Ruhollah Ebrahimi Gorji; Hamed Soleimani; Behrouz Afshar-Nadjafi
Abstract
In today's competitive market, reducing costs and time is one of the most important issues that has occupied the minds of managers and researchers. This issue is especially important in the field of supply chain management and transportation because by reducing time and cost, manufacturers and service ...
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In today's competitive market, reducing costs and time is one of the most important issues that has occupied the minds of managers and researchers. This issue is especially important in the field of supply chain management and transportation because by reducing time and cost, manufacturers and service providers can gain a competitive advantage over competitors. Accordingly, vehicle routing issues are one of the most important issues in this field because it is directly related to the time of service or product delivery and also by optimizing the network, reduces the cost of the entire network. Therefore, in this study, the intention was to evaluate the problem of vehicle routing (trucks) by considering the time constraints and using a multi-objective approach. Therefore, we discussed each of the factors separately based on the issue. The results of this study show. In this research, the model with two objective functions will be solved by two metaheuristic algorithms NSGA-II and MOPSO Managers are concerned with time and cost management in today's competitive markets, which is seen as a source of competitive advantage. The present study aims to find a solution to a bi-objective function model by employing two metaheuristic algorithms, NSGA-II and MOPSO. Additionally, a criterion for comparing algorithms is presented. The findings show that the MOPSO algorithm yields the optimal solution. The contribution of the present study in comparison with other previous studies can be summarized as follows: Environmental protection based on reducing pollution and its effects as well as reducing costs. Finding the desired route taking into account the complexity and difficulty of the route. Managers are concerned with time and cost management in today's competitive markets, which is seen as a source of competitive advantage. The present study aims to find a solution to a bi-objective function model by employing two metaheuristic algorithms, NSGA-II and MOPSO. Additionally, a criterion for comparing algorithms is presented. The findings show that the MOPSO algorithm yields the optimal solution. The contribution of the present study compared to other previous studies can be environmental protection and cost reduction that the two factors are compared and the results of the two methods are analyzed.
M. Sayyah; H. Larki; M. Yousefikhoshbakht
Volume 3, Issue 1 , June 2016, , Pages 15-38
Abstract
One of the most important extensions of the capacitated vehicle routing problem (CVRP) is the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) where customers require simultaneous delivery and pick-up service. In this paper, we propose an effective ant colony optimization (EACO) ...
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One of the most important extensions of the capacitated vehicle routing problem (CVRP) is the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) where customers require simultaneous delivery and pick-up service. In this paper, we propose an effective ant colony optimization (EACO) which includes insert, swap and 2-Opt moves for solving VRPSPD that is different with common ant colony optimization (ACO). ACO is a meta-heuristic algorithm inspired by the foraging behavior of real ants. Artificial ants are used to build a solution for the problem by using the pheromone information from previously generated solutions. An extensive numerical experiment is performed on 68 benchmark problem instances involving up to 200 customers available in the literature. The computational result shows that EACO not only presented a very satisfying scalability, but also was competitive with other meta-heuristic algorithms such as tabu search, large neighborhood search, particle swarm optimization and genetic algorithm for solving VRPSPD problems.