International Journal of Research in Industrial Engineering
Volume:2 Issue: 3, Summer 2013

The fuzzy primal simplex method [15] and the fuzzy dual simplex method [17] have been proposed to solve a kind of fuzzy linear programming (FLP) problems involving symmetric trapezoidal fuzzy numbers. The fuzzy simplex method starts with a primal fuzzy basic feasible solution (FBFS) for FLP problem and moves to an optimal basis by walking truth sequence of exception of the optimal basis obtained in fuzzy primal simplex method don’t satisfy the optimality criteria for FLP problem. Also this method has no efficient when a primal fuzzy basic FBFS is not at hand. The fuzzy dual simplex method needs to an initial dual FBFS. Furthermore, there exists a shortcoming in the fuzzy dual simplex method when the dual feasibility or equivalently the primal optimality is not at hand and in this case, the fuzzy dual simplex method can’t be used for solving FLP problem. In this paper, a fuzzy Big-M method is proposed to solve these problems in which the primal FBFS is not readily available. A numerical example is given to illustrate the proposed method.

The global marketplace has witnessed an increased pressure from customers and competitors in manufacturing .In this age of agile manufacturing the global competition characterized by both technology push and market pull had forced the companies to achieve world-class performance through continuous improvement in their products and processes, reduction in cost, reduce wastes, increase quality, increasing of effectiveness, safety increasing, reduction of the pollution, increase decision making power and improved methodology. To want all this changes, there is need to adopt new the management system. In management system there are many types of techniques. The present work is dedicated to study and implement 5S methodology. In this work, proper system will be analyzed to reduce the wastes, reduction in cost, processes, and increase decision making power by implementing 5S techniques. Apart from this, theoretical results compared with original data from industry before and after implementation.

In the present paper the step fixed charge transportation problem under uncertainty, particularly when variable and fixed cost are given in fuzzy forms, is formulated. In order to solve the problem, two metaheuristic, simulated annealing algorithm (SA) and variable neighborhood search (VNS), are developed for this NP-hard problem. Due to the significant role of parameters and operators on the algorithm’s quality, an extensive calibration in both SA and VNS is carried out with the aid of a set of experimental design. Through extensive computational experiments, appropriate parameter values of the proposed algorithms were chosen. For this purpose, twenty eight problems with different configuration have been generated at random and then the effectiveness of the proposed algorithms was evaluated using the relative percentage deviation (RPD) method.

Equipment selection is one of the most important aspects of open pit design. The selection of equipment for mining applications is not a well-defined process and because it involves the interaction of several subjective factors or criteria, decisions are often complicated and may even embody contradictions. The aim of this study is introducing a multi-criteria decision making method for selecting the most appropriate combination of drilling, loading and haulage equipment using a state of the art comprehensive model. The proposed method consists of two stages, first is determining the weight of each criteria which affects the decision using fuzzy analytic network process (FANP), the next step is calculating the score of each possible combination of mining equipment using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed methodology is applied for Sungun copper mine which is the largest open-cast copper mine in Iran and is among the most important copper deposits in Middle East and finally the most appropriate combination of mining equipment is determined for this open-pit mine.

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.