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 efficient hybrid solution ALGORITHM based on several meta-heuristics and an exact ALGORITHM is put forward. This ALGORITHM generates neighborhoods by combining the main concepts of VARIABLE neighborhood SEARCH, simulated annealing, and tabu SEARCH and finds the local optima by using an ALGORITHM that uses an exact method in its framework. Finally, to test the ALGORITHMs’ performance, we apply numerical experiments on both randomly generated and standard test problems. Computational experiments show that our ALGORITHM is more effective and efficient in term of CPU time and solutions quality in comparison with CPLEX solver.

Hydrokinetic energy harnessing has emerged as a promising renewable energy that utilizes the kinetic energy of moving water to generate electricity. Nevertheless, the variation and fluctuation of water velocity and turbulence flow in a river is a challenging issue, especially in designing a control system that can harness the maximum output power with high efficiency. Besides, the conventional Hill-climbing SEARCH (HCS) MPPT ALGORITHM has weaknesses, such as slow tracking time and producing high steady-state oscillation, which reduces efficiency. In this paper, the VARIABLE-Step Hill Climbing SEARCH (VS-HCS) MPPT ALGORITHM is proposed to solve the limitation of the conventional HCS MPPT. The model of hydrokinetic energy harnessing is developed using MATLAB/Simulink. The system consists of a water turbine, permanent magnet synchronous generator (PMSG), passive rectifier, and DC-DC boost converter. The results show that the power output achieves a 28 % increase over the system without MPPT and exhibits the lowest energy losses with a loss percentage of 0.9 %.

The purpose of solving the problem of vehicle routing is to find a suitable route taking into account the existing conditions in the transportation problem. In this case, considering the routing conditions with several depots along with imposing traffic restrictions on some vehicles on some routes, will create quite real and complex conditions. Furthermore, in some cases, it is necessary to deliver the customer demand by visiting several times. For this purpose, in this reSEARCH, by simultaneous considering of multiple depots, split delivery and traffic restrictions, it has been tried to bring the conditions of the routing problem very close to real-world problems. In this paper, after presenting a mathematical model, the problem is solved in small-size instances using CPLEX solver. Then, due to NP-Hardness of considered problem, to solve it on a larger size instance, a VARIABLE neighborhood SEARCH ALGORITHM is proposed. Finally, the simulated annealing ALGORITHM is used to validate and evaluate the quality of the proposed ALGORITHM. The computational results show that the proposed ALGORITHM has good performance in terms of runtime and solution quality.

In this reSEARCH, a job shop scheduling problem with an assembly stage is studied. The objective function is to find a schedule that minimizes the completion time of all products. At first, a linear model is introduced to express the problem. Then, in order to confirm the accuracy of the model and to explore the efficiency of the ALGORITHMs, the model is solved by GAMS. Since the job shop scheduling problem with an assembly stage is considered as an NP-hard problem, a hybrid ALGORITHM is used to solve the problem in medium to large sizes in a reasonable amount of time. This ALGORITHM is based on genetic ALGORITHM and parallel VARIABLE neighborhood SEARCH. The results of the proposed ALGORITHMs are compared with those of genetic ALGORITHM. Computational results showed that, for small problems, both HGAPVNS and GA have approximately the same performance. In addition, in medium to large problems, HGAPVNS outperforms GA.

Random based inventive ALGORITHMs are being widely used for optimization. An important category of these ALGORITHMs comes from the idea of physical processes or the behavior of beings. A new method for achieving quasi-optimal solutions related to optimization problems in various sciences is proposed in this paper. The proposed ALGORITHM for optimizing the orientation game is a series of optimization ALGORITHMs that are formed with the idea of an old game and the SEARCH operators are an arrangement of players. These players are displaced in a certain space, under the influence of the referee's orders. The best position would be achieved by following the game laws. In this paper, the real version of the ALGORITHM is presented. The optimization results of a set of standard functions confirm the optimal efficiency of the proposed method, as well as the superiority of the proposed method over the other well-known metaheuristic ALGORITHMs.

Task assignment problem (TAP) involves assigning a number of tasks to a number of processors in distributed computing systems and its objective is to minimize the sum of the total execution and communication costs, subject to all of the resource constraints. TAP is a combinatorial optimization problem and NP-complete. This paper proposes a hybrid meta-heuristic ALGORITHM for solving TAP in a heterogeneous distributed computing system. To compare our ALGORITHM with previous ones, an extensive computational study on some benchmark problems was conducted. The results obtained from the computational study indicate that the proposed ALGORITHM is a viable and effective approach for the TAP.

The vehicle routing problem (VRP), a well-known combinatorial optimization problem, holds a central place in logistics management. A typical VRP aims to find a set of tours for several vehicles from a depot to a lot of customers and return to the depot without exceeding the capacity constraints of each vehicle at minimum cost. Since the customer combination is not restricted to the selection of vehicle routes, VRP is considered as a combinatorial optimization problem where the number of feasible solutions for the problem increases exponentially with the number of customers increasing. Many meta-heuristic approaches like particle swarm optimization, simulated annealing, genetic ALGORITHMs, tabu SEARCH, and ant colony optimization (ACO) have been proposed to solve VRP. Ant ALGORITHM is a distributed meta-heuristic approach that has been applied to various combinatorial optimization problems, including traveling salesman problem and quadratic assignment problem. In this reSEARCH, a hybrid ant colony optimization (HACO) for solving the VRP is proposed. In the proposed ALGORITHM (PA), the concept of VARIABLE neighborhood SEARCH (VNS) is used in order to move from the current solution to next solution. Furthermore, several types of local SEARCH ALGORITHMs including insert, swap, 2-opt are applied for more improving of the PA. The proposed metaheuristic ALGORITHM is tested on the well-known VRP instances involving 14 benchmark problems from 50 to 199 customers. The computational results show that our HACO yields better than other metaheuristic ALGORITHMs in terms of solution quality. Furthermore, the gap of the HACO stays on average almost 1% of the execution time and also ten best known solutions of the benchmark problems are found by the proposed ALGORITHM.