نشریه مدیریت صنعتی
سال سوم شماره 6 (بهار و تابستان 1390)

The basic type of Assembly Line Balancing (ALB) problem is the allocation of work elements to a set of production work stations considering cycle time, precedence constraints, and assembly diagram to achieve predefined performance criteria such as line efficiency and smoothness index. In this paper, a mixed integer programming model for solving multiple product ALB problems with processing times larger than cycle time as well as technological constraints is presented. The performance of presented model in finding optimal solution is compared with models presented in the literature by solving sample problems. The obtained results were quite satisfactory. Also, the relative advantages of the proposed model in comparison with some approaches in the literature are verified and shown using Kruskal-Wallis non-parametric testing approach. This was accomplished by calculating the measures of performance such as line smoothness, line efficiency and deviations of solved sample problems.

In a logistic system, decisions on location of depot, vehicle routing and assignment of routes to vehicles should be considered simultaneously. The location-routing problems are defined to find the optimal number and locations of depots, and at the same time with the distribution routes for the vehicles. Because of the complexity and the time consuming computation, this problem belongs to a class of combinatorial optimization and NP-hard problems. Finding an optimal solution for this type of complex, huge problem in reasonable computational time by using traditional and exact approaches and optimization tools is very difficult. So a Meta heuristic algorithm based on ant colony system is proposed to solve this problem. According to the experiments, this proposed algorithm reports near-optimal and promising solutions in reasonable time. Also in this paper the assumption of assigning several routes to a vehicle during its available time is considered. Finally, the computational results obtained by this algorithm are compared with the results reported by Lingo software.

The selection of ERP systems has been considered as one of the most influential and risky managerial decisions due to its heavy burden of purchase and implementation costs. The purpose of this paper is designing a fuzzy expert system for selecting the appropriate ERP system based on the internationally selected and validated criteria for minimizing the risks of inappropriate selection. If an option does not have the sufficient correspondence to the organizational criteria; the fuzzy logic provides the opportunity to the other options to taken into account for not only cover the weaknesses of measurement, but also easily lower the opportunity of selecting the appropriate alternatives. This leads to a comprehensive fuzzy evaluation of all of the alternatives. From another point of view, the fuzzy expert system rules evaluate the options according to the demands and requirements of organization and accordingly, the selection process will require no direct bi-comparison of options for the desired organizational status. For the sake of better validation and presentation, this paper has been conducted and provided through a case study of one of the Iranian industrial organizations.

In this paper has been used a new integrated method of fuzzy TOPSIS and zero-one integer linear programming for location selection of Bahman Group company facilities and manufacturing stations of affairs. Selected criteria and constraints are dividing to the explicit and implicit categories. Candidate alternatives have been selected from current and useable points of company. The first criteria have been exploited from interview with experts and used as triangular fuzzy numbers in the fuzzy TOPSIS. In this research 10 candidate alternatives have been ranked through fuzzy TOPSIS. Then coefficient of closeness and coefficient of variance have been used in the maximization and minimization objective functions in the ZOLP. Also, the second criteria have been used as constraints of ZOLP. Finally ZOLP model has been run and presented the best alternative for industrial facility location.

One of the most effective tools for decline cost and time, quality improvement and consequently project performance enhancement especially in construction projects, is value engineering. There are linguistic judgments in many cases of value engineering studies in construction projects such as interior design, mechanical installations design, and etc. Therefore multiple attribute decision-making techniques with fuzzy approach can be used in decision-making stages in value engineering process. In this paper, value engineering is combined with fuzzy MADM techniques. In this hybrid, fuzzy AHP technique is used in order to determine the weight of evaluation criteria recommended in creativity phase and fuzzy TOPSIS is used for ranking of alternatives. This approach was applied in design phase of mechanical installations of Mouj tower project in Sadaf town, one of the under construction projects in union sector of the state. According to the paper results, estimation of value engineering team, leads to 21.45 % of decreasing in project’s costs and 17.65% of decreasing in project’s duration.

This paper submitting the computation of optimize point of ordering in inventory control systems and particularly for continuous review of ordering system (r,Q) by fuzzy logic method which similarly can be used for other ordering systems. Since in the ordering models we cannot forecast the parameters like cost exactly, we have ambiguity. In this case using precise parameters may cause wrong decision making. In other hand using the statistical methods for parameters like cost is not proper. Therefore in this paper we have provided a method for using fuzzy numbers in the decision making about the ordering systems. For fuzzification we have used trapezoidal fuzzy number and for defuzzification we have used signed distance method. Finally there has been presented a numerical example in order to explain the given model.

The Investigation of Dimensional Efficiency for Branches of Maskan Bank in Resources Collection and Facilities Allocation: Approach of Super Efficiency Model with Weight Restrictions. The scarcity of financial resources of banks, especially in developing countries like as Iran, leads to planning for improving efficiency and productivity. Improving efficiency needs to performance measurement and recognize of strength and weakness of bank branches. This paper has been used DEA and AHP models for investigating of dimensional efficiency of bank branches in resources collection and resources allocation. In this paper efficient and non efficient branches are determined and finally some recommendations are presented.

This paper has been offered a technique for proper locating of Taliya Company’s sales shops and the supporting service centers. The researcher has discussed to know the related fundamental elements through library researches, interviews with Taliya professionals which are classified in two main categories of attributes and constraints. The attributes are consisting of marketing & customer’s satisfaction and constraints are regarding to the financial, commercial, and topological facilities. 66 cases are chosen to be studied containing three sales shops in each of 22 areas of Tehran. The cases were selected advised by out letting and real estate consultants. For selecting optimized centers, there was used the combined pair wise comparison and TOPSIS to calculate attributes weight. Then zero-one integer programming was used for optimized centers. Finally six shops have been selected as optimize centers.

This paper has revised the combined model of Chan & QI with supply chain operations reference (SCOR). This combined model with hierarchical structure, has three levels and seven criteria that some criteria are quantitative (cost, use of source) and others of them are qualitative (quality, flexibility, tangible, validity and innovation). In order to improvement of this model, this paper has combined this model with fuzzy logic. The goal of this paper is propose an innovative measurement method and provide meaningful composite indices for supply chain by use of fuzzy logic. Finally this model was tested in a real case and the results have validated the proposed model.

This paper presents a model predictive control (MPC) approach for production and inventory control systems. Model predictive control previously has been successfully applied to supply chain problems; however most systems that have been proposed so far possess no information on future demand. The incorporation of a forecasting methodology in an MPC framework can promote the efficiency of control actions by providing more insights in the future demands. In this paper, we investigate the applicability of advanced and traditional methods to forecasting lumpy demand at supply chains. Neural networks can be better model for forecast the nonlinear and chaotic behavior of lumpy demands in comparison with traditional and usual methods such as exponential smoothing. This paper efficiently employs neural networks as the main core in the proposed control design. To highlight efficiency of the proposed method, real datasets are used for investigation and comparison studies.