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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]

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]

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]

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]

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]

Due to various interference factors, a pre-planned assembly scheme and its cycle time can be disturbed, resulting in the failure of product delivery on schedule. However, when assembly data of the production line can be obtained in real time, the balance of the assembly line could be dynamically adjusted in case of experiencing serious interferences to optimize its cycle time in time and improve its production efficiency. Therefore, this paper proposes a dynamic rebalancing framework by integrating real time manufacturing data into a novel serial two-stage adaptive alternate genetic fireworks algorithm for solving a stochastic type-II simple assembly line balancing problem (SSALBP-II). MES (manufacturing execution system) is used to obtain some real time data such as the resource status, operation information and task information and to judge abnormal phenomenon and the overdue delivery caused by interferences. On this basis, the stochastic type-II simple assembly line balance model is constructed, with a new serial two-stage adaptive alternate genetic fireworks algorithm (STAGFA). This new algorithm can incorporate both genetic algorithm and fireworks algorithm to solve the model according to the transformation of population diversity discrimination index. Through the comparison between STAGFA and other algorithms such as fireworks algorithm, genetic algorithm, other improved intelligent algorithms, it is proved that the STAGFA is effective and superior in solving the assembly line (re)balance problem. Then, the rebalanced scheme verified by simulation is dispatched to control the physical assembly line and realize dynamic rebalancing cycle time effectively. [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]

With the continuous upgrading of the manufacturing system, the assembly line balancing problem (ALBP) is gradually complicated, and the researches are constantly deepened in the application theory and solution methods. In order to clarify the research direction and development status of assembly line balancing, 89 articles are read and studied. We classify ALBPs to construct the network structure of research from horizontal classification and vertical thinking. The ALBP framework is horizontally given according to the number of models (i.e. the number of products), the layout shape of the assembly line, and the data of task time. The "seven steps for scientific paper" is vertically proposed according to the research steps to comb the research path of scientific and technological literature. The horizontal and vertical extension crosses and constructs the network structure of the ALBP. Any horizontal problem intersects with any step of the vertical "seven steps for scientific paper" to form a research point. We analyze 89 articles according to the development path from the straight line to U-shaped line and then to two-sided U-shaped/parallel U-shaped assembly line, summarize the research algorithm of assembly line balance and count the number of articles, and point out the latest research direction and algorithm development trend of assembly line balance. [ABSTRACT FROM AUTHOR]

The simple assembly line balancing (SALB) problem is a significant challenge faced by industries across various sectors aiming to optimise production line efficiency and resource allocation. One important issue when the decision-maker balances a line is how to keep the cycle time under a given time across all cells, even though there is variability in some parameters. When there are stochastic elements, some approaches use constraint relaxation, intervals for the stochastic parameters, and fuzzy numbers. In this paper, a three-part algorithm is proposed that first solves the balancing problem without considering stochastic parameters; then, using simulation, it measures the effect of some parameters (in this case, the inter-arrival time, processing times, speed of the material handling system which is manually performed by the workers in the cell, and the number of workers who perform the tasks on the machines); finally, the add-on OptQuest in SIMIO solves an optimisation problem to constrain the cycle time using the stochastic parameters as decision variables. A Gearbox instance from literature is solved with 15 tasks and 14 precedence rules to test the proposed approach. The deterministic balancing problem is solved optimally using the open solver GLPK and the Pyomo programming language, and, with simulation, the proposed algorithm keeps the cycle time less than or equal to 70 s in the presence of variability and deterministic inter-arrival time. Meanwhile, with stochastic inter-arrival time, the maximum cell cycle is 72.04 s. The reader can download the source code and the simulation models from the GitHub page of the authors. [ABSTRACT FROM AUTHOR]

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]

Currently, the largest percentage of the employed workforce in the manufacturing industry is involved in the assembly process, making ergonomics a key factor when dealing with assembly-related problems. During these processes, repetitive tasks and heavy component handling are frequent for workers, who may result overloaded from an energetic point of view, thus affecting several aspects not only relating to the human factor but also to potentially reduced productivity. Different organizational strategies and technological solutions could be adopted to overcome these drawbacks. For these purposes, the present paper proposes a genetic algorithm for solving the typical problem of assembly line balancing, taking into account job rotation and human–robot collaboration for enhancing ergonomics of workers. The objectives of the problem are related to both economic aspects and human factor: (i) the cost for implementing the assembly line is minimized, evaluated on the basis of the number of workers and differentiated by skill levels and on equipment installed on workstations, including collaborative robots, and (ii) the energy load variance among workers is also minimized, so as to smooth their energy expenditure in performing the assigned assembly operations, calculated according to their movements, physiological characteristics, job rotations and degree of collaboration with robots. The paper finally presents and discusses the application of the developed tool to an industrial assembly case. [ABSTRACT FROM AUTHOR]

The research on the robotic assembly line balancing problem (RALBP) was originated for the first time nearly three decades ago. This problem is under the umbrella of the assembly line balancing problem in which robots and automated equipment are employed to take on human workers' roles to form a flexible assembly line. In this review paper, the development and generalisation throughout the time of the RALBP are addressed. To make the review easy to comprehend and effective, the RALBP is first classified based on the types of layouts and then further dividing up according to the 4 M (Man, Machine, Material and Method) concept. The main contributions of different articles are chronologically summarised in the form of a table. Besides, the research contribution precedence diagram is used to illustrate the sequential order and linkage relationship among researches. Finally, from the findings of the review, future research directions are pinpointed and discussed. [ABSTRACT FROM AUTHOR]

Numerous research studies have focused on the Simple Assembly Line Balancing Problem (SALBP). Although many of these studies have attempted to minimise the number of workstations for a given cycle time (SALBP-1) or min-imise the cycle time for a given number of workstations (SALBP-2), few have focused on the E-type of the SALBP, which is a general NP-hard problem. Therefore, a Systematic Literature Review (SLR) in this area is extremely important. The SALB-E problem involves scheduling a given set of tasks in an orderly sequence of workstations, where task precedence relations are satisfied, and finding the optimal pair of number of workstations and cycle time that maximises line efficiency. The aim of this systematic review was to examine existing research on SALB-E issues and predict future directions for studies on this topic. A systematic methodology was used to review papers published between 1995 and 2023, selected from the Scopus, ScienceDirect, and Google Scholar data-bases. The review showed that future studies should focus on multi- and mixed-model U-shaped and two-sided lines in the SALBP-E. In addition, it may be important to evaluate multiple objective functions rather than just a single one. To solve this problem, it is preferable to develop and refine genetic algorithms and investigate new approaches within the metaheuristic framework. [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]

This paper presents an integrated approach for the design of human-robot collaborative workstations in industrial shop floors. In particular, the paper presents how to use virtual reality (VR) technologies to support designers in the creation of interactive workstation prototypes and in early validation of design outcomes. VR allows designers to consider and evaluate in advance the overall user experience, adopting a user-centered perspective. The proposed approach relies on two levels: the first allows designers to have an automatic generation and organization of the workstation physical layout in VR, starting from a conceptual description of its functionalities and required tools; the second aims at supporting designers during the design of Human-Machine Interfaces (HMIs) by interaction mapping, HMI prototyping and testing in VR. The proposed approach has been applied on two realistic industrial case studies related to the design of an intensive warehouse and a collaborative assembly workstation for automotive industry, respectively. The two case studies demonstrate how the approach is suited for early prototyping of complex environments and human-machine interactions by taking into account the user experience from the early phases of design. [ABSTRACT FROM AUTHOR]

Assembly line balancing plays a significant role in mass production systems due to production efficiency and productivity. A number of researchers have recently shown great interest in this topic, especially in the area of simple assembly line balancing problems (SALBP), and several academic papers have been published on this topic using different exact, heuristic, and metaheuristic methods. Recently, Rashid et al. (Int J Adv Manuf Technol 59:335-349, ) reviewed one decade (2000-2010) of published studies on assembly sequence planning and assembly line balancing optimization, those studies that applied soft computing approaches. The scope of the Rashid et al. review in the area of assembly line balancing problems was reported to be on SALBP. In this paper, we suggest that the review by Rashid et al. and the conclusion drawn regarding SALBP are inaccurate in some parts, and some revisions to Rashid et al. (Int J Adv Manuf Technol 59:335-349, ) are necessary. Accordingly, a revision to Rashid et al.'s review paper is proposed, and a guide to future research is presented. Moreover, to have up-to-date information, the review is extended to also include the published studies in the period of 2011-2013. [ABSTRACT FROM AUTHOR]

In this paper, a neural-network-based control method to achieve trajectory tracking and balancing of a ball-balancing robot with uncertainty is presented. Because the ball-balancing robot is an underactuated system and has nonlinear couplings in the dynamic model, it is challenging to design a controller for trajectory tracking and balancing. Thus, various approaches have been proposed to solve these problems. However, there are still problems such as the complex control system and instability. Therefore, the objective of this paper was to propose a solution to these problems. To this end, we developed a virtual angle-based control scheme. Because the virtual angle was used as the reference angle to achieve trajectory tracking while keeping the balance of the ball-balancing robot, we could solve the underactuation problem using a single-loop controller. The radial basis function networks (RBFNs) were employed to compensate uncertainties, and the controller was designed using the dynamic surface control (DSC) method. From the Lyapunov stability theory, it was proven that all errors of the closed-loop control system were uniformly ultimately bounded. Therefore, the control system structure was simple and ensured stability in achieving simultaneous trajectory tracking and balancing of the ball-balancing robot with uncertainty. Finally, the simulation results are given to verify the performance of the proposed controller through comparison results. As a result, the proposed method showed a 19.2 % improved tracking error rate compared to the existing method. [ABSTRACT FROM AUTHOR]

With the growth of industrialization, the global manufacturing industry is continually evolving and reforming in the direction of intelligence and green production. Industrial robots have replaced human workers because of the benefit of production efficiency. However, the large-scale application of robots requires a large amount of energy consumption and generates a large amount of CO2, which will lead to energy waste and environmental pollution. In addition, in term of performing some particular tasks, current robot technology cannot achieve the same level of intelligence as human. Therefore, the design trend of assembly lines in industry has shifted from traditional configuration to human-robot collaboration to achieve higher productivity and flexibility. This paper investigates the human-robot collaboration (HRC) assembly line balancing problem, taking cycle time and carbon emission as primary and secondary objectives. A new mixed-integer programming model that features a cross-station design is formulated. A particle swarm algorithm (PSO) with two improvement rules is designed to solve the problems. The comparative experiments on ten benchmark datasets are conducted to assess the performance of the proposed algorithm. The experimental results indicate that the improved particle swarm algorithm is superior to the other two heuristics: simulated annealing (SA) and the late acceptance hill-climbing heuristic (LAHC). [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]

Frequent changes in customer needs and large product variety are forcing manufacturing companies to move from mass production to mass customization. Customized production can be achieved by introducing reconfigurable production systems (RMS). The customized flexibility and several characteristics of RMSs provide many opportunities in terms of process and production planning. However, those characteristics greatly increase the complexity of the design and planning of production systems. This paper presents a decision support system relying on a skill-based approach to design a reconfigurable assembly line considering the planning of assembly processes and monitoring. The proposed decision aid system is modular in design and is composed of four modules. The main input data is a CAD model of a new product variant for the identification of the assembly and monitoring requirements. Besides, a current assembly system layout with its resource descriptions exists. In the first developed module, assembly-by-disassembly and a skill-based approach are used to generate different assembly plans. In the second module, feature recognition and skill-based approaches generate process monitoring alternatives. The third module uses a linear program (LP) that aims to minimize the total cost of workstation activation and reconfiguration, as well as cycle time, and to maximize the process quality of the assembly tasks. A user-based generative model design approach is applied to optimize the values of three objective functions. In the fourth and final module, a simulation of the optimized assembly plan allows either the validation of the assembly plan and process monitoring plan or initiates a new iteration due to their infeasibility. To further demonstrate how the proposed methodology works, some computational experiments are provided for two use cases. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of assembly line and optimize the layout, this paper presents a collaborative optimization model for a two-sided U-type assembly line and a novel design with p-l partition layout is proposed to minimize number of workstations without increasing the length of the assembly line. Considering the task orientation and time sequencing in cross-workstation, the mathematical model of two-sided U-type assembly line balancing problem is derived. A multi-level priority rule heuristic algorithm is developed to drive the optimization process. The multi-level priority rule heuristic algorithm, modified particle swarm optimization algorithm, and the bi-objective integer programming method are applied to 20 classic examples, respectively. The calculation results suggest that the optimal results of the proposed method account for 90%, which verifies the rationality of the collaborative optimization model and algorithm, and provides a useful reference for the modeling and solution of the two-sided U-type assembly line balancing problems. [ABSTRACT FROM AUTHOR]

In selective serial disassembly sequence planning, when the target node (component) is reached, the selective disassembly task is completed and the refurbished component is repaired, reused or remanufactured. Since the efficient utilization of existing resources is necessary, it is crucial to predict disassembly operation times and the condition of joints for recycling, reusing or remanufacturing. The method of estimating the disassembly times of a joint if it is intended for remanufacturing, recycling and reuse is an important and urgent requirement for research development and results. The aim of the paper is to investigate the disassembly system with predicted operation times and the quality of product connections (joints) in order to balance the line smoothness index, to minimize a line time factor, line efficiency and profit and minimize an ex post error. Disassembly times for remanufacturing, recycling and reuse are estimated separately based on the historical data of disassembly times and the quality of joints. The presented estimation method of disassembly operation times increases the reliability and efficiency of elaborated balances of tasks in lines. Underestimated disassembly operation times can be compensated for during the idle points in the successive cycles, provided that the transport operations are performed manually and that travel time determines the cycle time. [ABSTRACT FROM AUTHOR]

Cycle time minimization is one of the major goals to achieve in manufacturing. Maximizing feedrate is the direct solution; however, physical motions need to be under the specified motion limits to avoid high-frequency vibration, causing machining error. In this paper, a time-optimal feedrate scheduling approach with corner smoothing for 5-axis G1 toolpaths is presented for 5-axis machining. One key feature of the proposed method is to describe the orientation toolpath in the spherical coordinate system (SCS), instead of the commonly used unit sphere. With this, the position and orientation of the 5-axis toolpath are integrated to become a hyper-curve in a 5-dimensional (5-D) space. The feedrate scheduling and corner smoothing are designed for this 5-D hyper-curve. Thus, no synchronization of position and orientation feedrate scheduling is needed, making the feedrate scheduling easier. A quintic B-spline corner smoothing method is utilized to smoothen sharp corners in the toolpath. Then, the S-shape feedrate profile of each block is optimized under the actuator motion constraints, with the objective of minimizing the cycle time. Particle swarm optimization (PSO) is used to provide the optimized solution. Experiments are conducted to validate the proposed approach, and the results are compared with two other existing approaches. It is found that the proposed method can achieve shorter cycle time and less contour errors, showing the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Purpose: One of the topics for manufacturers is to discuss the diversity of customer tastes. To manage this situation with the least change in products, multiple assembly lines make the necessary flexibility to produce the products. In multi-model assembly lines, different product types in different batches are produced and there is a setup time to prepare assembly lines between two types of products to produce another product type. This paper aims to investigate multi-model assembly lines and their sequencing, balancing, and worker assignment due to the existence of various tasks for workers according to learning and disremembering effects. Frequent changes in the product design of multi-model assembly lines according to customer demands can reduce the learning effect of workers and increase task times, while in another view, repeating tasks, particularly for products with more demands can increase the learning effect and reduce the task times. Therefore, in this study, the effects of workers' learning and disremembering multi-model assembly line balancing, sequencing, and worker assignment are investigated to minimize the number of workstations for a given cycle time not only to cover the different tastes of customers, but also indirectly minimize the costs of building stations, hiring, and employing manpower. Design/methodology/approach: In this paper, as an innovation, a mixed-integer mathematical model for multi-model assembly line balancing, sequencing, and worker assignment with different workers' skill levels and learning and disremembering rates has been developed to minimize the number of stations. Based on the nature of the multi-model, random demand for each product has been considered. After mathematical modeling, different small-sized problems have been solved by the GAMS software. Results and sensitivity analysis underlined the validity of the proposed model. Since this problem is typically NP-hard, GAMS software cannot solve medium and large-sized problems in a reasonable time. Therefore, the Krill herd optimization and Particle Swarm Optimization (PSO) algorithms have been used for medium and large-sized problems, which have not been used earlier in similar cases. The Krill herd optimization algorithm has been used as the proposed algorithm and PSO has been used as a competing algorithm. The parameters of both algorithms have been adjusted by the Taguchi method, and the best level has been selected for each parameter. Findings: 12 test problems were solved with different sizes. Results indicated that only five GAMS problems could reach the optimal solution. For better comparison of the Krill herd optimization and the particle swarm optimization algorithm, each test problem was run 30 times and minimum, maximum, and average objective function and their running times were reported. The results indicated that the objective function of both metaheuristic algorithms was the same but the Krill herd optimization algorithm can achieve optimal or near-optimal answers in less time than GAMS and the PSO algorithm declared the efficiency of the proposed algorithm in solving these problems. Research limitations/implications: One of the limitations in this research was the lack of cooperation of factories whose assembly lines were similar to the problem considered in this study, and in this regard, the real-world data was not accessible. Therefore, the standard test problems were used that existed in the famous database of assembly line balancing problems. Since the problem in this paper was new, some other required data, and different examples in different ways needed to be considered, randomly. Another limitation of using this research in a real-world situation was the challenge of exact determination of learning and disremembering rate of each worker which can be solved by using experts in the field of assessment and training. Originality/value: In this paper, a mathematical model was developed for multi-model assembly line balancing, sequencing, and worker assignment according to the learning and disremembering effect. Since the problem was NP-Hard, as well as GAMS software, two metaheuristic algorithms were applied for a similar problem, and their efficiency was compared with each other. The two-mentioned algorithms have not been used in previous studies. Both academic researchers and production managers can benefit from applying the findings of this study. [ABSTRACT FROM AUTHOR]

Some feasible mathematical model solution results in a recent paper (Fattahi et al., Int J Adv Manuf Technol, 53:363-378, 2011) are incorrect. In this note, we show by counterexamples that the results are incorrect and mathematical formulation is corrected by additional constraints. [ABSTRACT FROM AUTHOR]

Assembly process is one of the important aspects in manufacturing industries. Industries are extensively using advanced technologies in assembly lines recently such as robots instead of human labor. Cost associated with human labor such as wages, training, safety, and employee management are eliminated with the help of robots. Investments on assembly lines are cost intensive, and industries continuously need to maximize their utilization. In this paper, a cost-based robotic assembly line balancing (RALB) problem with an objective of minimizing assembly line cost and cycle time is addressed. Moreover, there is no research reported on concurrently optimizing cycle time and assembly line cost for a robotic assembly line system to date. The objective of this paper is to propose models with dual focus on time and cost to minimize the cycle time and total assembly line cost simultaneously. Time-based model with the primary focus to optimize cycle time and the cost-based model with the primary focus to optimize total assembly line cost are developed. Due to NP-hard nature, differential evolution (DE) is the algorithm used to solve the RALB problem. Straight and U-shaped robotic assembly line problems are solved using the proposed algorithm, and the detailed comparisons of the results obtained are presented. While comparing straight and U-shaped RALB problems, assembly line cost and cycle time obtained by U-shaped RALB problems are better than the straight RALB problems. The proposed models have significant managerial implications, and these have been discussed in detail. [ABSTRACT FROM AUTHOR]

Reducing the time and cost involved in the product development is very important to stay competitive in the market. Design for Manufacturing and Assembly (DFMA) is a cost-reduction framework for designers to evaluate manufacturing aspects of a product design. A case study on an in-market food processor, this paper aims to demonstrate the significance of DFMA implementation on an Indian consumer product. Even though many DFMA case studies have been published till date, very few have dealt with the Indian consumer durables category. In this paper, various cost-reduction opportunities are identified in the design of a food processor manufactured by a reputed company in India. Using the DFMA study, design ideas are developed with an objective of reducing the overall manufacturing cost of the product. As a result of DFMA implementation, significant improvement in terms of the product’s architecture, assembly time and design efficiency is identified. Overall cost reduction of 0.25 USD (United States Dollar) was achieved and an improvement in the Design for Assembly (DFA) index from 15.99 to 19.93 is reported. This procedure utilized in this paper can be adopted for any consumer durable products of similar type and design improvements and cost reduction can be achieved. [ABSTRACT FROM PUBLISHER]

This paper deals with the mixed-model assembly line balancing problem. This type of line is applied to more than one similar model of a product in an intermixed order. Despite their widespread use, these lines have received little attention in the literature. Metaheuristics, heuristics, and mathematical programming techniques are developed to solve these types of assembly line balancing problems. However, linear physical programming method has never been used. In this paper, a linear physical programming model is proposed for balancing a mixed-model assembly line. The performance of the proposed model is applied to a numerical example to analyze the usage of the methodology. Five objectives are considered in the model, and the outperformance of the methodology is demonstrated by comparing it to a different approach. According to the results, it has been seen that the proposed linear physical programming model is practical and useful approach for mixed-model assembly line balancing problems. [ABSTRACT FROM AUTHOR]

The U-shaped assembly lines help to have more flexibility than the straight assembly lines, where the operators can perform tasks in both sides of the line, the entrance and the exit sides. Having more than one operator in any station of the line can reduce the line length and thereby affects the number of produced products. This paper combines the U-shaped assembly line balancing problem with the multi-manned assembly line balancing problem in one problem. In addition, the processing times of the tasks are considered as stochastic, where they are represented as random variables with known means and variances. The problem is formulated as a mixed-integer linear programming and the cycle time constraints are formulated as chance-constraints. The proposed algorithm for solving the problem is a differential evolution algorithm. The parameter of the algorithm is optimized using experimental design and the computational results are done on 71 adapted problems selected from well-known benchmarks. [ABSTRACT FROM AUTHOR]

Assembly optimisation activities occur across development and production stages of manufacturing goods. Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) problems are among the assembly optimisation. Both of these activities are classified as NP-hard. Several soft computing approaches using different techniques have been developed to solve ASP and ALB. Although these approaches do not guarantee the optimum solution, they have been successfully applied in many ASP and ALB optimisation works. This paper reported the survey on research in ASP and ALB that use soft computing approaches for the past 10 years. To be more specific, only Simple Assembly Line Balancing Problem (SALBP) is considered for ALB. The survey shows that three soft computing algorithms that frequently used to solve ASP and ALB are Genetic Algorithm, Ant Colony Optimisation and Particle Swarm Optimisation. Meanwhile, the research in ASP and ALB is also progressing to the next level by integration of assembly optimisation activities across product development stages. [ABSTRACT FROM AUTHOR]

This paper proposes a constructive heuristic approach for the assembly line worker assignment and balancing problem (ALWABP). ALWABP arises when the operation time for every task differs according to the worker who executes the task. Since the operation times of tasks vary due to the workers, the problem requires a simultaneous solution to the double assignment problem. Tasks must be assigned to workers and workers to stations, concurrently. This problem is especially proposed in sheltered work centers for the disabled. However, it is not only important for the assembly lines with the disabled, but also for manually operated assembly lines with high labor turnover. In this paper, a multiple-rule based constructive randomized search (MRBCRS) algorithm is proposed in order to solve the ALWABP. Thirty nine task priority rules and four worker priority rules are defined. Performance of the proposed MRBCRS is compared with the relevant literature on benchmark data. Experimental results show that the proposed MRBCRS is very effective for benchmark problems. The results show that the algorithm improves upon the best-performing methods from the literature in terms of solution quality and time. [ABSTRACT FROM AUTHOR]

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Numerous clothing enterprises in the market have a relatively low efficiency of assembly line planning due to insufficient optimization of bottleneck stations. As a result, the production efficiency of the enterprise is not high, and the production organization is not up to expectations. Aiming at the problem of flexible process route planning in garment workshops, a multi-object genetic algorithm is proposed to solve the assembly line balance optimization problem and minimize the machine adjustment path. The encoding method adopts the object-oriented path representationmethod, and the initial population is generated by random topology sorting based on an in-degree selection mechanism. The multi-object genetic algorithm improves the mutation and crossover operations according to the characteristics of the clothing process to avoid the generation of invalid offspring. In the iterative process, the bottleneck station is optimized by reasonable process splitting, and process allocation conforms to the strict limit of the station on the number of machines in order to improve the compilation efficiency. The effectiveness and feasibility of themulti-object genetic algorithm are proven by the analysis of clothing cases. Compared with the artificial allocation process, the compilation efficiency of MOGA is increased by more than 15% and completes the optimization of the minimum machine adjustment path. The results are in line with the expected optimization effect. [ABSTRACT FROM AUTHOR]

This paper addresses a stochastic assembly line balancing problem with flexible task times and zoning constraints. In this problem, task times are regarded as interval variables with given lower and upper bounds. Machines can compress processing times of tasks to improve the line efficiency, but it may increase the equipment cost, which is defined via a negative linear function of task times. Thus, it is necessary to make a compromise between the line efficiency and the equipment cost. To solve this problem, a bi-objective chance-constrained mixed 0-1 programming model is developed to simultaneously minimize the cycle time and the equipment cost. Then, a hybrid Particle swarm optimization algorithm is proposed to search a set of Pareto-optimal solutions, which employs the simulated annealing as a local search strategy. The Taguchi method is used to investigate the influence of parameters, and accordingly a suitable parameter setting is suggested. Finally, the comparative results show that the proposed algorithm outperforms the existing algorithms by obtaining better solutions within the same running time. [ABSTRACT FROM AUTHOR]

Robotic assembly lines are widely used in manufacturing industries. The robotic assembly line balancing (RALB) problem aims to balance the workloads among different workstations and optimize the assembly line efficiency. This paper addresses a particular type of RALB problem, which minimizes the assembly line cycle time by determining the task and robot assignment in each workstation under precedence constraints. To solve the problem, we present an effective hybrid algorithm fusing the estimation of distribution algorithm and branch-and-bound (B&B) based knowledge. A problem-specific probability model is designed to describe the probabilities of each task being assigned to different workstations. Based on the probability model, an incremental learning method is developed and a sampling mechanism with B&B based knowledge is proposed to generate new feasible solutions. The fuse of B&B based knowledge is able to reduce the search space of EDA while focusing the search on the promising area. To enhance the exploitation ability, a problem-specific local search is developed based on the critical workstation to further improve the quality of elite solutions. The computational complexity of the proposed algorithm is analyzed, and the effectiveness of the B&B based knowledge and the problem-specific local search is demonstrated through numerical experiments. Moreover, the performance of the proposed algorithm is compared with existing algorithms on a set of widely-used benchmark instances. Comparative results demonstrate the effectiveness and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper examines the effect of stochastic task times on the total operating costs of a continuously paced assembly line under the assumption that the line is stopped whenever at least one work station requires more time than allotted. A comprehensive stochastic cost function is integrated into an efficient balancing algorithm to enable an approximately minimum cost balance to be obtained. An experiment was conducted to determine the cost savings resulting from using the stochastic method as compared to two deterministic methods. The stochastic method of this paper produced lower cost balances in most cases studied. [ABSTRACT FROM AUTHOR]

Balancing assembly line in a mass production system plays a vital role to improve the productivity of a manufacturing system. In this paper, a single model assembly line balancing problem (SMALBP) is considered. The objective of this problem is to group the tasks in the assembly network into a minimum number of workstations for a given cycle time such that the balancing efficiency is maximized. This problem comes under combinatorial category. So, it is essential to develop efficient heuristic to find the near optimal solution of the problem in less time. In this paper, an attempt has been made to design four different genetic algorithm (GA)-based heuristics, and analyze them to select the best amongst them. The analysis has been carried out using a complete factorial experiment with three factors, viz. problem size, cycle time, and algorithm, and the results are reported. [ABSTRACT FROM AUTHOR]

The assembly of large and complex products such as cars, trucks, and white goods typically involves a huge amount of production resources such as workers, pieces of equipment, and layout areas. In this context, multi-manned workstations commonly characterize these assembly lines. The simultaneous operators' activity in the same assembly station suggests considering compatibility/incompatibility between the different mounting positions, equipment sharing, and worker cooperation. The management of all these aspects significantly increases the balancing problem complexity due to the determination of the start/end times of each task. This paper proposes a new mixed-integer programming model to simultaneously optimize the line efficiency, the line length, and the workload smoothness. A customized procedure based on a simulated annealing algorithm is developed to effectively solve this problem. The aforementioned procedure is applied to the balancing of the real assembly line of European sports car manufacturers distinguished by 665 tasks and numerous synchronization constraints. The experimental results present remarkable performances obtained by the proposed procedure both in terms of solution quality and computation time. The proposed approach is the practical reference for efficient multi-manned assembly line design, task assignment, equipment allocation, and mounting position management in the considered industrial fields. [ABSTRACT FROM AUTHOR]

In the UAV (often called drones) industry a zero defects culture is required. However, process improvement through the DMAIC lean methodology has not been widely studied in this industry. This paper examines how to solve the increasing quality problems of a small drones' assembly company. A circular DMAIC methodology was used as an adaptation of the DMAIC methodology. Principal results obtained demonstrate a reduction of the lead time and an increment in the leanness degree level from 23% to 60%. Multifunctional worker rate increased from 75% to 83% and motion waste was reduced almost in a 50%. Line balancing and Simulation were applied. [ABSTRACT FROM AUTHOR]

In most mass-production assembly lines, workers perform a set of tasks repetitively predefined by assembly line balancing techniques. The static task assignment often leads to low productivity when the assembly system faces disruptions or uncertainties such as machine breakdown and uneven worker capabilities. The idea of bucket brigades (BB) has been introduced to address the static assignment problems where cooperative behavior of ants is applied to flow line control. This paper examines possible efficiency losses associated with the existing BB-based assembly cell and presents an improved version for assembly cells under uncertain environments. The new system attempts to enhance productivity by assigning assembly tasks to workers dynamically and possibly adding buffers for decoupling consecutive workers. The proposed assembly system is evaluated through simulation experiments under various manufacturing environments. The experimental results show that the new system provides higher productivity than the naïve BB-based assembly cell as well as traditional assembly cells, especially for uncertain assembly environments. [ABSTRACT FROM AUTHOR]

This paper addresses an assembly line balancing problem in which each station is allowed to have more than one worker, with minimization of two objectives: (i) the number of workers used in the line and (ii) the number of stations opened in the line. Each station has an own workpiece, and workers at the same station simultaneously perform different assembly tasks on its workpiece. For solving the balancing problem of these lines, a new mixed integer programming formulation is presented, and then a simulated annealing based heuristic method is proposed. The new formulation requires less number of variables and constraints. The basic property of the proposed heuristic is that it is directly applied to Gantt representations of problem's solutions. Moreover, the paper includes some important neighborhood generation properties for Gantt representations of solutions. Both methods are compared with existing methods available in the literature. Experimental study indicates that the proposed methods can yield promising results. [ABSTRACT FROM AUTHOR]

Assembly line balancing (ALB) is concerned with assigning tasks within an assembly line to meet the required production rate for optimization purposes. On the other hand, two-sided ALB performs double-sided assembly operation on a single assembly line. In this paper, we have focused the survey on two-sided assembly line balancing (2S-ALB) research problems. The numerous factors mentioned in 2S-ALB literature were actually based on problem resolutions, and this paper will quote any preferred literature considering the frequent citation. In particular, this review explores in detail the ALB problems, optimization methods, objective functions, and specific constraints used in solving 2S-ALB problems. Among the purposes of ALB problems is that it traditionally focuses on simple ALB with various engaging approaches. General ALB comes second because of its complexity and nondeterministic polynomial (NP)-hard-classified problems. However, due to the current manufacturing issues, GALB problems, such as 2S-ALB, are forced to be examined and this comprehensive literature will specify anything necessary for the optimization purposes. Finally, future research direction has been discovered and put forward as the suggestion. [ABSTRACT FROM AUTHOR]

This paper addresses an existing but unsolved problem: dynamic task reassignment under changing task performance times. This situation is relevant to automated flexible assembly systems with collaborative learning. The length of the line can be reduced during the period of production by dynamically rebalancing the assembly line because of the changing task times. A task reassignment procedure is designed to balance such an assembly line where task times are being reduced during small lot size production. Backward induction rules, which can be combined with prevalent algorithms, are proposed to form such a procedure. A case study is provided to show that the procedure significantly increases the efficiency of the assembly line in terms of the length of the production line and system idle times. [ABSTRACT FROM AUTHOR]

This paper presents a new extension of SALBP-2, so called assembly line resource assignment and balancing problem of type 2 (ALRABP-2). Two main differences from the existing literature are revealed in this work. The first is on the objective function which is a multiple one. It is aimed here to minimize both the cycle time and the cost per time unit (hour) of a line for a fixed number of stations to satisfy the constraints of precedence between tasks and compatibility between resources. The second difference lies in the proposed method to solve this problem. A new version of multi-objective genetic algorithm (MOGA) called hybrid MOGA (HMOGA) is elaborated. Full experiment design is used to obtain a better MOGA parameters combination. The effectiveness of the HMOGA was assessed through a set of literature problems. The performance of HMOGA shows a good quality of the fronts generated and a better problem-solving capacity for two optimisations. [ABSTRACT FROM AUTHOR]

In this paper, we propose an intelligent battery control system that incorporates an active balancing technique and a fuzzy controller to manage and extend the life of an agricultural drone battery efficiently. The control system includes the following key features: A battery pack balancing algorithm based on a bidirectional DC/DC converter; a cell balancing system to prevent overcharge/discharge and ensure equal control of the cell voltages; and a monitoring system and wireless link to track the real-time status of the battery, temperature, and acceleration while the drone is in operation. Each battery pack consists of six lithium polymer batteries, one fuzzy controller per cell, and supports active balancing using the developed balancing technique. The capacity of each of the battery packs in the system is 11,000 mAh, and two can be combined to provide a total capacity of 22,000 mAh. [ABSTRACT FROM AUTHOR]