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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Water Cycle Algorithm,Linearization,Lot sizing,Distributed permutation flow shops,Monarch butterfly optimization
- چکیده:
- چکیده انگلیسی: This paper addresses a new mixed integer nonlinear and linear mathematical programming economic lot sizing and scheduling problem in distributed permutation flow shop problem with number of identical factories and machines. Different products must be distributed between the factories and then assignment of products to factories and sequencing of the products assigned to each factory has to be derived. The objective is to minimize the sum of setup costs, work-in-process inventory costs and finished products inventory costs per unit of time. Since the proposed model is NP-hard, an efficient Water Cycle Algorithm is proposed to solve the model. To justify proposed WCA, Monarch Butterfly Optimization (MBO), Genetic Algorithm (GA) and combination of GA and simplex are utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure according to Response Surface Methodology is executed.
- انتشار مقاله: 03-06-1396
- نویسندگان: Mohammad Alaghebandha,Bahman Naderi,Mohammad Mohammadi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,Scheduling,Variable neighborhood search,mixed integer linear programming,No-idle hybrid flow shops
- چکیده:
- چکیده انگلیسی: Although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. But, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. This paper considers the problem of scheduling no-idle hybrid flow shops. A mixed integer linear programming model is first developed to mathematically formulate the problem. Using commercial software, the model can solve small instances to optimality. Then, two metaheuristics based on variable neighborhood search and genetic algorithms are developed to solve larger instances. Using numerical experiments, the performance of the model and algorithms are evaluated.
Although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. But, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. This paper considers the problem of scheduling no-idle hybrid flow shops. A mixed integer linear programming model is first developed to mathematically formulate the problem. Using commercial software, the model can solve small instances to optimality. Then, two metaheuristics based on variable neighborhood search and genetic algorithms are developed to solve larger instances. Using numerical experiments, the performance of the model and algorithms are evaluated.- انتشار مقاله: 08-10-1394
- نویسندگان: Mehdi Yazdani,Bahman Naderi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Lot streaming,Mixed Integer Linear Programming Model,Flexible flow line scheduling,Artificial bee colony optimization
- چکیده:
- چکیده انگلیسی: Although lot streaming scheduling is an active research field, lot streaming flexible flow lines problems have received far less attention than classical flow shops. This paper deals with scheduling jobs in lot streaming flexible flow line problems. The paper mathematically formulates the problem by a mixed integer linear programming model. This model solves small instances to optimality. Moreover, a novel artificial bee colony optimization is developed. This algorithm utilizes five effective mechanisms to solve the problem. To evaluate the algorithm, it is compared with adaptation of four available algorithms. The statistical analyses showed that the proposed algorithm significantly outperformed the other tested algorithms.
- انتشار مقاله: 17-04-1393
- نویسندگان: Bahman Naderi,Mehdi Yazdani
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,Imperialist competitive algorithm,Simulated Annealing,Mixed Integer Programming,Project portfolio selection and scheduling
- چکیده:
- چکیده انگلیسی: This paper investigates the problem of selecting and scheduling a set of projects among available projects. Each project consists of several tasks and to perform each one some resource is required. The objective is to maximize total benefit. The paper constructs a mathematical formulation in form of mixed integer linear programming model. Three effective metaheuristics in form of the imperialist competitive algorithm, simulated annealing and genetic algorithm are developed to solve such a hard problem. The proposed algorithms employ advanced operators. The performance of the proposed algorithms is numerically evaluated. The results show the high performance of the imperialist competitive algorithm outperforms the other algorithms.
- انتشار مقاله: 30-02-1391
- نویسندگان: Bahman Naderi,Bahman Naderi,Bahman Naderi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Total Weighted Tardiness,makespan,No-wait hybrid flowshop scheduling,Multi-objective simulated annealing algorithm
- چکیده:
- چکیده انگلیسی: This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presents a new multi-objective simulated annealing algorithm (MOSA). A set of experimental instances are carried out to evaluate the algorithm by advanced multi-objective performance measures. The algorithm is carefully evaluated for its performance against available algorithm by means of multi-objective performance measures and statistical tools. The related results show that a variant of our proposed MOSA provides sound performance comparing with other algorithms.
- انتشار مقاله: 27-02-1390
- نویسندگان: Bahman Naderi,Hassan Sadeghi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial Engineering International
- نوع مقاله: Journal Article
- کلمات کلیدی: Scheduling,mixed integer linear programming,Open shop with no buffer,Electromagnetism algorithm
- چکیده:
- چکیده انگلیسی: This paper considers open-shop scheduling with no intermediate buffer to minimize total tardiness. This problem occurs in many production settings, in the plastic molding, chemical, and food processing industries. The paper mathematically formulates the problem by a mixed integer linear program. The problem can be optimally solved by the model. The paper also develops a novel metaheuristic based on an electromagnetism algorithm to solve the large-sized problems. The paper conducts two computational experiments. The first includes small-sized instances by which the mathematical model and general performance of the proposed metaheuristic are evaluated. The second evaluates the metaheuristic for its performance to solve some large-sized instances. The results show that the model and algorithm are effective to deal with the problem.
- انتشار مقاله: 10-07-1399
- نویسندگان: Bahman Naderi,Esmaeil Najafi,Mehdi Yazdani
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,Order allocation,Supplier Selection,Fuzzy programming
- چکیده:
- چکیده انگلیسی: In this paper, we study a supplier selection and order allocation problem with in a multi-objective and fuzzy environment. Transportation costs and quantity discount are taken into account in the problem. We assume four common objectives as total costs, on-time delivery rate, defective rate, and purchasing value. We utilize a max-min approach such that the min-operator finds the fuzzy decision that simultaneously satisfies all the fuzzy objectives. Then the maximizing decision is determined to be the maximum degree of membership for the fuzzy decision. We use the non-linear S-shape membership functions to express the vague aspiration levels of the DM’s objective. According to the defined fuzzy membership functions and applying Bellman–Zadeh’s maximization principle, the fuzzy multi objective model is transformed into a single objective model. A genetic algorithm is applied to solve the multi objective fuzzy supplier selection and order allocation problem. Computational results are presented using numerical examples.
- انتشار مقاله: 14-04-1397
- نویسندگان: Mohammad Ali Sobhanolahi,Ahmad Mahmoodzadeh,Bahman Naderi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: healthcare management,organ transplant supply chain,location efficiency,bi-objective MIP optimization
- چکیده:
- چکیده انگلیسی: Nowadays, working alone on a context is not sufficient and reaching good and worthy results demands cooperation of multi sciences. Healthcare supply chain is one of these sciences that bridges engineering and healthcare sciences. This paper proposes a new multi–objective model for organ transplant supply chain, which is one of consequential fields in Healthcare supply chain, by aiming at having a more effective system. First objective function tries to minimize costs of opened centers, shipping organs, information, and allocations. In this regard, to increase number of transplantations and decrease shortage of demands, a penalty figure is also considered for remained inventory at the end of each period. The second objective function considers three important aspects of location in organ transplant supply chain which have not been studied yet, including; expected number of donors, coverage of other locations by taking into consideration the maximum remaining time for each organ out of body, and safety index. The last objective function tries to find routs with final total minimum time. At the end, some numerical experiments are done with using GAMS optimization software.
- انتشار مقاله: 15-01-1396
- نویسندگان: Seyed Mahdi Aghazadeh,Mohammad Mohammadi,Bahman Naderi
- مشاهده