We present a framework for decision making under uncertainty where the priorities of the alternatives can depend on the situation at hand. We design a logic-programming language, DOP-CLP, that allows the user to specify the static priority of each rule and to declare, dynamically, all the alternatives for the decisions that have to be made. In this paper we focus on a semantics that reflects al...

in this research, an integrated inventory problem is formulated for a single-vendor multiple-retailer supply chain that works according to the vendor managed inventory policy. the model is derived based on the economic order quantity in which shortages with penalty costs at the retailers` level is permitted. as predicting customer demand is the most important problem in inventory systems and th...

This paper proposes a Stochastic Chance-Constrained Programming Model (SCCPM) for the supplier selection problem to select best suppliers offering incremental volume discounts in a conflicting multi-objective scenario and under the event of uncertainty. A Fast Non-dominated Sorting Genetic Algorithm (NSGA-II), a variant of GA, adept at solving Multi Objective Optimization, is used to obtain the...

In this paper we used genetic algorithms to1 find the solution of game theory. We proposed new method foe solving game theory and find the optimal strategy for player A or player B. We can benefit from the relationship between game theory and the linear programming to find the fitness function and tested this fitness function at different examples .

In an environment with strategic interaction, multiple equilibria yields a coordination problem and strategic uncertainty, even in the idealized setting of complete information. The resulting disequilibrium of beliefs leads to coordination failure, as demonstrated by many experiments. This paper examines two such coordination problems: the threshold game with a unitary public good and the marke...

This paper employs uncertain programming to deal with optimal assignment problem with uncertain profits. Within the framework of uncertain programming, a concept of expected optimal assignment for uncertain optimal assignment problem is proposed, and then an expected value model is constructed. Taking advantage of properties of uncertainty theory, this model can be transformed into a correspond...

hub covering location problem, network design,   single machine scheduling, genetic algorithm,   shuffled frog leaping algorithm   hub location problems (hlp) are synthetic optimization problems that appears in telecommunication and transportation networks where nodes send and receive commodities (i.e., data transmissions, passengers transportation, express packages, postal deliveries, etc.) th...

The multi-objective optimization for a multi-product multi-period four-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) and retailers each with uncertain services and uncertain customer nodes are aimed in this paper. The two objectives are minimization of the total supply chain cost and maximization of the average number of products dispatched to custo...

This paper describes the recently developed "genetic programming" paradigm which genetically breeds populations of computer programs to solve problems. In genetic programming, the individuals in the population are hierarchical computer programs of various sizes and shapes. Three applications to problems in artificial intelligence are presented. Introduction and Overview This paper describes the...