JOURNAL OF OPTIMIZATION IN INDUSTRIAL ENGINEERING (JOURNAL OF INDUSTRIAL ENGINEERING)
Year:2012 | Volume:6 | Issue:2 (11)|Papers Count:8

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 FMEA which 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 continuous review inventory control problem within the batch arrival queuing approach (MQr/M/1) is modeled to find the optimal quantities of the maximum inventory. The objective function is to minimize the total costs of ordering, holding and shortage under warehouse space and 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 developed to solve the model. Moreover, to justify the proposed ICA, a simulated annealing algorithm is utilized, and to determine the best values of algorithm parameters that may result in a better solution, a fine-tuning procedure is followed. Finally, the performance of the proposed ICA is assessed through some numerical examples.

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 the business-to-business (B2B) setting. In order to continue its business in a manufacturer-retailer-consumer chain, a manufacturer should improve its relationship with retailers. In this regard, 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. Therefore, this study proposes a methodology for retailer segmentation based on the value-based segmentation and the analytical hierarchy process (AHP). The proposed methodology is implemented by using the data of a firm in the hygienic industry in Iran. As a result, having found six groups of retailers and labelled them according to their performance, we provide some possible measures that can be taken in order to improve the relationship between the firm and its retailers.

This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the make span 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 a reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper, therefore, 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. In fact, the performance of the algorithm is carefully evaluated in terms of the available algorithms by means of multi-objective performance measures and statistical tools. The results show that compared with other algorithms, a variant of our proposed MOSA produces a good performance.

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.