Abstract
Multi-Depot Vehicle Routing Problem (MDVRP) is a familiar combinative optimization problem that simultaneously determines the direction for different vehicles from over one depot to a collection of consumers. Researchers have suggested variety of meta-heuristic and heuristic algorithms to elucidate MDVRP, but none of the existing technique has improved the fitness of the solution at the time of initial population generation. This motivates to propose an enhanced ODV based population initialization for Genetic Algorithm (GA) to solve MDVRP effectively. The Ordered Distance Vector (ODV) based population seeding method is a current and effective population initialization method for Genetic Algorithm to produce an early population with quality, individual diversity and randomness. In the proposed model, the customers are first grouped based on distance to their nearest depots and then routes are scheduled and optimized using enhanced ODV based GA. The experiments are performed based on different types of instances of Cordeau. From the experimental results, it is very clear that the proposed technique outperforms the existing techniques in terms of convergence rate, error rate and convergence diversity.
Highlights
Dantzig and Ramser [1] in 1959 introduced the Vehicle Routing Problem (VRP) which was derived from the traveling salesman problem
Around ten to twelve variants are introduced in VRP such as Open vehicle Routing Problem (OVRP), Capacitated Vehicle Routing Problem (CVRP), Period vehicle routing problem (PVRP), Split delivery vehicle routing problem (SDVRP), Time Dependent Vehicle Routing Problem with Time Windows (TDVRPTW), Vehicle Routing Problem with Time
The Ordered Distance Vector (ODV) Equi-begin and variable diversity (EV) population seeding technique based on Genetic Algorithm (GA) is proposed to solve Multi-Depot Vehicle Routing Problem (MDVRP)
Summary
Dantzig and Ramser [1] in 1959 introduced the Vehicle Routing Problem (VRP) which was derived from the traveling salesman problem. It is one of the most challenging optimization problems. Windows (VRPTW), Vehicle Routing Problem with Pickup and Delivery (VRPPD), and Multi-Depot Vehicle Routing Problem (MDVRP) and many other variants are there. In 1969, Tillman studied Multi-Depot Vehicle Routing Problem (MDVRP) which is a main variant in VRP. It is a well-known combinatorial optimization problem to simultaneously determine the routes for fleet of vehicles from over one depot to serve the customers. Cost is closely related with distance the objective is to reduce the full distance travelled by vehicle fleet
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