A community of agents as a tool to optimize industrial districts logistics

The aim of this paper is to find an optimal solution to operational planning of freight transportation in an industrial district. We propose a system architecture that drives agents – the industrial district firms - to cooperate in logistic field, to minimize transport and environmental costs. The idea is to achieve logistics optimization setting up a community made of district enterprises, preserving a satisfactory level of system efficiency and fairness. We address the situation in which a virtual coordinator helps the agents to reach an agreement. The objectives are: maximizing customers satisfaction, and minimizing the number of trucks needed. A fuzzy clustering (FCM), two Fuzzy Inference System (FIS) combined with a Genetic Algorithm (GA), and a greedy algorithm are thus proposed to achieve these objectives, and eventually an algorithm to solve the Travelling Salesman Problem is also used. The proposed framework can be used to provide real time solutions to logistics management problems, and negative environmental impacts

VISTA: a visual interactive method for solving MCDM problems

Ankara : The Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 1994.Thesis (Master's) -- Bilkent University, 1994.Includes bibliographical references leaves 91-94.In this thesis, recognizing the need of interaction with DM (Decision Maker) in solving MCDM (Multiple Criteria Decision Making) problems, a practical interactive algorithm called VISTA (Visual Interactive Sequential Tradeoffs Algorithm) is developed, and a DSS (Decision Support System) is designed to assist DM to use judgement effectively. The algorithm operates by successively optimizing a chosen objective function while the remaining objectives are converted to constraining objectives by setting their satisficing values, one of which is parametrically varied. By plotting the maximum value of the main objective function versus the parameter varied, a tradeoff curve is constructed between the optimized and the parametrized objective, while assuring constraining objectives (satisficing values guaranteed). This tradeoff curve is presented to the DM, and the DM is asked to choose a compromise solution between these two objectives. This chosen point is used as the new satisficing value of the parametrized objective, and a new tradeoff curve is generated by parametrizing another constraining objective function’s right hand side and .so on. This interactive procedure is continued until the DM is satisfied with the current decision or some other termination criterion is met. Special features to facilitate the DM’s judgement (MRS (Marginal Rate of Substitution) Curve, Multiple Comparison Plots, Convergence Plots), and the start and the termination (Start, Terminate, a Hybrid Approach) of the algorithm are provided. Two example problems are worked out with VISTA to demonstrate the practicality of the algorithm. The model and the entire procedure are validated.Tabanoğlu, AslıhanM.S