Hybrid Optimization Algorithm for Vehicle Routing Problem with Simultaneous Delivery-Pickup

In order to provide reasonable and effective decision support for logistics enterprises in vehicle distribu-tion path planning, this paper studies the vehicle routing problem with simultaneous delivery-pickup and time windows (VRPSDPTW) for single distribution center distribution mode, and establishes a mathematical model with the objective of minimizing the total distribution cost. According to the characteristics of the model, a hybrid optimization algorithm (SA-ALNS) based on the combination of simulated annealing (SA) and adaptive large-scale neighborhood search (ALNS) is proposed. An insertion heuristic algorithm based on time and distance weighting is used to construct the initial solution of the problem. A variety of delete and insert operators are introduced to optimize the path with adaptive selection strategy. Through the feedback mechanism, the probability of each operator being selected is gradually adjusted to make the algorithm more inclined to choose the operator with better optimization effect. The Metropolis criterion of simulated annealing mechanism is used to control the solution updating. In the simulation experiment, 56 large-scale examples are tested, and other intelligent optimization algori-thms such as p-SA algorithm, DCS algorithm and VNS-BSTS are compared and statistically analyzed. The results show that the algorithm is feasible and superior in solving the vehicle routing problem with simultaneous delivery-pickup and time windows. The research results greatly enrich the related research of vehicle routing problem (VRP)

Industrial and Tramp Ship Routing Problems: Closing the Gap for Real-Scale Instances

Recent studies in maritime logistics have introduced a general ship routing problem and a benchmark suite based on real shipping segments, considering pickups and deliveries, cargo selection, ship-dependent starting locations, travel times and costs, time windows, and incompatibility constraints, among other features. Together, these characteristics pose considerable challenges for exact and heuristic methods, and some cases with as few as 18 cargoes remain unsolved. To face this challenge, we propose an exact branch-and-price (B&P) algorithm and a hybrid metaheuristic. Our exact method generates elementary routes, but exploits decremental state-space relaxation to speed up column generation, heuristic strong branching, as well as advanced preprocessing and route enumeration techniques. Our metaheuristic is a sophisticated extension of the unified hybrid genetic search. It exploits a set-partitioning phase and uses problem-tailored variation operators to efficiently handle all the problem characteristics. As shown in our experimental analyses, the B&P optimally solves 239/240 existing instances within one hour. Scalability experiments on even larger problems demonstrate that it can optimally solve problems with around 60 ships and 200 cargoes (i.e., 400 pickup and delivery services) and find optimality gaps below 1.04% on the largest cases with up to 260 cargoes. The hybrid metaheuristic outperforms all previous heuristics and produces near-optimal solutions within minutes. These results are noteworthy, since these instances are comparable in size with the largest problems routinely solved by shipping companies

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 Modified Meta-Heuristic Approach for Vehicle Routing Problem with Simultaneous Pickup and Delivery

The aim of this work is to develop an intelligent optimization software based on enhanced VNS meta-heuristic to tackle Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). An optimization system developed based on enhanced Variable Neighborhood Search with Perturbation Mechanism and Adaptive Selection Mechanism as the simple but effective optimization approach presented in this work. The solution method composed by combining Perturbation based Variable Neighborhood Search (PVNS) with Adaptive Selection  Mechanism (ASM) to control perturbation scheme. Instead of stochastic approach, selection of perturbation scheme used in the algorithm employed an empirical selection based on each perturbation scheme success along the search. The ASM help algorithm to get more diversification degree and jumping from local optimum condition using most successful perturbation scheme empirically in the search process. A comparative analysis with a well-known exact approach is presented to test the solution method in a generated VRPSPD benchmark instance in limited computation time. Then a test to VRPSPD scenario provided by a liquefied petroleum gas distribution company is performed. The test result confirms that solution method present superior performance against exact approach solution in giving best solution for larger sized instance and successfully obtain substantial improvements when compared to the basic VNS and original route planning technique used by a distributor company

An Adaptive Tabu Search Heuristic for the Location Routing Pickup and Delivery Problem with Time Windows with a Theater Distribution Application

The time constrained pickup and delivery problem (PDPTW) is a problem of finding a set of routes for a fleet of vehicles in order to satisfy a set of transportation requests. Each request represents a user-specified pickup and delivery location. The PDPTW may be used to model many problems in logistics and public transportation. The location routing problem (LRP) is an extension of the vehicle routing problem where the solution identifies the optimal location of the depots and provides the vehicle schedules and distribution routes. This dissertation seeks to blend the PDPTW and LRP areas of research and formulate a location scheduling pickup and delivery problem with time windows (LPDPTW) in order to model the theater distribution problem and find excellent solutions. This research utilizes advanced tabu search techniques, including reactive tabu search and group theory applications, to develop a heuristic procedure for solving the LPDPTW. Tabu search is a metaheuristic that performs an intelligent search of the solution space. Group theory provides the structural foundation that supports the efficient search of the neighborhoods and movement through the solution space

An adaptive large neighborhood search for a full truckload routing problem in public works

International audienceThis paper presents a truck routing and scheduling problem faced by a public works company. Itconsists of optimizing the collection and delivery of materials between sites, using a heterogeneousfleet of vehicles. These flows of materials arise in levelling works and construction of roads networks.As the quantity of demands usually exceeds the capacity of a truck, several trucks are needed tofulfill them. As a result, demands are split into full truckloads. A set of trucks routes are needed toserve a set of demands sharing a set of resources, available at pickup or delivery sites, which can beloaders or asphalt finishers in our application cases. Thus, these routes need to be synchronized ateach resource. We propose an Adaptive Large Neighborhood Search (ALNS) to solve this problem.This approach is evaluated on real instances from a public work company in France