Genetic algorithms are one example of the use of a random element within an algorithm for combinatorial optimization. We consider the application of the genetic algorithm to a particular problem, the Assembly Line Balancing Problem. A general description of genetic algorithms is given, and their specialized use on our test-bed problems is discussed. We carry out extensive computational testing to find appropriate values for the various parameters associated with this genetic algorithm. These experiments underscore the importance of the correct choice of a scaling parameter and mutation rate to ensure the good performance of a genetic algorithm. We also describe a parallel implementation of the genetic algorithm and give some comparisons between the parallel and serial implementations. Both versions of the algorithm are shown to be effective in producing good solutions for problems of this type (with appropriately chosen parameters). Subject classifications: Programming: combinatorial ...

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this paper, a multi-objective genetic agorithm to solve assembly line balancing problems is proposed. The performance criteria considered are the number of workstations, the line efficiency, the smoothness index before trade and transfer, and the smoothness index after trade and transfer. The developed genetic algorithm is compared with six popular heuristic algorithms, namely, ranked positional weight, Kilbridge and Wester, Moodie and Young, Hoffmann precedence matrix, immediate update first fit, and rank and assign heuristic methods. For comparative evaluation, 20 networks are collected from open literature, and are used with five different cycle times. All the six heuristics and the genetic algorithm are coded in C++ language. It is found that the proposed genetic algorithm performs better in all the performance measures than the heuristics. However, the execution time for the GA is longer, because the GA searches for global optimal solutions with more iterations

This paper addresses the single-product assembly system design problem (ASDP) which seeks to minimize total cost by optimally integrating design (selecting the machine type to locate at each activated station) and operating issues (assigning tasks to observe precedence relationships and cycle time restrictions). We propose an effective branch-and-cut approach for solving single-product ASDPs, adapting inequalities known to be valid for embedded line balancing structures to form inequalities that are valid for the ASDP. The implementation also involves a specialized preprocessor, a heuristic, separation procedures, and an enumeration scheme. Computational tests establish benchmark results for this first implementation of cutting planes for solving the ASDP.

Abstract. We present a rough-cut analysis tool that quickly determines a few potential cost-effective designs at the initial design stage of flexible assembly systems (FASs) prior to a detailed analysis such as simulation. It uses quantitative methods for selecting and configuring the components of an FAS suitable for medium to high volumes of several similar products. The system is organized as a series of assembly stations linked with an automated material-handling system moving parts in a unidirectional flow. Each station consists of a single machine or of identical parallel machines. The methods exploit the ability of flexible hardware to switch almost instantaneously from product to product. Our approach is particularly suitable where the product mix is expected to be stable, since we combine the hardware-configuration phase with the task-allocation phase. For the required volume of products, we use integer programming to select the number of stations and the number of machines at each station and to allocate tasks to stations. We use queueing network analysis, which takes into account the mean and variance of processing times among different products to determine the necessary capacity of the material-handling system. We iterate between the two analyses to find the combined solution with the lowest costs. Work-in-process costs are also included in the analysis. Computational results are presented. Key Words: assembly-line balancing, closed queueing networks, flexible assembly systems, integer programming, minimum cost design. I.

In this paper the Simple Assembly Line Balancing Problem 1 is considered. This well known NP-hard problem has been widely studied in the last thirty years. Therefore several sequential algorithms have been designed for solving it. But although these algorithms have become more and more efficient they cannot cope with a lot of instances of realistic size. For these examples the algorithms achieve only poor results or need unreasonable time for finding solutions of acceptable quality. The consequence is to use not only one processor. Based on the observation that Local-Area-Networks (LAN) with often more than 100 computers are installed in many companies today the use of distributed algorithms appears to be a promising approach for solving production-planning-problems in practice. Therefore an extended version of the up to now most efficient Branch&Bound procedure for SALBP-1 has been parallelized. Although the searching-tree of the sequential algorithm is extremly unbalanced the new di...

Abstract: As part of manufacturing systems, the assembly line has become one of the most valuable researches to accomplish the real world problems related to them. Many efforts have been made to seek the best techniques in optimizing assembly lines. Problem statement: Since it was published by Salveson in 1955, some methods and techniques have been developed based on mathematical modeling. In recent years, some researches in Assembly Line Balancing (ALB) have been conducted using Soft Computing (SC) approaches. However, there is no comprehensive survey studies conducted regarding the use of SC in ALB problems, which is became the aim of this study. Approach: This study reviewed published literatures and previous related works that applied SC in solving ALB problems. Main outcomes: This study looks into the suitability of SC approaches in several types of ALB problems. Furthermore, this study provides the classification of ALB problems that can facilitate distinguishing those problems as fields of research. Result: This study found that Genetic Algorithms (GAs) are predominantly applied to solve ALB problems compared to other SC approaches. This high suitability in ALB refers to GAs ’ main characteristics that include its robustness and flexibility. These SC approaches have mostly been applied to simple ALB problems, which are not problems that are covered in a real complex manufacturing environment. Conclusion/Recommendations: This study