An efficient heuristic for the multi-vehicle one-to-one pickup and delivery problem with split loads

In this study, we consider the Multi-vehicle One-to-one Pickup and Delivery Problem with Split Loads (MPDPSL). This problem is a generalization of the one-to-one Pickup and Delivery Problem (PDP) where each load can be served by multiple vehicles as well as multiple stops by the same vehicle. In practice, split deliveries is a viable option in many settings where the load can be physically split, such as courier services of third party logistics operators. We propose an efficient heuristic that combines the strengths of Tabu Search and Simulated Annealing for the solution of MPDPSL. Results from experiments on two problems sets in the literature indicate that the heuristic is capable of producing good quality solutions in reasonable time. The experiments also demonstrate that up to 33\% savings can be obtained by allowing split loads; however, the magnitude of savings is dependent largely on the spatial distribution of the pickup and delivery points

A Metaheuristic for the Pickup and Delivery Problem with Split-Loads and its Extension

In this dissertation, we study improvements in the Pickup and Delivery Problem that can be achieved by allowing multiple vehicle trips to serve a common load. We explore how costs can be reduced through the elimination of the constraint that a load must be served by only one vehicle trip. Specifically, we investigate the problem of routing vehicles to serve loads that have distinct origins and destinations, with no constraint on the amount of a load that a vehicle may serve at a time. We develop a metaheuristic to solve large scale practical size problems in this form and apply the metaheuristic to randomly generated data sets. The metaheuristic is based on a predetermined fixed number of restarts of annealing-like procedure with tabu-lists to avoid cycling in the search process and the annealing-like procedure is to guide the local search in three neighborhoods defined to solve the problem. We test the algorithm on several sets of problem instances generated with different transportation requests and over different load size ranges. The experimental results on these problem sets have shown that benefits are common if split loads are adopted in designing practical sized transportation network for different load size configurations, and the most benefit is achieved when all the loads are just a little above half of the vehicle capacity and have small variations, and this most benefit is around 33% for all the three 75-, 100-, and 125-request problem sets, which overtakes the one reported in previous literature. In a more general setting when some load sizes are greater than the vehicle capacity and have to be split, there are also certain cost reduction if split loads are applied. We also generate numeral tests on different load size ranges and split the loads that are greater than the vehicle capacity using different ”splitting” strategy, in term of how much amount to split from the original load to form a new load, and find that there seem to be no optimal ”splitting” strategy, which can assure the best quality of solutions using the metaheuristic developed in the dissertation

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

Capacitated vehicle routing problem with time windows

Vehicle Routing Problem with Time Windows (VRPTW) is an extension of the Capacitated Vehicle Routing Problem. The objective is to design optimal routes that satisfy all of the constraints. In this study, a linear IP model and hybrid heuristics for the VRPTW are proposed. The objective function considered in the model is the total distance traveled by all vehicles. Vehicles are identical, capacities of the vehicles are finite and the time window constraints are assumed to be strict. The proposed hybrid heuristics are combined by two parts. The first part, which has both parallel and sequential versions, finds an initial solution. Both parallel and sequential initial solution algorithms are based on the idea of clustering the customers while doing the insertion. Second part is an improvement heuristic, which is a combination of three procedures: Inter-route exchanges, inter-route moves and intra-route exchanges. In the proposed heuristics, these operators are used nested with each other. There are two improvement heuristics proposed that use these operators in different ways. The improvement algorithms are supported with a restart mechanism called diversification in order to escape the local optima and widen the search space. In this study, two diversification methods are proposed. The hybrid algorithms in this study are the combinations of the initial solution, improvement and diversification methods proposed. The algorithms have been tested on the 56 benchmark problem instances of Solomon (1987), which were used widely in the literature. The hybrid algorithms are proven to give better results when compared to not only some known metaheuristics, but also to the best known results in the literature

Efficient neighborhood evaluations for the vehicle routing problem with multiple time windows

In the vehicle routing problem with multiple time windows (VRPMTW), a single time window must be selected for each customer from the multiple time windows provided. Compared with classical vehicle routing problems with only a single time window per customer, multiple time windows increase the complexity of the routing problem. To minimize the duration of any given route, we present an exact polynomial time algorithm to efficiently determine the optimal start time for servicing each customer. The proposed algorithm has a reduced worst-case and average complexity than existing exact algorithms. Furthermore, the proposed exact algorithm can be used to efficiently evaluate neighborhood operations during a local search resulting in significant acceleration. To examine the benefits of exact neighborhood evaluations and to solve the VRPMTW, the proposed algorithm is embedded in a simple metaheuristic framework generating numerous new best known solutions at competitive computation times

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

Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving