Högskolan i Skövde

The advent of Industry 5.0 and advancements in collaborative robot (cobot) technology have driven many industries to adopt human–robot collaboration (HRC) in their assembly lines. This collaborative approach, which combines human expertise with robotic precision, necessitates an optimized method for balancing and scheduling tasks and operators across stations. This study proposes various constraint programming (CP) models tailored to straight and U-shaped assembly layouts, with objectives such as minimizing the number of stations, reducing cycle time, and minimizing costs. To enhance real-world applicability, the models consider the presence of diverse humans and cobots with varying skills and energy requirements working collaboratively or concurrently on assembly tasks. Additionally, practical constraints are addressed, including robot tool changes, zoning, and technological needs. Computational results demonstrate the superior efficiency of the proposed CP models over state-of-the-art mixed-integer programming models, validated through a case study and a comprehensive set of test problems. The results indicate that U-shaped layouts offer greater flexibility than straight-line configurations, particularly in reducing cycle time. Furthermore, higher HRC levels, including more humans and cobots, can significantly improve the number of stations, cycle time, and cost by up to 50%, 29%, and 36%, respectively.

In the rapidly evolving landscape of hyperconnected digital manufacturing, known as Industry 4.0, achieving energy efficiency has become a critical priority. As manufacturers worldwide strive to meet sustainable development goals, enhancing energy efficiency is essential for reducing operational costs and minimizing environmental impact. In this context, line balancing is a pivotal strategy for optimizing energy consumption within manufacturing processes. This study presents a comprehensive literature review on the Line Balancing Problems (LBPs) focused on enhancing energy efficiency. The review aims to provide a holistic understanding of this domain by examining past, present, and future trends. A systematic literature review is conducted using the PRISMA method, incorporating both qualitative and quantitative analyses. The quantitative analysis identifies prevalent patterns and emerging trends in energy efficiency optimization within the LBP domain. Concurrently, the qualitative analysis explores various aspects of existing studies, including configurations of lines, managerial considerations, objectives, solution methodologies, and real-world applications. This review synthesizes current knowledge and highlights potential avenues for future research, underlining the importance of energy efficiency in driving sustainable practices in Industry 4.0 and the emerging Industry 5.0 paradigm.

In today’s advanced manufacturing landscape, optimizing production processes is crucial for maintaining competitiveness. Among various optimization challenges, production sequencing in make-to-order hybrid flow shops (HFSs) stands out as particularly complex. This study investigates production sequencing in an HFS from the marine engine production industry, characterized by feed-forward quality inspection (FFQI). In FFQI, rejected engines must be repaired rather than scrapped. The complexity is further heightened by the fact that repair capacity is usually limited to a few engines and rejection at quality inspection leads to sequence scrambling at downstream stations. To address this issue, this study employs simulation-based, knowledge-driven optimization that utilizes real-world data on the rejection rates of different engine variants. This data is used to cluster the variants into three groups with different risks of rejection at quality inspection, informing production sequencing decisions. A non-dominated sorting genetic algorithm, enhanced with anti-block (AB) and anti-delay (AD) strategies (NSGAIIAB-AD), is developed to optimize throughput and delivery delay. AB aims to mitigate the succession of high-risk product variants, minimizing blockage probabilities in the quality inspection stage. AD prioritizes engines with earlier due dates from the same risk category to prevent unnecessary delivery delays. The study also evaluates the impact of extending planning horizons beyond the current 3-day standard. Results demonstrate the effectiveness of the AB and AD strategies, yielding a 10% improvement in average current throughput. Moreover, adopting a 5-day planning horizon leads to an 18% decrease in average delay compared to the current 3-day horizon.

Harvest scheduling and transport are crucial for the delivery performance of a wood supply chain, ensuring that product volumes are delivered on time and in the right quality. This paper suggests three delivery performance objectives for the wood supply chain: service level, lead time, and throughput. It presents a framework for optimizing these objectives by finding trade-off solutions using simulation-based multi-objective optimization. Due to the complexity of the wood supply chain, discrete-event simulation is used to evaluate delivery performance from harvesting to customer delivery. The harvest scheduling problem is formulated as a permutation optimization solved by a customized NSGA-II algorithm with a comparison of three crossover mechanisms implemented: Random Key Simulated Binary Crossover, Order Crossover, and Partially Mapped Crossover, specifically designed for general forestry permutation optimization problems. Analyzed with a heatmap for the visualization of the mapping of the decision space to the Pareto-optimal solutions, the results indicate that the Partially Mapped Crossover performs best. Other simulation-optimization generated data are processed and visualized in an interactive, web-based dashboard for decision-makers, such as forest managers, allowing them to analyze meta-heuristically optimized solutions in both the solution and decision spaces, guiding them to find the most suitable harvest schedules. 

The Transfer Line Balancing Problem (TLBP) is characterized as the challenge of optimally distributing tasks across various workstations in an automated machining line to ensure its maximum efficiency. This problem holds pivotal importance for industries reliant on high-volume production, such as the automotive and aerospace sectors, where it directly influences the overall productivity and cost efficiency of the manufacturing process. TLBP has been studied for over two decades, and many problem variants and solution approaches have been devised to address real-world challenges. Despite the long history of the topic, no review study exists to shed light on its past, current, and future developments. This study conducted a systematic literature review on TLBP to identify and address the research gaps, focusing on classifying existing studies. A tuple notation classification framework has been introduced to organize TLBP research based on system configuration, problem characteristics, and optimization objectives. This framework offers a structured overview of the field, clarifying the current state of research and highlighting prospective research pathways. Consequently, this review study establishes itself as a foundational guide for academics and industry professionals interested in TLBP studies.

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.

The recent Industry 4.0 trend, followed by the technological advancement of collaborative robots, has urged many industries to shift towards new types of assembly lines with human-robot collaboration (HRC). This type of manufacturing line, in which human skill is supported by robot agility, demands an integrated balancing and scheduling of tasks and operators among the stations. This study attempts to deal with these joint problems in the straight and U-shaped assembly lines while considering different objectives, namely, the number of stations (Type-1), the cycle time (Type-2), and the cost of stations, operators, and robot energy consumption (Type-rw). The latter type often arises in the real world, where multiple types of humans and robots with different skills and energy levels can perform the assembly tasks collaboratively or in parallel at stations. Additionally, practical constraints, namely robot tool changes, zoning, and technological requirements, are considered in Type-rw. Accordingly, different mixed-integer linear programming (MILP) models for straight and U-shaped layouts are proposed with efficient lower and upper bounds for each objective. The computational results validate the efficiency of the proposed MILP model with bounded objectives while addressing an application case and different test problem sizes. In addition, the analysis of results shows that the U-shaped layout offers greater flexibility than the straight line, leading to more efficient solutions for JIT production, particularly in objective Type-2 followed by Type-rw and Type-1. Moreover, the U-shaped lines featuring a high HRC level can further enhance the achievement of desired objectives compared to the straight lines with no or limited HRC.

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.

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.

The transfer lines are among highly complex automated production systems that manufacture a large volume of identical or similar products with high demand. Recently, the design of environmentally friendly production systems has become the focus of more and more enterprises as one of the sustainable manufacturing strategies. In light of the recent energy-efficient manufacturing trends, this paper investigates the transfer line balancing problem (TLBP), considering both efficiency and energy aspects. The problem arises in the automated transfer lines equipped with dedicated machining centers and automated material handling systems, producing a large volume of specific products. The operations are performed at machines by realizing particular processing requirements, including machining features, inclusions, and exclusions considerations. The objectives to be minimized are the cycle time and the total energy consumption. The latter objective consists of machines’ operating and non-operating energy costs. The problem is first formulated as a mixed-integer linear programming model. Due to the problem’s complexity, an efficient multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm (NSGA-II) is also proposed. The performance of the proposed algorithm is compared with the e-constraint method in terms of the Pareto-front metrics while solving various test problems and a case study. The computational results show the effectiveness of the proposed algorithm in dealing with the TLBP.