دانلود مقاله ISI انگلیسی شماره 7738
عنوان فارسی مقاله

مخلوط مدل بالانس خط مونتاژ با استفاده از بهینه سازی رویکرد چند هدفه کلونی مورچه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
7738 2011 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Mixed-model assembly line balancing using a multi-objective ant colony optimization approach
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 38, Issue 10, 15 September 2011, Pages 12453–12461

کلمات کلیدی
بالانس خط مونتاژ - مدل مختلط - بهینه سازی کلونی مورچه ها - چند هدفه
پیش نمایش مقاله
پیش نمایش مقاله مخلوط مدل بالانس خط مونتاژ با استفاده از بهینه سازی رویکرد چند هدفه کلونی مورچه

چکیده انگلیسی

Mixed-model assembly lines are production systems at which two or more models are assembled sequentially at the same line. For optimal productivity and efficiency, during the design of these lines, the work to be done at stations must be well balanced satisfying the constraints such as time, space and location. This paper deals with the mixed-model assembly line balancing problem (MALBP). The most common objective for this problem is to minimize the number of stations for a given cycle time. However, the problem of capacity utilization and the discrepancies among station times due to operation time variations are of design concerns together with the number of stations, the line efficiency and the smooth production. A multi-objective ant colony optimization (MOACO) algorithm is proposed here to solve this problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved. The results show that the MOACO algorithm is an efficient and effective algorithm which gives better results than other methods compared.

مقدمه انگلیسی

Mixed-model assembly lines are needed for the assembly of products that are demanded in variety of models with comparatively lower prices. The assembly lines on which a mixed order of various models of similar products is produced sequentially, are called mixed-model assembly lines. Mixed-model assembly lines are commonly used for their flexibility with respect to model changes, for reducing the final product inventories and for a continuous flow of materials. However, ineffective use of the capacity of mixed-model assembly lines with reduction in productivity results in high unit costs due to higher initial investment costs. Therefore, in order for the mixed-model assembly lines to function properly and productively under the time, space and location constraints, the MALBP arises to distribute the work content evenly among the stations. The MALBP types found in the literature are (Scholl, 1995): 1. MALBP-I: The number of stations is to be minimized for a given cycle time (i.e., production rate). 2. MALBP-II: The cycle time is to be minimized for a given number of stations. 3. MALBP-E: The cycle time and the number of stations are to be minimized at the same time. In the literature, there exists numerous methods developed for assembly line balancing, but the majority of these are for the single-model assembly line balancing (SALBP). Since balancing problems are usually NP-hard, finding an optimal solution is hardly possible, therefore near optimal heuristic approaches are preferred. Thomopoulos (1967) is known to be the first to work on mixed-model assembly line balancing and sequencing. In his work, mixed-model line was treated as a single-model line. In a follow up work, Thomopoulos (1970) proposed smoothening for task assignments in mixed-model lines. It is suggested that all of the feasible solutions be searched for smoothening. In a work by Macaskill (1972), groups of tasks were assigned to stations in order to maximize the efficiency of the mixed-model lines but no smoothening was considered. Chakravarty and Shtub (1985) worked on balancing mixed-model lines with work-in-process inventory where labour, inventory and set-up costs are aimed at for minimization using algorithms which determine task assignments, cycle time, and the number of stations. In a following cost minimization work by Chakravarty and Shtub (1986) operation times are taken to be normally distributed rather than deterministic as compared to their previous work. Fokkert and Kok (1997) gave a review of the literature on the mixed-model and multi-model assembly line balancing problem and compared several heuristics based on the combined precedence diagram. Gokcen and Erel, 1997 and Gokcen and Erel, 1998 developed a goal programming model and a binary integer programming model for the mixed-model line balancing problem, respectively. Katayama (1998) presented a conventional algorithm to minimize the number of stations and an effective sequencing logic based on the original Target Chasing Method. Both of these algorithms are integrated in a two-stage hierarchical structure. Erel and Gokcen (1999) presented a shortest-route formulation for the mixed-model line balancing problem. Merengo, Nava, and Pozzetti (1999) presented a new balancing and production sequencing method for manual mixed-model assembly lines. The balancing method minimizes the number of station on the line; the sequencing method provides a uniform part usage. Kim, Kim, and Kim (2000) proposed a new method using a co-evolutionary algorithm that can simultaneously solve balancing and sequencing problems in mixed-model assembly lines. Karabati and Sayin (2003) formulated the MALBP with the objective of minimizing total cycle time by combining the cyclic sequencing information. They proposed a mathematical model and an alternative heuristic approach to minimize the maximum sub-cycle time. Bukchin and Rubinovitz (2006) developed a backtracking branch-and-bound algorithm to minimize stations and task duplication cost. Haq, Jayaprakash, and Rengarajan (2006) proposed a genetic algorithm to minimize the number of workstations. Recently, Xu and Xiao (2009) introduce robust optimization approaches to balance mixed model assembly lines with uncertain task times and daily model mix changes. This work considers the mixed-model assembly line balancing problem to minimize the balance delay and the smoothness index for a given cycle time (MALBP-I). A multi-objective ant colony optimization algorithm is proposed to solve this problem. A comparison of the performance of the proposed algorithm given at this study shows that proposed algorithm is more effective than other methods compared. The remainder of the paper is organized as follows. In Section 2, the basis of ACO is explained. In Section 3, MALBP-I is defined. The proposed algorithm (MOACO) for MALBP-I is given in Section 4. The algorithm is tested on problems of varying task numbers in order to examine the performance of the proposed approach in Section 5. Finally, conclusions and directions for future research are pointed out in Section 6.

نتیجه گیری انگلیسی

The significance of mixed-model type assembly lines is quite obvious when the current consumer trends require variety of models of a product to be supplied instantly at even lower prices. Production facilities with assembly lines must continuously meet the requirements of improved productivity and efficiency in a competitive environment. Line balancing is an approach to improve the effectiveness of such facilities. In this study, we consider the mixed-model assembly line balancing problem to minimize the balance delay, the smoothness index between stations and the smoothness index within stations for a given cycle time. In recent work of others, metaheuristic approaches are commonly preferred to solve combinatorial optimization problems. In this paper, the proposed algorithm to solve this problem which is known as NP-hard type is based on ACO metaheuristic. In order to verify the performance of the proposed algorithm, computational experiments are conducted on test problems. Results show that the proposed ant colony algorithm MOACO performs better than RPWM heuristic, GA and AIS algorithm for MALBP-I with acceptable computation times. Obtaining a perfect balance in assembly lines is not a sufficient condition for effective mixed-model line utilization. Our future work will concentrate on the integration of line balancing and sequencing for new models within a hierarchical planning structure, since effective line sequencing can only be done on a well-balanced line. Additionally, a parameter tuning analysis can be considered for improving the solution performance, together with solving many variants of the problem for serial lines, parallel lines and U-lines.

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