Journal of Optimization in Industrial Engineering
Volume:5 Issue: 11, Summer and Autumn 2012

Failure mode and effects analysis (FMEA) is a method based on teamwork to identify potential failures and problems in a system, design, process and service in order to remove them. The important part of this method is determining the risk priorities of failure modes using the risk priority number (RPN). However, this traditional RPN method has several shortcomings. Therefore, in this paper we propose a FMEAwhich uses generalized mixture operators to determine and aggregate the risk priorities of failure modes. In a numerical example, a FMEA of the LGS gas type circuit breaker product in Zanjan Switch Industries in Iran is presented to further illustrate the proposed method. The results show that the suggested approach is simple and provides more accurate risk assessments than the traditional RPN.

In this paper, a multi-product continues review inventory control problem within batch arrival queuing approach (MQr/M/1) is modeled to find the optimal quantities of maximum inventory. The objective function is to minimize summation of ordering, holding and shortage costs under warehouse space, service level, and expected lost-sales shortage cost constraints from retailer and warehouse viewpoints. Since the proposed model is Np-Hard, an efficient imperialist competitive algorithm (ICA) is proposed to solve the model. To justify proposed ICA, a simulated annealing algorithm has been utilized. In order to determine the best value of algorithms parameters that result in a better solution, a fine-tuning procedure is executed. Finally, the performance of the proposed ICA is analyzed using some numerical illustrations.

Data mining techniques have been used widely in the area of customer relationship management (CRM). In this study, we have applied data mining techniques to address a problem in business-to-business (B2B) setting. In a manufacturer-retailer-consumer chain, a manufacturer should improve its relationship with retailers to continue its business. Segmentation is a useful tool for identifying groups of similar retailers in order to improve retailer loyalty by developing and implementing segment-specific marketing strategies. In this study, we have proposed a methodology for retailer segmentation based on their LRFM variables (relation Length, Recency, Frequency and Monetary) and analytical hierarchy process (AHP). The proposed methodology has been implemented by using data of a firm from a hygienic industry in Iran. The empirical results indicated that there are six groups of retailers. After analyzing each segment according to LRFM values, we labeled each retailer group according to its performance. Furthermore, we provided some possible actions that can be taken in order to improve the relationship between the firm and retailers.

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.

This study aims to minimize the total cost of a four-echelon supply chain including suppliers, an assembler, distributers, and retailers. The total cost consists of purchasing raw materials from the suppliers by the assembler, assembling the final product, materials transportation from the suppliers to the assembler, product transportation from the assembler to the distributors, product transportation from the distributors to the retailers, and product holding and stock-out in the distribution centers. To this end, having modeled the problem addressed, a numerical example including ten suppliers, an assembler, three distributors and eight retailers in the chain is solved for four periods of time. Then the model is solved by a simulated annealing-based heuristic and LINGO. Finally, a set of 30 numerical problems of small and large sizes are developed and solved. The results indicate that simulated annealing-based heuristic provides near optimal solutions.

In this paper, we aim to overcome three major shortcomings of the FDH (Free Disposal Hull) directional distance function through developing two new, named Linear and Fractional CDFDH, complete FDH measures of efficiency. To accomplish this, we integrate the concepts of similarity and FDH directional distance function. We prove that the proposed measures are translation invariant and unit invariant. In addition, we present effective enumeration algorithms to compute them. Our proposed measures have several practical advantages such as: (a) providing closest Pareto-efficient observed targets (b) incorporating the decision maker’s preference information into efficiency analysis and (c) being flexible in computer programming. We illustrate the newly developed approach with a real world data set.

Data envelopment analysis (DEA) is a method for measuring the relative efficiencies of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. In this paper, we study the measurement of DMU performances in DEA in situations where input and/or output values are given as imprecise data. By imprecise data we mean situations where we only know that the actual values lie in certain intervals, or cases in which data are given only as ordinal relationships. In this paper, we present two distinct approaches obtaining the upper and lower bounds of efficiency which the DMU under evaluation can have with imprecise data. The optimistic approach seeks the best score among the various values of the efficiency score, while the pessimistic approach seeks the worst score. The main idea of the paper is illustrated using an example. Also, two real-world cases are presented to demonstrate how the efficiency interval is interpreted. The efficiency interval not only describes the actual situation in more detail, but also relieves the psychological pressure on all the evaluated DMUs and the decision-maker.

Decision making on budgeting is one of the most important issues for executing managers. Budgeting is a major tool for planning and control of projects. In public and non-profit organizations and institutions, estimating the costs and revenues plays an important role in receiving credit and budgeting. In this regard, in the present paper the case of Isfahan municipality is considered. One of the main expenditures of the 14 districts of Isfahan is the costs related to water. Predicting the total cost of water helps the municipality of Isfahan to optimize the water use in its 14 urban zones. Thus, in this study the total cost of water in the districts of Isfahan is estimated using regression analysis and neural network models. Then the results of the methods are compared with each other to minimize the deviations from the approved budget. Finally, the neural network method is selected as the main simulation method for forecasting the total cost of water in the districts of Isfahan.