Abstract
An effective and fast hybrid metaheuristic is proposed for solving the pickup and delivery problem with time windows. The proposed approach combines local search, large neighbourhood search and guided ejection search in a novel way to exploit the benefits of each method. The local search component uses a novel neighbourhood operator. A streamlined implementation of large neighbourhood search is used to achieve an effective balance between intensification and diversification. The adaptive ejection chain component perturbs the solution and uses increased or decreased computation time according to the progress of the search. While the local search and large neighbourhood search focus on minimising travel distance, the adaptive ejection chain seeks to reduce the number of routes. The proposed algorithm design results in an effective and fast solution method that finds a large number of new best-known solutions on a well-known benchmark dataset. Experiments are also performed to analyse the benefits of the components and heuristics and their combined use to achieve a better understanding of how to better tackle the subject problem.
Highlights
The pickup and delivery problem (PDP) is a vehicle routing problem in which customers are paired together and a pair must be serviced by the same vehicle (Savelsbergh and Sol 1995)
The algorithmic design proposed here incorporates specialised neighbourhood operators to enhance the effectiveness of the local search, adaptive ejection search to reduce the number of routes and streamlined large neighbourhood search to enhance the efficiency of the search
This paper proposes an effective and fast hybrid metaheuristic algorithm to tackle the pickup and delivery problem with time windows (PDPTW)
Summary
The pickup and delivery problem (PDP) is a vehicle routing problem in which customers are paired together and a pair must be serviced by the same vehicle (Savelsbergh and Sol 1995). A large number of techniques have been developed for PDPTW These include approaches based on exact methods as well as heuristics. The exact based methods have advantages such as solving to provable optimality or providing bounds. They tend to perform very well on smaller and medium sized instances. It could be argued that some heuristic methods are easier to develop and maintain and to adapt to new problem requirements. These could be the reasons that the majority of commercial vehicle routing software packages use heuristic-based methods (Hall and Partyka 2016)
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