This paper deals with the vehicle routing problem with time windows (VRPTW). The VRPTW routes a set of vehicles to service customers having two-sided time windows, i.e. earliest and latest start of service times. The demand requests are served by capacitated vehicles with limited travel times to return to the depot. The purpose of this paper is to develop a hybrid algorithm that uses the modified push forward insertion heuristic (MPFIH), a λ-interchange local search descent method (λ-LSD) and a genetic algorithm to solve the VRPTW with two objectives. The first objective aims to determine the minimum number of vehicles required and the second is to find the solution that minimizes the total travel time. A set of well-known benchmark problems are used to compare the quality of solutions. The results show that the proposed algorithm provides effective solutions compared with best found solutions and better than another heuristic used for comparison.
This paper presents an algorithm for the vehicle routing problem with soft time windows (VRPSTW). It involves serving a set of customers, with earliest and latest time deadlines, which may be violated if a penalty is paid, and a constant service time at the customer site. Customer demands are served by capacitated vehicles. The purpose of this research is to develop a hybrid algorithm that includes an insertion heuristic, a local search algorithm and a meta-heuristic algorithm to solve VRPSTW problems with more than one objective. The first priority aims to find the minimum number of vehicles required and the second priority aims to search for the solution that minimizes the total travel time. Performance of the algorithmic approach is measured by two criteria: solution quality and *284 T. MANISRI, A. MUNGWATTANA, G. K. JANSSENS AND A. CARIS run time. A set of well-known benchmark data and a genetic algorithm are used to compare the solution quality and running time of the algorithm. Results show a trade-off can be made between total cost and service when considering soft time windows. Running time results display that the hybrid algorithm has a higher performance than the genetic algorithm when the number of customers is less than 25.
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