Linear models

This paper is the second part of a comprehensive survey on pickup and delivery models. Basically, two problem classes can be distinguished. The first part dealt with the transportation of goods from the depot to linehaul customers and from backhaul customers to the depot. In this class four subtypes were considered, namely the Vehicle Routing Problem with Clustered Backhauls (VRPCB all linehauls before backhauls), the Vehicle Routing Problem with Mixed linehauls and Backhauls (VRPMB any sequence of linehauls and backhauls permitted), the Vehicle Routing Problem with Divisible Delivery and Pickup (VRPDDP customers demanding delivery and pickup service can be visited twice), and the Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP customers demanding both services have to be visited exactly once). The second part now considers all those problems where goods are transported between pickup and delivery locations, denoted as Vehicle Routing Problems with Pickups and Deliveries (VRPPD). These are the Pickup and Delivery VRP (PDVRP unpaired pickup and delivery points), the classical Pickup and Delivery Problem (PDP paired pickup and delivery points), and the Dial-A-Ride Problem (DARP paired pickup and delivery points and user inconvenience taken into consideration). A single as well as a multi vehicle mathematical problem formulation for all three VRPPD types is given, and the respective exact, heuristic, and metaheuristic solution methods are discussed.

/pdf/a-survey-on-pickup-and-delivery-problems-part-ii-s8djcjjy1t.pdf

A survey on pickup and delivery problems: Part II: Transportation between pickup and delivery locations

This paper is the first part of a comprehensive survey on pickup and delivery problems. Basically, two problem classes can be distinguished. The first class, discussed in this paper, deals with the transportation of goods from the depot to linehaul customers and from backhaul customers to the depot. This class is denoted as Vehicle Routing Problems with Backhauls (VRPB). Four subtypes can be considered, namely the Vehicle Routing Problem with Clustered Backhauls (VRPCB – all linehauls before backhauls), the Vehicle Routing Problem with Mixed linehauls and Backhauls (VRPMB – any sequence of linehauls and backhauls permitted), the Vehicle Routing Problem with Divisible Delivery and Pickup (VRPDDP – customers demanding delivery and pickup service can be visited twice), and the Vehicle Routing Problem with Simultaneous Delivery and Pickup (VRPSDP – customers demanding both services have to be visited exactly once). The second class, dealt with in the second part of this survey, refers to all those problems where goods are transported between pickup and delivery locations. These are the Pickup and Delivery Vehicle Routing Problem (PDVRP – unpaired pickup and delivery points), the classical Pickup and Delivery Problem (PDP – paired pickup and delivery points), and the Dial-A-Ride Problem (DARP – passenger transportation between paired pickup and delivery points and user inconvenience taken into consideration). Single as well as multi vehicle versions of the mathematical problem formulations are given for all four VRPB types, the corresponding exact, heuristic, and metaheuristic solution methods are discussed.

/pdf/a-survey-on-pickup-and-delivery-problems-4jg10ds2xx.pdf

A survey on pickup and delivery problems

In this paper, a new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms' strength Some benchmark test functions are used to compare the hybrid algorithm with both the standard PSO and GSA algorithms in evolving best solution The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard PSO and GSA

/pdf/a-new-hybrid-psogsa-algorithm-for-function-optimization-17tsmnq1kv.pdf

A new hybrid PSOGSA algorithm for function optimization

https://www.cirrelt.ca/DocumentsTravail/CIRRELT-2011-61.pdf

A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows

A genetic and set partitioning two-phase approach for the vehicle routing problem with time windows

This paper presents a hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). The main idea is to integrate PSO with GA mutation method. Simulations for a series of benchmark test functions show that the hybrid proposed method possess better ability to find the global optimum than the standard PSO algorithm.

Hybrid Evolutionary Algorithm Based on PSO and GA Mutation

The vehicle routing problem with time windows (VRPTW) is a well-known and complex combinatorial problem, which has received considerable attention in recent years Results from exact methods have been improved exploring parallel implementations and modern branch-and-cut techniques However, 23 out of the 56 high order instances from Solomon's test set still remain unsolved Additionally, in many cases a prohibitive time is needed to find the exact solution Many efficient heuristic methods have been developed to make possible a good solution in a reasonable amount of time Using travel distance as the main objective, this paper proposes a robust heuristic approach for the VRPTW using an efficient genetic algorithm and a set partitioning formulation The tests were produced using both real numbers and truncated data type, making it possible to compare the results with previous heuristic and exact methods published Furthermore, computational results show that the proposed heuristic approach outperforms all previous known heuristic methods in the literature, in terms of the minimal travel distance

A two-phase genetic and set partitioning approach for the vehicle routing problem with time windows

The Vehicle Routing Problem with Time Windows (VRPTW) is a well-known and complex combinatorial problem, which has received considerable attention in recent years. The VRPTW benchmark problems of Solomon (1987) have been most commonly chosen to evaluate and compare all exact and heu- ristic algorithms. A genetic algorithm and a set partitioning two phases approach has obtained competitive results in terms of total travel distance minimization. However, a great number of heuristics has used the number of vehicles as the first objective and travel distance as the second, subject to the first. This paper proposes a three phases approach considering both objectives. Initially, a hierarchical tournament selection genetic algorithm is applied. It can reach all best results in number of vehicles of the 56 Solomon's prob- lems explored in the literature. After then, the two phase approach, the genetic and the set partitioning, is applied to minimize the travel distance as the second objective.

A Hybrid Algorithm for the Vehicle Routing Problem with Time Window

The vehicle routing problem with time windows (VRPTW) is a well-known and complex combinatorial problem. The static version of this problem has received special attention. However, most real world vehicle routing problems are dynamic, where information relevant to the routing can change after the initial routes have been constructed. The common approach in this scenario is to restart the algorithm when novel information arrives, but this type of restart methods does not use the memory to improve the quality of the final result. In this paper, the hybrid CGH column generation heuristic (Alvarenga et al., 2005) is modified in order to use the routes generated during the optimization process.

Guilherme Bastos Alvarenga

Papers

A genetic and set partitioning two-phase approach for the vehicle routing problem with time windows

Hybrid Evolutionary Algorithm Based on PSO and GA Mutation

A two-phase genetic and set partitioning approach for the vehicle routing problem with time windows

A Hybrid Algorithm for the Vehicle Routing Problem with Time Window

A hybrid approach for the dynamic vehicle routing problem with time windows