Högskolan i Skövde

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.

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.

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.

This paper studies the assembly line balancing problem with collaborative robots in light of recent efforts to implement collaborative robots in industrial production systems under random processing time. A stochastic version with uncertain human processing time is considered for the first time. The issue is defined by the potential for simultaneous human and robot task execution at the same workpiece, either in parallel or in collaboration. We provide stochastic mixed-integer programming based on Monte Carlo sampling approach for the balancing and scheduling of collaborative robot assembly lines for this novel issue type. In order to minimise the line cost including fixed workstation operating costs and resource costs caused by exceeding cycle time, the model determines both the placement of collaborative robots at stations and the distribution of work among humans and robots.

In line with Industry 5.0, ergonomic factors have recently received more attention in balancing assembly lines to enhance the human-centric aspect. Meanwhile, today’s mass-customized trend yields manufacturers to offset the assembly lines for different product variants. Thus, this study addresses the mixed-model assembly line balancing problem (MMALBP) by considering worker posture. Digital human modeling and posture assessment technologies are utilized to assess the risks of work-related musculoskeletal disorders using a method known as rapid entire body analysis (REBA). The resulting MMALBP is formulated as a mixed-integer linear programming (MILP) model while considering three objectives: cycle time, maximum ergonomic risk of workstations, and total ergonomic risks. An enhanced non-dominated sorting genetic algorithm (E-NSGA-II) is developed by incorporating a local search procedure that generates neighborhood solutions and a multi-criteria decision-making mechanism that ensures the selection of promising solutions. The E-NSGA-II is benchmarked against Epsilon-constraint, MOGA, and NSGA-II while solving a case study and also test problems taken from the literature. The computational results show that E-NSGA-II can find promising Pareto front solutions while dominating the considered methods in terms of performance metrics. The robustness of E-NSGA-II results is evaluated through one-way ANOVA statistical tests. The analysis of results shows that a smooth distribution of time and ergonomic loads among the workstations can be achieved when all three objectives are simultaneously considered.