JOURNAL OF INDUSTRIAL MANAGEMENT STUDIES
Year:2017 | Volume:14 | Issue:43 |Papers Count:7

By applying fuzzy set theory in game theory, player’s strategies are determined by fuzzy variable with a definite membership function, where the degree of non-membership is 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 deals 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 structure of two-level programming in a Stackelberg game form. Finally, using a numerical example, structural validation and the effectiveness of proposed model is shown in product pricing process.

In this paper, multi-site aggregate production planning for the production of perishable products such as gifts of New Year, calendars and maturities by postponement policy in uncertainty conditions is determined. 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” are used to validate and show the efficiency of the proposed model. Results show that the proposed model of this paper can use for similar factories which are active in the field of aggregate production planning with considering uncertainty in the parameters.

In this paper hybrid flowshop scheduling problem where some jobs, not all, have to follow no-wait restriction (that is, the operations of that job must be processed with no stop) is examined. In the literature, all papers assume that all jobs of the shops have to follow no-wait restrictions. First, this paper mathematically formulates the problem with two different mixed integer linear models under proposed considerations. The models are evaluated using two performance measures of size complexity and computational complexity. The small instances of the problem are solved using commercial software of mathematical programming. To solve larger instances of problem, two solution algorithms have been developed. These two algorithms are based on imperialist competitive algorithm and simulated annealing. A comprehensive numerical experiment including small and large instances is conducted to evaluate the models and algorithms. The results show that the imperialist competitive algorithm outperforms simulated annealing.

The 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 of the efficient periodicity maintenance for all of component of a system which has antagonistic objectives is not a trivial task. The aim of this paper is to presenting scheduling model of preventive maintenance and replacement for series system by variable and not predetertmined periods and also by applying inflation in the model. The model is designed based on fuzzy multi-objective and integer non-linear programming. Also the proposed model is solved for a case study with numerical example and furthermore, analysis of various senarios, the effects of inflation on the model will be considered. The results show that applying inflation in the model changes not only scheduling for replacement and maintenance, but also affects the effective age of components.

By developing information technology and emerging online markets, Planning for these markets and analyzing their details has been became a priority for beneficiary organizations. One of most important online markets in Iran is SIM card credit services that are considered as online stores. Regarding to numerous and increasing number of online malls, grouping and classification of online malls from supplier's point of view in order to forecasting cooperation is essential. In this paper, around three thousand online stores has been studied and analyzed by using one of famous supplier data and it has been clustered according to supplier's indexes. Clustering process has been done by using SOM Neural Network in two levels by K-means algorithm since it facilitates analyzing of clusters. Although various validation indicators has been developed to determine the best number of clusters but in this paper, an optimizing model of compensatory approach to indexes is presented by using combination 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 product design documentation. Process planning and scheduling optimization is done with Considering qualitative parameters which are affective on job shop and flexible system. Fuzzy Inference System and Meta-heuristic algorithms with multiple objective and goal functions are used to solving problem. Objective functions include minimization of cost and time of processing parts and maximizing the utility of the process design. based on the illustrated numerical example simulated annealing algorithm has better efficiency than imperialist competitive algorithm, particle swarm, bee colony.

One of the important issues to reducing waste and therefore increasing productivity in manufacturing enterprises is good production layout. Organizations can make production flow layouts productive by following up lean thinking model, thereby 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, transport, lead time and product quality. To evaluate and choosing layouts based on the forgoing criteria, a fuzzy inference system was employed. The system input is rating of each layout of production flow for each criterion, which was 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 case firm.