Profit distribution in collaborative multiple centers vehicle routing problem

A Tabu Search algorithm for the vehicle routing problem with discrete split deliveries and pickups

A logistics distribution region partitioning model is developed.This model is to minimize the cost of two-echelon logistics distribution network.A hybrid algorithm with PSO and GA is proposed.The empirical results reveal that EPSO-GA algorithm outperforms other algorithms. Two-echelon logistics distribution region partitioning is a critical step to optimize two or multi-echelon logistics distribution network, and it aims to assign distribution unit to a certain logistics facility (i.e. logistic center and distribution center). Given the partitioned regions, vehicle routing problem can be further developed and solved. This paper established a model to minimize the total cost of the two-echelon logistics distribution network. A hybrid algorithm named as the Extended Particle Swarm Optimization and Genetic Algorithm (EPSO-GA) is proposed to tackle the model formulation. A two-dimensional particle encoding method is adopted to generate the initial population of particles. EPSO-GA combines the merits of Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) with both global and local search capability. By updating the inertia weight and exchanging best-fit solutions and worst-fit solutions between PSO and GA, EPSO-GA algorithm is able to converge to an optimal solution with a reasonable design of termination and iteration rules. The computation results from a case study in Guiyang city, China, reveal that EPSO-GA algorithm is superior to the other three algorithms, Hybrid Particle Swarm Optimization (HPSO), GA, and Ant Colony Optimization (ACO), in terms of the partitioning schemes, the total cost and number of iterations. By comparing with the exact method, the proposed approach demonstrates its capability to optimize a small scale two-echelon logistics distribution network. The proposed approach can be readily implemented in practice to assist the logistics operators reduce operational costs and improve customer service. In addition, the proposed approach is of great potential to apply in other research domains.

Two-echelon logistics distribution region partitioning problem based on a hybrid particle swarm optimization-genetic algorithm

This paper presents an improved differential evolution algorithm with local search DELS for solving the capacitated vehicle routing problem CVRP. The CVRP is a classical vehicle routing problem with additional constraint where the capacity of the vehicle travelling on a specific route cannot exceed the maximum vehicle capacity. Local search procedures help to explore new search areas and refine the solutions found. The proposed algorithm is tested on CVRP instances described by Augerat et al. and Christofides and Eilon. The proposed DELS approach generate quality solutions for the benchmark problems tested and are comparable to the algorithms reported in the literature.

Differential evolution algorithm with local search for capacitated vehicle routing problem

To split or not to split: that is the question

The vehicle routing problem (VRP) is a well-known combinatorial optimization issue in transportation and logistics network systems. There exist several limitations associated with the traditional VRP. Releasing the restricted conditions of traditional VRP has become a research focus in the past few decades. The vehicle routing problem with split deliveries and pickups (VRPSPDP) is particularly proposed to release the constraints on the visiting times per customer and vehicle capacity, that is, to allow the deliveries and pickups for each customer to be simultaneously split more than once. Few studies have focused on the VRPSPDP problem. In this paper we propose a two-stage heuristic method integrating the initial heuristic algorithm and hybrid heuristic algorithm to study the VRPSPDP problem. To validate the proposed algorithm, Solomon benchmark datasets and extended Solomon benchmark datasets were modified to compare with three other popular algorithms. A total of 18 datasets were used to evaluate the effectiveness of the proposed method. The computational results indicated that the proposed algorithm is superior to these three algorithms for VRPSPDP in terms of total travel cost and average loading rate.

A two-stage heuristic method for vehicle routing problem with split deliveries and pickups

Considering real-time requests and multiple truck–drone delivery modes, we propose an online delivery problem using a truck and some drones, which form a hybrid truck–drone delivery collaboration system comprising independent and truck-carried drones. Considering this problem, we focus on how to schedule the vehicles to serve real-time requests, with the objective of minimizing the time of the latest vehicle’s return to the delivery station. First, we proved the lower bound of this problem to be 1.5. Second, we designed an online re-planning algorithm and proved its competitive ratio to be 2.5. As the online re-planning algorithm invokes an offline algorithm, an offline model was established, and an offline drone priority algorithm was designed. Then, we verified the effectiveness of the offline algorithm by comparing it with the CPLEX solution, and the stability of the online re-planning algorithm with different input parameters was studied through MATLAB simulation. Finally, the minimal latest time saving was calculated by comparing the hybrid truck–drone collaboration system with a truck-only delivery system. This research provides theoretical support for addressing the hybrid truck–drone delivery problem.

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Hai-yan Yu

Papers

A two-stage heuristic method for vehicle routing problem with split deliveries and pickups

Online Delivery Problem for Hybrid Truck–Drone System with Independent and Truck-Carried Drones