optimization models have been used to support decision making in production planning for a long time. however, several of those models are deterministic and do not address the variability that is present in some of the data. robust optimization is a methodology which can deal with the uncertainty or variability in optimization problems by computing a solution which is feasible for all possible ...

in this paper, the capacitated location-routing problem with fuzzy demands (clrp-fd) is considered. the clrp-fd is composed of two well-known problems: facility location problem and vehicle routing problem. the problem has many real-life applications of which some have been addressed in the literature such as management of hazardous wastes and food and drink distribution. in clrp-fd, a set of c...

Abstract: Hub location problem is one of the most important problems in the areas of logistics and telecommunication network design. In this paper, we address the single allocation maximal covering hub location problem in which the objective is to maximise the amount of covered flows associated with origin-destination pairs in a hub-and-spoke network using a fixed number of hub facilities. Two ...

The optimal reactive power dispatch (ORPD) is a very important problem aspect of power system planning and is a highly nonlinear, non-convex optimization problem because consist of both continuous and discrete control variables. Since the power system has inherent uncertainty, hereby, this paper presents both of the deterministic and stochastic models for ORPD problem in multi objective and sin...

Integration of wind units into power system introduces various sources of technical and economical challenges to operate power system due to its inherent uncertainty and variability. In operation of power system, stochastic wind generation can affect the system security. In this paper, optimal Security-Constrained Unit Commitment (SCUC) in presence of Demand Response (DR) program based on stoch...

Multistage stochastic integer programming (MSIP) combines the difficulty of uncertainty, dynamics, and non-convexity, and constitutes a class of extremely challenging problems. A common formulation for these problems is a dynamic programming formulation involving nested cost-to-go functions. In the linear setting, the cost-to-go functions are convex polyhedral, and decomposition algorithms, suc...

This paper presents a tutorial on the state-of-the-art software for solution of two-stage (mixed-integer) linear stochastic programs and provides list designed this purpose. The methodologies are classified according to decomposition alternatives types variables in problem. We review fundamentals Benders decomposition, dual progressive hedging, as well possible improvements variants. also prese...

This study investigates the minimization of total electricity cost in a two-machine permutation flow shop scheduling problem under most common tariff, Time-of-Use (ToU). We provide new property and solution approach to enhance existing methods literature. First, we an overview optimal cases for some specific ToU pricing structures that consist only two intervals. When price decreases, Johnson’s...

Integration of wind units into power system introduce various sources of technical and economical challenges to operation of power system due to its inherent uncertainty and variability. In operation of power system, stochastic wind generation can affect on the system security. In this paper, optimal security-constrained unit commitment (SCUC) in presence of demand response program (DR) based o...

Constraint Integer Programming (CIP) is a generalization of mixed-integer programming (MIP) in the direction of constraint programming (CP) allowing the inference techniques that have traditionally been the core of CP to be integrated with the problem solving techniques that form the core of complete MIP solvers. In this paper, we investigate the application of CIP to scheduling problems that r...