A hybrid solution approach for the 3L-VRP with simultaneous delivery and pickups

This paper deals with a special vehicle routing problem with backhauls where each customer receives items from a depot and, at the same time, returns items back to the depot. Moreover, time windows are assumed and three-dimensional loading constraints are to be observed, i.e. the items are three-dimensional boxes and packing constraints, e.g. regarding load stability, are to be met. The resulting problem is the vehicle routing problem with simultaneous delivery and pickup (VRPSDP), time windows, and three-dimensional loading constraints (3L-VRPSDPTW). This problem occurs, for example, if retail stores are supplied by a central warehouse and wish to return packaging material.A particular challenge of the problem is to transport delivery and pickup items simultaneously on the same vehicle. In order to avoid any reloading effort during a tour, we consider two different loading approaches of vehicles: (i) loading from the back side with separation of the loading space into a delivery section and a pickup section and (ii) loading at the long side. A hybrid algorithm is proposed for the 3L-VRPSDPTW consisting of an adaptive large neighbourhood search for the routing and different packing heuristics for the loading part of the problem. Extensive numerical experiments are conducted with VRPSDP instances from the literature and newly generated instances for the 3LVRPSDPTW

A Hybrid Algorithm for the Vehicle Routing Problem with Pickup and Delivery and 3D Loading Constraints

In this paper, we extend the classical Pickup and Delivery Problem (PDP) to an integrated routing and three-dimensional loading problem, called PDP with 3D loading constraints (3L-PDP). A set of routes of minimum total length has to be determined such that each request is transported from a loading site to the corresponding unloading site. In the 3L-PDP, each request is given as a set of 3D rectangular items (boxes) and the vehicle capacity is replaced by a 3D loading space. We investigate which constraints will ensure that no reloading effort will occur, i.e. that no box is moved after loading and before unloading. A spectrum of 3L-PDP variants is introduced with different characteristics in terms of reloading effort. We propose a hybrid algorithm for solving the 3L-PDP consisting of a routing and a packing procedure. The routing procedure modifies a well-known large neighborhood search for the 1D-PDP. A tree search heuristic is responsible for packing boxes. Computational experiments were carried out using 54 newly proposed 3L-PDP benchmark instances

Solving the Pickup and Delivery Problem with 3D Loading Constraints and Reloading Ban

In this paper, we extend the classical Pickup and Delivery Problem (PDP) to an integrated routing and three-dimensional loading problem, called PDP with 3D loading constraints (3L-PDP). A set of routes of minimum total length has to be determined such that each request is transported from a loading site to the corresponding unloading site. In the 3L-PDP, each request is given as a set of 3D rectangular items (boxes) and the vehicle capacity is replaced by a 3D loading space. This paper is the second one in a series of articles on 3L-PDP. In both articles we investigate which constraints will ensure that no reloading effort will occur, i.e. that no box is moved after loading and before unloading. In this paper, the focus is laid on the so-called reloading ban, a packing constraint that ensures identical placements of same boxes in different packing plans. We propose a hybrid algorithm for solving the 3L-PDP with reloading ban consisting of a routing and a packing procedure. The routing procedure modifies a well-known large neighborhood search for the 1D-PDP. A tree search heuristic is responsible for packing boxes. Computational experiments were carried out using 54 3L-PDP benchmark instances

Hybrid Algorithms for the Vehicle Routing Problem with Pickup and Delivery and Two-dimensional Loading Constraints

We extend the classical Pickup and Delivery Problem (PDP) to an integrated routing and two-dimensional loading problem, called PDP with two-dimensional loading constraints (2L-PDP). A set of routes of minimum total length has to be determined such that each request is transported from a loading site to the corresponding unloading site. Each request consists of a given set of 2D rectangular items with a certain weight. The vehicles have a weight capacity and a rectangular two-dimensional loading area. All loading and unloading operations must be done exclusively by movements parallel to the longitudinal axis of the loading area of a vehicle and without moving items of other requests. Furthermore, each item must not be moved after loading and before unloading. The problem is of interest for the transport of rectangular-shaped items that cannot be stacked one on top of the other because of their weight, fragility or large dimensions. The 2L-PDP also generalizes the well-known Capacitated Vehicle Routing Problem with Two-dimensional Loading Constraints (2L-CVRP), in which the demand of each customer is to be transported from the depot to the customer’s unloading site.This paper proposes two hybrid algorithms for solving the 2L-PDP and each one consists of a routing and a packing procedure. Within both approaches, the routing procedure modifies a well-known large neighborhood search for the one-dimensional PDP and the packing procedure uses six different constructive heuristics for packing the items. Computational experiments were carried out using 60 newly proposed 2L-PDP benchmark instances with up to 150 requests

A matheuristic approach to the integration of three-dimensional Bin Packing Problem and vehicle routing problem with simultaneous delivery and pickup

This work presents a hybrid approach to solve a distribution problem of a Portuguese company in the automotive industry. The objective is to determine the minimum cost for daily distribution operations, such as collecting and delivering goods to multiple suppliers. Additional constraints are explicitly considered, such as time windows and loading constraints due to the limited capacity of the fleet in terms of weight and volume. An exhaustive review of the state of the art was conducted, presenting different typology schemes from the literature for the pickup and delivery problems in the distribution field. Two mathematical models were integrated within a matheuristic approach. One model reflects the combination of the Vehicle Routing Problem with Simultaneous Delivery and Pickup with the Capacitated Vehicle Routing Problem with Time Windows. The second one aims to pack all the items to be delivered onto the pallets, reflecting a three-dimensional single bin size Bin Packing Problem. Both formulations proposed—a commodity-flow model and a formulation of the Three-Dimensional Packing Problem must be solved within the matheuristic. All the approaches were tested using real instances from data provided by the company. Additional computational experiments using benchmark instances were also performed.This research was funded by national funds through FCT—Fundação para a Ciência e a Tecnologia, under the projects UIDB/00285/2020, UIDB/00319/2020. This work was supported by the Research Unit on Governance, Competitiveness and Public Policies (UIDB/04058/2020) + (UIDP/04058/2020), funded by national funds through the Foundation for Science and Technology, IP. This work was also funded by FEDER in the frame of COMPETE 2020 under the project POCI-01-0247-FEDER-072638

Thirty years of heterogeneous vehicle routing

It has been around thirty years since the heterogeneous vehicle routing problem was introduced, and significant progress has since been made on this problem and its variants. The aim of this survey paper is to classify and review the literature on heterogeneous vehicle routing problems. The paper also presents a comparative analysis of the metaheuristic algorithms that have been proposed for these problems

Low Carbon Logistics Optimization for Multi-depot CVRP with Backhauls - Model and Solution

CVRP (Capacitated Vehicle Routing Problems) is the integrated optimization of VRP and Bin Packing Problem (BPP), which has far-reaching practical significance, because only by taking both loading and routing into consideration can we make sure the delivery route is the most economic and the items are completely and reasonably loaded into the vehicles. In this paper, the CVRP with backhauls from multiple depots is addressed from the low carbon perspective. The problem calls for the minimization of the carbon emissions of a fleet of vehicles needed for the delivery of the items demanded by the clients. The overall problem, denoted as 2L-MDCVRPB, is NP-hard and it is very difficult to get a good performance solution in practice. We propose a quantum-behaved particle swarm optimization (QPSO) and exploration heuristic local search algorithm (EHLSA) in order to solve this model. In addition, three groups of computational experiments based on well-known benchmark instances are carried out to test the efficiency and effectiveness of the proposed model and algorithm, thereby demonstrating that the proposed method takes a short computing time to generate high quality solutions. For some instances, our algorithm can obtain new better solutions

A hybrid algorithm for the vehicle routing problem with three-dimensional loading constraints and mixed backhauls

In this paper, a variant of the vehicle routing problem with mixed backhauls (VRPMB) is presented, i.e. goods have to be delivered from a central depot to linehaul customers, and, at the same time, goods have to be picked up from backhaul customers and brought to the depot. Both types of customers can be visited in mixed sequences. The goods to be delivered or picked up are three-dimensional (cuboid) items. Hence, in addition to a routing plan, a feasible packing plan for each tour has to be provided considering a number of loading constraints. The resulting problem is the vehicle routing problem with three-dimensional loading constraints and mixed backhauls (3L-VRPMB)

Vehicle routing problem considering reconnaissance and transportation

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. 문일경.Troop movement involves transporting military personnel from one location to another using available means. To minimize damage from enemies, the military simultaneously uses reconnaissance and transportation units during troop movements. This thesis proposes vehicle routing problem considering reconnaissance and transportation (VRPCRT) for troop movements in wartime. VRPCRT is formulated as a mixed-integer programming model for minimizing the completion time of wartime troop movements. For this thesis, an ant colony optimization (ACO) algorithm for the VRPCRT was also developed and computational experiments were conducted to compare the performance of the ACO algorithm and that of the mixed-integer programming model. Furthermore, a sensitivity analysis of the change in the number of reconnaissance and transportation vehicles was performed, and the effects of each type of vehicle on troop movement were analyzed.Abstract iii Contents iv List of Tables vi List of Figures vii Chapter 1 Introduction 1 1.1 Research Motivation and Contribution 4 1.2 Organization of the Thesis 5 Chapter 2 Literature Review 6 2.1 Review of pickup and delivery problem 6 2.2 Review of ant colony optimization algorithms 9 Chapter 3 Mathematical model 10 3.1 Problem description 10 3.2 The model formulation 14 3.3 Numerical example 17 Chapter 4 Ant colony optimization algorithm 20 4.1 Construction of a solution 21 4.2 Pheromone updating 23 Chapter 5 Computational experiment 26 5.1 Experiment 1 26 5.2 Experiment 2 29 Chapter 6 Conclusion 34 5.1 Findings 34 5.2 Future direction 35 Bibliography 36 국문초록 40 감사의 글 41Maste

The split delivery vehicle routing problem with three-dimensional loading constraints

 The Split Delivery Vehicle Routing Problem with three-dimensional loading constraints (3L-SDVRP) combines vehicle routing and three-dimensional loading with additional packing constraints. In the 3L-SDVRP splitting deliveries of customers is basically possible, i.e. a customer can be visited in two or more tours. We examine essential problem features and introduce two problem variants. In the first variant, called 3L-SDVRP with forced splitting, a delivery is only split if the demand of a customer cannot be transported by a single vehicle. In the second variant, termed 3L-SDVRP with optional splitting, splitting customer deliveries can be done any number of times. We propose a hybrid algorithm consisting of a local search algorithm for routing and a genetic algorithm and several construction heuristics for packing. Numerical experiments are conducted using three sets of instances with both industrial and academic origins. One of them was provided by an automotive logistics company in Shanghai; in this case some customers per instance have a total freight volume larger than the loading space of a vehicle. The results prove that splitting deliveries can be beneficial not only in the one-dimensional case but also when goods are modeled as three-dimensional items