This paper proposes a new linear programming method entitled by LP-GW-AHP for weights generation in analytic hierarchy process (AHP) which employs concepts from data envelopment analysis. We propose four specially constructed linear programming (LP) models which are used to derive weight vector from a pair-wise comparison matrix or a group of them. We can use both interval and relative importance weights for each decision maker in LP-GW-AHP. In this method, solving only one LP model is enough for local weights derivation from pair-wise comparison matrices. Five numerical examples are examined to illustrate the potential applications of the LP-GW-AHP method. We show that not only derived weights of the new method have slight differences than Saaty's eigenvector weights but sometimes they are better than eigenvector method weights in the fitting performance index as well. LP-GW-AHP is compared with a method which has been recently proposed for the weights derivation in the group AHP and it becomes obvious that LP-GW-AHP provides better weights. The simple additive weighting method is utilized to aggregate local weights without the need to normalize them.

Nowadays, all market components are competing with each other. Supplier selection among different manufacturers is one of the most important aspects of competition among market participants. In this paper, we study a supplier selection taking into consideration three key criteria: price, quality and delivery performance. A game theory-based model is proposed for the problem in such a way that each buyer tries not only to optimise his/her own problem but also to remove the other buyers from their optimum points. We apply a combination of multi-criteria decisionmaking methods and an extension of the ordinal game theory to find the Nash equilibrium for selecting the right supplier in a competitive market. The success of this model will be supported by a numerical example.

The dynamic facility layout problem considers flow over multiple time periods in an environment where the material flow between departments changes over time. If this complex problem is combined with the other system problems, such as material handling system designing, this results in a more complicated problem. In most recently proposed approaches for these two synchronized problems, the integrated formulations have been used where the objective function is a weighted sum of some conflicting objectives that may belong to either one or different decision makers. In the former case, the poor practicability of such an approach is due to the difficulty of normalizing these functions and of quantifying the weights. In addition to these problems, in multiagent systems with a strong cooperation, there is another important problem in incorporating the conflicting objectives of conflicting decision makers that is not considered in the existing researches. By these descriptions, developing some other solution strategies, such as iterative or hierarchical methods, are necessary for these cases. In this paper, a bilevel model is developed. The experimental results reveal significant enhancements with respect to more classical approaches (i.e., integrated formulation and Pareto optimal solutions) based on the proposed bilevel model and algorithms.Index Terms-Bilevel programming, bi-particle swarm optimization (PSO) algorithm, coevolutionary algorithm, dynamic facility layout problem (DFLP), material handling system (MHS) design. 0018-9391

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This paper presents a model of the plant-within-a-plant (PWP) design problem and demonstrates a heuristic for analyzing the problem. Although the benefits of a manufacturing focus have been articulated in the literature, methods for implementation with consideration for resource requirements have not been developed previously. In this study, we discuss the importance of including resource considerations and propose a methodology that can help managers arrive at a facility design with a high degree of focus and minimum resource needs. A heuristic is developed that incorporates the concept of order-winning criteria and volume into the focus design. The heuristic not only recognizes the effects of conflicting manufacturing tasks, but also considers resource costs and material flows between PWP units. Experimental results show that the proposed methodology offers managers the opportunity to generate and assess alternative PWP designs, which are otherwise unavailable. Overall, this research provides an analytical framework for further research in focused manufacturing.