فصلنامه مطالعات مدیریت صنعتی
پیاپی 39 (زمستان 1394)

Outranking based models as one of the most important multicriteria decision methods need the definition of large amount of preferential information called “parameters” from decision maker. Because of the multiplicity of parameters, their confusing interpretation in problem context and the imprecise nature of data, Obtaining all these parameters simultaneously specially in large scale realistic credit problems which requires real time decision making is very complex and time-consuming.
Preference Disaggregation approach infers these parameters from the holistic judgements provided by decision maker. This approach within multicriteria decision methods is equivalent to machine learning in artificial intelligence discipline.
Under this approach this paper proposes a new learning method in which Genetic Algorithm(GA) in an evolutionary process induces all , ELECTRE TRI model parameters from training set then at the end of this process, classification is done on testing set by inferred parameters. Experimental analysis on credit data shows high quality and competitive results compared with some standard classification methods.

Today¡ companies have accepted that the maintenance and repairs are profitable business elements and therefore¡ the role of maintenance and repairs has become more important in modern manufacturing systems. Maintenance and repair play important roles in achieving organizational goals and improving the indicators of reliability¡ reducing the equipment downtime¡ products quality¡ increasing productivity¡ safety equipment and etc. In this regard¡ the maintenance and repairs and their strategies have the particular importance in the industry. As a result¡ the main purpose of this study is selecting and ranking the best repairs and maintenance strategies using a combination of confirmatory factor analysis methods (FA)¡ AHP and TOPSIS in oil refinery of Ray. According to that many variables such as safety¡ cost¡ added value¡ etc. are affective in choosing a maintenance and repairs strategy¡ in the present study at first has been identified these variables by the aid of literature review and experts opinion and then has been addressed to select the best maintenance strategy by AHP and TOPSIS techniques and try to offer suggestions for improving refinery maintenance system.

This paper presents a mathematical model for the multi-mode resource-constrained project scheduling problem with maximizing the net present value of project. The proposed model is inspired by MRCPSP-GPR. Firstly, we presented an exact model solving MRCPSP_GPR then we expanded the model to estimate other cost-related options, penalty-related expenditure is a case in point. As a result of estimating different factors which impact on project scheduling such as reward and penalty of finishing the project, managers can efficiently make a better decision. For better adaptation with real conditions, we consider two payment methods in two objective functions. The adjusted schedule by proposed model and solving time was logical. Moreover, to verify the proposed model, a numerical example is solved in small size and the related computational results are illustrated in terms of schedules. In addition, computational results with a set of 36 test problems in various sizes are reported and the results analyzed.

In this study, the strategy of effective asset allocation under uncertainty with the capability of risk control, transaction cost reduction and favorable return realization is investigated. In order to implement this strategy and to overcome the shortfalls of classic portfolio optimization models in dealing with uncertainty, the formation of an index fund using a robust approach and considering cardinality constraint became the agenda. Accordingly, in order to solve the index tracking problem, a linear programming model as minimizing the absolute deviation between the expected return of the index fund and that of the benchmark is presented. Considering the dimension of the solution space, a Meta heuristic genetic algorithm was implemented to solve the robust counterpart of the problem. The results of the analysis imply on the selection of 20 stocks as the index fund composition and indicate good performance of the index tracking funds based on criteria such as correlation, root mean square error and the excess return using out of sample data.

Improving products quality and services is the best and most important factor to win competitors and get majority of the market share. In this regard, Failue mode and Effect analysis is an efficient tool to improve the quality products. Considering many criticisms to taraditional method, the risk priority number in FMEA is formed by multiplying of three factors (seveirity, Occurrence and Detect). In order to existing defects, a new method to calculate the risk priority number in FMEA based on data envelopment analysis method is introduced. The aim of this study is to provide a new kind of risk priority number by assigning different weights to each of the risk factors. Also according to severity, Occurance and detection numbers that are achieved by a team of experts and are not a constant and certain factor, in this research has been used Robust optimization because of covering the result of DEA and less complexity. The results of example indicate that, proposed model is more effective than traditional RPN and provide a full ranking.

Many companies are pursuing agile manufacturing in order to reduce costs, improve customer service and attaining competitive advantage. After reviewing theoretical background and literature on agility, this paper presents agility attributes, criteria and enablers. Then a framework developed in order to prioritize and analyze agility indices with considering major competitive advantage. Hybrid Fuzzy Quality Function Deployment (FQFD) and Gap Analysis (GA) approach used to prioritize indices based on experts in an organization. Findings point out that “manufacturing management agility” has lowest maturity among agility enablers that lead this enabler to takes maximum importance. Furthermore, “manufacturing management agility” obtained most weight by FQFD, meaning this enabler has maximum priority and importance for organization. Interestingly, “knowledge management criterion” got first priority among all criteria by regarding maximum gap in gap analysis approach.

This paper presents a mathematical model for a redundancy allocation problem (RAP) for the series-parallel system with k-out-of-n subsystems and failure rate depends on working components of system. It means that failure rate of components increases when a component fails. The subsystems may use either active or cold-standby redundancy strategies, which considered as a decision variable for individual subsystems. Thus, the proposed model and solution methods are to select the best redundancy strategy among active or cold-standby, component type, and levels of redundancy for each subsystem. The objective function is to maximize the system reliability under cost and weight constraints. To solve the model, since RAP belongs to Np-Hard class of the problems, one effective meta-heuristic algorithm named genetic algorithm (GA)is proposed. Then, response surface methodology is applied for algorithm parameter tuning.Finally, we consider the results of solving presented model with a numerical example.