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Decision process includes complexity in which all of environmental conditions are evaluated in accordance with objectives. The aim of this study is to present a multi-objective decision making (MODM) process by using fuzzy set applications. Process is considered in the concept of two person non-constant sum games and all phases are executed in game theoretic perspective by evaluating the mutual strategies of players. To demonstrate the validity and applicability of proposed methodology for competition environments, a case and a numerical example is given. [ABSTRACT FROM AUTHOR]

Decision process can be summarized as a process that helps for choosing the optimal alternatives according to suggested objectives by evaluating all environmental effects in problem solving. Nowadays, since environmental effects are more complex, imprecise and multilateral, fuzzy set theory and game theory are widely preferred instruments in decision making process. The aim of this study is to present a hybrid multi-criteria decision making approach which uses artificial intelligence techniques such as fuzzy logic and game theory. This process is considered in two person non-constant sum game theory perspective. The methods in literature about related topics (such as scenario planning and fuzzy TOPSIS) are examined and a hybrid decision making methodology that comprises many decision methods is formed. All phases of this approach are executed in game theory perspective by evaluating mutual strategies of players. In the study, the methodology is explained and a fictitious international disagreement case is used as a numerical example to demonstrate the validity and applicability. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new hybrid meta-heuristic algorithm based on simulated annealing supplemented with tabu list for the Multi Objective Facility Layout Problem (MOFLP). It is a SA approach supplemented with a tabu list. The main advantage of this approach is that a short term memory provided by the tabu list can be used to avoid solution re-visits while preserving the stochastic nature of the SA method. The goal of the new hybrid heuristic algorithm is to find the solutions forming the set of efficient solutions (pareto optimal set) of the MOFLP's in a relatively short time. The hybrid meta-heuristic is illustrated with a practical example of plant with 8 departments and 4 objectives. An efficient set of layouts corresponding to pre-determined weights are obtained. And also the set of efficient pareto-optimal solutions of the problem are determined. [ABSTRACT FROM AUTHOR]

In this article, we consider the facility layout problem which combines the objective of minimization of the total material handling cost and the maximization of total closeness rating scores. Multi-objective optimization is the way to consider the two objectives at the same time. A simulated annealing (SA) algorithm is proposed to find the non-dominated solution (Pareto optimal) set approximately for the multi-objective facility layout problem we tackle. The Pareto optimal sets generated by the proposed algorithm was compared with the solutions of the previous algorithms for multi-objective facility layout problem. The results showed that the approximate Pareto optimal sets we have found include almost all the previously obtained results and many more approximate Pareto optimal solutions. [ABSTRACT FROM AUTHOR]

Automated guided vehicles (AGVs) have become quite popular with their applications in automatic materials handling systems, in flexible manufacturing systems and even in seaports. In consequence of their widespread employment, the problems in these systems have emerged increasingly. In this study, a mathematical programming model is proposed for setting up an AGV system. When an AGV system is to be set up, flow path design and determination of the numbers and types of the required vehicles are crucial decisions for the system efficiency. In the literature, no model is present that solve these problems simultaneously. Therefore here an integrated mixed integer linear programming model is proposed to solve the foregoing problems for the preliminary design. The solvability of the model is shown on the randomly generated test problems. [ABSTRACT FROM AUTHOR]

Traditional supply chain network design problems are often taken as a single objective. However, supply chains are complex networks formed by organizations having conflicting objectives with each other in real life. In this study, a multi-product, multi-stage and multi-period planning model is proposed to achieve multiple incommensurable goals in an integrated supply chain network with uncertain market demands. The supply chain planning model is constructed as a mixedinteger nonlinear programming problem to satisfy several conflicting objectives with each other. The proposed model consists of two objective functions. The first one is minimizing the fixed opening and operating costs with transportation costs determined depending on distances. Second one is minimizing the purchasing, ordering, inventory and backlogging costs according to Economic Production Quantity (EPQ) model. Fuzzy goal programming approach is used in order to include decision maker's imprecise goal values in proposed model. The model is solved using GAMS optimization program. The application results presented in this study, demonstrates that fuzzy modeling and solution approaches could be used in the creation of more realistic models of the supply chain. [ABSTRACT FROM AUTHOR]

Traditional supply chain network design problems are often taken as a single objective. However, supply chains are complex networks formed by organizations having conflicting objectives with each other in real life. In this study, a multi-product, multi-stage and multi-period planning model is proposed to achieve multiple incommensurable goals in an integrated supply chain network with uncertain market demands. The supply chain planning model is constructed as a mixedinteger nonlinear programming problem to satisfy several conflicting objectives with each other. The proposed model consists of two objective functions. The first one is minimizing the fixed opening and operating costs with transportation costs determined depending on distances. Second one is minimizing the purchasing, ordering, inventory and backlogging costs according to Economic Production Quantity (EPQ) model. Fuzzy goal programming approach is used in order to include decision maker's imprecise goal values in proposed model. The model is solved using GAMS optimization program. The application results presented in this study, demonstrates that fuzzy modeling and solution approaches could be used in the creation of more realistic models of the supply chain. [ABSTRACT FROM AUTHOR]

In this study, an algorithm is developed to solve traditional assembly line balancing problem with fuzzy operation times. Variability and uncertainty in the assembly line balancing problem is traditionally modelled through the use of statistical distributions. This may not be feasible in cases where no historical data exists. For this reason, the proposed algorithm uses fuzzy operation times determined by an expert to balance the new assembly line. The developed solution algorithm is coded by Java programming language and is tested in a factory to balance a new assembly line. [ABSTRACT FROM AUTHOR]