R. Golestaneh; A. Jafari; M. Khalilzadeh; H. Karimi
Volume 2, Issue 3 , September 2013, , Pages 47-57
Abstract
In this paper, we study a resource-constrained project-scheduling problem in which the objective is minimizing total Resource Tardiness Penalty Costs. We assume renewable resources that are limited in number, are restricted to very expensive equipment and machines, therefore they are rented and used ...
Read More
In this paper, we study a resource-constrained project-scheduling problem in which the objective is minimizing total Resource Tardiness Penalty Costs. We assume renewable resources that are limited in number, are restricted to very expensive equipment and machines, therefore they are rented and used in other projects, and are not available in all project periods. In other words, there exists a predefined ready-date as well as a due date for each renewable resource type. In this way, no resource is utilized before its ready date. Nevertheless, resources are allowed to be used after their due date by paying penalty costs depending on the resource type. The objective is to minimize the costs of renewable resource usages. We formulated and mathematically modeled this problem as an integer-Linear programming model. Since our problem is NP-hard and also exact methods are only applicable in small scale, therefore metaheuristic methods are practical approaches for this problem; this means that metaheuristics are better for this problem. In order to authenticate the model and solution algorithm in small scale, we consider a network with low activity, and then solve the model of this network with both exact algorithms and SA-GA-TS metaheuristic algorithms. For more activities, as well as getting closer to the real world, we present a Simulated Annealing Algorithm to solve this problem. In order to examine the performance of this algorithm, data that had been derived from studied literature were used, and their answers were compared with Genetic Algorithm (GA) and Tabu Search Algorithm (TS). Results show that in average, quality of SA answers was better than those of the GA and TS algorithms. In addition, we use relaxation method to achieve an even higher validation for the SA algorithm. Finally all results in this paper indicate that both model and solution algorithm have high validity.
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, ...
Read More
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 ...
Read More
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.
