فصلنامه مطالعات مدیریت صنعتی
پیاپی 43 (زمستان 1395)

By applying fuzzy set theory in game theory, players’ strategies are determined by fuzzy variables with a definite membership function, where the degree of non-membership is only stated by supplement of degree of membership, while determining the value of uncertain parameters of decisions may be associated with a hesitation degree. Therefore, in this paper, intuitionistic fuzzy variables are used to better describe vague and imprecise information, and also to deal with uncertainty and ambiguity in product pricing process. To provide the proposed model, a two-echelon supply chain with one manufacturer and one retailer is considered. For designing the proposed pricing game, we have considered the structure of two-level programming in a Stackelberg game form. Finally, the structural validation and the effectiveness of proposed model is shown in product pricing process using a numerical example.

In this paper, multi-site aggregate production planning for the production of perishable products such as gifts of New Year, calendars and maturities is determined by postponement policy in uncertainty conditions. The production process for these products is proposed to be divided into two phases including the production of final and semi-finished products with applying the concept of postponement. So, there are three production activities including direct production, the production of semi-finished products and final assembly. Also, a robust optimization model to solve aggregate production planning problem for these products is developed. Finally, a set of real data from a calendar producing company in Tehran called “NIK Calendar” is used to validate and show the efficiency of the proposed model. Results show that the proposed model of this paper can be used for similar factories which are active in the field of aggregate production planning with considering uncertainty in the parameters.

In this paper, hybrid flow shop scheduling problem with parallel machines in each work station is examined. This problem considered two works: no-wait restriction jobs and typical jobs. In no-wait restriction jobs, the operations must be processed with no stop. This paper mathematically formulates the problem with two different mixed integer linear models under proposed considerations. The small instances of the problem are solved and performance results are analyzsed using commercial software of mathematical programming. To solve larger instances of the problem, two solution algorithms have been developed. These two algorithms are based on imperialist competitive algorithm and simulated annealing. A comprehensive numerical experiment is conducted to evaluate algorithms. The results show that the imperialist competitive algorithm outperforms simulated annealing.

Scheduling of preventive maintenance and replacement is one of the crucial issues in the literature of system reliability and maintenance engineering. The importance of this issue is more evident in series system in which by stopping a component the whole system will stop. However, the quest for the efficient periodic maintenance for all components of a system which has antagonistic objectives is not a trivial task. The aim of this paper is to present scheduling model of preventive maintenance and replacement for series systems by variable and not-predetermined periods and also by applying inflation in the model. The proposed multi-objective model minimizes breakdown, maintenance, replacement and fixed costs and also maximizes system's reliability. The model is designed based on fuzzy multi-objective and integer non-linear programming. The proposed model is solved with a numerical case. Furthermore, analysis of various scenarios and the effects of inflation on the model have been considered. The results show that applying inflation in the model changes not only scheduling for replacement and maintenance, but also affect the effective age of components.

By developing information technology and emerging online markets, planning for these markets and analyzing their details has become a priority for beneficiary organizations. One of the most important online markets in Iran is SIM card credit services that are considered as online stores. Regarding numerous and increasing number of online malls, grouping and classification of online malls from the supplier's point of view is essential to forecast cooperation. In this paper, around three thousand online stores have been studied and analyzed by using the data from one of famous suppliers and they have been clustered according to supplier's indexes. Clustering process has been done using SOM Neural Network in two levels by K-means algorithm since it facilitates analysis of clusters. Although various validation indicators have been developed to determine the best number of clusters. In this paper, an optimizing model of compensatory approach to indexes is presented through combination of multi criteria decision making and aggregation of various indexes.

Process planning involves determining the most suitable and efficient manufacturing (assembly) processes and their sequence in order to produce a product (part). These processes should be compatible with required attributes in the product design documentation. In this paper, process planning and scheduling optimization is done through tacking qualitative parameters which are effective on job shop and flexible system. Fuzzy Inference System and Meta-heuristic algorithms with multiple objectives and goal functions are used to solve the problem. Objective functions include minimization of cost and time of processing parts and maximizing the utility of the process design. Results from solving numerical example, show that simulated annealing algorithm has better efficiency than imperialist competitive, particle swarm and bee colony algorithms.

One of the important issues to reduce waste and therefore increase productivity in manufacturing enterprises is good production layout. Organizations can make production flow layouts productive by following lean thinking pattern, which can result in producing final products with minimal waste. This paper aims to provide a model to aid industry managers’ decision-making process for choosing the most appropriate production flow layout pattern with respect to the amount of waste in production units. This model considers inventory level, transportation, lead time, and product quality. To evaluate and choosing layouts based on the considered criteria, a fuzzy inference system was employed. The system input is calculated through rating of each layout of production flow for each criterion, which is obtained by ANP method, and its output is the amount of waste of each layout for production flow. In order to simulate the proposed fuzzy inference system, Matlab software was utilized. Finally, product layout with a quantitative difference compared to group technology (cellular) layout, took up the least amount of waste and was considered as the most suitable of lean production flow layout for the investigated firm.