JOURNAL OF INDUSTRIAL MANAGEMENT STUDIES
Year:2016 | Volume:14 | Issue:42 |Papers Count:7

Supplier selection and quota allocation is an important decision in supply chains. This decision can be considered as a complex multi-criteria group decision making problem. This decision in many practical situations is very difficult for vague and uncertain environment. This vagueness and uncertainty can be handled by using fuzzy set theory. Therefore, this paper proposed a fuzzy MCDM model to evaluate candidate suppliers and quota allocation. A hybrid ANP-VIKOR method in fuzzy environment applied first with 16 criteria to evaluate suppliers. Then, a fuzzy multi-objective mathematical model is used to quota allocation. Finally, the fuzzy model is solved by Tiwari method. An illustration with a data set from a realistic situation is presented to demonstrate the effectiveness of the proposed model.

During the recent years, extensive research has been done on the field of project scheduling. There is always uncertainty in the area of project scheduling that causes a deviation in the real plan from the scheduled plan. One of the solutions to deal with this uncertainty is using the critical chain method (CCM) in project scheduling. This method which is derived from the theory of constraints (TOC) is a new method in project control which was first proposed by Goldartt in 1997.In this research we attempt to use the principals of critical chain in resource-constrained project scheduling problem. The main innovation in this research is presentation of critical chain project scheduling problem model with consideration of feeding buffer and using float as a supplement for feeding buffer. For this matter, the project scheduling under resources constraints with critical chain approach was first written and its reliability was evaluated using the Lingo software. In the next step the solution algorithm of this model was developed using the genetic algorithm and finally different sample issues were investigated. The results of this research show the efficiency of the presented genetic algorithm.

In this paper, a mathematical model is proposed for project portfolio selection and resource availability cost problem to scheduling activities in order to maximize net present value of the selected projects preserving precedence and resource constraints. Since the developed model belongs to NP-hard problems list, so a genetic based meta-heuristic algorithm is proposed to tackle the developed model. In the proposed algorithm beside common operators of genetic algorithms such as crossover & mutation, some intelligent operators are utilized for local search in computed resources and shifting the activities with negative cash flows. The key parameters of the algorithm are calibrated using Taguchi method to accelerate convergence of the proposed algorithm. Then, the algorithm is used to solve 90 test problems consisting 30 small-scale, 30 middle-scale and 30 large scale problems to examine the algorithm’s performance. It is observed that, in small problems, the obtained solutions from the proposed genetic algorithm have been comparably better than the local optimum solutions stemmed from Lingo software. On the other hand, for the middle and large size problems which there is no local optimum available within the limited CPU time, robustness of the proposed algorithm is appropriate.

A redundancy allocation problem is a well-known NP-hard problem that involves the selection of elements and redundancy levels to maximize the system reliability under various system-level constraints. In many practical design situations, reliability apportionment is complicated because of the presence of several conflicting objectives that cannot be combined into asingle-objective function. A stele communications, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. In this paper, a new hybrid multi-objective competition algorithm (HMOCA) based on competitive algorithm (CA) and genetic algorithm (GA) is proposed for the first time in multi-objective redundancy allocation problems. In the multi objective formulation, the system reliability is maximized while the cost and volume of the system are minimized simultaneously. Additionally, ay RSMis employed to tune the CA parameters. The proposed HMOCA is validated by some examples with analytical solutions. It shows its superior per for mance compared to a NSGA-II and PAES algorithms. Finally, the conclusion is given.

In this paper, a methodology is proposed to measure the efficiency of national energy sector in IRAN. The technical and environmental performance of the oil refineries in IRAN as a major producer of energy and fuel are evaluated based on data from years 2010 to 2013. In this study, a fuzzy multi-objective multi-period common weight network data envelopment analysis approach is proposed and customized to evaluate the performance of oil refineries. A certain scenario, called food-production in which a refinery is assumed as a decision making unit (DMU) consuming inputs to produce outputs, is considered to evaluate the technical and environmental performance in presence of undesirable outputs. The main contribution of this study are summarized as: (1) Proposing a multi-objective common weight DEA model in order to determine the weights of inputs and outputs in a single run; (2) Calculating the long term efficiency scores during a multiple-periods of planning incorporating dynamic nature of inputs and outputs; (3) Handling a compromise solution using fuzzy mathematical programming to address multi-objective mathematical programming; (4) Proposing a linear mathematical programming to achieve the global optimum solutions; (5) Enhancing the discrimination power of the DEA models; (6) Reducing the computational time of modeling and solution procedure; (7) incorporating effective criteria in the modeling procedure. The analysis of case study presents the efficacy and applicability of proposed method in comparison with existing classic models.

Regarding to the increasing importance of supply chain risk in the last decade in general and projects especially, in this research the dimensions and elements of supply chain risk of projects have been determined. Therefore, Fars Gas Transfer Pipeline projects were considered to discover codes and themes according to the phenomena by using Grounded Theory (Corbin and Strauss method). Based on final paradigm model, project supply chain risk management issue was categorized in 6 main dimensions, 19 sub-dimensions and 57 codes. The results show that supply chain risks as a core phenomenon, consists of environmental risks, organizational risks and network risks. To do this, we use theoretical sampling and interview with 10 experts from Gasstate company and some suppliers. These interview was deep one because we employed open questionnaire. Then by using open encoding, axial encoding and selective encoding, paradigm model was extracted. The main phenomenon placed the center of model and other themes joined to that.

Decision making (DM) is an important problem in most of the army operations. One of the challenging issues in this area is uncertainty in wars with uncertain information which causes many destructive effects on the results of strategies in battlefields. In the Heravi et al. article’s, published in the year 2013, utilizing a combination of Cognitive Agent (CA) and Classification based on Fuzzy Association Rules (CFAR) as the most effective and widely used methods, was able to relatively reduce this problem and tried to reduce uncertainty. But still in critical condition, can’t deny the need to act quickly and remove most invalid and inefficient rules extracted in the effective decisions.This paper aims to utilize the capabilities of Genetic Algorithm (GA) in amore realistic selection rules as a meta-heuristic way to combine complementary methods to minimize the uncertainty in DM. In comparison with previous method, experimental results achieved, clearly show that this combination in addition to the advantages of the previous method, due to the further reduction of production rules for DM, are more understandable and accurate and has more rational risk acceptance.