The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper, we propose a mixed singlestage R&D projects and multi-stage securities portfolio selection model. Specifically, we present a bi-objective mixed-integer stochastic programming mode...

In recent years, Supplier evaluation and selection, an important element in supply chain management, has been gaining attention in both academic literature and industrial practice. The Mixed integer multi-Objective non-Linear programming model (MIMONLP) presented in this paper aimed to evaluate and select the appropriate set of suppliers considering quantitative and qualitative criteria and in ...

an interactive heuristic approach can offer a practical solution to the problem of linear integer programming (lip) by combining an optimization technique with the decision maker’s (dm) judgment and technical supervision. this is made possible using the concept of bicriterion linear programming (blp) problem in an integer environment. this model proposes two bicriterion linear programs for iden...

Although lot streaming scheduling is an active research field, lot streaming flexible flow lines problems have received far less attention than classical flow shops. This paper deals with scheduling jobs in lot streaming flexible flow line problems. The paper mathematically formulates the problem by a mixed integer linear programming model. This model solves small instances to optimality. Moreo...

In this paper recently developed mixed-integer programming (MIP) tools to the problem of optimal siting and sizing of distributed generators in a distribution network. Here three methodologies for solving the DGPP via mixed-integer linear programming (MILP) and mixed-integer nonlinear programming (MINLP) approaches are compared. The single MILP approach uses the well-known DC linear approximati...

Multi-objective optimization is the simultaneous consideration of two or more objective functions that are completely or partially inconflict with each other. The optimality of such optimizations is largely defined through the Pareto optimality. Multiple objective integer linear programs (MOILP) are special cases of multiple criteria decision making problems. Numerous algorithms have been desig...

multi-objective optimization is the simultaneous consideration of two or more objective functions that are completely or partially inconflict with each other. the optimality of such optimizations is largely defined through the pareto optimality. multiple objective integer linear programs (moilp) are special cases of multiple criteria decision making problems. numerous algorithms have been desig...