Multi-restart iterative search for the pickup and delivery traveling salesman problem with FIFO loading

•A multi-restart iterative search approach is presented.•A threshold-based exploration phase is combined with a descent-based improvement phase.•An adaptive threshold accepting method is presented.•Improved best upper bounds are reported for 20 out of 42 benchmarks.•The key components of the propose...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Computers & operations research Ročník 97; s. 18 - 30
Hlavní autoři: Lu, Yongliang, Benlic, Una, Wu, Qinghua
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.09.2018
Pergamon Press Inc
Témata:
Algorithms
FIFO
First-in-first-out loading
Graphs
Heuristics
Operations research
Pickup and delivery
Searching
Studies
Threshold search
Traveling salesman problem
Upper bounds
First-in-first-out loading
Traveling salesman problem
Pickup and delivery
Threshold search
Heuristics
ISSN:0305-0548, 1873-765X, 0305-0548
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:•A multi-restart iterative search approach is presented.•A threshold-based exploration phase is combined with a descent-based improvement phase.•An adaptive threshold accepting method is presented.•Improved best upper bounds are reported for 20 out of 42 benchmarks.•The key components of the proposed algorithm are analyzed. The pickup and delivery traveling salesman problem with FIFO loading (TSPPDF) is a variant of the classic traveling salesman problem with pickup and delivery arising from several practical applications where services have to be carried out in the first-in-first-out fashion. In this paper, we present a multi-restart iterative search approach (MIS) for TSPPDF based on a combined use of 6 move operators. The basic idea behind MIS is to alternate between a threshold-based exploration phase, during which degrading solutions are considered as long as they satisfy a quality threshold, and a descent-based improvement phase that only accepts solutions that improve on the quality of the current solution. A dedicated restart strategy is applied to generate a new starting point for the next round of the iterative search as soon as the search is deemed trapped into a deep local optimum. Extensive experiments on a set of 42 benchmark instances from the literature show that the proposed approach competes very favorably with the existing methods from the literature. In particular, it reports new upper bounds (improved best-known solutions) for 20 instances, while matching the best-known result for the remaining instances. The key components of MIS are analyzed to shed light on their impact to the overall algorithmic performance.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2018.04.017