Optimizing departure times in vehicle routes

Most solution methods for the vehicle routing problem with time\ud windows (VRPTW) develop routes from the earliest feasible departure time. However, in practice, temporal traffic congestions make\ud that such solutions are not optimal with respect to minimizing the\ud total duty time. Furthermore, VRPTW solutions do not account for\ud complex driving hours regulations, which severely restrict the daily\ud travel time available for a truck driver. To deal with these problems,\ud we consider the vehicle departure time optimization (VDO) problem\ud as a post-processing step of solving a VRPTW. We propose an ILP-formulation that minimizes the total duty time. The obtained solutions are feasible with respect to driving hours regulations and they\ud account for temporal traffic congestions by modeling time-dependent\ud travel times. For the latter, we assume a piecewise constant speed\ud function. Computational experiments show that problem instances\ud of realistic sizes can be solved to optimality within practical computation times. Furthermore, duty time reductions of 8 percent can\ud be achieved. Finally, the results show that ignoring time-dependent\ud travel times and driving hours regulations during the development of\ud vehicle routes leads to many infeasible vehicle routes. Therefore, vehicle routing methods should account for these real-life restrictions

Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care

International audienceThis paper addresses a vehicle scheduling problem encountered in home health care logistics. It concerns the delivery of drugs and medical devices from the home care company's pharmacy to patients' homes, delivery of special drugs from a hospital to patients, pickup of bio samples and unused drugs and medical devices from patients. The problem can be considered as a special vehicle routing problem with simultaneous delivery and pickup and time windows, with four types of demands: delivery from depot to patient, delivery from a hospital to patient, pickup from a patient to depot and pickup from a patient to a medical lab. Each patient is visited by one vehicle and each vehicle visits each node at most once. Patients are associated with time windows and vehicles with capacity. Two mixed-integer programming models are proposed. We then propose a Genetic Algorithm (GA) and a Tabu Search (TS) method. The GA is based on a permutation chromosome, a split procedure and local search. The TS is based on route assignment attributes of patients, an augmented cost function, route re-optimization, and attribute-based aspiration levels. These approaches are tested on test instances derived from existing VRPTW benchmarks

Sequential Simulated Annealing for the Vehicle Routing Problem with Time Windows

This article presents a new simulated annealing algorithm that provides very high quality solutions to the vehicle routing problem. The aim of described algorithm is to solve the vehicle routing problem with time windows. The tests were carried out with use of some well known instances of the problem defined by M. Solomon. The empirical evidence indicates that simulated annealing can be successfully applied to bi-criterion optimization problems

A hybrid algorithm for a vehicle routing problem with realistic constraints

Proliferation of multi-national corporations and extremely competitive business environments have led to an unprecedented demand for third-party logistics services. However, recent studies on the vehicle routing problem (VRP) have considered only simple constraints. They also do not scale well to real-world problems that are encountered in the logistics industry. In this paper, we introduce a novel vehicle routing problem with time window and pallet loading constraints; this problem accounts for the actual needs of businesses in the logistics industry such as the delivery of consumer goods and agricultural products. To solve this new VRP, we propose a hybrid approach by combining Tabu search and the artificial bee colony algorithm. A new benchmark data set is generated to verify the performance of the proposed algorithm because the proposed VRP has never been reported in the literature. Experiments are performed for a data set of Solomon's 56 vehicle routing problem with time windows. Our approach is superior to a number of other heuristic algorithms in a comparison on Solomon's VRPTW instances. © 2017 Elsevier Inc