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In the very few recently published paper by Alavidoost et al. [1] , they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2] , as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption. [ABSTRACT FROM AUTHOR]

The paper [[10]] considers an assignment problem and some modifications which can be converted to routing, distribution, or scheduling problems. This special issue of I Algorithms i is dedicated to recent developments of scheduling algorithms and new applications. References 1 Werner F., Burtseva L., Sotskov Y. Special Issue on Algorithms for Scheduling Problems. For this problem, a hybrid metaheuristic algorithm is presented which combines a genetic algorithm with a so-called spotted hyena optimization algorithm. [Extracted from the article]

This paper considers the optimisation of Mixed-model assembly lines with continuous paced line control. The two minimisation goals have a mixed-integer linear multi-objective dispute. The paper proposes a criterion-space method to define the Pareto front for this class of problems. The method combines Pareto fronts obtained from integer solutions and gradually refines them until the instance's global front is determined. Comparing paced to unpaced line controls can be challenging, since they can produce the same cycle time given sufficiently long line lengths or buffers. Hence, determining Pareto fronts between cycle time and line length for paced lines allows meaningful comparisons between line controls. An industrial case study shows that line length acts as continuously distributable buffers for paced lines, leading to weaker diminishing returns. This result suggests that paced lines are more efficient than unpaced ones for lower cycle time ranges. [ABSTRACT FROM AUTHOR]

As the core issue for improving manufacturing system performance (such as product quality, production efficiency and cost, etc.), modeling, analysis and optimization of manufacturing systems have always been studied towards smart manufacturing. The paper I KGAssembly: Knowledge graph-driven assembly process generation and evaluation for complex components i , by Bin Zhou, Jinsong Bao, Zhiyu Chen and Yahui Liu, proposes a knowledge graph-driven assembly process generation and evaluation method for complex components. [Extracted from the article]

Industry 4.0 enables the manufacturing of the right products/services in the right quantity, to be delivered to the right place for the right customers in the right time with right manufacturing costs, while keeping the environment safe. Service and manufacturing industries have changed significantly in the recent years, resulting in new manufacturing concepts such as Industry 4.0. Industry 4.0 has shown great potential to transform traditional manufacturing systems into advanced manufacturing and service systems providing effective and efficient production, planning and control. [Extracted from the article]

Classical assembly line balancing (ALB) models assume constant cycle times during production. However, this assumption oversimplifies the actual situation, especially in small batch production of up to a few hundred units, since employees can significantly improve their performance thanks to the learning effect, causing task times to decrease. Several researchers have realised the importance of the effect of learning in ALB. However, only a limited number of papers have so far addressed this issue. This is problematic, since ignoring the learning effect in ALB may lead to inaccurate results and by extension misleading conclusions. This study summarises the main contributions in the field of ALB that focus on the learning effect. First, assembly lines (ALs) and ALB problems are characterised. Next, the importance of the learning effect in ALB is highlighted, and the main learning curve (LC) models are introduced. Finally, an exhaustive review of the main contributions in the field of ALB and learning effect is provided. The results highlight that many problems in this area need to be investigated further, in relation to both conceptual model building and the development of algorithms for solving practical size problems. [ABSTRACT FROM AUTHOR]

Issues 1 and 2 of 2024 are composed of papers selected for the special section in memory of Dr. Jean-Marie Proth. Some papers are extended versions of articles presented at the 10th IFAC MIM 2022 conference (hub.imt-atlantique.fr/mim2022/) in the invited sessions in memory of Dr. Proth. [ABSTRACT FROM AUTHOR]

The Robotic Assembly Line Balancing Problem (RALBP) is a joint optimisation problem that is concerned with assigning both assembly operations and robots to workstations that are placed within a straight line. RALBP-2 is the particular problem where the cycle time, which is the maximum time spent on a workstation by the product being assembled, is minimised while the number of workstations is fixed. Sequence-dependent setup times are considered which raises the problem of sequencing the operations assigned to each workstation. Both the durations of the operations and the setup times depend on the robot. Two different variants are identified from literature. The first variant assumes that, given a set of types of robots, each type of robot can be assigned to multiple workstations without any limitation. Given a set of robots, the second variant forces each robot to be assigned to at most one workstation. Both assumptions are studied in this paper. The particular case of a given giant sequence of operations is solved thanks to a polynomial optimal algorithm. The latter algorithm, called split, is then embedded in a metaheuristic framework that explores the space of giant sequences. Benchmark data sets from literature are considered in the experimental section. A comparative study with other methods from literature shows the competitiveness of the suggested approach. [ABSTRACT FROM AUTHOR]

Collaborative robots (cobots) are important components of the Industry 4.0 paradigm and smart manufacturing. Cobots are known for their ability to interact with the operators in a shared workspace. Due to their spread in the last decade, cobot research proliferated. However, most individual studies focused on specific aspects of cobot deployment, and only scant attention was given to their evaluation (mostly not based on productivity criteria). Thus, better support is needed for cobot acquisition and deployment decisions. This paper answers this need by presenting a summary of the major considerations related to cobots acquisition and deployment, and providing a productivity analysis procedure that supports cobot acquisition and deployment decisions. Defining the cobots' required characteristics and capabilities, effectively narrows the possible selection of cobots. However, it does not give information as to the economic value of acquiring and deploying a specific cobot. So, in addition to cobots' characteristics and capabilities, this paper presents computational techniques to analyse and support this decision for a single workstation per se, and for a station in an assembly line. The difference between these two cases is discussed and analysed, and corresponding models are presented with computational examples. [ABSTRACT FROM AUTHOR]

The solution to the assembly line balancing with the hierarchical worker assignment problem (ALBHWP) provides the optimal allocation of workers and tasks to the stations that minimise the total worker cost. In the ALBHWP, tasks differ in terms of the qualification requirements of workers, and the qualification levels of workers are hierarchical. In the hierarchical workforce structure, a lower qualified worker can be replaced by higher qualified ones with higher costs, while the vice versa is not applicable. This problem has only been studied for straight assembly lines so far. In this paper, we introduce the ALBHWP for U-shaped assembly lines. We developed integer and constraint programming models for solving the ALBHWP and compared their effectiveness using an extensive set of benchmark instances. We solved the ALBHWP for straight and U-shaped assembly lines comparatively. Constraint programming models have been statistically proven to provide better quality solutions faster than integer programming models. Besides, the CP model outperforms the only available metaheuristic in the literature for the S-ALBHWP in almost all problem sizes. Another observation is that a U-shaped line design is more cost-effective than a straight line design, but solving the ALBHWP for U-shaped lines is more difficult regarding computational complexity. [ABSTRACT FROM AUTHOR]

Energy efficiency has become a major concern for manufacturing systems, due to industry being the largest user of scarce, finite energy sources, and also to recent events which have pushed energy prices to alarming levels. In the present Industry 4.0 context, Reconfigurable Manufacturing Systems (RMS) are therefore one of the most promising manufacturing paradigm. In this paper, we investigate the suitability of one of the most common types of RMS, the Parallel-Serial manufacturing line with Crossover, to help minimise the peak of the electric power consumption. More specifically, the balancing of such a production line is studied, so as to integrate power peak minimisation from the design stage. Thus, we define the Parallel-Serial-with-Crossover Assembly Line Balancing Problem with Power Peak Minimization, a new combinatorial NP-hard problem. We also propose a suitable time-indexed Integer Linear Program that integrates balancing and scheduling decisions and a matheuristic algorithm designed to tackle large-size instances. Both approaches are tested on a wide set of instances. The computational results show that relevant power peak reductions can be achieved (33% on average), opening up promising perspectives from both algorithmic and managerial viewpoints. [ABSTRACT FROM AUTHOR]

This paper proposes new algorithms for the assembly line balancing problem with hierarchical worker assignment (ALBHW). The ALBHW appears in real industrial contexts, where companies deal with a multi-skilled workforce. It considers task execution times that vary depending on the worker type to whom the task is assigned. Qualification levels among workers are ranked hierarchically, where a lower qualified worker costs less but requires larger execution times then a higher qualified one. The aim is to assign workers and tasks to the stations of an assembly line, in such a way that cycle time and precedence constraints are satisfied, and the total cost is minimised. In this paper, we first present a mathematical model and improve it with preprocessing techniques. Then, we propose a constructive heuristic and a variable neighbourhood descent that are useful to solve large instances. Extensive computational experiments on benchmark instances prove the effectiveness of the algorithms. [ABSTRACT FROM AUTHOR]

An important and highly complex process in the automotive industry is the balancing of the assembly lines. Optimally distributing jobs among the lines in order to obtain the highest efficiency is mostly done manually, taking a lot of time. This paper aims to automate the process of line balancing fora real-world test case. Automotive assembly lines are highly complex, and multiple factors have to be considered while balancing the lines. All factors relevantin a case study at VDL Nedcar are considered, namely, mixed-model production, sequence-dependent setup times, variable workplaces with multiple operators and multiple assignment constraints. A Genetic Algorithm (GA) is proposed to solve the formulated balancing problem and to act as a decision support system. Results on newly proposed benchmark instances show that the solution is dependent on the relation between the takt time and processing time of jobs, as well as the setup times. In addition, results of a real-life case study show that the proposed GA is effective in balancing a real-world assembly line and that it can both increase the efficiency of the line and decrease the variance in operating time between all model variants when compared to current practice. [ABSTRACT FROM AUTHOR]

Assembly lines are production flow systems wherein activities are organised around a line consisting of various workstations through which the product flows. At each station, the product is assembled through a subset of operations. The assembly line balancing problem (ALBP) consists of allocating operations between stations to maximise the system efficiency. In this study, a variable-depth local search algorithm is proposed for solving simple assembly line balancing problems (SALBPs), which are the most widely studied versions of the ALBP. Although the state-of-the-art techniques for solving the SALBP consist of exact enumeration-based methods or heuristics, this paper proposes a local search-based heuristic using variable-length sequences that allow the solution space to be efficiently explored. The proposed algorithm improves the best solution known for multiple instances reported in the literature, indicating that its efficiency is comparable to those of the state-of-the-art method for solving the SALBP. Moreover, the characteristics of the instances for which the proposed procedure provides a better solution than previously reported construction procedures are investigated. [ABSTRACT FROM AUTHOR]

In the mixed-model assembly line balancing and sequencing problem (MALBSP), workstations are assumed to be constantly available. The failure of any workstation will make the entire assembly line stop working. Preventive maintenance (PM) is a way to maintain the workstation before its failure, reduce unexpected downtime, and prolong its useful life. Previous studies have considered PM scenarios (PMS) in the simple and U-shaped assembly line to improve production efficiency and smoothness effectively, but not in the mixed-model assembly line. This paper fills this research gap, and the MALBSP considering PMS (MALBSP_PMS) is studied in this paper. A mixed-integer linear programming model is proposed to minimize makespan and task alteration. A migrating birds optimization algorithm is improved (IMBO) to obtain well-distributed Pareto frontier solutions. This algorithm designs a restart mechanism and an intra-population crossover operator to avoid falling into the local optimal and enhance its searchability. Experimental results demonstrate the effectiveness of two improvements and the IMBO algorithm. In addition, a real-world case study is introduced to illustrate the importance of considering PM scenarios in MALBSP. [ABSTRACT FROM AUTHOR]

Assembly lines (conveyors) are traditional means of large-scale and mass-scale productions. An assembly line balancing problem is needed for optimizing the assembly process by configuring and designing an assembly line for the same or similar types of final products. This problem consists of designing the assembly line and distributing the total workload for manufacturing each unit of the fixed product to be assembled among the ordered workstations along the constructed assembly line. The assembly line balancing research is focused mainly on simple assembly line balancing problems, which are restricted by a set of conditions making a considered assembly line ideal for research. A lot of published research has been carried out in order to describe and solve (usually heuristically) more realistic generalized assembly line balancing problems. Assembly line designing, balancing and scheduling problems with not deterministic (stochastic, fuzzy or uncertain) parameters have been investigated in many published research works. This paper is about the design and optimization methods for assembly and disassembly lines. We survey the recent developments for designing, balancing and scheduling assembly (disassembly) lines. New formulations of simple assembly line balancing problems are presented in order to take into account modifications and uncertainties characterized by real assembly productions. [ABSTRACT FROM AUTHOR]

In recent years, environmental concerns have increased the need for design and optimisation of closed-loop supply chain (CLSC) networks. Majority of the existing research papers consider the CLSC network designing and line balancing decisions separately. However, this approach may lead to sub-optimal designs due to the interdependency of these decisions. To this end, this paper investigates a CLSC network designing problem integrated with assembly and disassembly line balancing under demand and return uncertainty. The proposed CLSC network contains manufacturers, remanufacturers, assembly centres, intermediate centres (where disassembly lines are located), and customer centres. A new mixed integer non-linear programming model for the proposed problem is developed. Furthermore, an enhanced decomposition approach is developed to solve the proposed model. Computational results, based on randomly generated problem instances, show the efficiency of proposed enhanced decomposition approach. Specifically, results shows that the proposed enhanced decomposition approach leads to significantly smaller running times in comparison with an existing decomposition approach. Results also highlight the importance of integrating supply chain network designing and line balancing decisions. [ABSTRACT FROM AUTHOR]

In an assembly system, balancing the line and designing the part feeding setting play a fundamental role in guaranteeing efficiency and effectiveness. Although these two aspects are interrelated, they are usually performed subsequent to each other, with the part feeding decisions only being made once the line has already been balanced. However, the simultaneous solving of the assembly line balancing and part feeding problems can lead to interesting benefits. In this paper, an innovative approach for solving the Joint Assembly Line Balancing and Feeding Problem (JALBFP) is proposed, to achieve an integrated balancing of assembly tasks and parts picking/kitting and a fully synchronised assembly-feeding system. The aim is to minimise the operational system cost, including workforce, space and transportation costs. The Mixed Integer Linear Programming (MILP) model is applied to several instances and two case studies and compared to a hierarchical approach. The proposed model leads to a significant improvement in reducing the number of assembly stations required and the inventory level at the line, where space is notoriously scarce, also avoiding workforce oversizing in the feeding system. The strength of the model relies on its formulation and on its versatility, which makes it applicable to different assembly sectors. [ABSTRACT FROM AUTHOR]

There is a disruptive impact of smart technologies in the 21st century, which is transforming the traditional industry and the healthcare sector into the Industry 5.0 and the Healthcare 5.0. Acknowledgments The Guest Editors would like to thank all authors and reviewers for their invaluable contributions towards the success of this special issue. [Extracted from the article]

Two-sided assembly lines balancing problem has been extensively studied in recent years. However, only limited attention has been paid to balancing mixed-model two-sided assembly lines problem (MTALBP). Moreover, the majority of balancing research assumes the setup as negligible, although it is ubiquitous in the assembly process. As the non-increment activities, setups occur in two ways: forward and backward setups. According to our best knowledge, no published work in literature on MTALBP has simultaneously considered forward and backward setups. In this paper, the problem of balancing mixed-model two-sided assembly lines with setups (MTALBPS) is considered. The purpose of this paper is twofold. The primary objective is to develop a mixed-integer programming (MIP) mathematical model to formulate the type-I problem of MTALBPS. The secondary objective is to propose an effective variable neighbourhood search (VNS) algorithm to solve it, especially for the large-sized problems. In addition, to test the effectiveness of the proposed approaches, a number of test problems from the literature with up to 148 tasks are solved and compared with the lower bound. The results demonstrate that the proposed algorithm is effective and produces very close results to the lower bound in a reasonable time. [ABSTRACT FROM AUTHOR]

Smoothing the workloads among the stations of an already installed assembly line is one of the major objectives in assembly line (re-)balancing. In order to find a feasible task-station assignment that distributes the total workload as equal as possible, two exact task-oriented branch-and-bound algorithms have recently been proposed. In this paper, we systematically analyse their effectiveness in solving the workload smoothing problem on simple assembly lines. In our experiments, we also examine the performance of a state-of-the-art mathematical programming solver and a 'combined' exact branch-and-bound procedure that integrates components of the two algorithms from the literature. In terms of theory, we show the equivalence of two recently developed local lower bounding arguments and suggest a slight improvement of the bound. We also propose an enhanced feasibility test. [ABSTRACT FROM AUTHOR]

Purpose: In a kitting supply system, the occurrence of material-handling errors is unavoidable and will cause serious production losses to an assembly line. To minimize production losses, this paper aims to present a dynamic scheduling problem of automotive assembly line considering material-handling mistakes by integrating abnormal disturbance into the material distribution problem of mixed-model assembly lines (MMALs). Design/methodology/approach: A multi-phase dynamic scheduling (MPDS) algorithm is proposed based on the characteristics and properties of the dynamic scheduling problem. In the first phase, the static material distribution scheduling problem is decomposed into three optimization sub-problems, and the dynamic programming algorithm is used to jointly optimize the sub-problems to obtain the optimal initial scheduling plan. In the second phase, a two-stage rescheduling algorithm incorporating removing rules and adding rules was designed according to the status update mechanism of material demand and multi-load AGVs. Findings: Through comparative experiments with the periodic distribution strategy (PD) and the direct insertion method (DI), the superiority of the proposed dynamic scheduling strategy and algorithm is verified. Originality/value: To the best of the authors' knowledge, this study is the first to consider the impact of material-handling errors on the material distribution scheduling problem when using a kitting strategy. By designing an MPDS algorithm, this paper aims to maximize the absorption of the disturbance caused by material-handling errors and reduce the production losses of the assembly line as well as the total cost of the material transportation. [ABSTRACT FROM AUTHOR]

The objective of assembly line balancing (ALB) is to minimize the number of workstations organized to perform tasks with precedence constraints. An important element of proper assignment of tasks to workstations is the consideration of skill requirement of tasks, and skill level of workers. Some tasks may require special skills, some workers might not be able to perform complicated tasks, and some workers might be specialized for specific tasks. This paper provides a general framework to model skill requirements and skill conditions for assembly line balancing models. Three types of skill constraints are defined. Low skill constraints determine workstations for workers who are able to perform only some simple tasks. High skill constraints consider tasks which require higher than average skills of workers. Finally, exclusive skill constraints consider situations where a group of workers is specialized in a subset of tasks. The paper summarizes the mathematical description of the different skill constraints, and shows how simple assembly line balancing models can be completed with skill considerations. The effect of skill constraints on the optimal solution is illustrated with a sample problem. [ABSTRACT FROM AUTHOR]

Assembly and disassembly are important activities in the manufacturing/remanufacturing process. Although the line balancing problems of them have been extensively discussed in the existing literature, they are rarely integrated into one system. In this paper, a hybrid production line balancing problem is adopted while considering the similarity between the assembly and disassembly tasks. First, to better reflect the uncertainty existing in the actual production environment, a mathematical model of the multi-objective stochastic hybrid production line balancing problem is presented, in which task disassembly times are assumed to be random variables with known normal probability distributions. Then, a hybrid VNS-NSGA II algorithm combining variable neighbourhood search (VNS) and non-dominated sorting genetic algorithm II (NSGA II) is proposed to solve the problem. VNS is embedded into NSGA II as a local search to improve the quality of the solutions found by the NSGA II at each generation. Finally, the effectiveness of the proposed method is verified by a case study, and the superiority of hybrid production line is reflected by comparing the solutions of the independent production line with the hybrid production line. Computational comparisons demonstrate the potential benefits of the hybrid production line and the proposed method. [ABSTRACT FROM AUTHOR]

Assembly lines have been critical to the successful operation of manufacturing plants over the years. This is mainly attributed to the ability of assembly lines to adjust to the needs of the industry at any given time. Therefore, it is imperative that assembly lines adjust to the era of smart manufacturing commonly termed as Industry 4.0. This latest trend in industrial revolution has many facets, changing the way products are designed, manufactured and delivered. One such standout feature of Industry 4.0, is how it has changed the way products are ordered. Industries are moving from a conventional make-to-stock approach to a make-to-order approach. This paper looks at how a customized ordering system can be modelled for a mixed model assembly line. The research is based on a case study using a water bottling plant which can bottle 500 ml and 750 ml bottles. The economic viability and a preliminary model for the project was done and documented previously. This paper is structured such that it initially provides an introduction to the research. Secondly, it provides an overview of the previous model and its limitations. Thirdly, the paper shows how the current model has been developed in Simulink to introduce customized ordering. Finally, the results obtained from the simulation are shown. The primary aim of this research is to develop a Simulink model for customizing the input of a water bottling plant. This model can then be used to optimize the time to manufacture water bottles and contribute to the broader research area of Mixed Model Stochastic Assembly Line Balancing. [ABSTRACT FROM AUTHOR]

Assembly line balancing usually presupposes binary task-station assignments. Some authors have previously described efficiency increases due to fractional task allocations or work-sharing. However, the internal storage requirements for such efficiency increases have not been analytically described. This paper defines the Fractional Allocation Assembly Line Balancing Problem and presents mixed-integer linear programming models to bridge that gap. The main opportunity afforded by the studied flexibility is increased throughput, which is associated to higher internal storage costs. Worst-case analyses define mathematical expressions for these costs both for paced (line length) and unpaced lines (buffers). A screening on a 1050-instance dataset is conducted. Results suggest that fractional allocations can often allow better resource utilisation with relatively low costs: the higher space requirement costs are often one-time investments, while lower cycle time represents fundamentally continuous gains. Lastly, the proposed formulation was adapted and applied to industrial data. This mixed-model assembly line case study suggests that fractional allocations can also lead to more robust balancing regarding demand uncertainty. [ABSTRACT FROM AUTHOR]

The assembly line balancing problem can completely vary from one production line to the other. This paper deals with a realistic assembly line for the automotive industry inspired by Fiat Chrysler Automotive in North America and Parskhodro in Iran (both large-scale automotive companies). This problem includes some specific requirements that have not been studied in the literature. For example, the assembly line is five-sided, and workers can move along these sides. Due to the limited workspace, all the sides cannot work simultaneously at one station. First, a mixed integer linear programming model is proposed for the problem. Then, the model is improved to have a tighter linear relaxation. Moreover, an effective logic-based Benders' decomposition algorithm is developed. After careful analysis of problem's structure, three propositions are introduced. The master problem is well restricted by eight valid inequalities. Two different sub-problem types are defined to extract more information from the master problem's solution. In this case, the algorithm adds effective cuts that reduce the solution space to the extent possible at each iteration. Thus, the number of iterations is significantly cut down. The performance of the model and algorithm, as well as improvement made on both, is evaluated. [ABSTRACT FROM AUTHOR]

Due to various complexities in real-world industry and service systems, many optimization problems cannot be solved effectively by traditional methods. The third paper titled "Multi-objective microservice deployment optimization via a knowledge-driven evolutionary algorithm" by Ma et al. presents a knowledge-driven evolutionary algorithm to solve the problem model and seek the optimal deployment and startup strategy of microservice instance in different resource centers. The proposed algorithm outperforms nine ABC algorithms and three differential evolution algorithms in solving 32 benchmarking function optimization instances. [Extracted from the article]

Some of the solutions reported in a recent paper (Akyol and Baykasoglu in J Intell Manuf 30(2):557–573, 2019. https://doi.org/10.1007/s10845-016-1262-6) are infeasible, jeopardising the conclusion that "best results for the 75% of the 320 test instances for the ALWABP-2" were obtained. In this note, we explore the contributions of the paper, show that some of the solutions are indeed infeasible and point out possible inconsistencies in the employed heuristic that may be causing this issue. [ABSTRACT FROM AUTHOR]

Ergonomics has a significant impact on productivity and human safety in manual assembly lines. For this reason, several studies in recent years have proposed including ergonomics in assembly lines. However, most works are focused on simple assembly lines, while only a few studies exist for mixed-model assembly lines (MMALs). Thus, in this paper, we propose a new methodological approach to include physical fatigue and rest allowance (RA) as ergonomic parameters in MMAL problems. For the balancing problem, we propose a linear mathematical model that minimises the cycle time by including RA. For the sequencing decision, we develop a heuristic approach that assigns workers to workstations according to the workers' age and their related maximum physical capacity. Then, we propose a linear sequencing model that minimises the work-overload by also evaluating RA to assign each worker according to her or his features. Finally, to complete the study, we test the mathematical models in a real case application and provide a detailed discussion of the results to highlight the benefits we can achieve with this approach. [ABSTRACT FROM AUTHOR]