TY - JOUR
T1 - Exact and heuristic algorithms for the fleet composition and periodic routing problem of offshore supply vessels with berth allocation decisions
AU - Vieira, Bruno S.
AU - Ribeiro, Glaydston M.
AU - Bahiense, Laura
AU - Cruz, Roberto
AU - Mendes, André B.
AU - Laporte, Gilbert
N1 - Funding Information:
This work was conducted during a scholarship supported by the International Cooperation Program CAPES/COFECUB at the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), and financed by CAPES (grant 88881.186960/2018-01) - Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Ministry of Education of Brazil. The support of the Canadian Natural Sciences and Engineering Research Council (grant 2015-06189 ) is gratefully acknowledged. This work was partially supported by the National Council for Scientific and Technological Development - CNPq, under grants #309661/2019-6 and #307835/2017-0. Thanks are due to the editor and to the referees for their valuable comments.
PY - 2021/8/13
Y1 - 2021/8/13
N2 - This paper presents a branch-and-cut algorithm and an adaptive large neighborhood search (ALNS) heuristic for the periodic supply vessel planning problem (PSVPP) arising in the upstream offshore petroleum logistics chain. Platform supply vessels support the offshore oil and gas exploration and production activities by transporting all the necessary material and equipment back and forth between offshore units and an onshore supply base according to a delivery schedule. The PSVPP consists of solving a periodic vehicle routing problem and simultaneously determining an optimal fleet size and mix of heterogeneous offshore supply vessels, their weekly routes and schedules for servicing the offshore oil and gas installations, and the berth allocations at the supply base. The branch-and-cut algorithm considers a reduced formulation for the problem which performs much better than the complete one, and easily finds optimal solutions for the smaller and most of the clustered instances. The ALNS heuristic contains new features which include multiple starts and spaced local searches. These algorithms were tested on instances with up to 79 offshore units, providing better results than the best available.
AB - This paper presents a branch-and-cut algorithm and an adaptive large neighborhood search (ALNS) heuristic for the periodic supply vessel planning problem (PSVPP) arising in the upstream offshore petroleum logistics chain. Platform supply vessels support the offshore oil and gas exploration and production activities by transporting all the necessary material and equipment back and forth between offshore units and an onshore supply base according to a delivery schedule. The PSVPP consists of solving a periodic vehicle routing problem and simultaneously determining an optimal fleet size and mix of heterogeneous offshore supply vessels, their weekly routes and schedules for servicing the offshore oil and gas installations, and the berth allocations at the supply base. The branch-and-cut algorithm considers a reduced formulation for the problem which performs much better than the complete one, and easily finds optimal solutions for the smaller and most of the clustered instances. The ALNS heuristic contains new features which include multiple starts and spaced local searches. These algorithms were tested on instances with up to 79 offshore units, providing better results than the best available.
KW - ALNS heuristic
KW - B&C algorithm
KW - Berth allocation
KW - Heterogeneous fleet sizing
KW - Offshore supply vessel planning
KW - OR in maritime industry
KW - Periodic VRP
UR - http://www.scopus.com/inward/record.url?scp=85104118899&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2021.03.022
DO - 10.1016/j.ejor.2021.03.022
M3 - Article
AN - SCOPUS:85104118899
SN - 0377-2217
VL - 295
SP - 908
EP - 923
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 3
ER -