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Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times.

In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times.

The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost.

Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case.

As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.

Describes a systematic procedure for the design of a manufacturing assembly system, which has been developed in response to the problems associated with the allocation of tasks to workstations, under conditions of uncertainties (and, hence, risks) in some key system parameters. Adopts the methodology of stochastic modelling, whereby various probability distributions are integrated within a modified COMSOAL algorithm, as a means of addressing the uncertainties associated with key manufacturing assembly system variables, such as cycle time and task times. The proposed computer‐oriented methodology is code‐named CIMASD, and incorporates four basic objective criteria options: minimizing the number of workstations; minimizing the balance delay; minimizing the cycle time; or a combination of two or more. Discusses four variants of the CIMASD methodology, designed and equipped to reflect on various uncertainty circumstances under which manufacturing assembly system designs are performed in practice. Demonstrates the efficacy of the CIMASD methodology by applying two of its variants to a case study. Shows that the proposed methodology is capable of facilitating far more informative manufacturing system design than would otherwise be possible: CIMASD can incorporate effective cost saving features, which are useful in the planning, designing and scheduling of workstation tasks, in a typical manufacturing assembly system design.

Recognising the enormous potential of just‐in‐time (JIT) concepts for boosting productivity and quality, an increasing number of US and European firms consider adopting JIT concepts in manufacturing. However, the transfer of a manufacturing policy from traditional to JIT always requires radical structural changes in a production line design. One typical example of these changes is uniform assembly which does not allow high variability in the production schedules. Consequently, major hindrance to uniform assembly is a random fluctuation of task processing times in assembly line balancing. This article proposes a heuristic which takes into account stochastic task processing times and further develops a work assignment with the lowest expected total cost as well as an assignment with the highest work completion probability crucial for the success of JIT manufacturing.

This research aims to address the cost-oriented stochastic assembly line balancing problem (ALBP) and propose a chance-constrained programming model.

The cost-oriented stochastic ALBP is solved for small- to medium-sized problems. Owing to the non-deterministic polynomial-time (NP)-hardness problem, a multiple rule-based genetic algorithm (GA) is proposed for large-scale problems.

The experimental results show that the proposed GA has superior performance and efficiency compared to the global optimum solutions obtained by the IBM ILOG CPLEX optimization software.

To the best of the authors’ knowledge, only one study has discussed the cost-oriented stochastic ALBP using the new concept of cost. Owing to the NP-hard nature of the problem, it was necessary to develop a heuristic or meta-heuristic algorithm for large data sets; this research paper contributes to filling this gap.

Various approaches and algorithms have been proposed since the 1950s to solve the assembly line (AL) balancing problem. These methods have established an AL configuration from the beginning. However, a prebalanced AL may have to be rebalanced in real life for many reasons, such as changes in the cycle time, production demand, product features or task operation times. This problem has increasingly attracted the interest of scientists in recent years. This study aims to offer a detailed review of the assembly line rebalancing problems (ALRBPs) to provide a better insight into the theoretical and practical applications of ALRBPs.

A structured database search was conducted, and 41 ALRBP papers published between 2005 and 2022 were classified based on the problem structure, objective functions, problem constraints, reasons for rebalancing, solution approaches and type of data used for solution evaluation. Finally, future research directions were identified and recommended.

Single model, straight lines with deterministic task times were the most studied type of the ALRBPs. Eighteen percent of the studies solved worker assignment problems together with ALRBP. Product demand and cycle time changes were the leading causes of the rebalancing need. Furthermore, seven future research opportunities were suggested.

Although there are many review studies on AL balancing problems, to the best of the authors’ knowledge, there have been no attempts to review the studies on ALRBPs.

The purpose of this paper is to develop a computer simulation model to evaluate increasing versus decreasing mean operation times assembly line arrangement for normal and exponential distributions and the variances equal to 1 and 16.

The model was developed on the basis of a realistic case problem and applied to a six‐station assembly line. The evaluation criteria were: the minimization of the total elapsed time; the maximization of the average percentage of working time; and the minimization of the average time in the system.

The increasing mean operation times line arrangement is superior to the decreasing mean operation times line arrangement for the normal and exponential distributions and the variances equal to 1 and 16, in terms of the total elapsed time and the average percentage of the working time evaluation criteria. The decreasing mean operation times lines is marginally superior to the increasing operation times line for the normal distribution for the variances equal to 1 and 16, in terms of the average time in the system evaluation criterion. The above inference can be made for the exponential distribution for the variance 16, but no definitive conclusion can be made for the variance 1. Overall, the increasing mean operation times line arrangement has proven to be superior to the decreasing operation times line arrangement for both the stated distributions and variances, in terms of the important evaluation criteria.

The paper contributes to the computer simulation approach to solving assembly line problems that deal with the impact of normally and exponentially distributed operation times, with variances equal to 1 and 16, on the increasing and decreasing mean operation times assembly line arrangements.

This paper briefly reviews the assembly line balancing techniques developed over the last 30 years. It attempts to establish the direction of research, to identify unexplored areas with potential for study and recommends future courses of action.

The purpose of this paper is to apply particle swarm optimization (PSO) a known combinatorial optimization algorithm to multi‐objective (MO) balancing of large assembly lines.

A novel approach based on PSO is developed to tackle the simple assembly line balancing problem (SALBP), a well‐known NP‐hard production and operations management problem. Line balancing is considered for two‐criteria problems utilizing cycle time and workload smoothing as performance criteria, as well as for three‐criteria problems involving the balance delay time of the line together with cycle time and workload smoothing. Emphasis is on seeking a set of diverse Pareto optimal solutions for the bi‐criteria SALBP.

Experiments carried out on multiple test problems taken from the open literature are reported and discussed. Comparisons between the proposed PSO algorithm and two existing MO population heuristics show a quite promising higher performance for the proposed approach.

Artificial particles (potential solutions “flown” by PSO though hyperspace) are encoded to actual ALB solutions via a novel representation mechanism. A new scheme for generating and maintaining diverse Pareto ALB solutions is proposed. For the case of the two‐criteria ALBPs, the individual objectives are summed to a weighted combination with the weight coefficients being dynamically adapted using a novel weighted aggregation method. This weighted method can be applied on any bi‐criteria optimization problem.

Cycle time fluctuations in assembly lines are one of the important reasons of re‐balancing. As a result of re‐balancing of assembly lines, it will be necessary to change task sequences or equipment locations. The purpose of this paper is to find the task sequence which enables assembly line balancing (ALB) with minimum number of stations (NS) for different cycle times such that tasks and equipment or fixture locations remain unchanged.

In this paper a heuristic which consist of two stages is proposed to find a common task sequence for different cycle times in assembly lines.

It is shown that optimal NS for different cycle times can be achieved with a fixed task sequence.

The approach is limited to a single model case. Model variety together with cycle time variety can be investigated in further studies.

Assembly lines which require less time and cost for re‐balancing can be easily designed by the proposed approach.

ALB problem is handled with a new viewpoint. Also, it is observed that the proposed approach serves as a bridge between assembly line design and balancing. In this regard, it is thought to have an important place in the ALB literature.

The problem of assembly line balancing is to assign different tasks to individual workstations for ensuring the sum of task times at any station not exceeding the station time. Standard minute time is generally used in the clothing industry as a predictor of sewing speed and production efficiency. In the clothing industry, the standard minute time derived from the work study methods is generally assumed as a constant for line balancing. However, a lot of factors cause variations on operational time of the same task such as the fabrics and sub‐materials, performance of the machinery, working environment and quality level of the product. With the aid of an illustrating example selected from a men’s shirt manufacturing factory, the effect of time variations for assembly line balancing has been studied in this paper.