Stochastic multi-depot vehicle routing problem with pickup and delivery: an ILS approach

• Autorzy: Rios, Brenner H. O. , Xavier, Eduardo C. , Miyazawa, Flávio K. , Amorim, Pedro
• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

We present a natural probabilistic variation of the multi-depot vehicle routing problem with pickup and delivery (MDVRPPD). In this paper, we present a variation of this deterministic problem, where each pair of pickup and delivery points are present with some probability, and their realization are only known after the routes are computed. We denote this stochastic version by S-MDVRPPD. One route for each depot must be computed satisfying precedence constraints, where each pickup point must appear before its delivery pair in the route. The objective is to find a solution with minimum expected traveling distance. We present a closed-form expression to compute the expected length of an a priori route under general probabilistic assumptions. To solve the S-MDVRPPD we propose an Iterated Local Search (ILS) that uses the Variable Neighborhood Descent (VND) as local search procedure. The proposed heuristic was compared with a Tabu Search (TS) algorithm based on a previous work. We evaluate the performance of these heuristics on a data set adapted from TSPLIB instances. The results show that the ILS proposed is efficient and effective to solve S-MDVRPPD.

Słowa kluczowe

PL

algorytmy deterministyczne; prawdopodobieństwo; problemy wyszukiwawcze; procesy stochastyczne; problem komiwojażera; wyznaczanie tras pojazdów

EN

deterministic algorithms; probability; search problems; stochastic processes; travelling salesman problem; vehicle routing

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 307--315
• Opis fizyczny: Bibliogr. 20 poz., wz., il., tab.

Twórcy

autor

Rios, Brenner H. O.

• Institute of Computing, University of Campinas, São Paulo, Brazil,

autor

Xavier, Eduardo C.

• Institute of Computing, University of Campinas, São Paulo, Brazil,

autor

Miyazawa, Flávio K.

• Institute of Computing, University of Campinas, São Paulo, Brazil,

autor

Amorim, Pedro

• INESC TEC, Faculty of Engineering, University of Porto, Porto, Portugal,

Bibliografia

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Uwagi

1. This work is financed by FAPESP (Proc. 2015/11937-9, 2016/01860-1, 2018/08879-5), CNPq (Proc. 314366/2018-0, 425340/2016-3, 304856/2017-7), ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-031908.

2. Track 1: Artificial Intelligence

3. Technical Session: 13th International Workshop on Computational Optimization

4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).

Identyfikatory

DOI

10.15439/2020F127