In this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which the uncertainty is characterized by given finite numbers of scenarios. In this model, the objective function minimizes the total expected costs of transportation and opening facilities subject to the robustness constraint. To tackle the problem efficiently and effectively, an effici...

The human has always been to find the best in all things. This Perfectionism has led to the creation of optimization methods. The goal of optimization is to determine the variables and find the best acceptable answer Due to the limitations of the problem, So that the objective function is minimum or maximum. One of the ways inaccurate optimization is meta-heuristics so that Inspired by nature, ...

This paper proposes a new mixed-integer model for the multi-skill resource-constrained project scheduling problem (MSRCPSP). The interactions between workers are represented as undirected networks. Therefore, for each required skill, an undirected network is formed which shows the relations of human resources. In this paper, community detection in networks is used to find the most compatible wo...

An important problem in nonlinear science is the unknown parameters estimation in Loranz chaotic system. Clearly, the parameter estimation for chaotic systems is a multidimensional continuous optimization problem, where the optimization goal is to minimize mean squared errors (MSEs) between real and estimated responses for a number of given samples. The Bees algorithm (BA) is a new member of me...

Meta-heuristics are commonly applied to solve various global optimization problems. In order make the meta-heuristics performing a search, balancing their exploration and ability is still an open avenue. This manuscript proposes novel Opposition-based learning scheme, called “PCOBL” (Partial Centroid Learning), inspired by partial centroid. PCOBL aims improve performance through maintaining eff...

As exact algorithms are unfeasible to solve real optimization problems, due their computational complexity, meta-heuristics usually used them. However, choosing a meta-heuristic particular problem is non-trivial task, and often requires time-consuming trial error process. Hyper-heuristics, which heuristics choose heuristics, have been proposed as means both simplify improve algorithm selection ...

This paper presents a new mathematical model, in which the location of hubs is fixed and their capacity is determined based on facilities and factories allocated to it. In order to feed the client's nodes, different types of vehicles of different capacities are considered, in which the clients are allocated to hubs, and types and numbers of vehicles are allocated to the factory's facilities. To...

This paper develops Order Acceptance for an Integrated Production-Distribution Problem in which Batch Delivery is implemented. The aim of this problem is to coordinate: (1) rejecting some of the orders (2) production scheduling of the accepted orders and (3) batch delivery to maximize Total Net Profit. A Mixed Integer Programming is proposed for the problem. In addition, a hybrid meta-heuristic...

The Robust Coloring Problem (RCP) is a generalization of the well-known Graph Coloring Problem where we seek for a solution that remains valid when extra edges are added. The RCP is used in scheduling of events with possible last-minute changes and study frequency assignments of the electromagnetic spectrum. This problem has been proved as NP-hard and in instances larger than 30 vertices, meta-...