An adaptive large neighbourhood search heuristic for routing and scheduling feeder vessels in multi-terminal ports

Hellsten, Erik Orm; Sacramento, David; Pisinger, David

doi:10.1016/j.ejor.2020.04.050

Cited by 27 publications

(24 citation statements)

References 38 publications

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“…The authors formulate the problem as a Mixed Integer Programming (MIP) model and propose an ALNS heuristic for solving large instances. The results in [18] highlight the effectiveness of the heuristic, finding high-quality solutions and outperforming the mathematical model in almost all cases. In this paper, we study the application of CP techniques for solving the PSP.…”

Section: Literature Reviewmentioning

confidence: 79%

“…In the following, we briefly describe the main characteristics of the problem. For a more thorough definition of the problem, we refer the reader to [18].…”

Section: Problem Definitionmentioning

confidence: 99%

“…The PSP becomes relevant in this case. Hellsten et al [18] introduced the PSP, which was defined in collaboration with representatives of the feeder line industry. The PSP accounts for most of the practical constraints derived from reallife operations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This paper studies the problem of scheduling the operations of feeder vessels in multi-terminal ports. This problem was first introduced in [18], who defined the Port Scheduling Problem (PSP). In this paper, we consider a Constraint Programming (CP) formulation of the same problem.…”

Section: Introductionmentioning

confidence: 99%

“…A matheuristic is an optimisation algorithm made by the interoperation of metaheuristics and mathematical programming techniques, combining the strengths of mathematical programming methods to intensify the search and the strengths of metaheuristics to exploit a large solution space [6]. In the matheuristic proposed in this paper, we explore the solution space using an adapted version of the ALNS heuristic from [18]. The main adaptions of the ALNS heuristic are described in Section 4.1.…”

Section: Introductionmentioning

confidence: 99%

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Constraint Programming and Local Search Heuristic: a Matheuristic Approach for Routing and Scheduling Feeder Vessels in Multi-terminal Ports

Sacramento

Solnon

Pisinger

2020

SN Oper. Res. Forum

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In the liner shipping business, shipping ports represent the main nodes in the maritime transportation network. These ports have a collection of terminals where container vessels can load and discharge containers. However, the logistics and planning of operations differ depending on the vessel size. Large container vessels visit a single terminal, whereas smaller container vessels, or feeder vessels, visit several terminals to transport all containers within the multiple terminals of the port. In this paper, we study the Port Scheduling Problem, the problem of scheduling the operations of feeder vessels in multi-terminal ports. The resulting problem can be identified as a version of the General Shop Scheduling Problem. We consider a Constraint Programming formulation of the problem, and we propose a matheuristic solution approach for solving large instances. The proposed matheuristic is a hybrid solution method that combines Constraint Programming with a local search heuristic. The solution approach benefits from the fast search capabilities of local search algorithms to explore the solution space using an Adaptive Large Neighbourhood Search heuristic. During the search, we further use the Constraint Programming model as an intensification technique, every time a new best-known solution is found. We conduct detailed computational experiments on the PortLib instances, showing that the incorporation of Constraint Programming within the heuristic search can result in significant benefits. The high instability in solution quality obtained by local search heuristics can be lowered by a simple combination of both methods.

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Section: Literature Reviewmentioning

confidence: 79%

“…In the following, we briefly describe the main characteristics of the problem. For a more thorough definition of the problem, we refer the reader to [18].…”

Section: Problem Definitionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations