A review on hybrid metaheuristics in solving assembly line balancing problem (original) (raw)
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This paper presents a comprehensive review and evaluation of heuristics and meta-heuristics for the two-sided assembly line balancing problem. Though a few reviews have been presented, some latest methods are not included and there is no comparison of the meta-heuristics in terms of their performances. Furthermore, since various kinds of encoding schemes, decoding procedures and objective functions have been applied, the results cannot be generalized and the published comparison might be unfair. This paper contributes to knowledge by comparing the published methods, ranging from well-known simulated annealing to recent published iterated local search, and evaluating the six encoding schemes, 30 decoding procedures and five objective functions on the performances of the meta-heuristics meanwhile. The experimental design approach is applied to obtain valid and convincing results by testing algorithms under four termination criteria. Computational results demonstrate that the proper selection of encoding scheme, decoding procedure and objective function improves the performance of the algorithms by a significant margin. Another unique contribution of this paper is that 15 new best solutions are obtained for the large-sized type-II two-sided assembly line balancing problem during the re-implementation and evaluation of the meta-heuristics tested. Please cite this article as: Zixiang Li , Ibrahim Kucukkoc , J. Mukund Nilakantan , Comprehensive review and evaluation of heuristics and meta-heuristics for two-sided assembly line balancing problem ,
Comparative Analysis of Different Heuristics for Cost Oriented Assembly Line Balancing Problems
Abstract— Assembly line balancing problem consists of a finite set of tasks, where each of them has a duration time and precedence relations, which specify the acceptable ordering of the tasks. Line balancing is an attempt to locate tasks to each workstation on the assembly line. The basic ALB problem is to assign a set of tasks to an ordered set of workstations, so that the precedence relationships were satisfied, and performance factors were optimized. This paper shows the comparison of different heuristics for cost oriented assembly line balancing problem which has been taken from literature. Comparison is done on the basis of their smoothness index, cost and line efficiency. The computational results show that the proposed heuristic performs better and minimizes the production cost.
A note on "A multi-objective genetic algorithm for solving assembly line balancing problem
2010
Assembly line balancing has a considerable place in industrial importance. Hence, a lot of researchers are interested in this subject and several papers have been published so far. Many exact, heuristic, metaheuristic, and hybrid approaches have been used to solve this type of problems. Recently Ponnanbalam et al. (Int J Adv Manuf Technol 16:341-352, 2000) have considered a multiobjective genetic algorithm utilizing several simple heuristic rules for solving the simple assembly line balancing problems, one of these rules was "rank positional weight (RPW)" originally published in Helgeson and Birnie (J Ind Eng 12(6): [394][395][396][397][398] 1961). Through providing two possible justifications, this note suggests that the mentioned rule can be mistakenly utilized and some revisions in Ponnanbalam et al. (Int J Adv Manuf Technol 16:341-352, 2000) seem to be necessary.
2008
Although genetic algorithm (GA) has been widely used to address assembly line balancing problems (ALBP), not much attention has been given to the population initialization procedure. In this paper, a comparison is made between a randomly generated initial population and a heuristics-treated initial population. A heuristics-treated population is a mix of randomly and heuristics generated individuals in the initial population. Both populations are tested with a proposed GA using established test problems from literature. The GA, using a fitness function based on realized cycle time is capable of generating good solutions.
A dynamic programming based heuristic for the assembly line balancing problem
European Journal of Operational Research, 2009
The simple assembly line balancing problem is the simplification of a real problem associated to the assignment of the elementary tasks required for assembly of a product in an assembly line. This problem has been extensively studied in the literature for more than half a century. The present work proposes a new procedure to solve the problem we call Bounded Dynamic Programming. This use of the term Bounded is associated not only with the use of bounds to reduce the state space but also to the reduction of such space based on heuristics. This procedure is capable of obtaining an optimal solution rate of 267 out of 269 instances, which have been used in previous works, thus obtaining the best-known performance for the problem. These results are an improvement from any previous procedure found in the literature even when using smaller computing times.
A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II
Computers & Industrial Engineering, 2004
Mixed-model assembly lines allow for the simultaneous assembly of a set of similar models of a product, which may be launched in the assembly line in any order and mix. As current markets are characterized by a growing trend for higher product variability, mixed-model assembly lines are preferred over the traditional single-model assembly lines. This paper presents a mathematical programming model and an iterative genetic algorithm-based procedure for the mixed-model assembly line balancing problem (MALBP) with parallel workstations, in which the goal is to maximise the production rate of the line for a pre-determined number of operators.
An enumerative heuristic and reduction methods for the assembly line balancing problem
European Journal of Operational Research, 2003
A new heuristic algorithm and new reduction techniques for the type 1 assembly line balancing problem are presented. The new heuristic is based on the well-known Hoffmann heuristic and builds solutions from both sides of the precedence network to choose the best. The reduction techniques aim at augmenting precedences, conjoining tasks and increasing operation times. The heuristic is tested on its own and also in combination with the reduction techniques. The tests, which are carried out on a well-known benchmark set of problem ...
Application of Simple Genetic Algorithm to U-Shaped Assembly Line Balancing Problem of Type II
IFAC Proceedings Volumes, 2014
Process of assigning tasks to workstations arranged along a U-shaped assembly line is known in literature as U Assembly Line Balancing Problem (UALBP). Maximizing the line efficiency by way of minimizing number of workstations (m) for a given cycle time (c) leads to UALBP-I while targeting the same objective through minimization of 'c' for given 'm' is known as UALBP-II. Although, quite a good amount of research has been reported on UALBP-I since the first published work on U lines in 1994; very little work has been reported on type II. Type E (minimizing 'c' and 'm' together) and type F (finding feasible line balance for given 'c' and 'm') problems which represent other two types of U line balancing problems also have not received any attention. This paper reports the initial efforts towards application of metaheuristics for solving UALBP-II problem which is encountered when the line already exists.
Heuristic approach for balancing mixed-model assembly line of type I using genetic algorithm
The mixed model assembly line is becoming more important than the traditional single model due to the increased demand for higher productivity. In this paper, a set of procedures for mixed-model assembly line balancing problems (MALBP) is proposed to make it efficiently balance. The proposed procedure based on the meta heuristics genetic algorithm can perform improved and efficient allocation of tasks to workstations for a pre-specified production rate and address some particular features, which are very common in a real world mixed model assembly lines (e.g. use of parallel workstations, zoning constraints, resource limitation). The main focus of this study is to study and modify the existing genetic algorithm framework. Here a heuristic is proposed to reassign the tasks after crossover that violates the constraints. The new method minimises the total number of workstation with higher efficiency and is suitable for both small and large scale problems. The method is then applied to solve a case of a plastic bag manufacturing company where the minimum number of workstations is found performing more efficiently.