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An advanced hybrid meta-heuristic algorithm for the vehicle routing problem with backhauls and time windows

A new model is built up for vehicle routing problem with backhauls and time windows.A hybrid algorithm which includes tabu search and simulated annealing is proposed.The nearest neighbor method is improved for initial solution generation.Proposed hybrid meta-heuristic algorithm outperforms the existing methods.34 new best solutions are obtained for 45 instances. This paper presents an advanced hybrid meta-heuristic algorithm (HMA) to solve the vehicle routing problem with backhauls and time windows (VRPBTW). The VRPBTW is an extension of the vehicle routing problem with time windows (VRPTW) and the vehicle routing problem with backhauls (VRPB) that includes capacity, backhaul and time window constraints. In this problem, the customers are divided into two subsets consisting of linehaul and backhaul customers. Each vehicle starts from the depot, and goods are delivered from the depot to the linehaul customers. Goods are subsequently returned to the depot from the backhaul customers. The objective is to minimize the total distance that satisfies all of the constraints. The proposed meta-heuristic method is tested on a problem data set obtained from Solomons VRPTW benchmark problems which includes 25, 50 and 100 demand nodes. The results of the computational studies show that the HMA outperforms the existing studies and provides better solutions than the best known solutions in practical computational times.

A memory structure adapted simulated annealing algorithm for a green vehicle routing problem

Currently, reduction of carbon dioxide (CO2) emissions and fuel consumption has become a critical environmental problem and has attracted the attention of both academia and the industrial sector. Government regulations and customer demands are making environmental responsibility an increasingly important factor in overall supply chain operations. Within these operations, transportation has the most hazardous effects on the environment, i.e., CO2 emissions, fuel consumption, noise and toxic effects on the ecosystem. This study aims to construct vehicle routes with time windows that minimize the total fuel consumption and CO2 emissions. The green vehicle routing problem with time windows (G-VRPTW) is formulated using a mixed integer linear programming model. A memory structure adapted simulated annealing (MSA-SA) meta-heuristic algorithm is constructed due to the high complexity of the proposed problem and long solution times for practical applications. The proposed models are integrated with a fuel consumption and CO2 emissions calculation algorithm that considers the vehicle technical specifications, vehicle load, and transportation distance in a green supply chain environment. The proposed models are validated using well-known instances with different numbers of customers. The computational results indicate that the MSA-SA heuristic is capable of obtaining good G-VRPTW solutions within a reasonable amount of time by providing reductions in fuel consumption and CO2 emissions.

Two-stage optimisation method for material flow and allocation management in cross-docking networks

Cross-docking is a relatively new logistics strategy in which items are moved from suppliers to customers through cross-docking centres without putting them into long-term storage. An important decision during the planning of cross-docking operations is related to the material flow management in the network, which has great potential to reduce transportation costs. However, until now, there has been a lack of studies regarding operations for both transportation of trucks between locations and trans-shipment of items in cross-docking centres. This study presents a novel two-stage mixed integer linear mathematical model for the transportation problem of cross-docking network design integrated with truck–door assignments to minimise total transportation costs from suppliers to customers. This model also considers incoming/outgoing truck-loading plans and product allocations in the cross-docking area with regard to the two-dimensional physical constraints. Due to the complexity of the problem, a genetic algorithm (GA) is proposed to solve large-sized problems. Computational studies are conducted to examine the validity of the two-stage model and performance of the GA. The computational studies show that the introduced model provides a comprehensive plan for material flow management in cross-docking networks and proposed GA is capable of obtaining effective results for the problem within a short computational time.

A hybrid metaheuristic algorithm for the green vehicle routing problem with a heterogeneous fleet

In this study, the green vehicle routing problem (GVRP) with a heterogeneous fleet is presented for both capacity and time-windows constraints to reduce fuel consumption and consequently to minimise CO2 emissions. A hybrid metaheuristic algorithm (HMA) is developed to solve this problem to analyse the effect of a heterogeneous fleet on reducing the fuel consumption for the specified variants of GVRP, such as GVRP with capacity constraints and GVRP with time-windows constraints. The proposed HMA is validated using well-known instances with different numbers of customers and fleet configurations. The computational results indicated that the HMA is capable of obtaining good results for GVRP variants within a reasonable amount of time by providing remarkable reductions in fuel consumption and greener fleet configurations.