This paper presents a novel mathematical model for a stochastic location-routing problem (SLRP) that minimizes the facilities establishing cost and transportation cost, and maximizes the probability of delivery to customers. In this proposed model, new aspects of a location-routing problem (LRP), such as stochastic availability of facilities and routes, are developed that are similar to real-word problems. The proposed model is solved in two stages: (i) solving the facility location problem (FLP) by a mathematical algorithm and (ii) solving the multi-objective multi-depot vehicle routing problem (MO-MDVRP) by a simulated annealing (SA) algorithm hybridized by genetic operators, namely mutation and crossover. The proposed SA can find good solutions in a reasonable time. It solves the proposed model in large-scale problems with acceptable results. Finally, a trade-off curve is used to depict and discuss a large-sized problem. The associated results are compared with the results obtained by the lower bound and Lingo 8.0 software.
In this paper, a mathematical model is presented for project scheduling with multiple purposes based on considering cost payment and resource constrains and since this this problem is considered as complex optimization in NP-Hard context, in order to solve proposed method from NSGA-II algorithm and the results are compared with GAMS software in some problems. The proposed method is a Converge to the optimum and efficient solution algorithm. Besides this algorithm is used in some parts of refinery project.
The purpose of this paper is to determine the location of facilities and routes of vehicles, in which the facilities and routes are available within probability interval (0, 1). Hence, this study is coherent the stochastic aspects of the location problem and the vehicle routing problem (VRP). The location problem is solved by optimization software. Because of the computational complexity of the stochastic vehicle routing problem (SVRP), it is solved by a meta-heuristic algorithm based on simulated annealing (SA). This hybrid algorithm uses genetic operators in order to improve the quality of the obtained solutions. Our proposed hybrid SA is more efficient than the original SA algorithm. The associated results are compared with the results obtained by SA and optimization software.
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