A. Jafari; P. Chiniforooshan; F. Zabihi
Volume 2, Issue 1 , February 2013, , Pages 45-62
Abstract
This paper investigates the problem of designing an integrated production-distribution system which supports strategic and tactical decision levels in supply chain management. This overall optimization is achieved using mathematical programming for modeling the supply chain functions such as location, ...
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This paper investigates the problem of designing an integrated production-distribution system which supports strategic and tactical decision levels in supply chain management. This overall optimization is achieved using mathematical programming for modeling the supply chain functions such as location, production, and distribution functions. Our model intends to minimize the total cost including production, location, transportation, and inventory holding costs. In view of the NP-hard nature of the problem, this paper provides a hybrid algorithm incorporates Genetic Algorithm into Lagrangian Relaxation method (namely HLRGA) to update the lagrangian multipliers and improve the performance of LR method. The effectiveness of HLRGA has been investigated by comparing its results with those obtained by CPLEX, hybrid genetic algorithm, and simulated annealing on a set of supply chain network problems with different sizes. Finally, an industrial case demonstrates the feasibility of applying the proposed model and algorithm to the real-world problem in a supply chain network.
A. Jafari; P. Chiniforooshan; F. Mousavinejad; Sh. Shahparvari
Volume 1, Issue 3 , December 2012, , Pages 1-25
Abstract
In this paper, we consider the fuzzy open shop scheduling problem with parallel machines in each working stage where processing times are vague and are represented by fuzzy numbers. An open shop scheduling problem with parallel machines in each working stage under this condition is close to the real ...
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In this paper, we consider the fuzzy open shop scheduling problem with parallel machines in each working stage where processing times are vague and are represented by fuzzy numbers. An open shop scheduling problem with parallel machines in each working stage under this condition is close to the real production scheduling conditions. A mixed-integer fuzzy programming (MIFP) model is presented to formulate this problem with the objective of minimizing makespan. To solve small-sized instances, an interactive fuzzy satisfying solution procedure is applied. Since this problem is known as a class of NP-hard, a novel discrete electromagnetism-like (DEM) is proposed to solve medium to large size examples. The DEM algorithm employs a completely difference approach. It makes use the crossover operators to calculate force and move particle is used. We employ Taguchi method to evaluate the effects of different operators and parameters on the performance of DEM algorithm. Finally to assess the performance of the algorithm, the results are compared with an existing EM algorithm from the literature and benchmark problems. The result exhibited the ability of the proposed DEM algorithm to converge to the efficient solutions.