This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a fun...

This paper describes a new approach for reducing the number of the fitness function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation. The additions involve memory as a function of both d...

Nowadays, there is a great deal of attention for regulations of carbon emissions to enforce the decision-makers of production and distribution networks to redesign their systems satisfactorily. The literature has seen a rapid interest in developing novel metaheuristics to solve this problem as a complicated optimization problem. Such difficulties motivate us to address a production-distribution...

Brief description of the topic: Many real-world decision-making situations possess both a discrete or combinatorial structure and involve the simultaneous consideration of conflicting objectives. Problems of this kind are in general of large size and contains several objectives to be “optimized”. Although Multiple Objective Optimization is a well-established field of research, one branch, namel...

Discrete-continuous optimization problems in process systems engineering are commonly modeled in algebraic form as mixed-integer linear or nonlinear programming models. Since these models can often be formulated in different ways, there is a need for a systematic modeling framework that provides a fundamental understanding on the nature of these models, particularly their continuous relaxations...

INTRODUCTION The Metaheuristics are general strategies for designing heuristic procedures with high performance. The term metaheuristic, which appeared in 1986 for the first time (Glover, 1986), is compound by the terms: " meta " , that means over or behind, and " heuristic ". Heuristic is the qualifying used for methods of solving optimization problems that are obtained from the intuition, exp...

In this paper we consider a particular class of nonlinear optimization problems involving both continuous and discrete variables. The distinguishing feature of this class of nonlinear mixed optimization problems is that the structure and the number of variables of the problem depend on the values of some discrete variables. In particular we define a general algorithm model for the solution of t...

Metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and Stochastic Partitioning Methods are introduced, and their recent applications to a wide class of combinatorial optimization problems under uncertainty are reviewed. The flexibility of metaheuristics in being adapted to different modeling approaches and problem formulations emerges clea...

Nowadays, cross-docking is one of the main concepts in supply chain management in which products received to a distribution center by inbound trucks which are directly to lead into outbound trucks with a minimum handling and storage costs as the main cost of a cross-docking system. According to the literature, several metaheuristics and heuristics are attempted to solve this optimization model....

Duality is an important notion for constrained optimization which provides a theoretical foundation for a number of constraint decomposition schemes such as separable programming and for deriving lower bounds in space decomposition algorithms such as branch and bound. However, the conventional duality theory has the fundamental limit that it leads to duality gaps for nonconvex optimization prob...