Robust supplier selection problem, in a scenario-based approach has been proposed, when the demand and exchange rates are subject to uncertainties. First, a deterministic multi-objective mixed integer linear programming is developed; then, the robust counterpart of the proposed mixed integer linear programming is presented using the recent extension in robust optimization theory. We discuss dec...

in this paper, we consider a multi-site production planning problem subject to uncertainty in demand and workforce expenses. in our new mathematical model, we presented a production planning system considering failure in rework and breakdown. we also survey human workforce allocation and its expenses which are considered uncertain due to some tradeoff between company’s benefits and workforce :u...

Production planning and control (PPC) systems have to deal with rising complexity and dynamics. The complexity of planning tasks is due to some existing multiple variables and dynamic factors derived from uncertainties surrounding the PPC. Although literatures on exact scheduling algorithms, simulation approaches, and heuristic methods are extensive in production planning, they seem to be ineff...

the revenue management concept and techniques are applied to model the coordination of supply chain elements. the fundamental premise of this approach is synchronization of a group of business entities consist-ing of a manufacturer and multiple suppliers to achieve an optimal supply chain capacity plans. the output of the supply chain can be various products and thus it is measured in terms of ...

perishability of blood products as well as uncertainty in demand amounts complicate the management of blood supply for blood centers. this paper addresses a mixed-integer linear programming model for blood platelets production planning while integrating the processes of blood collection as well as production/testing, inventory control and distribution. whole blood-derived production methods for...

This paper deals with an investigation of combinatorial and robust optimization models to solve rolling-stock planning problems for passenger trains. Here robustness means that rolling-stock can better deal with some disruptions of the railway system. The proposed method is based on optimization and simulation techniques to handle the problem with imperfect information on data. The goal of the ...