Journal of Operational Research in its Applications
Year:2018 | Volume:14 | Issue:4 (55) |Papers Count:10

In Supply chain management, decision making for the selection of suppliers is of such a key importance that has developed into a strategic objective for organizations in recent years. The issue of selecting a supplier, due to multiplicity and diversity of qualitative and quantitative indices, is a multiattribute decision making question and as a result, known techniques of this field of study can be used in the process of problem-solving as well. The present study intends to present a new and scientific algorithm for evaluation and selection of suppliers by combining multiattribute decision making techniques and improving their implementation process. For this purpose, similar studies were reviewed in the first step and then, assisted by organizational connoisseurs, eight important and effective indices were extracted for the selection of suppliers includinmg price, quality, good delivery, geographic location, payment terms, device technological capabilities, and after-sales services. In the second step, using real data provided by YOUTAB Company, the suggested algorithm was explained. To this end, firstly, applying Entropy technique (completing decision matrix by Company's experts), the initial weight of indices was extracted; then with the help of AHP (conducting an opinion poll of the company's chief executive officer) a new weight for indices was obtained, and then they were combined with initial weights so that the final weights could be calculated. After this, the revised PROMETHEE technique was used to evaluate the four existing suppliers and analyze the results achieved.

Since most real-world decision problems, because of incomplete information or the existence of linguistic information in the data, are including uncertainties, stochastic programming and fuzzy programming as two conventional approaches to such issues have been raised. Stochastic programming deals with optimization problems where some or all the parameters are random. In this paper, a method is provided for solving multi-objective stochastic programming where the unknown parameters have been considered as normal random variables. In this model, it is assumed that the parameters are specified by the relevant professionals. Since there are not enough ways to solve such problems directly, the corresponding model using chance-constraint approaches, are converted to a certain multi-objective problem. Then, a fuzzy programming technique for solving the certain multi-objective model will be utilized. In this paper, the hyperbolic membership function is used. The final method can be solved by standard methods of nonlinear programming. Finally, numerical examples are provided to illustrate the operation of the proposed method.

Let P1  and P 2 be two gamma independent populations that Pi has unknown scale parameter qi and common and known shape parameter a . Suppose that X1 and X2 are drown from P1 and P2, respectively, and the population corresponding to X (1)=min (X1, X2) and X (2)=max (X1 ,X2) is selected. The aim of this paper is to find the optimal decisions for the scale parameters qM and qJ from the selected gamma population under a generalized scale invariant loss function. To do this, we obtain admissible and inadmissible decisions of the form cX (1) or cX (2) in a subclass of invariant decisions qM or qJ. Moreover, Bayes and generalized Bayes decisions of qM and qJ are obtained and we show that these decisions are admissible. We apply our results for censored data and also for Weibull and Rayleigh distributions.

Selecting the right suppliers is one of the most important decision-making problems to achieve competitive advantage in the supply chain of every company and organization. In order to achieve these goals, the decision-makers (DMs) should apply the best method and accurate criteria for analyzing and solving supplier selection problems. Meanwhile, due to their flexibility, the use of hybrid models has always been welcomed. The present paper employs the integrated analytical hierarchy process (AHP) technique, Taguchi loss function, and multi-choice goal programming (MCGP) with utility functions to evaluate and select the best supplier. The proposed model will enable the decision-maker to choose the appropriate supplier (s) based on the lowest loss and the highest level of utility to achieve the most suitable policy for his goals.

Shortest path problem is among the most interesting problems in the field of graph and network theory. There are many efficient matrices-based algorithms for detecting of shortest path and distance between all pairs of this problem in the literature of research. In this paper, a new exact algorithm, named the Cascade Rectangle Algorithm, is presented by using the main structure of previous exact algorithms and developing some new techniques. In cascade rectangle algorithm, all mathematical calculations and operations execute in multirectangle structure. This algorithm is an efficient exact algorithm with simple procedure and fast running time. Furthermore, a new matrix on the basis of route matrix, called the Transposition Matrix, is defined to apply the sensitivity analysis and reoptimization of the all pairs shortest path networks. Finally, one illustrative example is also solved in a step by step procedure to show the details of the cascade rectangle algorithm, floyd-warshall algorithm, revised matrix algorithm, and Transposition Matrix.

These days, one of the biggest reasons for the success of large companies is their successful managers. The more powerful and efficient managers of an organization are, without doubt, the more efficient manpower and other resources under their supervision will be. This research in terms of purpose is an applied study and in terms of method is a descriptive-analytical study, which has been conducted as a case study to investigate the performance of the managers of a big building company. At first, by reviewing domestic and foreign literature, 84 criteria were identified for assessing the performance of managers. After obtaining experts opinion (in the company studied), 27 sub-criteria in 5 main groups or criteria were selected to assess the performance of managers (including director personality traits, communication skills, decision making skills, management skills, and professional capabilities and skills). Then, using the fuzzy TOPSIS technique and obtaining 11 experts’ opinion, the main criteria and sub-criteria were prioritized, and the weight of each criteria was calculated. Among the identified criteria the decision making skill of managers gained the highest rating. Fuzzy DEA model, BCC, CCR output oriented method, and GAMS software were used in order to rank the performance of 20 managers, and according to the similar returns to the scale and the similar results of both matrices, the general performance matrix of 20 directors of the company was composed, and ultimately, company managers were ranked based on their performance and the aforementioned criteria.

One of the most important applications of the mathematical sciences in industrial and engineering sectors is the determination of a set of Pareto optimal points in multiobjective optimization problems with at least two conflicted and inappropriate objective functions. In this paper, a numerical method based on Pascoletti-Serafini approach for finding an approximation of the efficient frontier in multiobjective optimization problems is presented. The relationship between the optimal solutions of the scalarized problem and the Pareto solutions of the multiobjective optimization problem is presented by several theorems. In numerical simulations section, the effectiveness of the proposed method is demonstrated by comparing it with mNBI and NC, which have nonlinear and nonconvex Pareto optimal frontier, respectively.

Through Employing Bootstrap Data Envelopment Analysis and LSW Algorithm this study aimed at estimating bias corrected efficiency, technical efficiency, managerial and scale efficiencies for firms with 10 or more labor forces during the year 2011. Bootstrap is a resampling technique applied to estimate the distribution properties of an estimator. Such methods are used when dependency and sensitivity of ranking and efficiency values of each economical firm changes in sample compositions and also when it is necessary to correct generated bias caused by calculation of efficiency values in conventional methods of input-data envelopment analysis. The results showed that the average of managerial and scale efficiencies of our country’s industry section are 77% and 94% respectively. This shows that the main reason for technical inefficiency is inefficient management.9% of our country’s industry sector, functions with complete technical efficiency, and 48% of the sector functions with less than 70% of technical efficiency.