Journal of Operational Research in its Applications
Year:2017 | Volume:14 | Issue:3 (54) |Papers Count:9

Choosing the right options for the best use of available resources has always been a concern for managers. There are different attitudes to the maximum use of these resources. The limitation of capital, manpower, energy, competitive market conditions, and so forth, has led executives to find a solution to the optimal solution. Analytic Hierarchy Process is one of the most widely used methods for choosing options, which uses a paired comparison between options and criteria to prioritize them. On the other hand, in the real world, the opinions of decision-makers of all levels are not the same and due to the use of the paired comparison matrix, the process of hierarchical analysis is time-consuming and may lead to incompatibility due the to inaccuracy in its completion. This paper presents a method for analyzing Group Analytic Hierarchy Process with an unequal level of decision-makers using preferential voting, as illustrated by a numerical example.

A considerable amount of perishable products, especially in the food and agricultural sector, is corrupted annually due to the lack of effective mechanism in the supply chain. So, in this study, we tried to improve these unfavorable conditions by designing an efficient supply chain network minimizing costs for perishable products. Due to lack of adequate research in the field of perishable supply chain network design, this study can be considered as one of the basic research issues in this field. In order to analyze and verify the proposed model, a case study in Mazandaran province has been applied. Since the proposed model on a large scale problem is NP-hard, the meta-heuristic algorithms are developed to solve the problem. It should be noted that in order to compare the performance of these algorithms on small size problems, the branch and bound method via Lingo software is used. Finally, conclusions and suggestions for future research are presented.

Original data envelopment analysis (DEA) models are to evaluate each decision-making unit (DMU) with a set of most favorable weights of performance indices to finding worst-practice DMUs. Indeed, classical DEA models evaluate each of the DMU compared to the most effective one. Since in this way the relative efficiency is calculated, at least one of the DMUs are located on the efficiency frontier. In comparison to classical DEA models, there are other DEA models, which evaluate DMUs based on unfavorable scenario and by making the inefficiency frontier, identify the DMUs with the worst-practice performance. The efficient DMUs obtained from the original DEA construct an efficient (best-practice) frontier. In this paper, by using the robust optimization approaches, we propose two models to evaluate DMUs in the worst-practice sense, and our aim is to obtain DMUs with the worst-practice performance in problems that faced with uncertainty in data. Also, to rank the DMUs with worst-practice we use the super-efficiency concept and called it super-inefficient. By using of two numerical examples, we demonstrate the capability of the proposed models in the presentation of reliable ranking and finding the worst-practice DMUs.

Data Envelopment Analysis (DEA) is a non-parametric method for the estimation of production function. In DEA, each decision making unit (DMU) has a number of inputs and outputs and deals with performance evaluation of DMUs using frontier efficiency. Each one of the inputs and outputs plays a basic role in the performance evaluation of DMU, which can be named as direct impact factor. Inputs and outputs of the DMU have, also, an indirect impact on the performance evaluation of DMUs, which one of blind spots of DEA models is not in consideration of these factors; they are known as indirect impact factor. Due to their essence and nature, indirect impact factors have influences on the efficiency negatively, and these factors cannot be taken into consideration as input or output or, controllable input or outputs. Recognition of indirect impact factors plays a remarkable role in the performance evaluation and ranking of DMUs. Therefore, some methods have been presented in this paper for performance evaluation and ranking of DMUs based on indirect impact factors.

Set covering problem has many applications such as emergency systems, retailers’ facilities, hospitals, radar devices, and military logistics, and it is considered as an Np-Hard problems. The goal of set covering problem is to find a subset such that the union of the subset members cover the whole set. In this paper, we present a new heuristic algorithm to solve the set covering problem. In the heuristic algorithm, the amounts of improvement are calculated for any of the vertices in the graph. Based on the improvement, we consider vertices in the subset. The amounts of improvement are updated in each iteration to find near optimal solution. A simulated annealing algorithm, whose parameters are tuned by Taguchi method, is presented to compare with our proposed heuristic algorithm. The computational results show that the heuristic algorithm works better than the simulated annealing algorithm in both quality of solution and time view.

Measurements of some variables in statistical analyses are often encountered with random errors. Therefore, investigating the effects of these errors seems to be important. This event in regression analysis seems to be more necessary because the aim of the fitting a regression model is estimating the effect of an independent variable on a response variable. Then measurements of an independent variable in a regression model are subject to random error, which may affect the parameter estimating processes. In this article, we first investigate how random errors occur on the measurements of a random variable. Then we show that such an error exist on the measurements of the independent variable which has an impact on the estimating of the parameters, such that it makes the solution of the estimating equations for the estimating of the regression model parameters possible. We also show that with an optimization procedure by solving the estimating equations, the estimation of model parameters can be achieved. Finally, we test the results of the optimization procedure on two practical examples, and we illustrate the effects of ignoring random errors in estimating model parameters in these two examples.

Current studies regarding to school bus routing problem are basically about travel time and cost of travel minimization. In this paper, we pay more attention to the challenges of transportation company in getting students to their school in a way that maximizes the company profit as well as minimizing the cost of students’ assignment. In this study, for the first time the transportation company is allowed to pay a fine to students in order to not serving them. In the investigated school bus routing problem, the following will be considered simultaneously: 1. Enabling potential stations (location) 2. Determining which students to be assigned to which stations and which students to be paid by company for not being served (allocation) 3. Determining routs among stations so that the total traveled distance be minimized (routing) A single objective mix integer programming of this problem is developed. Finally, an exact approach and a metaheuristic procedure are proposed for solving the problem. The results of this two approaches are studied in five generated samples, and the results indicate good performance of metaheuristic procedure.

In this article, centralized resource allocation (CRA) models based on the value efficiency in DEA and DEA-R are recommended. In general, if the input and output data of decision-making units are available, DEA models provide targets of units on the efficiency frontier in addition to the efficiency of units. However, if only a ratio of the input data to output data, or vice versa, is available, DEA models cannot determine the efficiency and target of units. In order to overcome this problem, DEA-R models are utilized. With a linear programming problem, centralized resource allocation models can achieve the projection of all decision-making units on the efficiency frontier. Therefore, in the present article, the projection of inefficient units in DEA and DEA-R is achieved using the CRA models based on the value efficiency (considering units that the manager defines as MPS). Finally, a case study is carried out for clothing companies of a specific brand.