Heuristics for dynamic and stochastic routing in industrial shipping

Tirado, Gregorio; Hvattum, Lars Magnus; Fagerholt, Kjetil; Cordeau, Jean‐François

doi:10.1016/j.cor.2012.06.011

Cited by 34 publications

(25 citation statements)

References 36 publications

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“…Another tabu search method, considering flexible cargo quantities, is proposed and presented by [49]. On the domain of dynamic and stochastic problems, Tirado et al [50] proposes three heuristic procedures-myopic dynamic heuristic, multiple scenario with consensus and branch-and-regret-for PDP with dynamic requests and stochastic processes for the arrival of new cargoes. Additionally, Agra et al [51] proposes a stochastic approach for maritime inventory routing with uncertainty in sailing and port service times at the archipelago of Cape Verde.…”

Section: Literature Reviewmentioning

confidence: 99%

Ship Routing with Pickup and Delivery for a Maritime Oil Transportation System: MIP Model and Heuristics

Rodrigues

Morábito

Yamashita

et al. 2016

Systems

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This paper examines a ship routing problem with pickup and delivery and time windows for maritime oil transportation, motivated by the production and logistics activities of an oil company operating in the Brazilian coast. The transportation costs from offshore platforms to coastal terminals are an important issue in the search for operational excellence in the oil industry, involving operations that demand agile and effective decision support systems. This paper presents an optimization approach to address this problem, based on a mixed integer programming (MIP) model and a novel and exploratory application of two tailor-made MIP heuristics, based on relax-and-fix and time decomposition procedures. The model minimizes fuel costs of a heterogeneous fleet of oil tankers and costs related to freighting contracts. The model also considers company-specific constraints for offshore oil transportation. Computational experiments based on the mathematical models and the related MIP heuristics are presented for a set of real data provided by the company, which confirm the potential of optimization-based methods to find good solutions for problems of moderate sizes.

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Section: Literature Reviewmentioning

confidence: 99%

Ship Routing with Pickup and Delivery for a Maritime Oil Transportation System: MIP Model and Heuristics

Rodrigues

Morábito

Yamashita

et al. 2016

Systems

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“…The dynamic and stochastic maritime pickup and delivery problem considered here was dened in (Tirado et al, 2013). The setting is industrial shipping, where a company operates a eet of vessels to transport its own cargoes.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The dynamic and stochastic maritime routing and scheduling problem considered in this paper was dened by Tirado et al (2013), who tested three dierent heuristics, namely the myopic dynamic heuristic (MDH), the branch-and-regret heuristic (BRH), and the multiple scenario approach with consensus (MSAC). These heuristics are briey described in Section 4.…”

Section: Literature Reviewmentioning

confidence: 99%

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Determining departure times in dynamic and stochastic maritime routing and scheduling problems

Tirado

Hvattum

2016

Flex Serv Manuf J

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In maritime transportation, decisions are made in a dynamic setting where many aspects of the future are uncertain. However, most academic literature on maritime transportation considers static and deterministic routing and scheduling problems. This work addresses a gap in the literature on dynamic and stochastic maritime routing and scheduling problems, by focusing on the scheduling of departure times. Five simple strategies for setting departure times are considered, as well as a more advanced strategy which involves solving a mixed integer mathematical programming problem. The latter strategy is significantly better than the other methods, while adding only a small computational eort.

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“…Another interesting algorithm is the branch-and-regret heuristic (BRH) which is a mix between the Consensus and the Restricted Expectation algorithms (see [Hvattum et al, 2007] and [Tirado et al, 2013]). This algorithm also mimics a branching scheme.…”

Section: Scenario Hedging and Branch-and-regret Algorithmsmentioning

confidence: 99%

Multi-period stochastic optimization problems in transportation management

Pironet¹

2014

4OR-Q J Oper Res

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Heuristics for dynamic and stochastic routing in industrial shipping

Cited by 34 publications

References 36 publications

Ship Routing with Pickup and Delivery for a Maritime Oil Transportation System: MIP Model and Heuristics

Ship Routing with Pickup and Delivery for a Maritime Oil Transportation System: MIP Model and Heuristics

Determining departure times in dynamic and stochastic maritime routing and scheduling problems

Multi-period stochastic optimization problems in transportation management

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