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In the complex and ever-changing manufacturing environment, maintaining the long-term steady and efficient work of the assembly line is the ultimate goal pursued by relevant enterprises, the foundation of which is a balanced load. Therefore, this paper carries out research on the two-sided assembly line balance problem (TALBP) for load balancing. At first, a mathematical programming model is established with the objectives of optimizing the line efficiency, smoothness index, and completion time smoothness index of the two-sided assembly line (TAL). Secondly, a deep reinforcement learning algorithm combining distributed proximal policy optimization (DPPO) and the convolutional neural network (CNN) is proposed. Based on the distributed reinforcement learning agent structure assisted by the marker layer, the task assignment states of the two-sided assembly and decisions of selecting tasks are defined. Task assignment logic and reward function are designed according to the optimization objectives to guide task selection and assignment. Finally, the performance of the proposed algorithm is verified on the benchmark problem. [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 industrial technique known as assembly line balancing is used to increase the efficiency of balancing in production lines. By increasing line efficiency, the assembly line balance problem reduces the number of workstations. The assembly line balancing technique improves line production by utilizing the priority limits for task assignment in the workstation. By distributing the tasks among the workstations according to priority constraints, assembly line balancing improves the efficiency of the line. Line balance issues are observed to fall under the category of sequence-dependent issues while deciding which part numbers to allocate in the workstation. When choosing which part numbers to allocate in the workstation, line balancing concerns are seen to fall under the category of sequence-dependent issues. Mathematical models, algorithms, and simulation software are just a few of the techniques that have been used in the past to handle assembly line balancing concerns. In order to decrease workstation loads and boost assembly line productivity, an effort has been made to review the PCB manufacturing assembly line balancing problem in the current research study. The goal of the study is to cut down on the workloads and waiting times for each task on a manufacturing line. In order to reduce job waiting times and maximize station workloads, U Type Assembly Line is preferred in this study. [ABSTRACT FROM AUTHOR]

This research was conducted to enhance the productivity of the targeted sewing line at the ABC textile company. The existing SAMs, the capacity of the sewing line for various operations, and several required machines were collected. Moreover, the obtained figures were observed and analyzed by using time study and motion study, and certain improvements were made at the sewing line. Results indicated that SAMs for operations A and B were minimized by -13.64% and -14.54% respectively; whereas, SAM for operation C was increased by 16.67%. Machine requirement for operation C was increased by 100%; moreover, the capacity for operations A, B, and C was increased by 12%, 12.69%, and 40% respectively. When it is to production activities, the little improvements play a significant role in boosting the productivity of the production system. Optimal allocation of human resources, machines, and time are the benefits of line balancing. This can be made possible with the application of the line-balancing framework. With every passing day, the nature and type of article vary at the sewing line thus it is highly needed for the company to get the model developed for an automated line balancing application. Moreover, this work can be extended by the development of a line-balancing framework considering the nature of production.

This study presents an approach to solving the assembly line balancing problem (ALBP) using the Methods-Time Measurement (MTM) time standard and simulation software. ALBP is a common problem in manufacturing where a set of tasks with fixed times must be assigned to a series of sequential workstations in order to minimize the total idle time and reduce the assembly cost per product. This study uses MTM, a widely used production process scheduling method, to create a new time analysis of an assembly process that was previously balanced using the Work-Factor method and time study. This literature review shows that there are a lack of combinations of updated time analyses with newer simulation approaches in the current literature, and this was the motivation for the present work. An assembly line simulation was performed using Simio software to evaluate different design options and operating scenarios. The results show that the use of MTM and simulation can help minimize idle time and improve assembly line performance, thereby reducing costs and increasing efficiency. This study shows that the approach of using MTM and simulation is effective in solving ALBP and is a useful tool for manufacturing companies to improve the performance of their assembly lines and reduce costs. [ABSTRACT FROM AUTHOR]

Conflict mitigation is significant for project performance and sustainable relationships in construction subcontracting. The contract, whose design is often characterized by contractual complexity, is among the most important governance mechanisms influencing the behaviors of the general contractor and the subcontractor. However, inconsistent findings still exist in the relationship between contractual complexity (i.e., the explicitness and elaborateness of a contract) and conflict in terms of whether the relationship is "positive" or "negative." To revisit the relationship in the construction subcontracting context, this study adopts a transaction governance perspective and focuses on the effects of formal rules (reflected as contractual complexity and contractual enforcement) and informal rules (reflected as organizational culture) to propose a research framework. To test the framework, the authors conducted a questionnaire survey, receiving 270 quantitative responses from general contractors and subcontractors. The results show that contractual complexity has U-shaped relationships with task conflict and relationship conflict, respectively. The optimal degrees of contractual complexity for reducing task conflict and relationship conflict are within the moderate range. The increase in contractual enforcement strengthens the curvilinearity of the relationship between contractual complexity and task conflict and moves the optimal degree of contractual complexity to the left. The increase in organizational culture distance weakens the curvilinearity of the respective relationships between contractual complexity and task conflict and relationship conflict, and moves the optimal degrees of contractual complexity to the right. This study contributes to the literature on conflict and transaction governance, and also helps general contractors and subcontractors draft contracts with the optimal degree of complexity for conflict mitigation according to their contractual enforcement and organizational culture distance. [ABSTRACT FROM AUTHOR]

This paper presents the novel method variable neighbourhood strategy adaptive search (VaNSAS) for solving the special case of assembly line balancing problems type 2 (SALBP-2S), which considers a limitation of a multi-skill worker. The objective is to minimize the cycle time while considering the limited number of types of machine in a particular workstation. VaNSAS is composed of two steps, as follows: (1) generating a set of tracks and (2) performing the track touring process (TTP). During TTP the tracks select and use a black box with neighborhood strategy in order to improve the solution obtained from step (1). Three modified neighborhood strategies are designed to be used as the black boxes: (1) modified differential evolution algorithm (MDE), (2) large neighborhood search (LNS) and (3) shortest processing time-swap (SPT-SWAP). The proposed method has been tested with two datasets which are (1) 128 standard test instances of SALBP-2 and (2) 21 random datasets of SALBP-2S. The computational result of the first dataset show that VaNSAS outperforms the best known method (iterative beam search (IBS)) and all other standard methods. VaNSAS can find 98.4% optimal solution out of all test instances while IBS can find 95.3% optimal solution. MDE, LNS and SPT-SWAP can find optimal solutions at 85.9%, 83.6% and 82.8% respectively. In the second group of test instances, we found that VaNSAS can find 100% of the minimum solution among all methods while MDE, LNS and SPT-SWAP can find 76.19%, 61.90% and 52.38% of the minimum solution. [ABSTRACT FROM AUTHOR]

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This study aims to improve the assembly manufacturing process to solve the workload imbalances by combining value stream mapping (VSM) and ranked positional weight (RPW). An empirical study was conducted in a defense manufacturing firm located in Indonesia. The study specifically focused on 155 components and 56 tasks distributed among 43 assembly workstations in one weapon product. The results of the analysis showed a significant reduction in the total cycle time, from 5121 s (85.35 min) to 3620 s (60.33 min), or a decrease of 29%. Additionally, the study found improvements in the balance of the assembly line as measured by balance delay, line efficiency, and smoothness index performance indicators. The application of VSM and RPW in this study is unique in the context of the defense industry, as it provides empirical analysis on cycle time and assembly line balance, which is rarely studied. The results of this study contribute to the advancement of literature in the field and provide valuable insights for other organizations in the defense industry and other manufacturing industries. By improving the efficiency and balance of the weapon assembly line, this study has the potential to increase productivity and reduce waste. [ABSTRACT FROM AUTHOR]

At present, the old production management mode has become a stumbling block to the development of enterprises, and the high-end manufacturing technology is still not mature enough. This research mainly discusses the intelligent manufacturing management system based on data mining in artificial intelligence energy-saving resources. The enterprise business management system cannot accurately and timely grasp the actual situation of the production site, and the accuracy and feasibility of the upper-level planning cannot be guaranteed. At the same time, on-site personnel and equipment cannot get practical production plans and production instructions in time, resulting in product backlogs and excessive inventory. On the other hand, equipment is idle and resources are wasted, and the workshop scheduling system loses the corresponding scheduling role. The development of this system is mainly composed of front-end technology, back-end technology and front-end and back-end interaction technology. The interface design of the front end is mainly completed by the windows form application in c#. The interaction between the front and back ends is mainly realized by programming in each control of the form application. Back-end technology is the core content of the system, mainly including two key technologies: mixed programming of C #. Net and MATLAB and C # connecting SQL Server database. The system mainly includes five sub-functional modules: order management, material management, mixed model assembly line balance, assembly line logistics scheduling and system management. Order management and material management are the basis of the system, which provides parameter input for the balance of assembly line and logistics scheduling. The balance of mixed model assembly line is the core function of the system. The balance of mixed model assembly line is carried out by calling the intelligent algorithm written in MATLAB, and the optimal assembly scheme of workstation is displayed to the front end of the system, which reflects the intelligent characteristics of production control system for intelligent manufacturing. The logistics scheduling of assembly line takes the balance result of mixed model assembly line as the premise, takes the balance result as the task sequence input of logistics scheduling, and optimizes the operation efficiency of logistics system (driving path and running time of AGV). The operation results show that the comprehensive energy consumption of 10,000 yuan industrial output value is 401.19 kg standard coal/10,000 yuan, a year-on-year decrease of 6.96%. This study is helpful to the fine management of manufacturing industry. [ABSTRACT FROM AUTHOR]

Multi-scale defect features, blurred edges and inability to locate geometric features have been the three key factors limiting the detection of surface defects on quality control system in the industrial manufacturing process. In this study, a method based on the fusion of multi-scale features and pixel-level semantic segmentation is proposed for the detection of surface defects. The proposed method firstly fuses multi-level feature maps to balance the expressiveness of multi-scale features, then adds a boundary refinement module to enhance the accurate inference of edge fine-grained, and finally adopts an en-decoder architecture to locate geometric features at the pixel-level for each type of defects, realizing intelligent detection of geometric features of end-to-end multi-scale defects on the surface of parts. We conduct experiments using the collected parts datasets to evaluate the effectiveness of our framework. The experimental results show that the proposed model achieves MIoU of 80.1%, the recognition accuracy reaches more than 95 %, and a detection rate of up to 29.64 FPS, demonstrating the advancement and effectiveness of the proposed method with less misclassification and superior generalization performance and has progress and effectiveness in detecting surface defects of multi-scale features. It provides a research idea for the subsequent realization of surface quality inspection in the manufacturing process system. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to propose a new multiobjective optimization metaheuristic based on the tiki-taka algorithm (TTA). The proposed multiobjective TTA (MOTTA) was implemented for a simple assembly line balancing type E (SALB-E), which aimed to minimize the cycle time and workstation number simultaneously. Design/methodology/approach: TTA is a new metaheuristic inspired by the tiki-taka playing style in a football match. The TTA is previously designed for a single-objective optimization, but this study extends TTA into a multiobjective optimization. The MOTTA mimics the short passing and player movement in tiki-taka to control the game. The algorithm also utilizes unsuccessful ball pass and multiple key players to enhance the exploration. MOTTA was tested against popular CEC09 benchmark functions. Findings: The computational experiments indicated that MOTTA had better results in 82% of the cases from the CEC09 benchmark functions. In addition, MOTTA successfully found 83.3% of the Pareto optimal solution in the SALB-E optimization and showed tremendous performance in the spread and distribution indicators, which were associated with the multiple key players in the algorithm. Originality/value: MOTTA exploits the information from all players to move to a new position. The algorithm makes all solution candidates have contributions to the algorithm convergence. [ABSTRACT FROM AUTHOR]

In the automotive industry, decentralized in-house logistic areas called supermarkets are used for parts feeding to mixed-model assembly lines. In the literature, generally, the number of supermarkets and their locations are determined according to a previously balanced assembly line. As a result, supermarket locations have to be completely dependent on the line balance. There may be more than one solution that gives the same performance criterion value in the line balancing. Among these alternative solutions, it is more reasonable to use a solution that provides lower logistics costs than others. For this purpose, in this study, the mixed-model assembly line balancing problem and the supermarket location problem are considered simultaneously. Accordingly, in order to minimize the total costs of the assembly line and supermarkets, a mixed-integer mathematical model is developed, and the developed model is solved by using constraint programming (CP). In addition, a new approach based on Ant Colony Optimization (ACO) and Simulated Annealing (SA) is presented for large-sized problems. An illustrative example problem is solved using both the developed model and the proposed algorithm. The effectiveness of the proposed approach is tested through a new data set of test problems presented to the literature for a computational experiment. The experimental results show that the total costs are reduced meaningfully and a more realistic and applicable structure is achieved by the proposed approach. [ABSTRACT FROM AUTHOR]

The ever-shorter time to market demanded by customers and an increasing number of variants lead to high-frequency product development and production system planning cycles. This article therefore presents an approach that aims at increasing efficiency in the repetitive planning of variant-specific assembly systems. Compared to the state-of-the-art manual, document-based planning prevailing in industry, formal optimization methods have the potential of considering multiple optimization criteria. This article therefore proposes an approach for assigning individual process steps to stations for a given customer cycle time while minimizing the number of stations as well as optimizing additional criteria such as increasing the production system flexibility, achieving good ergonomics or reducing the deviation within provided tolerances. The solution space of the optimization is constrained by the mathematical modeling of a limited number of stations. The operations research (OR) model developed for this purpose considers given assembly precedence graphs as an input and includes variables, parameters, objective function and constraints. Especially the latter considers the process precedence relations in terms of the Assembly Line Balancing (ALB) problem as well as a divisibility of process steps. The approach is applied to the variant-specific planning of a highly automated welding assembly in the automotive supplier industry as use case. The results show its applicability and potential in reducing planning time. [ABSTRACT FROM AUTHOR]

Assembly line balancing by workload smoothing is very common; it aims to appropriately assign tasks to the stations of an assembly line, to ensure as similar as possible workloads for the stations. A shortcoming of the mathematical models developed in the literature is the optimization of workload smoothing when only all potential stations are considered. Therefore, the obtained solution may not be the global optimal solution and also may not be effective for real world cases. To overcome this shortcoming, some new workload smoothing criteria and their related mixed integer linear mathematical models are developed for three different settings: (1) workload smoothing optimization, when all potential stations are considered, (2) workload smoothing and minimization of the number of stations are optimized simultaneously, (3) global workload smoothing optimization regardless of the number of considered stations. The proposed formulations are associated with fewer variables and constraints compared with models previously presented in the literature and lead to better results according to an extensive numerical study. The proposed models can be applied in practice for designing the assembly lines of, inter alia, automobile and electronic equipment production systems. [ABSTRACT FROM AUTHOR]

Biomechanical overload is considered a significant occupational risk in manufacturing and a potential cause of musculoskeletal disorders. This research aims to introduce new methodologies for the quantitative risk evaluation of biomechanical risk by combining surface electromyography with a motion acquisition system based on inertial measurement units. Due to the lack of experimental data in the literature acquired in a real industrial environment during the working shift, an on-the-field study regarding an automotive assembly line workstation has been carried out in collaboration with Fiat Chrysler Automobiles Italy S.p.A. Data related to the trunk flexion forward and the erector spinae muscle activity have been acquired for several consecutive working cycles by considering three different workers. Data analyses indicated kinematic and muscular activity patterns consistent with those expected and that the proposed wearable technologies can be integrated and used simultaneously during work activities. Furthermore, the results demonstrated data repeatability, strengthening the feasibility and usefulness of the combined use of kinematic and electromyography technologies to assess biomechanical overload in production lines. This study could lay the bases for the future definition of a method for assessing biomechanical overload due to awkward postures. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the maintenance policy for a two-machine one-buffer (2M1B) assembly line system. We assume that the observed quality states of the deteriorating machines in the system are characterized by multiple decreasing yield stages. A semi-Markov decision process (SMDP) model is used for describing the deteriorating process of the system. A heuristically accelerated multi-agent reinforcement learning (HAMRL) method is conducted to solve the problem model. The asynchronous updating rules are introduced in the HAMRL method, and the production time, preventive maintenance (PM) time and corrective repair (CR) time are random, and the deterioration mode of the device is not fixed. Meanwhile, a comparison with a simulated annealing search (SAS) based exploration algorithm and a neighborhood search (NS) based exploration algorithm in reinforcement learning (RL) is presented. The empirical results indicate that the proposed HAMRL algorithm can speed up the learning process, and has a certain advantage for the larger space and the more practical problem. And the maintenance strategy for the 2M1B assembly line system is obtained under the condition of convergent system average cost rate. This paper provides new and practical insights into the application and selection of techniques for maintenance policy of the 2M1B assembly line system. [ABSTRACT FROM AUTHOR]

Background: An assembly line is a technique used in mass production industries, especially in the automotive industry; it consists of many workstations organized along a conveyor belt system or other material handling equipment. The assembly line balancing problem (ALBP) involves assigning assembly tasks to workstations on the line while meeting optimization goals. It is considered a critical issue in operations management because it directly affects the productivity of the entire manufacturing system. Methods: Based on the mathematical model previously developed by (Esmaeilbeigi, Naderi, and Charkhgard 2015) for the E-type SALBP problem, we proposed a new model adaptable to the automotive sector. The proposed model uses new feasibility rules and optimizes constraints in order to propose better balancing results and efficiency. Results: A computational experiment is presented in this article, using the newly developed model to balance an assembly line in an automotive manufacturing plant consisting of 5 workstations. Conclusions: The experimental results show that the proposed model improved the line efficiency by 15%, which proves that the proposed method has good robustness. [ABSTRACT FROM AUTHOR]

This research deals with a U-shaped assembly line balancing problem in which items are fixed on the conveyor. Operators execute the assembly tasks without removing items from the conveyor as the assembly items are assumed to be unsuitable for manual handling. The operators have to walk beside the conveyor at the same speed as the conveyor while they execute assembly tasks. If the operators want to assemble other items, they may have to walk to other positions at their normal walking speed. Therefore, the cycle time of an operator should include assembly times and walking times between assembly tasks. In this research, a mathematical model of the proposed U-shaped conveyor assembly line balancing problem was developed. Given a number of operators, the mathematical model can optimize the sequence of tasks and allocation of tasks to workstations using the commercial software LINGO 17. Because LINGO 17 requires a long computing time, simulated annealing (SA), which can accept worse new solutions in the search procedure, is proposed. The experimental results show that the proposed SA can optimize the problem efficiently. [ABSTRACT FROM AUTHOR]

The present study discusses an extended problem of assembly line balancing with fractional task allocation. Fractional task allocation means that a task will be executed on one of paired consecutive stations. A fractional value gives the allocation ratio of the task on the upstream station against the downstream station. A mixed-model assembly line is assumed to accept the fractional allocation of a single task. Under the condition of mixed-model assembling, the duration of each task depends on the model. Therefore, buffer stations are necessary between stations to absorb the duration variation. However, preparing a buffer incurs a fixed cost, and thus it is a realistic assumption that the number of available buffers is limited. Therefore, examining the exact timing that a job arrives at each station and the buffer required is necessary to generate a feasible assembly schedule. The present study presents a mixed-integer programming model that includes three types of decisions; the allocation of tasks at each station, including a task with fractional allocation, the assignment of buffers, and the generation of a feasible assembly schedule. The objective is to minimize the cycle time under the given number of stations. Numerical experiments were conducted for problem instances with two and five models. Solutions support fractional allocation under the conditions of (i) there is a task that has a longer processing duration than the cycle time, and (ii) the model that involves such a task has a higher demand ratio. [ABSTRACT FROM AUTHOR]

The problems of parallel two-sided assembly lines balancing (TALB) are nonpolynomial (NP-hard) problems that belong to the field of combinatorial optimization. In most cases, these problems can be resolved with algorithms. To this end, this research adopts colored Petri nets (CPN) and a minimal spanning tree (MST) to address the type I problems for two-sided assembly line balancing problem (TALBP). This research proposes a three-step method. In the first step, a reachability tree chart is built to show potential tasks order assignment. In the second step, the color firing mechanism is employed to determine if the colored conditions are satisfied. The final step uses a MST and a least-time prioritized rule to select the proper tasks for assignment efficiently. These steps will be performed repetitively until all the tasks are properly assigned. Since the whole assignment process is based on graphical and paradigm methods, it can be adopted to lessen the burdens of complex calculations. The end of the research will present the comparison results between the examples from the literature and the method proposed in this research. As indicated by the research findings, employing CPN and an MST for TALBPs (type-I) can obtain even favorable and consistent results over legacy methods, and further optimize workstation design and enhance balance efficiency. [ABSTRACT FROM AUTHOR]

Assembly lines are widely used in industrial environments that produce standardised products in high volumes. Multi-manned assembly line is a special version of them that allows simultaneous operation of more than one worker at the same workstation. These lines are widely used in large-sized product manufacturing since they have many advantages over the simple one. This article has dealt with multi-manned assembly line balancing problem with walking workers for minimising the number of workers and workstations as the first and second objectives, respectively. A linear mixed-integer programming formulation of the problem has been firstly addressed after the problem definition is given. Besides that, a metaheuristic based on electromagnetic field optimisation algorithm has been improved. In addition to the classical electromagnetic field optimisation algorithm, a regeneration strategy has been applied to enhance diversification. A particle swarm optimisation algorithm from assembly line balancing literature has been modified to compare with the proposed algorithm. A group of test instances from many precedence diagrams were generated for evaluating the performances of all solution methods. Deviations from lower bound values of the number of workers/workstations and the number of optimal solutions obtained by these methods are concerned as performance criteria. The results obtained by the proposed programming formulations have been also compared with the solutions obtained by the traditional mathematical model of the multi-manned assembly line. Through the experimental results, the performance of the metaheuristic has been found very satisfactory according to the number of obtained optimal solutions and deviations from lower bound values. [ABSTRACT FROM AUTHOR]

The design of a production system is a strategic level decision. One of the key problems to solve is the line balancing problem that determines the efficiency of a production or assembly line. This class of problem has been widely studied in the literature. It determines important features, such as the number of stations, the takt time or the working conditions. Most of the variants of this problem consider only one objective function, but nowadays companies have to take into account different criteria. In this study, we consider a bi-objective variant of the simple assembly line balancing problem. We present a generic branch-and-bound method to solve exactly this problem. The objective functions are to minimise the takt time and the number of stations. To do so, bounds and bound sets are developed. The resulting method is numerically tested and compared to an ϵ-constraint method. These experiments show that the bi-objective branch-and-bound algorithm outperforms an ϵ-constraint method using a state-of-the-art single objective algorithm for more than 80% of the instances. Finally, we propose an analysis of the cases where the branch-and-bound method is outperformed. [ABSTRACT FROM AUTHOR]

Within U-shaped assembly lines, the increase of labour costs and subsequent utilisation of robots has led to growing energy consumption, which is the current main expense of auto and electronics industries. However, there are limited researches concerning both energy consumption reduction and productivity improvement on U-shaped robotic assembly lines. This paper first develops a nonlinear multi-objective mixed-integer programming model, reformulates it into a linear form by linearising the multiplication of two binary variables, and then refines the weight of multiple objectives so as to achieve a better approximation of true Pareto frontiers. In addition, Pareto artificial bee colony algorithm (PABC) is extended to tackle this new complex problem. This algorithm stores all the non-dominated solutions into a permanent archive set to keep all the good genes, and selects one solution from this set to overcome the strong local minima. Comparative experiments based on a set of newly generated benchmarks verify the superiority of the proposed PABC over four multi-objective algorithms in terms of generation distance, maximum spread, hypervolume ratio and the ratio of non-dominated solution. [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]

Characterising existing approaches to operational complexity, it can be stated that for those metrics are characterful different factors used as variables such as product structure, machine composition, number of technological functions performed by machine, and others. Moreover, the complexity metrics using the information content as a basis can be divided into two groups: those which define complexity as an absolute entropy quantity, and metrics defining complexity as the relative entropy magnitude. Our view on the operational complexity is based on an assumption that process complexity value obtained as a sum of the partial complexities is not so important than a balanced operational complexity value expressing relative quantity to equilibrium levels. Accordingly, this paper introduces the novel operational complexity measure that initially identifies operational complexities of individual machines based on the number of parts, machines and operations. In the subsequent steps, these sub-measures are used to define summary complexity measure involving two balanced operational complexity characteristics. The novel measure can be effectively used to find the most suitable layout design alternative. For the purpose to prove its effectiveness, on two practical cases where tested its practicability by comparing it to the complexity indicator expressing the sum of the partial complexities. [ABSTRACT FROM AUTHOR]

The optimisation problems related to the assignment of tasks to workstations in assembly and disassembly lines have been largely discussed in the literature. They are known, respectively, as Assembly Line Balancing and Disassembly Line Balancing Problems. In this study, both types of task performed on the identical product are integrated in a common hybrid production system. Therefore, the logistic process is simplified and disassembly tasks can supply easier the assembly tasks with the required components. The considered production system has the layout of two parallel lines with common workstations. The product flow is conventional in the assembly line and reverse in the disassembly line. The paper provides a new mathematical model for designing such a hybrid system and an approximate approach based on ant colony optimisation for solving large-scale instances. The solution method is tested in a case study. The obtained results are compared with the solution provided by the design of two independent lines. The analysis of the results highlights the potential benefits of the hybrid production system. [ABSTRACT FROM AUTHOR]

Assignment of worker appears as a new problem in the literature for assembly lines (AL) where the workforce is heterogeneously located. The difference of this line balancing from the traditional line is that the assembly task times of the workers to be assigned to the stations differ from each other. Especially assembly lines issues are generally a labor-intensive type of production. Therefore, for assembly line problems, balancing activity should be done considering the labor costs. Thus, the proposed solutions will be able to produce more realistic results for real life. For this purpose, in this study, a mixed-integer mathematical formulation is presented for assembly line worker assignment and balancing problems by considering the objective function of minimizing labor costs and cycle time. The efficiency of the proposed model and the improvements have been demonstrated by solving test instances in the literature.

The assembly line balancing problem (ALBP) is an eminent NP-hard topic that is discussed in mass production systems with low diversity. Primarily, two types of ALBPs are discussed in the literature as type I, which aims to find the minimum number of workstations for a given cycle time, and type II, which assigns some tasks to a given number of workstations such that the maximum workstation load is minimized. To solve ALBPs, various exact, heuristic, and metaheuristic methods have been proposed. However, these methods lose their efficiency when handling large-size problems. Therefore, researchers have focused on proposing heuristic and metaheuristic algorithms to solve large-size problems, especially when they deal with real-life case problems in the industry. This study aims to present a novel and competitive exact method for solving ALBP type II based on the lexicographic order of vectors for feasible solutions. To evaluate the performance of the developed method, a group of highly used standard test problems in the literature is utilized, and the results are compared and discussed in detail. The computational results in this study specify that the developed solution approach performs efficiently and yields the best global solution of all the ALB test problems, which proves the proposed method's potential and its competitive advantage.

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]

This article considers multi-manned assembly line balancing problems with walking workers. The objective of the problem is the minimization of number of workers and workstations simultaneously. Several exact-solution algorithms based on Benders' decomposition are proposed to solve the problem optimally. In one of the algorithms a constructive heuristic that generates effective task-worker assignments and some problem-specific symmetry breaking constraints are used. Moreover, the solutions obtained by meta-heuristic in the literature are used as starting points to increase the performance of proposed decomposition methods. A benchmark set of 99 instances are used to analyze the performance of the proposed exact methods, contribution of the developed heuristic and the ability of Benders' decomposition on improving the starting solutions. Our results indicate a significiant improvement in the optimal solvability of the problem for larger-sized instances. Suggested methods also improve the results of the meta-heuristic method for significant number of instances. Consequntly, proposed methods solved most of instances optimally and they are able to find the optimal solutions of 17 instances that cannot be solved optimally with previous methods. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, assembly processes involve most of the workforce to deal with the many manual operations. Thus, the design of workplaces must take into account ergonomics to promote workers well-being and safeguard their health and safety, also enhancing productivity. The occupational ergonomic risk not only depends on the physical workload of a task, but also on environmental characteristics of the workplace, including noise, the assessment of which may contribute to prevent workers from possible health issues associated to hearing injuries. In this regard, the present study proposes a software tool based on a genetic algorithm for solving the mixed-model assembly line balancing problem with job rotation and collaborative robots to improve workers' ergonomics, for the evaluation of which noise exposure is also considered. In particular, the objectives of the problem concern economic aspects, which are taken into account through the optimization of the cost of the line, and ergonomics, which is pursued by reducing and smoothing both workers' energy expenditure and noise exposure for performing operations on the line. To test the effectiveness of the proposed approach, an industrial case study is finally discussed. [ABSTRACT FROM AUTHOR]

According to the extant literature, improving the leanness of a production system boosts a company's productivity and competitiveness. However, such an endeavor usually involves managing multiple, potentially conflicting objectives. This study proposes a framework that analyzes lean production methods using simulation and data envelopment analysis (DEA) to accommodate the underlying multi-objective decision-making problem. The proposed framework can help identify the most efficient solution alternative by (i) considering the most common lean production methods for assembly line balancing, such as single minute exchange of dies (SMED) and multi-machine set-up reduction (MMSUR), (ii) creating and simulating various alternative assembly line configuration options via discrete-event simulation modeling, and (iii) formulating and applying DEA to identify the best alternative assembly system configuration for the multi-objective decision making. In this study, we demonstrate the viability and superiority of the proposed framework with an application case on an automotive spare parts production system. The results show that the suggested framework substantially improves the existing system by increasing efficiency while concurrently decreasing work-in-process (WIP). [ABSTRACT FROM AUTHOR]

The classical simple assembly line balancing problem (SALBP) has been extended with many real-life applications recently. One of these extensions is assembly line balancing with sequence-dependent setup times (ALBPS). In this study, ALBPS is extended with more than one resource at each workstation. Task is performed any resource at each workstation. More than one resource at each workstation can decrease the number of setups. Thus, the required number of the workstations on the line can reduce. The problem is called assembly line balancing with resource and sequence-dependent setup times (ALBPRS). ALBPRS is to assign the task to the workstations, to assign the tasks to the resource, and to sequence the tasks performed by the same resource at each workstation simultaneously. A mathematical model and a genetic algorithm are developed to solve the problem. The benchmark data set is generated for ALBPRS and the mathematical model and genetic algorithm are tested on the generated benchmark dataset. Results show that the proposed methods are efficient and using more than one resource at each workstation decreases the total number of the workstations in many test problems. [ABSTRACT FROM AUTHOR]

Assembly lines are often indispensable in factories, and a good assembly-line balancing model is very important for manufacturers to maximize their profit using limited resources in a competitive environment. To maintain a productive assembly line, multiple objectives with different importance must be considered at the same time. In this paper, a two-sided assembly-line balancing problem (TALBP) with multiple objectives is examined. A fuzzy multi-objective linear programming-weighted model (FMOLP-W) for solving the TALBP is constructed first with the consideration of the importance weights of the line balancing performance factors, including minimizing the number of workstations, minimizing cycle time, maximizing line efficiency, minimizing smoothness index and minimizing workstation idle time. An evolutionary genetic algorithm (GA) is proposed next to tackle large-scale problems when the problems are too complex to be solved by the FMOLP-W. [ABSTRACT FROM AUTHOR]

The mixed work assignment technique was applied to the modular production concept to solve the apparel assembly line balancing problem. Specifically, an algorithm was implemented to generate workstations by first classifying tasks into modules through an analysis of the manufacturing process and assigning grouped tasks in the single task--multiple workers and multiple tasks--single worker assignment methods. Then, worker assignment was sequentially performed considering the skill level of the worker. A simulation of producing 100 men's shirts was performed. Owing to line balancing, the total production time was considerably lower than that reported in previous studies. The enhanced performance was attributable to the fact that unlike previous studies that limited the apparel assembly line balancing problem to worker assignment, line balancing was performed through both task and worker assignment in this study. The results demonstrated that task assignment must be considered in the design stage in the apparel assembly line balancing problem. [ABSTRACT FROM AUTHOR]

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Demand trends towards mass customization drive the need for increasingly productive and flexible assembly operations. Walking-worker assembly lines can present advantages over fixed-worker systems. This article presents a multiproduct parallel walking-worker assembly line with shared automated stations, and evaluates its operational performance compared to semiautomated and manual fixed-worker lines. Simulation models were used to set up increasingly challenging scenarios based on an industrial case study. The results revealed that semiautomated parallel walking-worker lines could achieve greater productivity (+30%) than fixed-worker lines under high-mix low-volume demand conditions. [ABSTRACT FROM AUTHOR]

Robotic assembly lines are widely applied to mass production because of their adaptability and versatility. As we know, using robots will lead to energy-consumption and pollution problems, which has been a hot-button topic in recent years. In this paper, we consider an assembly line balancing problem with minimizing the number of workstations as the primary objective and minimizing energy consumption as the secondary objective. Further, we propose a novel mixed integer linear programming (MILP) model considering a realistic production process design—cross-station task, which is an important contribution of our paper. The "cross-station task" design has already been applied to practice but rarely studied academically in type-1 RALBP. A simulated annealing algorithm is developed, which incorporates a restart mechanism and an improvement strategy. Computational tests demonstrate that the proposed algorithm is superior to two other classic algorithms, which are the particle swarm algorithm and late acceptance hill-climbing algorithm. [ABSTRACT FROM AUTHOR]

One of the most recent challenges that manufacturers confront is to respond on time to the variety of customers' demands for different products. The Assembly line is the main element responsible for assembling products in manufacturing systems, and it requires good management to avoid several problems that could lead to production failures. The mixed-model assembly line balancing problem type-I (MiMALBP-I) occurs in the step of designing a new assembly line in which different models of one product are assembled in an intermixed sequence; it aims to optimize the number of workstations for a fixed known cycle time. In this paper, the authors propose a Hybrid Reactive Greedy Randomized Adaptive Search Procedure (HRGRASP) to solve this problem. The performance of the proposed algorithm is compared with those of the basic GRASP, heuristics based approach, and the Lingo solver using seven problems of different sizes. [ABSTRACT FROM AUTHOR]