Högskolan i Skövde

Simulation-based learning (SBL) presents a wide variety of opportunities to practice complex computer and networking skills in higher education, employing various platforms to enhance educational outcomes. The integration of SBL tools in teaching computer networking courses is useful for both instructors and learners. Furthermore, the increasing importance of SBL in higher education highlights the necessity to further explore the factors that affect the adoption of SBL technologies, particularly in the field of computer networking courses. Despite these advantages, minimal effort has been made to examine the factors that impact instructors' intentions to use SBL tools for computers and networking courses. The main objective of this study is to examine the factors that affect instructors' intentions to utilize SBL tools in computer networking courses offered by higher education institutions. By employing Interpretive structural modeling (ISM) and Matriced' Impacts Croise's Multiplication Appliquee a UN Classement (MICMAC) analysis, the research attempts to provide an in-depth understanding of the interdependencies and hierarchical associations among twelve identified factors. Results showed that system quality, self-efficacy, technological knowledge, and information quality have high driving power. This study offers valuable perspectives for higher education institutions and for upcoming empirical studies and aids in comprehending the advantages of using SBL tools in teaching and higher education. 

Application of reconfigurable manufacturing systems (RMS) plays a significant role in manufacturing companies’ success in the current fiercely competitive market. Despite the RMS’s advantages, designing these systems to achieve a high-efficiency level is a complex and challenging task that requires the use of optimization techniques. This study proposes a simulation-based optimization approach for optimal allocation of work tasks and resources (i.e., machines) to workstations. Three conflictive objectives, namely maximizing the throughput, minimizing the buffers’ capacity, and minimizing the number of machines, are optimized simultaneously while considering the system’s stochastic behavior to achieve the desired system’s configuration.

Reducing fuel consumption by transportation fleet in a supply chain, reduces transportation costs and consequently, the product final cost. Moreover, it reduces environmental pollution, and in some cases, it helps governments constitute less subsidies for fuels. In this paper, a supply chain scheduling is studied, with the two objective functions of minimizing the total fuel consumption, and the total order delivery time. After presenting the mathematical model of the problem, a genetic algorithm, named Social Genetic Algorithm (SGA) is proposed to solve it. The proposed algorithm helps decision makers determine the allocation of orders to the suppliers and vehicles and production and transportation scheduling to minimize total order delivery time and fuel consumption. In order for SGA performance evaluation, its results are compared with another genetic algorithm in the literature and optimal solution. Finally, a sensitivity analysis is performed on SGA. The results of comparisons also show the high performance of SGA. Moreover, by increasing the number of suppliers and vehicles and decreasing the number of orders, the value of the objective function is reduced.

Healthcare waste disposal center location (HCWDCL) impacts the environment and the health of living beings. Different and sometimes contradictory criteria in determining the appropriate site location for disposing of healthcare waste (HCW) complicate the decision-making process. This research presents a hybrid multi-criteria decision-making (MCDM) method, named PROMSIS, to determine the appropriate HCWDCL in a real case. The PROMSIS is the combination of two well-known MCDM methods, namely TOPSIS and PROMETHEE. Moreover, fuzzy theory is used to describe the uncertainties of the problem parameters. To provide a reliable decision on selecting the best HCWDCL, a comprehensive list of criteria is identified through a literature review and experts’ opinions obtained from the case study. In total, 40 criteria are identified and classified into five major criteria, namely economic, environmental, social, technical, and geological. The weight of the considered criteria is determined by the Analytical Hierarchy Process (AHP) method. Then, the score of the alternative HCWDCLs in each considered criterion is obtained. Finally, the candidate locations for disposing of HCWs are ranked by the proposed fuzzy PROMSIS method. The results show that the most important criteria in ranking the alternatives in the studied case are economic, environmental, and social, respectively. Moreover, the sub-criteria of operating cost, transportation cost, and pollution are identified as the most important sub-criteria, respectively.

This study aims at reducing energy consumption in supply chain networks by providing optimal integrated production and transportation scheduling. The considered supply chain consists of one main manufacturing center, multiple production units (i.e., suppliers), and multiple heterogeneous vehicles as the transportation fleet. To schedule this complex supply chain network in an energy-efficient way, several decisions should be made concerning the assignment of orders to suppliers and determining their production sequence, splitting orders, assigning orders to vehicles, and assigning delivery priority to orders. To cope with the problem, a mixed-integer linear programming model is presented. Due to the complexity of the problem, a novel development of the genetic algorithm named the Multiple Reference Group Genetic Algorithm (MRGGA) is also proposed. Four objectives are considered to be optimized to meet both suitability and energy-efficiency aspects in the supply chain network. These optimization objectives are to minimize the total orders' delivery times to the manufacturing center, fuel consumption by the vehicles, energy consumption at supplies, and maximize orders' quality. To analyze the performance of the proposed algorithm, a real case and a set of generated instances are solved. The results obtained by the proposed algorithm are compared with an existing genetic algorithm in the literature. Moreover, the results are also compared with the optimal solutions obtained from the mathematical model for small-size problems. The results of the comparisons show the efficiency of the proposed MRGGA in finding energy-efficient solutions for the considered supply chain network.

Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when considering a distributed manufacturing system (DMS). The present study aims to tackle the supply chain scheduling problem in a DMS while considering two essential sustainability aspects, namely environmental and economic. The economic aspect is addressed by optimizing the total delivery time of order, transportation cost, and production cost while optimizing environmental pollution and the quality of products contribute to the environmental aspect. To cope with the problem, it is mathematically formulated as a mixed-integer linear programming (MILP) model. Due to the complexity of the problem, an improved genetic algorithm (GA) named GA-TOPKOR is proposed. The algorithm is a combination of GA and TOPKOR, which is one of the multi-criteria decision-making techniques. To assess the efficiency of GA-TOPKOR, it is applied to a real-life case study and a set of test problems. The solutions obtained by the algorithm are compared against the traditional GA and the optimum solutions obtained from the MILP model. The results of comparisons collectively show the efficiency of the GA-TOPKOR. Analysis of results also revealed that using the TOPKOR technique in the selection operator of GA significantly improves its performance.

The Healthcare Waste Disposal Method Selection (HCWDMS) is a complicated problem due to multiple and often contradictory criteria with different importance degrees. Thus, decision-makers are restored to multi-criteria decision-making (MCDM) methods to prioritize and select the best HCW disposal methods. This study introduces an enhanced MCDM method to deal with the HCWDMS problem. To address the problem, a comprehensive list of criteria and HCW disposal methods are identified. All the criteria are categorized into four main criteria, and Fuzzy Analysis Hierarchy Process is used to determine the weights of considered criteria and sub-criteria. The study results show that environmental, economic, technical, and social criteria are the most important in selecting disposal methods, respectively. Moreover, the sub-criteria of ‘Health Risk’, ‘Release with health effects’, and ‘Capital cost’ have the highest importance, respectively. Additionally, the methods of ‘Microwave’, ‘Sterilization by autoclave’, and ‘Reverse polymerization’ have the highest priority, respectively.

The effective distribution of relief goods is critical in mitigating the impact of natural disasters and preserving human life. This study addresses a relief goods distribution problem, assuming the existence of multiple relief orders that must be delivered to various disaster-stricken regions from a network of warehouses using a fleet of diverse vehicles. The objective is to identify the most suitable warehouse for each relief order, allocate relief orders to vehicles, batch the orders in the designated vehicles, and devise routing plans to minimize the total delivery time. A mixed-integer linear programming model is formulated to tackle this problem. Owing to the problem’s NP-hard nature, a metaheuristic algorithm, known as the Multiple League Championship Algorithm (MLCA), is developed. Furthermore, two innovative variants of the MLCA , namely the League Base Multiple League Championship Algorithm (L- MLCA) and the Playoff Multiple League Championship Algorithm (P-MLCA), are introduced.Experimental results indicate that the P-MLCA outperforms the other two algorithms. The solutions derived from the P-MLCA are compared with the optimal solutions obtained by a commercial solver for small-scale problems. This comparative analysis demonstrates the promising performance of the P-MLCA in finding the optimal distribution of relief goods.

Preventing energy waste in residential and office buildings has emerged as a critical issue in both developed and developing countries over recent decades. The growing demand for oil and energy reserves has amplified the urgency of this concern. The deployment of building energy management systems (BEMSs) can lead to timely responses to changes in environmental conditions, the prevention of energy wastage, a reduction in CO2 emissions, and an increase in the longevity of building equipment. Despite the undeniable benefits of BEMSs, their market size remains small, creating challenges for providers in reaching potential customers. This research seeks to identify and prioritize the marketing strategies for BEMSs. A case study was conducted, employing the “Strengths, Weaknesses, Opportunities, and Threats” analysis as a tool for identifying marketing strategies related to BEMSs. This method resulted in the identification of 18 distinct marketing strategies. These strategies were subsequently prioritized using a novel fuzzy multicriteria decision-making technique, VIkor-topSIS, considering six specific criteria. The findings of the study suggested a hierarchical influence of six criteria on the BEMS market, arranged in the following order of significance: effectiveness, cost, attainability, complexity, timing, and popularity. Furthermore, the top three marketing strategies for BEMSs were found to be internet advertising strategies, discounts to consumers, and online sales. The analysis of the results has also offered valuable insights into the strengths and weaknesses of the studied BEMS provider, as well as the opportunities and threats present within the BEMS market.

This study aims to introduce a method to integrate and coordinate departments’ activities to enhance the service quality of organizations using Quality Function Deployment (QFD). To this purpose, the classical two-dimensional House Of Quality (HOQ) matrix is changed to a three-dimensional form (3D-HOQ). The 3D-HOQ is applied to the marketing and Human Resources (HR) departments of a bank to determine customers’ and employees’ demands, respectively. The 3D-HOQ is also employed to provide a unique list of technical requirements to satisfy the identified demands. Obtaining a unique list of technical requirements with the cooperation of both departments reduces the inconsistency between departments, saves cost and time by preventing reworks and parallel works, and increases the organization’s efficiency. Moreover, 3D-HOQ is combined with the SERVQUAL technique and fuzzy theory to determine the weight of obtained technical requirements. The study is conducted in four main steps, (1) identifying the customers’ and employees’ demands, (2) identifying the technical requirements for simultaneous satisfaction of both customers’ and employees’ demands, (3) determining the relationships between the technical requirements and the identified demands, and (4) prioritizing technical requirements. Applying the 3D-HOQ resulted in identifying 30 customers’ demands, 30 employees’ demands, and 50 technical requirements. The study results show that "using new banking technologies" has the highest weight among the customers’ demands, and "job security" has been found to have the highest weight among employees’ demands. Moreover, "Intra-organizational processes automation" has been identified as the technical requirement with the highest weight.

In the face of global market challenges, manufacturers place a high priority on the improvement of their production system efficiency to sustain their competitive stance. Flexible Transfer Lines (FTLs) stand out for their adaptability, enabled by cutting-edge Computer Numerical Control (CNC) technology, automated transport, and sophisticated control software, allowing for swift adjustments to changes in product specifications. These systems are identified as essential for industries dependent on mass production, such as the automotive and aerospace sectors, where a significant impact on productivity and cost efficiency is seen due to operational efficiency. This study introduces a heuristic approach for balancing FTLs. The heuristic is characterized by uniquely incorporating a broad spectrum of real-world considerations, including equipment-related, time-related, and operational-related characteristics. Through a detailed numerical example, the practical application and effectiveness of the heuristic are demonstrated, showcasing its capacity to produce a feasible solution.

In general, profitability and efficiency have been the main interest for organization. However, the increasing concerns for the environment from government, regulators, customers and other stakeholders has forced companies to seek for alternatives to achieve green objectives. The difficulties faced by organizations are lack of awareness and guideline in implementing green practices in their daily operation. Under constrained resources, employers are reluctant to spend money on something unclear. During the last decade, lean manufacturing seems to be visible trend in most of the manufacturing industries in Malaysia. As lean tends to emphasize on waste reduction, it provides similarity between lean and green. Therefore, it is a better atmosphere to deploy green practices and tools under existing lean manufacturing. The purpose of this paper is to present a review on the synergies between green and lean and identifying the determinants that affecting both lean and green manufacturing for Malaysian manufacturers. The determinant obtained are financial benefit, incentive, legislation, stakeholder, management commitment, technology, environmental awareness and brand image or competitiveness. Besides, the authors identified and suggested future research directions on developing an integrated lean-green model for daily operation. This study aims to assist researchers to identify the opportunities and challenges on lean-green model and this review is useful for manufacturers and government in developing manufacturing policies and guideline.

This study addresses the Distributed Flexible Job-Shop Scheduling Problem (DFJSP) while considering the delivery of customer orders and transportation time. To tackle the problem, a mathematical model is presented, and a novel variant of the Genetic Algorithm (GA), called Time-Travel GA (TTGA), is developed. The optimization objectives include minimizing the total orders’ delivery time to the related customers, reducing transportation and production costs, reducing manufacturing pollution, and maximizing the quality of completed orders. The performance of TTGA is tested by implementing it in a real-life manufacturing setting. Additionally, TTGA results are compared with those of four other algorithms over a set of test problems. Furthermore, the solutions obtained by TTGA are compared with the optimal solutions obtained by a commercial solver for small-size problems. The results of these comparisons collectively demonstrate the promising performance of TTGA in addressing the DFJSP.

Purpose – The purpose of this study is to firstly investigate the efficiency of the most commonly used performance measures for minimizing the Number of Workstations (NWs) in approaches addressing Simple Assembly Line Balancing Problem (SALBP) for both straight and U-shaped line. Secondly, this study aims to provide a comparative evaluation of 20 constructive heuristics to find solutions to the SALBP-1.

Design/methodology/approach – 200 problems are solved by 20 different constructive heuristics for both straight and U-shaped assembly line. Moreover, several comparisons have been made to evaluate the performance of constructive heuristics.

Findings – Minimizing the Smoothness Index (SI) is not necessarily equivalent to minimizing the NWs, therefore, it should not be used as the fitness function in approaches addressing the SALBP-1. Line efficiency (LE) and the idle time (IT) are indeed reliable performance measures for minimizing the NWs. The most promising heuristics for straight and U-shaped line configurations for SALBP-1 are also ranked and introduced.

Practical implications – Results are expected to help scholars and industrial practitioners to better design effective solution methods for having a most balance assembly line. This study will further help with choosing the most proper heuristic with regard to the problem specifications and line configuration.

Originality/value – There is limited research assessing the efficiency of the common objectives for SALBP-1. This study is among the first to prove that minimizing the workload smoothness is not equivalent to minimizing the NWs in SALBP-1 studies. This work is also one of the first attempts for evaluating the constructive heuristics for both straight and U-shaped line configurations.

In the past decade, the importance of teamwork training in higher education and employers’ enthusiasm for recruiting team players have been widely discussed in the literature. Yet, the process through which effective teamwork training is developed in a higher education setting has not yet been properly discussed. The present study aims to map the precedence relationships among the key determinants of teamwork training effectiveness and explain the process through which an effective teamwork training program can be developed. The study first conducted an extensive review of the literature to highlight the key determinants of effective teamwork training. Next, the study benefitted from an interpretive structural modeling technique and captured the opinions of a group of teamwork training experts to further map the interrelationships among the potential determinants that were identified. By listing the key determinants of effective teamwork training, mapping their interrelationships, and identifying their driving and dependence power, the present study is expected to help practitioners and academicians through providing a detailed understanding of the process through which an effective teamwork training program can be developed in a higher education context.

This study investigates the potential of Mixed Reality (MR) in the manual assembly processes and conducts a case study at a pump manufacturing plant in Sweden. An MR solution is developed to assist operators through visual instructions and guiding aides. The solution also captures the operator's motions using advanced hand and eye tracking features for real-time guidance and accurate time measurement. The proposed MR solution uses the build feature of HoLolens and a workstation editor, which facilitates the use of the solution in diverse assembly environments. The results of the experiments show that the developed MR solution can improve operator support, reduce errors, and enhance the overall efficiency of manual assembly processes. Moreover, it is shown to be an efficient tool for time measurement of the manual assembly process that has promising potential to replace sophisticated and time-consuming traditional time study methods.

The fourth industrial revolution and the digital transformation of consumer markets require contemporary manufacturers to rethink and reshape their business models to deal with the ever-changing customer demands and market turbulence. Manufacturers nowadays are inclined toward product differentiation strategies and more customer-focused approaches to stay competitive in the Industry 4.0 environment, and mass customization and product diversification are among the most commonly implemented business models. Under such circumstances, an economical material supply to assembly lines has become a significant concern for manufacturers. Consequently, the present study deals with optimizing the material supply to mixed-model assembly lines that contribute to the overall production cost efficiency, mainly via the reduction of both the material transportation and material holding costs across production lines, while satisfying certain constraints. Given the complexity of the problem, a novel two-stage heuristic algorithm is developed in this study to enable a cost-efficient delivery. To assess the efficiency and effectiveness of the proposed heuristic algorithm, a set of test problems are solved and compared against the best solution found by a commercial solver. The results of the comparison reveal that the suggested heuristic provides reasonable solutions, thus offering immense opportunities for production cost efficiency and manufacturing sustainability under the mass customization philosophy.

Manufacturers worldwide are nowadays in pursuit of sustainability. In the Industry 4.0 era, it is a common practice to implement decentralized logistics areas, known as supermarkets, to achieve production sustainability via Just-in-Time material delivery at assembly lines. In this environment, manufacturers are commonly struggling with the Supermarket Location Problem (SLP), striving to efficiently decide on the number and location of supermarkets to minimize the logistics cost. To address this prevalent issue, this paper proposed a Simulated Annealing (SA) algorithm for minimizing the supermarket cost, via optimally locating supermarkets in assembly lines. The efficiency of the SA algorithm was tested by solving a set of test problems. In doing so, a holistic performance index, namely the total cost of supermarkets, was developed that included both shipment cost and the installation cost across the assembly line. The effect of workload balancing on the supermarket cost was also investigated in this study. For this purpose, the SLP was solved both before and after balancing the workload. The results of the comparison revealed that workload balancing could significantly reduce the total supermarket cost and contribute to the overall production and economic sustainability. It was also observed that the optimization of material shipment cost across the assembly line is the most influencing factor in reducing the total supermarket cost.

This study is motivated by a real-world assembly line in an automotive manufacturing company and it addresses the simple assembly line balancing problem type-E (SALBPE). The SALBPE aims to maximize the balance efficiency (BE) through determining the best combinations of cycle time and station number. To cope with the problem, a mixed integer nonlinear programming (MINLP) model is proposed. The MINLP model differs from the existing ALBPE models as it includes the technological requirements of assembly tasks and optimizes the variation of workload beside the BE. The validity of the proposed model is tested by solving the real-world case study and a set of benchmark problems.

This study aims to bridge the gap between theory and practice by addressing a real-world assembly line balancing problem (ALBP) where task times are stochastic and there are zoning constraints in addition to the commonly known ALBP constraints. A mixed integer programming (MIP) model is proposed for each of the straight and U-shaped assembly line configurations. The primary objective in both cases is to minimize the number of stations; minimizing the maximum of stations’ mean time and the stations’ time variance are considered secondary objectives. Four different scenarios are discussed for each model, with differences in the objective function. The models are validated by solving a real case taken from an automobile manufacturing company and some standard test problems available in the literature. The results indicate that both models are able to provide optimum solutions for problems of different sizes. The technique for order preference by similarity to ideal solution (TOPSIS) is used to create reliable comparisons of the different scenarios and valid analysis of the results. Finally, some insights regarding the selection of straight and U-shaped layouts are provided.

• Purpose – This study aims to propose an efficient optimization algorithm to solve the assembly line balancing problem (ALBP). The ALBP arises in high-volume, lean production systems when decision makers aim to design an efficient assembly line while satisfying a set of constraints.
• Design/methodology/approach – An improved genetic algorithm (IGA) is proposed in this study to deal with ALBP in order to optimize the number of stations and the workload smoothness.
• Findings – To evaluate the performance of the IGA, it is used to solve a set of well-known benchmark problems and a real-life problem faced by an automobile manufacturer. The solutions obtained are compared against two existing algorithms in the literature and the basic genetic algorithm. The comparisons show the high efficiency and effectiveness of the IGA in dealing with ALBPs.
• Originality/value – The proposed IGA benefits from a novel generation transfer mechanism that improves the diversification capability of the algorithm by allowing population transfer between different generations. In addition, an effective variable neighborhood search is employed in the IGA to enhance its local search capability.

Assembly-line balancing is a significant issue in production systems. Employing industrial robots as the main production resource was a milestone in developing assembly lines, and emerging Industry 4.0 led industries to build collaborative assembly lines by combining robots and human operator skills. Recently, the majority of research on assembly line balancing has contributed to addressing aspects of utilizing robots in assembly lines and how they can increase line performance. Various models and methods are developed, considering different objectives and performance indicators. Despite the increasing number of studies in this area, a thorough literature review is lacking in identifying gaps, shedding light on research directions, and facilitating future development. This study systematically reviews assembly-line balancing studies targeted at assembly lines with industrial and collaborative robots. Studies are classified based on their objectives and reviewed for their solution method, line layout, and other essential specifications. A descriptive analysis is provided to assist researchers and practitioners in linking different properties of assembly lines to the objectives and applied methodologies. The results show that most studies developed models and solution methods that focused on simultaneously optimizing more than one objective. The review reveals that minimizing the cycle time is the most popular objective, and meta-heuristic algorithms are the dominant solution approaches. It is also observed that balancing assembly lines with collaborative robots has received more attention in the last five years with the emergence of Industry 4.0. The review also highlights gaps in the related literature and provides promising insights for future research.

This study is motivated by a real case study and addresses the material supply problem at assembly lines. The aim of the study is to optimally schedule the delivery of raw material at assembly lines while using the minimum number of vehicles. To cope with the problem an original mixed integer linear programming model has been proposed based on the assumptions and constraints observed in the case study. The validity of the model has been examined by solving several real cases and analysing different scenarios. The results of the study show the efficiency and effectiveness of the model.

Purpose – The purpose of this paper is to contribute to the existing knowledge about the relationships between information technology (IT), lean manufacturing (LM), organizational environmental issues and business performance.

Design/methodology/approach – A questionnaire-based survey was conducted to collect data from 122 elite manufacturers, and the hypothesized relationships were tested using partial least squares structural equation modeling. Findings – IT competence in LM acts as a lower-order organizational capability, and its business value should be recognized through the intermediate roles of LM effectiveness and environmental management capability. Findings recommend that the net benefits of LM are mainly materialized through waste and pollution reduction and simplified implementation of proactive environmental practices.

Research limitations/implications – Among other limitations, relying on a rather small sample size and cross-sectional data of this research, and lack of generalizability of findings, tends to have certain limitations. An interesting direction for future research would be to extend this research by assessing interaction of other types of IT resources with LM and organizational environmental issues.

Practical implications – Both LM and proactive environmental management are information-intensive. Investment in both technological and human aspects of IT resource aimed at increasing the effectiveness of LM activities and proactive environmental practices is imperative for contemporary manufacturers.

Originality/value – This study introduces the IT capability of IT competence in LM and two organizational capabilities of LM effectiveness and environmental management capability. By doing so, the study highlights the significant role of organizational environmental issues in devising firms’ IT and advanced manufacturing technology investment strategies in LM context.

purpose– The study demonstrates how small manufacturing firms can leverage their Information Technology (IT) resources to develop the lean-digitized manufacturing system that offers sustained competitiveness in the Industry 4.0 era.

Design/methodology/approach – The study performs an in-depth 5-years case study of a manufacturing firm, and reports its journey from failure in the implementation of enterprise resource planning to its success in integrating IT-based technology trends of Industry 4.0 with the firm’s core capabilities and competencies while pursuing manufacturing digitization.

Findings – Industry 4.0 transition requires the organizational integration of many IT-based modern technologies and the digitization of entire value chains. However, Industry 4.0 transition for smaller manufacturers can begin with digitization of certain areas of operations in support of organizational core strategies. Development of leandigitized manufacturing system is a viable business strategy for corporate survivability in the Industry 4.0 setting.

Research limitations/implications – Although the implementation of lean-digitized manufacturing system is costly and challenging, this manufacturing strategy offers superior corporate competitiveness in the long run. Since this finding is rather limited to the present case study, assessing the business value of lean-digitized manufacturing system in a larger-scale research context would be an interesting avenue for future research.

Practical implications – Industry 4.0 transition for typical manufacturers should commensurate with their organizational, operational, and technical particularities. Digitization of certain operations and processes, when aligned with the firm’s core strategies, capabilities, and procedures, can offer superior competitiveness even in Industry 4.0 era, meaning that the strategic plan for successful Industry 4.0 transition is idiosyncratic to each particular manufacturer.

Social implications – Manufacturing digitization can have deep social implications as it alters inter and intra organizational relationships, causes unemployment among low-skilled workforce, and raises data security and privacy concerns. Manufacturers should take responsibility for their digitization process and steer it in a direction that simultaneously safeguards economic, social, and environmental sustainability.

Originality/value – The strategic roadmap devised and employed by the case company for managing its digitization process can better reveal what manufacturing digitization, mandated by Industry 4.0, might require of typical manufacturers, and further enable them to better facilitate their digital transformation process.

Understanding the interactions of Industry 4.0 and sustainability is a cutting-edge research topic. The present study aims to contribute to this research topic by explaining how Industry 4.0 may contribute to energy sustainability. The present study performs a content-centric qualitative review of the extant digitalization literature to identify the primary energy sustainability functions of Industry 4.0. The interpretive structural modeling technique is further used for mapping the interrelationships among various energy sustainability functions identified. The interpretive model developed, and the Matrice d'Impacts Croisés Multiplication Appliquée àun Classement analysis offered exciting insights into the Industry 4.0-energy sustainability interactions. Findings show that Industry 4.0 promotes energy sustainability via a very complex mechanism that involves ten interrelated functions. Contrary to the general opinion, production efficiency offered by the digitalization of the manufacturing industry is not the immediate and most essential energy efficiency outcome of the digital industrial transformation. Industry 4.0 primarily contributes to energy sustainability by enabling the energy industry to reshape its operating landscape and enjoy more advanced, intelligent, and complicated energy production and distribution equipment. The digitalization of the energy demand sector, digitalization of the manufacturing industry, and the introduction of smarter and more sustainable products are among the main opportunities of Industry 4.0 for energy sustainability. Overall, the study and the ISM model of energy sustainability developed explains how Industry 4.0 contributes to energy sustainability via different functions and how each function is placed within the structural model based on its driving and dependence powers.

The present study addresses the issue of mobile banking customer retention by developing and empirically testing a theoretical model that describes the way mobile banking success is achieved. The data collection process was conducted via a web-based questionnaire survey through which 402 usable responses from users of application-based mobile banking services were collected. The data collected were further analyzed via covariance-based structural equation modeling. Results indicate that application-based mobile banking success can be defined in terms of the favorable attitude toward and repeated use of mobile banking applications. Experienced advantage, user satisfaction, and post-use trust toward mobile banking applications are among the critical enablers of application-based mobile banking success. The findings of this research can enable academicians and practitioners, banks, and financial institutions, in particular, to devise the mechanism through which the success of application-based mobile banking services can be facilitated.

Purpose – The purpose of this study is to contribute to the existing knowledge about the process ofachieving Lean Manufacturing (LM) success.

Design/methodology/approach – This study uses interpretive structural modeling and captures theopinions of a group of LM experts from a world-class Japanese automobile manufacturer, to map theinterrelationships among potential determinants of LM success. This study further uses the data from asurvey of 122 leading automobile part manufacturers by performing structural equation modeling toempirically test the research model proposed.

Findings – Management support and commitment, financial resources availability, information technologycompetence for LM, human resources management, production process simplicity, supportive culture andsupply chain-wide integration are the key determinants that directly or indirectly determine the level ofachievement of LMsuccess.

Research limitations/implications – The determinants of LM success as experienced by Asianautomobile manufacturers might be different from determinants of LM success as experienced byWestern automobile manufacturers. An interesting direction for future research would be to capturethe experts’ inputs from Western automobile manufacturers to complement the findings of thisstudy.

Practical implications – The practical contribution of this study lays in the development of linkagesamong various LM success determinants. Utility of the proposed interpretive structural modeling andstructural equation modeling methodologies imposing order, direction and significance of therelationships among elements of LM success assumes considerable value to the decision-makers and LMpractitioners.

Originality/value – Building on opinions of a group of LM experts and a case study of leading auto partmanufacturers, the present study strives to model the success of LM, a topic that has received little attentionto date.

The fourth industrial revolution, known as Industry 4.0, and the underlying digital transformation, is a cutting-edge research topic across various disciplines. Industry 4.0 literature is growing exponentially, overexpanding the current understanding of the digital industrial revolution through thousands of academic publications. This unprecedented growth calls for a systematic review of the concept, scope, definition, and functionality of Industry 4.0. Such systematic review should address the existing ambiguities and deliver a clear, comprehensive, and up-to-date overview of this phenomenon, including the possible implications for sustainability. Consistently, the present study carried out a systematic literature review of related articles, published online within the Industry 4.0 discipline until November 2020. The systematic literature review identified 745 eligible articles and applied extensive qualitative and quantitative data analysis methodically. The study provides a descriptive assessment of eligible articles’ properties and offers a unified conceptualization of Industry 4.0 and the underlying building blocks. The study explains how the implications of Industry 4.0 for value creation expand beyond the manufacturing industry. The study further describes the sustainability value drivers of the fourth industrial revolution and identifies the conditions on which digital industrial transformation success lays. Overall, findings reveal that Industry 4.0 transformation could address pressing issues of sustainable development goals, particularly concerning the manufacturing-economic development. The study also draws on the findings and offers important theoretical and practical implications, highlights the existing gaps within the literature, and discusses the possible future research directions.

Purpose: This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach. Design/methodology/approach: The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0. Findings: Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations. Practical implications: While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits. Originality/value: This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

Globalization, market turbulence, and global crisis such as the COVID-19 pandemic push the manufacturing industry to rush towards automation, data-centricity, and greater interconnectivity under the ongoing digital industrial transformation known as Industry 4.0. Smaller manufactures worldwide strive to keep up with the digitalization race and secure sustainable competitiveness. Fierce competition and market turbulence force Middle Eastern manufacturers to make the most of the digitalization race. The present study reports on the Industry 4.0 digital transformation and development of small and medium-sized manufacturing enterprises in Iran, one of the most economically influential countries within the Middle East. Smaller manufacturers in Iran consider Industry 4.0 to improve productivity, operate in the data-driven economy, and increase business resilience. Although the implementation rate of generic digital technologies has been somewhat promising among Iranian manufacturers, the present report identifies significant barriers slowing the implementation rate of more advanced Industry 4.0 technologies such as predictive analytics and blockchain technology. Overall, Industry 4.0 transformation has provided Iranian manufacturing enterprises with meaningful opportunities and benefits. Nonetheless, smaller manufacturers cannot generally develop an all-inclusive strategic plan for navigating digitalization efforts. This issue, along with many other barriers such as resource scarcity, digitalization immaturity, and cybersecurity risks, has slowed down the digital transformation of the Iranian manufacturing industry.

Purpose: The study seeks to understand the possible opportunities that Industry 5.0 might offer for various aspects of inclusive sustainability. The study aims to discuss existing perspectives on the classification of Industry 5.0 technologies and their underlying role in materializing the sustainability values of this agenda. Design/methodology/approach: The study systematically reviewed Industry 5.0 literature based on the PRISMA protocol. The study further employed a detailed content-centric review of eligible documents and conducted evidence mapping to fulfill the research objectives. Findings: The advancement of Industry 5.0 is currently underway, with noteworthy initial contributions enriching its knowledge base. Although a unanimous definition remains lacking, diverse viewpoints emerge concerning the recognition of fundamental technologies and the potential for yielding sustainable outcomes. The expected contribution of Industry 5.0 to sustainability varies significantly depending on the context and the nature of underlying technologies. Practical implications: Industry 5.0 holds the potential for advancing sustainability at both the firm and supply chain levels. It is envisioned to contribute proportionately to the three sustainability dimensions. However, the current discourse primarily dwells in theoretical and conceptual domains, lacking empirical exploration of its practical implications. Originality/value: This study comprehensively explores diverse perspectives on Industry 5.0 technologies and their potential contributions to economic, environmental and social sustainability. Despite its promise, the practical evidence supporting the effectiveness of Industry 5.0 remains limited. Certain conditions are necessary to realize the benefits of Industry 5.0 fully, yet the mechanisms behind these conditions require further investigation. In this regard, the study suggests several potential areas for future research. 

In this paper, we have considered that ranked set sampling is able to estimate the parameters of exponentiated Pareto distribution. The method with which the maximum likelihood estimators for the parameters of exponentiated Pareto distribution is studied is numerical since there is no presence or possibility of a closed-form at the hands of estimators or any other intellectual. The numerical approach is a well-suited one for this study as there has been struggles in achieving it with any other technique. In order to compare the different sampling methods, simulation studies are performed as the main technique. As for the illustrative purposes, analysis of a simulated dataset is desired for the objective of the presentation. The conclusion that we can reach based on these is that the estimators based on the ranked set sample have far better efficiency than the simple random sample at the same sample size.

This study aims to develop appropriate models for income distribution in Iran using the econophysics approach for the 2006–2018 period. For this purpose, the three improved distributions of the Pareto, Lognormal, and Gibbs-Boltzmann distributions are analyzed with the data extracted from the target household income expansion plan of the statistical centers in Iran. The research results indicate that the income distribution in Iran does not follow the Pareto and Lognormal distributions in most of the study years but follows the generalized Gibbs-Boltzmann distribution function in all study years. According to the results, the generalized Gibbs-Boltzmann distribution also properly fits the actual data distribution and could clearly explain the income distribution in Iran. The generalized Gibbs-Boltzmann distribution also fits the actual income data better than both Pareto and Lognormal distributions

Developing sustainable products and processes is essential for the survival of manufacturers in the current competitive market and the industry 4.0 era. The activities of manufacturers andtheir supply chain partners should be aligned with sustainable development goals. Manufacturershave faced many barriers and challenges in implementing sustainable practices along the entiresupply chain due to globalisation, outsourcing, and offshoring. Blockchain technology has thepotential to address the challenges of sustainability. This study aims to explain the applications ofblockchain technology to sustainable manufacturing. We conducted a systematic literature reviewand explained the potential contributions of blockchain technology to the economic, environmental,and social performances of manufacturers and their supply chains. The findings of the study extendour understanding of the blockchain applications in sustainable manufacturing and sustainablesupply chains. Furthermore, the study explains how blockchain can influence the sustainable performance of manufacturers by creating transparency, traceability, real-time information sharing, andsecurity of the data capabilities.

Purpose This study examines the importance of English Mobile Learning research as a foundation for lifelong and sustainable education from different points of view, including those of technology innovation experts, psychologists and educators. It aims to explore the current status and relevant research trends through the application of bibliometric mapping and bibliometric analysis.

Design/methodology/approach For this study, all Web of Science records (in total 5,343) from 2000 to 2020 in the field of English Mobile Learning were analyzed using the VOSviewer and CiteSpace software tools. The WoS built-in functions, including “Refine” and “Analyze,” were employed to perform the bibliometric analysis. The study further analyzed a sample of the five most-cited articles to identify the previous studies with the highest quality or impact.

Findings The results showed that research in English Mobile Learning is growing quickly and steadily with a noticeable emphasis on various device-based technologies and applications. The study also discusses the key implications for research institutions, education policymakers and academicians, and identifies the most prominent avenues for future research on English Mobile Learning. Moreover, the results shared in this review highlight the most important and emerging areas of research in the field.

Originality/value This article is the most recent bibliographic review of literature that particularly addresses the English Mobile Learning research during the past two decades.

Platform thinking involves creating an ecosystem that enables different participants to interact and co-create value. This mindset shift moves away from the traditional linear value chain model toward a more collaborative, networked approach. In the manufacturing sector, platform thinking has given rise to new business models like manufacturing as a service (MaaS) and cloud manufacturing (CMfg). In this paper, building upon existing literature, a system dynamics (SD) model is developed to analyze the acceptance of platform thinking as an innovative solution in manufacturing. In contrast to previous studies, the model considers the dynamics of customer behavior in addition to the roles of the platform provider (government) and service providers (manufacturers). The model incorporates four decision variables: commission rates, user membership fees, and subsidies for both cloud platform users and service providers, which can be adjusted to inform policymakers. Studying the effect of individual decision variables over a 30-year time frame using the SD model reveals that increasing commission rates and user membership fees negatively affect the platform size (number of manufacturers joining and staying on the platform), yet can result in increased long-term platform revenue. On the other hand, providing subsidies for cloud platform users and service providers has a positive impact on the platform size but leads to increased costs for the platform provider. A non-dominated sorting genetic algorithm (NSGA-II) is employed to optimize the system dynamics model and identify optimal policy settings that balance the interests of all stakeholders. This approach enables policymakers to make informed decisions that support the growth of CMfg and MaaS, fostering innovation and competitiveness in the manufacturing sector.

Bottleneck analysis, known as one of the essential lean manufacturing concepts, has been extensively researched in the literature. Recently, there has been a move towards using new Industry 4.0-based concepts and technologies in the development of bottleneck analysis. However, the interrelations between bottleneck analysis and Industry 4.0 have not been studied thoroughly. The present study addresses this gap and performs a systematic literature review on articles available in major scientific databases (i.e., Web of Science and Scopus) to investigate the impact of Industry 4.0 on the advancement of bottleneck analysis in production and manufacturing. Bibliometric analysis and content review were performed to extract the quantitative and qualitative data. Results revealed that only five out of 15 design principles and five out of eleven technologies of Industry 4.0 were addressed previously in developing bottleneck analysis methods. In addition to highlighting the existing gaps in the literature and proposing topics for future research, several potential development streams are proposed by studying the design principles and technologies of Industry 4.0, which have not been considered in bottleneck analysis before.

The performance of a production system is primarily evaluated by its throughput, which is constrained by throughput bottlenecks. Thus, bottleneck analysis (BA), encompassing bottleneck identification, diagnosis, prediction, and prescription, is a crucial analytical process contributing to the success of manufacturing industries. Nevertheless, BA requires a substantial quantity of information from the manufacturing system, making it a data-intensive task. Based on the dynamic nature of bottlenecks, the optimal strategy for BA entails making well-informed decisions in real-time and executing necessary modifications accordingly. The efficient implementation of BA requires gathering, storing, analyzing, and illustrating data from the shop floor. Utilizing Industry 4.0 technologies, such as cyber-physical systems and cloud technology, facilitates the execution of data-intensive operations for the successful management of BA in real-world settings. The main objective of this study is to establish a framework for BA through the utilization of Cloud-Based Cyber-Physical Systems (CB-CPSs). First, a literature review was conducted to identify relevant research and current applications of CB-CPSs in BA. Using the results of the review, a CB-CPSs framework was subsequently introduced for BA. The application of the framework was assessed via simulation in a real-world manufacturer of marine engines. The findings indicate that the implementation of CB-CPSs can contribute significantly to throughput improvement. 

Aircraft engine maintenance, repair, and overhaul (MRO) exemplifies a closed-loop remanufacturing system in which all components are recovered. As a critical process ensuring aircraft safety and reliability, MRO faces significant challenges due to the inherent uncertainty in maintenance workloads and the stochastic nature of the process. Aircraft engines contain life-limited parts, replaced at predetermined intervals, and on-condition parts, which are inspected during each maintenance visit and replaced as needed. The presence of on-condition components introduces additional uncertainty, as the full scope of required maintenance is only known after disassembly and inspection.

Consequently, effective buffer allocation between the disassembly, repair, and reassembly stages is crucial for absorbing this variability. To optimize buffer allocation in this stochastic environment, this study employed discrete-event simulation to model the detailed MRO process. A multi-objective meta-heuristic algorithm was then applied to identify near-optimal buffer allocations that simultaneously maximize engine inter-arrival rates and minimize work-in-process. The results demonstrate that strategically designed buffers, particularly between major process stages, can significantly enhance performance in the face of uncertainty inherent to MRO operations. This simulation-based optimization approach offers valuable insights for managing complex remanufacturing systems such as aircraft engine MRO.

Data-driven simulation (DDS) is fundamental to analytical and decision-support technologies in Industry 4.0 and smart manufacturing. This study investigates the potential of DDS for resource allocation (RA) in high-mix, low-volume smart manufacturing systems with mixed automation levels. A DDS-based decision support system (DDS-DSS) is developed by incorporating two RA strategies: simulation-based bottleneck analysis (SB-BA) and simulation-based multi-objective optimization (SB-MOO). To enhance the performance of SB-MOO, a unique meta-learning mechanism featuring memory, dynamic orthogonal array, and learning rate is integrated into the NSGA-II, resulting in a modified version of the NSGA-II with meta-learning (i.e., NSGA-II-ML). The proposed DSS also benefits from a post-optimality analysis that leverages a clustering algorithm to derive actionable insights. A real-life marine engine manufacturing application study is presented to demonstrate the applicability and exhibit efficacy of the proposed DSS and NSGA-II-ML. To this aim, NSGA-II-ML was tested against the original NSGA-II and differential evolution (DE) algorithm across a set of test problems. The results revealed that NSGA-II-ML surpassed the other two in terms of the number of non-dominated solutions and hypervolume, particularly in medium and large-sized problems. Furthermore, NSGA-II-ML achieved a 24% improvement in the best throughput found in the real case problem, outperforming SB-BA, NSGA-II, and DE. The post-optimality analysis led to the extraction of valuable knowledge about the key, influencing decision variables on the throughput.

The supplier selection problem (SSP) is a significant issue in renewable supply chain management (RSCM). Selecting a strategic green supplier can not only discover the sustainable development of supply chains but also optimize the consumption rate of resources and decrease the negative environmental effects, which adopts to the green development context. As a multiple criteria group decision-making (MCGDM) problem, choosing a strategic green supplier is important to renewable supply chains. However, how to choose a strategic green supplier for supply chains is a great effort. Hence, In the present work, evaluating a set of strategic suppliers is primarily based on green capabilities by using an integrated fuzzy Best Worst Method (FBWM) with the other two techniques, namely COPRAS (Complex Proportional Assessment of Alternatives) and WASPAS (Weighted Aggregated Sum-Product Assessment). Initially, nine strategic supplier selection criteria have been identified through literature review and a real-world case study of Iran's renewable energy supply chain is deliberated to exhibit the proposed framework's applicability. The applied methodology and its analysis will provide insight to decision-makers of strategic supplier selection. It may aid decision-makers and the procurement department in differentiating the significant strategic green supplier selection criteria and assess the strategic green supplier in the local and global market supply chain. Finally, the strengths and limitation of the framework are discussed by using comparative analysis with other methods.

Industry 4.0 has been associated with the rise of disruptive intelligence and informationtechnologies. These cutting-edge technologies have the potential to increase productivity whilesimultaneously having a significant impact on social and environmental sustainability. As a result,manufacturers must evaluate the role of these innovative technologies in sustainable development,as these technologies have the potential to address prevalent sustainability issues. A content-centricstudy of the implementation of these Industry 4.0 cutting-edge technologies in sustainable manufacturing is currently absent. A systematic literature study was conducted to explain the potentialcontribution of these novel technologies to the economic, social, and environmental dimensions ofmanufacturing industries. This study describes how these cutting-edge technologies are used insustainable manufacturing. The findings of this study are particularly beneficial to practitioners whoseek to apply one or more digital technologies to sustainable development.

In response to sustainability imperatives, manufacturers adapt production lines for enhanced energy efficiency. This study focuses on flexible transfer lines (FTL), which are renowned for flexibility and efficiency in mass-customized production. This study addresses the pivotal challenges of balancing and scheduling FTL, aiming to optimize cycle time and total energy cost. A novel mixed-integer linear programming model and a multi-objective optimization approach utilizing the epsilon-constraint method are introduced for solving small to medium-sized problems. The findings advance sustainable practices by exploring the impact of varying energy modes on FTL sustainability, offering manufacturers insights into energy-efficient production strategies.

This article aims to address the assembly line balancing problem (ALBP) and supermarket location problem (SLP) as two long-term interrelated decision problems considering the stochastic nature of the task times and demands. These problems arise in real-world assembly lines during the strategic decision-making phase of configuring new assembly lines from both line balancing and part-feeding (PF) aspects. A hierarchical mathematical programming model is developed, in which the first level resolves the stochastic ALBP by minimizing the workstation numbers and the second level deals with the stochastic SLP while optimizing the PF shipment, inventory and installation costs. The results of case data from an automotive parts manufacturer and a set of standard test problems verified that the proposed model can optimize the configuration of assembly lines considering both ALBP and SLP performance measures. This study also validates the effect of the stochastic ALBP on the resulting SLP solutions.

This paper aims to investigate the effect of station demands variations on supermarket location problem (SLP). This problem arises in the real-world assembly line part feeding (PF) context where supermarkets are used as the intermediate storage areas for stations. To this purpose a stochastic SLP model is developed to optimize the total cost of PF in terms of shipment, inventory and installation costs. The computational results over a real case as well as different test instances verify that the station demands variation has an effect on the SLP solutions.

Decentralised in-house logistics areas, known as supermarkets, are widely used in the manufacturing industry for parts feeding to assembly lines. In contrary to the literature and inspired by observation in a real case, this study relaxes the assumption of using identical transport vehicles when deciding on the supermarkets’ location by considering the availability of different vehicles. In this regard, this study deals with the integrated supermarket location and transport vehicles selection problems (SLTVSP). A mixed-integer programming (MIP) model of the problem is developed. Due to the complexity of the problem, a hybrid genetic algorithm (GA) with variable neighborhood search (GA-VNS) is also proposed to address large-sized problems. The performance of GA-VNS is compared against the MIP, the basic GA, and simulated annealing (SA) algorithm. The computational results from the real case and a set of generated test problems show that GA-VNS provides a very good approximation of the MIP solutions at a much shorter computational time while outperforming the other compared algorithms. The analysis of the results reveals that it is beneficial to apply different transport vehicles rather than identical vehicles for SLTVSP.

This paper aims to deal with assembly line design from both line balancing and parts feeding (PF) aspects as two-interrelated decision problems while supermarkets are used. These problems arise in the real-world assembly lines (ALs) where decision makers are planning to simultaneously determine the optimal number of stations and the optimal number of supermarkets so that the total installation costs of ALs including line balancing and PF costs are minimized. To this purpose an integrated mathematical model is proposed and its performance is tested through solving a number of benchmark problems and a real case taken from industry.

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has convinced many industries to shift towards semi-automated assembly lines with human-robot collaboration (HRC). In the HRC environment, robot agility can support human skill upon efficiently balancing tasks among the stations and operators. On the other hand, the robot energy consumption in today's energy crisis area demands that tasks be performed with as little energy utilization as possible by robots. In this context, the cycle time (CT) and total energy cost (TEC) of robots are among two conflicting objectives. Thus, this study balances HRC lines where a trade-off between CT and TEC of robots is sought. A mixed-integer linear programming model is proposed to formulate the problem. In addition, a multi-objective optimization approach based on ε-constraint is developed to address a case study from the automotive industry and a set of generated test problems. The computational results show that promising Pareto solutions in terms of CT and TEC can be obtained using the proposed approach.

In light of the Industry 5.0 trend towards human-centric and resilient industries, human-robot collaboration (HRC) assembly lines can be used to enhance productivity and workers’ well-being, provided that the optimal allocation of tasks and available resources can be determined. This study investigates the assembly line balancing problem (ALBP), considering HRC. This problem, abbreviated ALBP-HRC, arises in advanced manufacturing systems, where humans and collaborative robots share the same workplace and can simultaneously perform tasks in parallel or in collaboration. Driven by the need to solve the more complex assembly line-balancing problems found in the automotive industry, this study aims to address the ALBP-HRC with the cycle time and the number of operators (humans and robots) as the primary and secondary objective, respectively. In addition to the traditional ALBP constraints, the human and robot characteristics, in terms of task times, allowing multiple humans and robots at stations, and their joint/collaborative tasks are formulated into a new mixed-integer linear programming (MILP) model. A neighborhood-search simulated annealing (SA) is proposed with customized solution representation and neighborhood search operators designed to fit into the problem characteristics. Furthermore, the proposed SA features an adaptive neighborhood selection mechanism that enables the SA to utilize its exploration history to dynamically choose appropriate neighborhood operators as the search evolves. The proposed MILP and SA are implemented on real cases taken from the automotive industry where stations are designed for HRC. The computational results over different problems show that the adaptive SA produces promising solutions compared to the MILP and other swarm intelligence algorithms, namely genetic algorithm, particle swarm optimization, and artificial bee colony. The comparisons of human/robot versus HRC settings in the case study indicate significant improvement in the productivity of the assembly line when multiple humans and robots with collaborative tasks are permissible at stations.