دانلود مقاله ISI انگلیسی شماره 18997
عنوان فارسی مقاله

برنامه ریزی تولید کارگاهی انعطاف پذیر چند هدفه : طراحی و ارزیابی های مدل سازی شبیه سازی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
18997 2009 15 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling
منبع

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

Journal : Applied Soft Computing, Volume 9, Issue 1, January 2009, Pages 362–376

کلمات کلیدی
مدل سازی و شبیه سازی - بهینه سازی ترکیبی - برنامه ریزی تولید کارگاهی - بهینه سازی کلونی مورچه ها - بهینه سازی چند هدفه - دانش زمانبندی
پیش نمایش مقاله
پیش نمایش مقاله برنامه ریزی تولید کارگاهی انعطاف پذیر چند هدفه : طراحی و ارزیابی های مدل سازی شبیه سازی

چکیده انگلیسی

Flexible job shop schedule is very important in both fields of combinatorial optimization and production management. In this paper, a simulation model is presented to solve the multi-objective flexible job shop scheduling problem. The proposed model has been coded by Matlab which is a special mathematical computation language. After modeling the pending problem, the model is validated by five representative instances based on practical data. The results obtained from the computational study have shown that the proposed approach is a feasible and effective approach for the multi-objective flexible job shop scheduling problem.

مقدمه انگلیسی

The job shop scheduling problem (JSSP) is generally defined as decision-making problems with the aim of optimizing one or more scheduling criteria. JSSP is a branch of production scheduling which is among the hardest combinatorial optimization problems. Many different approaches have been successfully applied to JSSP, such as, simulated annealing (SA [1] and [2]), tabu search (TS [3] and [4]), genetic algorithm (GA [5], [6], [7], [8] and [9]), ant colony optimization (ACO [10] and [11]), neural networks (NN [12] and [13]), evolutionary algorithm (EA [14] and [15]) and other heuristic approach [16], [17], [18], [19] and [20]. Flexible job shop scheduling problem (FJSSP) is an extension of the classical JSSP which allows an operation to be processed by any machine from a given set. It is more complex than JSSP because of the addition need to determine the assignment of operations to machines. Bruker and Schlie [21] were among the first to address this problem. For solving the realistic case with more than two jobs, two types of approaches have been employed: hierarchical approaches and integrated approaches [22]. In hierarchical approaches, assignment of operations to machines and sequencing of operations on machines are treated separately. Brandimarte [23] was the first to use the decomposition for the FJSSP. He solved the routing sub-problem using some existing dispatching rules and then solved the scheduling sub-problem using a TS heuristic. Tung et al. [24] developed a similar approach for scheduling a flexible manufacturing system. Recently, Kacem et al. [25] and [26] proposed a GA controlled by the assigned model which is generated by the approach of localization. Xia and Wu [22] makes use of particle swarm optimization (PSO) to assign operations on machines and SA algorithm to schedule operations. Integrated approaches were used by considering assignment and scheduling at the same time. Hurink et al. [27] proposed a TS heuristic in which reassignment and rescheduling are considered as two different types of moves. The integrated approach presented by Dauzere-Peres and Paulli [28] was defined a neighborhood structure for the problem where there is no distinction between reassigning and resequencing an operation, and the TS procedure is proposed based on the neighborhood structure. Mastrolilli and Gambardella [29] improved Dauzere-Peres’ TS techniques and presented two neighborhood functions. Most researchers were interested in applying TS and GA techniques to FJSSP in the past [22]. A simulation model is proposed in our work for the FJSSP with multi-objectives of minimizing makespan, total workload of machines and workload of the critical machine. The remainder of this paper is organized as follows. The notation and problem formulation are introduced in Section 2. Section 3 describes the proposed simulation modeling. In Section 4, we present some improvements for the simulation model. In Section 5, computational experiments performed with our approach for some representative instances of FJSSP are reported followed by the comparison to other heuristic methods. Some concluding remarks are made in Section 6.

نتیجه گیری انگلیسی

Flexible job shop schedule is very important in both fields of combinatorial optimization and production management. At past, most researchers were interested in applying TS and GA techniques to solve FJSSP. In this paper, a simulation model is proposed for the multi-objective FJSSP. The notation and problem formulation are introduced firstly, and our simulation model is proposed secondly, then some improvements are presented for the simulation model thirdly, and finally computational experiments performed with our approach for some representative instances of FJSSP are reported followed by the comparison to other heuristic methods. The results obtained from the computational study have shown that the proposed approach is a feasible and effective approach for the multi-objective flexible job shop scheduling problem. The contribution of this paper can be summarized as follows. It presented a simulation model to solve the flexible job shop scheduling problems with multi-objectives of minimizing makespan, the total workload of machines and workload of the critical machine. Firstly, our work presented an efficacious approach for solving the multi-objective FJSSP, and anyone can employ our approach to solve the pending practical problems expediently. Secondly, our work proposed an impactful framework for solving the multi-objective FJSSP. The researcher can propose their new approach by modifying or improving some algorithms among our simulation models. Taking one with another, our research has important academic and practical significances. About future research direction, we should pay more attention to the following two points. The first one is optimizing these parameters of our proposed approach for its quick constringency. The second one is reducing the sensitivity to the initial solution of our proposed approach.

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