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Solving a multi-objective mixed-model assembly line sequencing problem by a fuzzy goal programming approach

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2008年第9-10期39卷 975-982页

作者： Javadi, Babak Rahimi-Vahed, Alireza Rabbani, Masoud Dangchi, Mostafa Univ Tehran Dept Ind Engn Fac Engn Tehran Iran Mazandaran Univ Sci & Technol Dept Ind Engn Babol Sar Iran

This paper presents a fuzzy goal programming approach to solve a multi-objective mixed-model assembly line sequencing problem in a just-in-time production system. A mixed-model assembly line is a type of production line capable of diversified small-lot production and is able to promptly respond to sudden demand changes for a variety of models. Determining the sequence of introducing models to such an assembly line is of particular importance for the efficient implementation of just-in-time (JIT) systems. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. Because of conflicting objectives, we propose a fuzzy goal programming-based approach to solve the model. This approach is constructed based on the desirability of decision maker (DM) and tolerances considered on goal values. To illustrate the behavior of the proposed model, some of instances are solved optimally and computational results reported.

关键词： Mixed-model assembly line multi-objective sequencing problem Just-in-time Fuzzy goal programming

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A multi-objective scatter search for a mixed-model assembly line sequencing problem

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ADVANCED ENGINEERING INFORMATICS 2007年第1期21卷 85-99页

作者： Rahimi-Vahed, A. R. Rabbani, M. Tavakkoli-Moghaddam, R. Torabi, S. A. Jolai, F. Univ Tehran Dept Ind Engn Tehran 4563 Iran

A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： mixed-model assembly line multi-objective sequencing problem just-in-time multi-objective scatter search multi-objective genetic algorithm

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A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2007年第4期53卷 642-666页

作者： Rahimi-Vahed, Alireza Mirzaei, Alil Hossein Univ Tehran Dept Ind Engn Tehran Iran Tarbiat Modares Univ Dept Ind Engn Tehran Iran

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers' demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： mixed-model assembly line multi-objective sequencing problem multi-objective shuffled frog-leaping algorithm bacterial optimization multi-objective genetic algorithm

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A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem

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SOFT COMPUTING 2007年第10期11卷 997-1012页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Rabbani, M. Univ Tehran Fac Engn Dept Ind Engn Tehran Iran

The sequencing of products for mixed-model assembly line in Just-in-Time manufacturing systems is sometimes based on multiple criteria. In this paper, three major goals are to be simultaneously minimized: total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Due to the NP-hardness of the problem, a new multi-objective particle swarm (MOPS) is designed to search locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three distinguished multi-objective genetic algorithms (MOGAs), i.e. PS-NC GA, NSGA-II, and SPEA-II. Comparison shows that MOPS provides superior results to MOGAs.

关键词： mixed-model assembly line multi-objective sequencing problem just-in-time multi-objective particle swarm multi-objective genetic algorithm

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A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem

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ENGINEERING OPTIMIZATION 2007年第8期39卷 877-898页

作者： Rahimi-Vahed, A. R. Mirghorbani, S. M. Rabbani, M. Univ Tehran Dept Ind Engn Tehran Iran

Mixed-model assembly line sequencing is one of the most important strategic problems in the field of production management where diversified customers' demands exist. In this article, three major goals are considered: (i) total utility work, (ii) total production rate variation and (iii) total setup cost. Due to the complexity of the problem, a hybrid multi-objective algorithm based on particle swarm optimization (PSO) and tabu search (TS) is devised to obtain the locally Pareto-optimal frontier where simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed algorithm in terms of solution quality and diversity level, the algorithm is applied to various test problems and its reliability, based on different comparison metrics, is compared with three prominent multi-objective genetic algorithms, PS-NC GA, NSGA-II and SPEA-II. The computational results show that the proposed hybrid algorithm significantly outperforms existing genetic algorithms in large-sized problems.

关键词： mixed-model assembly line multi-objective sequencing problem multi-objective particle swarm tabu search multi-objective genetic algorithm

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