An adaptive large neighborhood search heuristic for fleet deployment problems with voyage separation requirements

Bakkehaug, Rikard; Rakke, Jørgen Glomvik; Fagerholt, Kjetil; Laporte, Gilbert

doi:10.1016/j.trc.2015.06.019

Cited by 25 publications

(16 citation statements)

References 24 publications

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“…The fourth is that the authors brought no evidence on the possibility of solving large problems when more cargoes and ships are added. Bakkehaug et al (2016) and Vilhelmsen et al (2017) have developed a similar model to schedule the voyages of a fleet of ships considering a minimum time spread between some voyages. The former has used the Adaptive Large Neighborhood Search (ALNS) heuristic to solve the problem, while the latter has used a Decomposition approach with Dynamic Programming algorithm for column generation.…”

Section: Problem Statement and Review Of The Literature In Tramp Shipmentioning

confidence: 99%

Shipping Optimisation Systems (SOS): tramp optimisation perspective

Noshokaty¹

2017

J. shipp. trd.

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This research paper is to announce a new policy to all systems which are sensitive to time. In tramp cargo transportation, as an example, the current policy is to select for each ship the cargo mix which contributes more to a gross-profit objective, assuming deterministic cargo transport demand. Since tramp cargo transportation is sensitive to time, where time varies considerably from one alternative ship voyage to another. The new policy considers this objective less profitable than gross-profit-per-day objective, assuming both deterministic and stochastic cargo transport demand. To introduce this new policy, SOS; a suite of decision support systems, is developed to optimise tramp shipping using a stochastic gross-profit-per-day objective. For operational purposes, SOS selects the most profitable cargo mix. This mix is selected because of the higher gross profit it is expected to yield and the less number of days it takes to generate such profit. For long-term planning purposes, SOS uses the optimal gross profit of each ship voyage, created by the system, to allocate fleet units to cargo trade areas, specifying their frequency of calls to maximise fleet annual gross profit. A useful application of this fleet allocation is that the allocated frequency of calls may be considered as representing the demand on services of utilities of ports, canals, and straits, and may be used to assess the competitiveness of these utilities. Utility and logistics planner, via sensitivity and what-if analysis, can determine whether calling at a utility of a trade area is sensitive to changes made to utility dues and staying time, cargo quantities and freight rates, cargo handling rates and charges, and ship speed and fuel consumption. For appraising purposes, SOS includes new ships in the allocation process, in competition with old ones, to find the share each new ship adds to total gross profit each year. SOS then applies the Net Present Value formula to gross profit of each new ship, along with other cash flow and cost of investment. SOS similar systems may be tailored for other means of cargo transport; namely cargo airplanes, trains, and trucks. The impact of SOS on any logistics and supply chain system is that it maintains the shortest possible transportation time owners of transport units can afford. Case studies are brought to demonstrate research findings.

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Section: Problem Statement and Review Of The Literature In Tramp Shipmentioning

confidence: 99%

Shipping Optimisation Systems (SOS): tramp optimisation perspective

Noshokaty¹

2017

J. shipp. trd.

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“…Laake and Zhang (2013), Vilhelmsen et al (2015) have developed a linear model to handle the case where multiple cargoes can be carried simultaneously on board each ship. Bakkehaug et al (2016) and Vilhelmsen et al (2017) have developed a model to schedule the voyages of a fleet of ships considering a minimum time spread between some voyages. The former has used the adaptive large neighborhood search (ALNS) heuristic to solve the problem, while the latter has used a decomposition approach with dynamic programming algorithm for column generation.…”

Section: Cargo Ship Profitability Capacity and Speedmentioning

confidence: 99%

The implication of the stochastic gross-profit-per-day objective on the cargo ship profitability, capacity, and speed

Noshokaty¹

2019

MABR

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Purpose This paper aims to study the implication of the stochastic gross-profit-per-day objective on the ship profitability and the ship capacity and speed. Design/methodology/approach The paper has used the mathematical model and the solution methodology given by El Noshokaty, 2013, 2014, 2017a, 2017b, and SOS, 2019. Findings The paper finds that if the ship owner follows the rate concept and the cargo demand forecast, he can improve the profitability of his company and be able to select the proper capacities and speeds for the ships used. Research limitations/implications The findings are not only useful for the shipping or other cargo transport companies but also for businesses like gas reservoir development, car assembly lines in the industry, cooperative farming and crop harvesting in agriculture, port cargo handling in trade and road paving in construction. Originality/value The contribution of this paper lies in notifying the ship owners of the possible profitability improvement and the consequences of building ships of larger capacities and slower speeds.

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“…Additionally, Bakkehaug et al [46] applies the ANLS heuristic to a maritime setting, with a focus on fleet deployment and voyage (i.e., sailing on a route) separation. A multi-start local search heuristic was proposed by [47] for short-term ship scheduling problems in the tramp mode.…”

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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An adaptive large neighborhood search heuristic for fleet deployment problems with voyage separation requirements

Cited by 25 publications

References 24 publications

Shipping Optimisation Systems (SOS): tramp optimisation perspective

Shipping Optimisation Systems (SOS): tramp optimisation perspective

The implication of the stochastic gross-profit-per-day objective on the cargo ship profitability, capacity, and speed

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

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