pricing and advertising are two important marketing strategies in the supply chain management which lead to customer demand’s increase and therefore higher profit for members of supply chains. this paper considers advertising, and pricing decisions simultaneously for a three-level supply chain with one supplier, one manufacturer and one retailer. the amount of market demand is influenced by pri...

Supplier selection is one of the influential decisions for effectiveness of purchasing and manufacturing policies under competitive conditions of the market. Regarding the fact that decision makers (DMs) consider conflicting criteria for selecting suppliers, multiple-criteria programming is a promising approach to solve the problem. This paper develops a nadir compromise programming (NCP) model...

The facility location problem is a strategic decision-making for a supply chain, which determines the profitability and sustainability of its components. This paper deals with a scenario where two supply chains, consisting of a producer, a number of distribution centers and several retailers provided with similar products, compete to maintain their market shares by opening new distribution cent...

Game playing and genetic algorithms (GAs) are two important topics in artificial intelligence (AI). In this work we employ network tournament to assist in teaching these concepts associated with AI. Three exercises that implement a game-playing program are designed to help students learn relevant topics in AI. The first exercise involves game theory, e.g. mini-max search and alpha-beta pruning....

Most research on bilevel linear programming problem  is focused on its deterministic form, in which the coefficients and decision variables in the objective functions and constraints are assumed to be crisp. In fact, due to inaccurate information, it is difficult to know exactly values of coefficients that used to construct a bilevel model. The interval set theory is suitable for describing and...

A novel data-driven stochastic robust optimization (DDSRO) framework is proposed for optimization under uncertainty leveraging labeled multi-class uncertainty data. Uncertainty data in large datasets are often collected from various conditions, which are encoded by class labels. Machine learning methods including Dirichlet process mixture model and maximum likelihood estimation are employed for...

In this paper, the fixed charge solid transportation problem under uncertainty is investigated. As a result, three mathematical models are constructed for the problem, i.e., expected value model, chanceconstrained programming model and measure-chance programming model, where the supplies, demands, conveyance capacities, direct costs and fixed charges are regarded as uncertain variables. After t...

This paper draws on the “human reliability” concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of de...

Bilateral bargaining is the most common economic transaction. Customarily, it is formulated as a non-cooperative game with uncertain-information and infinite actions (offers are real-value). Its automation is a long-standing open problem in artificial intelligence and no algorithmic methodology employable regardless of the kind of uncertainty is provided. In this paper, we provide the first ste...