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

This article presents a novel approach for the automated design of assembly lines that combines the assembly line balancing problem with resource selection and the positioning of the chosen resources into one single optimisation problem. Existing approaches for the automated planning of assembly plants either focus on one planning step or work through different planning steps sequentially. So far, no method exists that sufficiently takes into account the interdependency between the selection and positioning of resources. This article addresses this problem by presenting a bi-level optimisation approach for the automated design of assembly lines. A nested genetic algorithm is used to solve an assembly line balancing problem that includes the selection of production resources while simultaneously considering the layouting options for the chosen resources. Three examples for the evaluation and validation of the algorithm are presented. The presented approach is economically promising as the design of assembly lines requires a lot of expert knowledge and is still mostly done manually. [ABSTRACT FROM AUTHOR]

Assembly line balancing is an important and well recognised operations research problem. The current line balance may not stay optimal, even feasible, due to the disruptions in one or more workstations. In this study, after the disruption, we aim to rebalance the assembly line by considering the trade-off between workload balancing (fairness measure) and total replacement distance for the tasks assigned to the different workstations (stability measure). We try to generate all non-dominated objective function vectors for the defined fairness and stability measures. Two algorithms are developed: exact algorithm (classical approach) and tabu search algorithm. The results of the experiments have shown that the classical approach returns exact non-dominated objective vectors with up to 40 tasks and 7 workstations in one hour, and the tabu search algorithm returns approximate non-dominated objective vectors that are very close to their exact counterparts and can solve large sized instances with up to 94 tasks and 7 workstations in less than 10 s. [ABSTRACT FROM AUTHOR]

The simple assembly line balancing problem (SALBP) considers work division among different workstations of a serially arranged assembly process to maximise its efficiency under workload (cumulative) and technological (precedence) constraints. In this work, we consider a variant of the SALBP which allows parallel workstations. To study the effect of parallel stations, we propose a new problem (the parallel station assembly line balancing problem or PSALBP) in which the objective is to minimise the number of parallel stations required to obtain the maximum theoretical efficiency of the assembly process. We study the complexity of the problem and identify a polynomially solvable case. This result is then used as a building block for the development of a heuristic solution procedure. Finally, we carry out a computational experiment to identify the characteristics of assembly lines that may benefit from station paralleling and to evaluate the performance of the proposed heuristic. [ABSTRACT FROM AUTHOR]

Given that a U-shaped assembly line is usually regarded as a serial flow production system, preventive maintenance (PM) on machines in any station of the line may lead to stoppage of the whole line and sequentially cause great loss of production cost. To improve production continuity during PM, this paper proposes multiple alternative assignment plans with interchangeable abilities to ascertain the production recovery from changes of line structure. A mixed-integer mathematical model is further formulated to make decisions on alternative assignment plans. In this model, cycle time and total assignment plan alteration cost are minimised simultaneously. And an enhanced JAYA algorithm is developed to obtain well-distributed Pareto frontier solutions effectively and efficiently. The proposed algorithm suggests a similarity-based selection and various crossover/neighbourhood search operators to enhance its search ability for global optima. Numerical experiment results prove the effectiveness of the proposed model and enhanced JAYA. This novel methodology of integration optimisation bridges assembly line balancing and PM, and thus promotes production continuity and achieves economic benefits under maintenance within large-scaled assembly lines in real situations. Furthermore, a real-world case study is conducted to illustrate the significance of this novel methodology. [ABSTRACT FROM AUTHOR]

In simple assembly line balancing problems, it is assumed that the resources required to perform the tasks are available at the relevant station for task assignment. However, each task may need different resource types depending on the difficulty, complexity and technical requirements of the tasks in real life. For this reason, tasks and resources belonging to these tasks must be assigned to the relevant station while balancing the line. In this study, the resource-constrained U-shaped assembly line balancing problem (U-GRCALBP) is discussed. According to the literature research, there is no study dealing with U-GRCALBP. Two different constraint programming (CP) models that define the resource constraints as "and/or" constraint types with concurrent resource types have been developed. In these models, the sum of resource usage costs and station opening cost minimization is aimed. The models are explained with an illustrative example and the efficiency of the models is tested by deriving new resource constraints on five different data set each taking into account four different cycle times and the number of two and four resource types. The number of stations obtained, the total number of resources used, total station opening and resource usage costs, and CPU time are used as performance criteria The results are compared with the traditional U-type assembly line balancing problem results. The proposed CP models give superior performance on all data sets, especially in terms of total cost. The numerical results show that both CP models are effective in solving the problem. Furthermore, some managerial implications are presented to be useful for professionals, organizations, and society. [ABSTRACT FROM AUTHOR]

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

Supply chain network design (SCND) problems are used to formulate and make strategic decisions on the number of facilities, network facility location and plant capacity, to ensure the optimal delivery of products from manufacturers to end consumers. On the other hand, assembly line balancing (ALB) problems provide details on operational decisions which encompass production levels and inventories and therefore should be flexible enough to react when changes in resource availability and product demands occur. Integration of the SCND and ALB problems has thus been explored to develop a more holistic model and to further improve the efficiency of the supply chain. This study develops a novel SCND-ALB optimization model which considers economic, environmental, and social objectives in determining the optimal design and operational conditions of the supply chain under flexible cycle time of the assembly lines. Furthermore, for the first time a zigzag type belief degree model is integrated to address system uncertainties of the SCND-ALB problem. Two multi-objective solution approaches are then considered to simultaneously account for the three sustainability objectives. A case study on the production of a nozzle is explored to demonstrate the capabilities of the model. [ABSTRACT FROM AUTHOR]

To keep up with the Industry 4.0 technological revolution and get the upper hand over competitors, manufacturing companies replace human workers with robots in their assembly processes. A popular approach in the manufacturing industry is to design an assembly line with human-robot collaboration. In this study, we investigate a robotic stochastic assembly line balancing problem (RSALBP), with the motivation to observe the effects of robots on the cycle time in stochastic assembly lines where human workers and robots operate in different workstations. In the literature, robotic assembly line balancing is only studied with deterministic task times. However, assembly line balancing contains stochastic processes in real life. We assume that the processing time of each task follows a normal distribution whose parameters depend on the type of the operator performing the task with robots having much less (possibly zero) variation in task times than human workers. It is assumed that human workers are fully capable while robots are able to perform a subset of the tasks. We study type-II RSALBP which aims to minimize the cycle time for an assembly line with stochastic task times, given a fixed number of workstations and robots. This problem is NP-hard and includes non-linearity. We propose a mixed-integer second-order cone programming formulation and a constraint programming formulation to solve the problem. Instances from the literature are used to test the effectiveness of the proposed formulations. Additionally, the effects of robots on cycle times are evaluated by conducting a computational study with a comprehensive experimental design. [ABSTRACT FROM AUTHOR]

This document discusses the role and challenges of human-robot collaboration (HRC) in the context of manufacturing and logistics. It highlights seven papers that were accepted for publication in the Flexible Services and Manufacturing Journal after a rigorous peer-review process. The papers cover various topics such as task planning and allocation, operational planning in a multi-level warehouse, assembly line balancing, workshop scheduling, rescheduling in the steelmaking process, and preventive maintenance in assembly lines. The aim of these papers is to propose solution techniques and identify how digitalized industrial scenarios operate in order to improve the interaction between humans and robots in complex systems. [Extracted from the article]

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

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]

Purpose: Assembly lines are widely employed in manufacturing processes to produce final products in a flow efficiently. The simple assembly line balancing problem is a basic version of the general problem and has still attracted the attention of researchers. The type-I simple assembly line balancing problems (SALBP-I) aim to minimise the number of workstations on an assembly line by keeping the cycle time constant. Design/methodology/approach: This paper focuses on solving multi-objective SALBP-I problems by utilising an artificial bee colony based-hyper heuristic (ABC-HH) algorithm. The algorithm optimises the efficiency and idleness percentage of the assembly line and concurrently minimises the number of workstations. The proposed ABC-HH algorithm is improved by adding new modifications to each phase of the artificial bee colony framework. Parameter control and calibration are also achieved using the irace method. The proposed model has undergone testing on benchmark problems, and the results obtained have been compared with state-of-the-art algorithms. Findings: The experimental results of the computational study on the benchmark dataset unequivocally establish the superior performance of the ABC-HH algorithm across 61 problem instances, outperforming the state-of-the-art approach. Originality/value: This research proposes the ABC-HH algorithm with local search to solve the SALBP-I problems more efficiently. [ABSTRACT FROM AUTHOR]

A great deal of work has been directed to assembly line balancing (ALB) using different heuristics. However, little work is made on evaluating the performance of ALB heuristics under various operating factors. In this paper, the performance of common heuristics in solving the single-model assembly line balancing problem (SLABP-1) is investigated using a dataset of randomly generated ALB problems. The dataset is sorted to obtain representative levels of cycle time, time variability (TV), number of tasks, and order strength. An experiment is then designed to investigate the effects of factors on the performance of heuristics with a focus on line efficiency, largest workstation idle time, smoothness index, total idle time, number of workstations, and west ratio. In this study, the optimal working settings for the balancing process are determined based on the performance metrics achieved. The results show that several heuristics can optimize multiple performance metrics given specific factor settings. Furthermore, findings reveal that some performance measures can be optimized at the same factor levels regardless of the heuristics used. TV is recognized to be a critical factor to ALB problem, with lower TV achieving better performance measures. Process designers should understand the impact of factors effects on choosing the ALB heuristic. A heuristic, which can optimize multiple performance measures simultaneously, is recommend. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problem of balancing the average weighted completion times of two classes of jobs is an NP-hard scheduling problem that was very recently introduced in the literature. Interpreted as a cooperative-type two-agent single-machine problem, its applications are in various practical contexts such as in logistics for balancing the delivery times, in manufacturing for balancing the assembly lines and in services for balancing the waiting times of groups of people. The only solution technique currently existing in the literature is a Lagrangian heuristic, based on solving a finite number of successive linear assignment problems, whose dimension depends on the total number of jobs. Since the Lagrangian approach has not appeared to be particularly suitable for solving large-scale problems, to overcome this drawback, we propose to face the problem by means of a genetic algorithm. Differently from the Lagrangian heuristic, our approach is found to be effective also for large instances (up to 2000 jobs), as confirmed by numerical experiments. [ABSTRACT FROM AUTHOR]

In this present work, the approach is for the adoption of virtual manufacturing in an assembly line system in which the assembly of Frame, Axle, Engine, Cab Body, and Trim is done over chassis in an automobile industry Trim Chassis and Frame shop. The major goal is to align the conveyor system's speed (Takt Time) and the Electrified Monorail Hoist System's speed with the conveyor system's speed. Various process parameter combinations are employed to achieve varied production rates while maintaining a smooth process flow among material handling equipment. Different volume levels were used to simulate the chassis assembly line. The system's efficiency was assessed using simulated data. By substituting manual processes with 3D simulation, Delmia Quest software aids in the detection of process planning flaws. The use of 3D simulation in the design of the process flow aids in the verification of equipment motion as well as the detection of crashing and trafficking on the assembly line system. CATIA® was used to design the assembly line, and Delmia QUEST was used to simulate it in real time. The proposed effort intends to provide a better understanding of assembly line production and the numerous manufacturing techniques and procedures that are necessary during the manufacturing process in order to reduce manufacturing costs and idle time while also increasing productivity. The operator loading improved by 8% after adding 313.92 min of new job content, while vehicle number increased by 5 vehicles each shift. [ABSTRACT FROM AUTHOR]

Copyright of Revista Producao Online is the property of Associacao Brasileira de Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents an integrated supply chain network with suppliers, manufacturers, assemblers, and customers. The proposed model considers a U-shaped assembly line with three sustainability objective functions. We consider assumptions considering different types of raw materials, multiple products, location selection of manufacturers, location selection of assemblers, and capacity of suppliers. The problem is formulated non-linear and then linearized as a multi-objective model. Some cost and demand parameters are considered uncertain and are represented by fuzzy sets and theory. The proposed uncertain model is first converted to a multi-objective crisp model by applying the modified robust possibilistic programming approach. Then, the obtained crisp multi-objective model is solved by an interactive-fuzzy optimization approach in the literature. For computational study, some test problems are generated and solved using an original deterministic formulation and the crisp form of the uncertain formulation. The obtained results are analyzed and compared according to the objective function values. Finally, an extensive sensitivity analysis is performed on the parameters of the models.

Assembly line efficiency is one of the most important parameters that determine the overall efficiency of a manufacturing company. The production of a product under optimum conditions is ensured by a balanced assembly. With a balanced assembly line, machinery, material and labour costs are reduced. Within the scope of this research, real data about the daily production capacity and assembly line efficiency of a company producing Emergency Luminaire were taken, the same assembly line was balanced with 4 different Heuristic ALB methods and the results were compared. According to the results obtained, a high line efficiency of 93.955% was achieved using the Hoffman, Comsoal and Moodie&Young (M&Y) methods, and 84.414% was achieved with the Ranked Positional Weight (RPW) method. As a result of this, it was observed that the daily production capacity increased from 250 units to 375 units. As a result of the study, it was revealed that the efficiency of the existing assembly line and accordingly the daily production capacity increased. In addition, the study results of this assembly line were taught to an artificial neural network model for training purposes, and the work station results of the operations of a different assembly line were obtained with 99.940 accuracy. In this context, it has been revealed that the artificial neural networks method can be used in addition to the use of the heuristic method in the solution of ALB problems.

In turbulent times featuring increased customisation, higher demand volatility, and shortened product life cycles, companies gain a competitive advantage by adopting a single yet highly flexible assembly line. A cornerstone of today's production systems is determining the optimal takt time (or cycle time) by aligning the assembly pace with the desired level of output. In practice, most companies rely on a fixed takt time, even when the work content between models varies considerably. We show that, in contrast to a fixed takt system, variable takt times reduce not only labour inefficiencies but also the complexity of the mixed-model assembly line balancing problem. Inspired by our industry partner Fendt, an innovation leader in the global agricultural machinery market and benchmark for a wide range of industries, we define a generalisable mixed-integer programming model that accounts for key conditions neglected in previous research – in particular, random customisation through configuration-specific task times and assembly quality by assigning operator workloads to 'zones'. Introducing such operator work zones reveals that firms need not face a time–quality trade-off whereby takt time must be prioritised over how well work is performed. Our numerical study and takt time sensitivity analysis document the effectiveness of this approach when its results are compared with those under Fendt's current takt times. [ABSTRACT FROM AUTHOR]

Multi-sided assembly line balancing problems usually occur in plants producing big-sized products such as buses, trucks, and helicopters. In this type of assembly line, in each workstation, it is possible to install several workplaces, in which a single operator performs his/her own set of tasks at an individual mounting position. In this way, the operators can work simultaneously on the same product without hindering each other. This paper considers for the first time the multi-sided assembly line balancing problem with the objective of minimising the cycle time, proposing a new mathematical formulation to solve small-sized instances of this problem. Besides, a metaheuristic algorithm based on variable neighbourhood search hybridised with simulated annealing is developed to solve large-sized instances. The algorithm is called adaptive because of the adopted neighbourhood selection mechanism. A novel three-string representation is introduced to encode the problem solutions and six different neighbourhood generation structures are presented. The developed approach is compared to other meta-heuristics, considering some well-known in literature test instance and a real world assembly line balancing problem arising in a car body assembly line. The experimental results validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

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

An efficient part feeding is among the top challenges of many mass producers applying mixed-model assembly lines, for instance, in the automotive industry. This paper introduces a novel part feeding policy applied by a large German assembly plant for car engines: In-line kitting. Under this policy, the first stations of the line do not execute assembly operations, but are reserved for picking parts while passing containers of stock-keeping units (SKUs) arranged along the line. In this way, the parts are collected in traveling kits moving along with each workpiece on the conveyor, so that later assembly stations have the required parts directly available and do not lose precious labor time for unproductive parts handling. A major operational challenge when applying this part feeding policy is the walking effort for the human pickers while putting the SKUs of their respective stations into the traveling kits of the passing workpieces. Due to a high product variety, a large number of comparatively bulky SKU containers have to fit into each station, so that the walking distance to be covered by a worker during a work shift exceeds multiple kilometers. We show that this physical burden can be reduced significantly by balancing the workload among stations and optimizing the storage assignment of SKU containers within each in-line kitting station. We formulate the resulting optimization problem and provide suited solution procedures. Our computational study shows that the walking distance of pickers can be reduced significantly without producing any additional costs. [ABSTRACT FROM AUTHOR]

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Data arrangement determines the capacity, resilience, and performance of a distributed storage system. A scalable self-managed system must place its data efficiently not only during stable operation but also after an expansion, planned downscaling, or device failures. In this article, we present Derrick, a data balancing algorithm addressing these needs, which has been developed for HYDRAstor, a highly scalable commercial storage system. Derrick makes its decisions quickly in case of failures but takes additional time to find a nearly optimal data arrangement and a plan for reaching it when the device population changes. Compared to balancing algorithms in two other state-of-the-art systems, Derrick provides better capacity utilization, reduced data movement, and improved performance. Moreover, it can be easily adapted to meet custom placement requirements. [ABSTRACT FROM AUTHOR]

The collaborative robots (cobots) are increasingly being utilized in industries due to the advancement in the field of robotic technology and also due to the increase in labor costs. The cobots on the assembly line can be utilized to complete the tasks independently or assist the workers to complete the tasks. This study considers the U-shaped assembly line balancing problem with cobots, where several cobots with different purchasing costs are selected under the budget constraint. Three mixed-integer programming models are formulated to optimize the cycle time, and the built models are capable of solving the small-sized instances optimally. Two algorithms, artificial bee colony algorithm and migrating bird optimization algorithm, are developed and improved to tackle the large-sized instances, where new encoding scheme and decoding procedure are developed for this new problem. The computational tests demonstrate that the utilization of collaborative robots reduces the cycle time effectively in the assembly line. The comparative study on a set of instances shows that the proposed methodologies obtain competing performance in comparison with other 12 implemented algorithms. [ABSTRACT FROM AUTHOR]

In multi-manned assembly lines, more than one operator is allowed on the same workstation. Limited research has been carried out on sequence-dependent set-up times. Therefore, this article optimizes the multi-manned assembly line balancing problem with sequence-dependent set-up times using an enhanced migrating birds optimization (EMBO) algorithm. Sequence-dependent set-up times are embedded in two ways: forward and backward set-ups. EMBO designs a mixed neighbourhood search and acceptance criteria to help it escape from local optima, and a diversification mechanism to enhance the global exploration ability. Three types of experiment and two scenarios, with low and high set-up times, are conducted to test the performance of the mixed-integer linear programming (MILP) model and EMBO. The first experiment compares three MILP models (no, low and high set-up times) to illustrate the significance of set-up times. The final two experiments demonstrate the effectiveness of EMBO by comparison with the MILP model and six state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the study is to fill a gap in the literature on mathematical production planning (joint balancing and sequencing) in the fashion industry. It considers in particular situations of mass customization, made-to-measure or small lot sizes. Design/methodology/approach: The paper develops a mathematical model based on product options and attributes instead of fixed variants. It proposes an easy-to-use genetic algorithm to solve the resulting optimization problem. Functionality and performance of the algorithm are illustrated via a computational study. Findings: An easy-to-implement, yet efficient algorithm to solve the multi-objective implementation of a problem structure that becomes increasingly relevant in the fashion industry is proposed. Implementation of the algorithm revealed that the algorithm is ideally suited to generate significant savings and that these savings are impervious to problem and thus company size. Practical implications: The solutions from the algorithm (Pareto-efficient frontier) offer decision-makers more flexibility in selecting those solutions they deem most fitting for their situation. The computational study illustrates the significant monetary savings possible by implementing the proposed algorithm to practical situations. Originality/value: In contrast to existing papers, for the first time, to the best of the author's knowledge, the focus of the joint balancing and sequencing approach has been applied in the fashion instead of the automotive industry. The applicability of the approach to specific fields of the fashion industry is discussed. An option and attributes-based model, rarely used in general assembly line sequencing per se, is used for more flexibility in representing a diverse set of model types. [ABSTRACT FROM AUTHOR]

This study addresses the integrated disassembly line balancing and pickup-delivery vehicle routing problem of companies with multiple disassembly centers. In a supply chain with distributed disassembly centers, the products to be disassembled must be collected from the factories where they are supplied and brought to the disassembly centers. Then, these collected products must be disassembled in the disassembly centers and these disassembled components must be distributed to the factories that demand the disassembled parts. Since there are distributed disassembly centers, factories that request components and factories that supply products should be assigned to the disassembly centers. This study aims to provide an integrated plan for the assignment, disassembly line balancing and collection-distribution processes. In this study, there are distributed disassembly centers with limited product supplies, and distribution and collection operations are considered together in the vehicle routing problem. The problem differs from the studies in the literature with these features. The simultaneous collection and distribution operations aim to save time and reduce transportation costs of vehicles. A mixed-integer nonlinear programming model, a mixed-integer linear programming model and a constraint programming model are presented to solve the integrated problem. The performance of the mixed-integer linear programming and constraint programming models has been evaluated using small-sized instances, and the computational findings indicate that both models can provide effective solutions for the problem. [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]

An assembly line is a serial production system that is meant to produce high-quality and usually complex products in mass quantities. Assembly lines play a crucial role in determining the profitability of a company, as they are utilised as the final stage of the system prior to shipping. In an assembly line balancing problem, the assembly tasks are allocated to workstations based on their processing times after considering the precedence relationships between them. There is a massive amount of research in the literature using deterministic task processing times, and many other works consider stochastic task times. This research utilises the uncertainty theory to model uncertain task times and considers incompatible task sets constraints. The problem is solved using a simulated annealing algorithm with problem-specific characteristics. Lower bounds are developed to accelerate the simulated annealing algorithm. A restart mechanism, which can escape the local optimum obtained by neighbourhood generation, is proposed. A repair mechanism is integrated to combine the workstations so as to further improve the quality of solutions. The numerical examples and experimental tests demonstrate the powerful solution-building capacity of the proposed simulated annealing algorithm over teaching–learning-based and genetic algorithms. The methodology proposed in this research is applicable to any industry (including the automotive industry) when the historical data on task processing times is very limited. [ABSTRACT FROM AUTHOR]

In this study, we develop a branch and bound solution algorithm to solve the workload smoothing problem. Our algorithm incorporates new formulas for dynamically computing a lower bound on the optimal value of the objective function and for determining the earliest workstations for tasks. It also uses a fast heuristic for computing a good initial upper bound. A comprehensive experimental analysis is conducted in this study. The analysis demonstrates the outstanding performance of the algorithm and its efficiency in solving medium-sized workload smoothing problems. [ABSTRACT FROM AUTHOR]

The assembly line balancing problem is always explored using the assumption that the processing ability of each workstation is constant. However, the initial workload balance can be easily broken by the changing processing condition of the machines, due to degradation. In the context of Industry 4.0, real-time information related to the machine health state is available. The aim is to improve the performance of the assembly process by making full use of the obtained real-time information. This research is the first exploration of real-time assembly line balancing with the changing health states of machines and the trigger point of adjustments to the assembly line. In this study, a fuzzy control system is developed to determine when to re-balance the assembly line and how to adjust the production rates to smooth the workloads of the workstations. The numerical results show that the assembly line with the proposed fuzzy control system satisfies the demand for most cases, and achieves higher utilisation of machines and lower buffer levels. Therefore, the real-time information brought by Industry 4.0 can be used to improve the performance of an assembly line. [ABSTRACT FROM AUTHOR]

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

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.

Ergonomic aspects have a crucial role in manual assembly systems. They impact on the workers' health, final product quality and productivity. For these reasons, there is the necessity to integrate them into the assembly line balancing phase as, whereas, only time and cost variables are considered. In this study, human energy expenditures are considered as ergonomic aspects and we integrate them, for the first time, into the assembly line balancing problem type 2 through the rest allowance evaluation. We consider as an objective function the minimization of the smoothness index. Firstly, a new optimal method based on mixed integer linear programming and a new linearization methodology are proposed. Then, a heuristic approach is introduced. To complete the study, a computational experimentation is presented to validate the mathematical model and to compare the methodologies proposed in terms of computational time, complexity and solution. Additionally, we provide a detailed analysis of the impact that rest allowance evaluation can have on productivity comparing the results obtained, taking into account the rest allowance integration before, during and after the assembly balancing process. [ABSTRACT FROM AUTHOR]

Asynchronous Mixed-Model Assembly lines are common production layouts dedicated to large-scale manufacturing of similar products. Cyclically scheduling such products is an interesting strategy to obtain high and stable throughput. In order to best optimise these lines, it is necessary to combine line balancing, model sequencing, and buffer allocation. However, few works integrate these three degrees of freedom, and evaluating steady-state performance as a consequence of these decisions is challenging. This paper presents a mathematical model that allows an exact steady-state performance evaluation of these lines, and hence their optimisation. While the combination of degrees of freedom is advantageous, it is also computational costly. An iterative decomposition procedure based on alternation between two mathematical models and on optimality cuts is also presented. The decomposition is tested against the proposed mathematical model in a 700-instance dataset. The developed methods obtained 142 optimal answers. Results show that the decomposition outperforms the monolithic mathematical model, in particular for larger and harder instances in terms of solution quality. The optimality cuts are also shown to help the decomposition steps in terms of solution quality and time. Comparisons to a sequential procedure further demonstrate the importance of simultaneously optimising the three degrees of freedom, as both the proposed model and the decomposition outperformed such procedure. [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]

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]