In this paper, we are interested in vehicle routing optimization which is an important problem in the fields of transportation .We will introduce the Vehicle Routing Problem with soft time windows, in both cases: static (VRPSTW) and dynamic (D-VRPSTW). Input information changes dynamically over time with the appearance of new customer requests at any point during the vehicle's route, that include real-life assumptions. On the other hand, soft time windows allow deliveries outside the boundaries against a penalty cost. This paper proposes the hybridization of the genetic method and the variable neighborhood search method to solve the two version of the problem. This algorithm reduces the transportation costs by using a fleet of vehicles, improves the quality of service by reducing the delay time for each customer and increase the stopping time for each vehicle. The solution quality of this method has been compared against existing results on benchmark problems.

No abstract

This paper is interested in pharmaceuticals distribution which is one of the most important activities and ensures the availability of drug products to a set of customers (pharmacies). The study introduces the Disrupted Vehicle Routing problem with Soft Time Windows since pharmaceutical distributors should respond to increased demands for products to ensure timely and efficient delivery to dynamic demands. We also propose an improved multiobjective local search (IMOLS), which uses methods of neighborhood search such as large neighborhood search (LNS) and variable neighborhood search (VNS) based on a hybrid approach in the optimization of vehicle routes. The algorithm is expected to achieve competitive results compared with previously published studies.

Constraint programming method (CPM) has gained much attention in the last years since it proved a great efficiency to model and solve many combinatorial problems like scheduling, assignment and routing problems. In this paper, we first present a CPM model for a real scheduling and routing problem (SRP) faced by a company of water and electricity distribution. The problem aims to minimise the total distance travelled by the company's technicians to the clients' locations in order to carry out some services. To solve the studied SRP three CPM-based solution methods are proposed: an exact method (branch and prune), a local search meta-heuristic and a hybrid algorithm which combine tabu search and constraint programming method. The proposed solution methods have been tested using three sets of instances. The two first sets are generated randomly, they represent small and large scale instances, while the third one contain the real instances provided by the company with which we are working. The results obtained by the three methods show clearly the superiority of the hybrid algorithm over the two others methods.

Constraint programming method (CPM) has gained much attention in the last years since it proved a great efficiency to model and solve many combinatorial problems like scheduling, assignment and routing problems. In this paper, we first present a CPM model for a real scheduling and routing problem (SRP) faced by a company of water and electricity distribution. The problem aims to minimise the total distance travelled by the company's technicians to the clients' locations in order to carry out some services. To solve the studied SRP three CPM-based solution methods are proposed: an exact method (branch and prune), a local search meta-heuristic and a hybrid algorithm which combine tabu search and constraint programming method. The proposed solution methods have been tested using three sets of instances. The two first sets are generated randomly, they represent small and large scale instances, while the third one contain the real instances provided by the company with which we are working. The results obtained by the three methods show clearly the superiority of the hybrid algorithm over the two others methods.