Benchmark Suite for Industrial and Tramp Ship Routing and Scheduling Problems

Hemmati, Ahmad; Hvattum, Lars Magnus; Fagerholt, Kjetil; Norstad, Inge

doi:10.3138/infor.52.1.28

Cited by 20 publications

(52 citation statements)

References 25 publications

(20 reference statements)

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“…Both models are nonlinear. Hemmati et al (2014) and Christiansen and Fagerholt (2014) have presented better tramp shipping characteristics. The former have used a linear objective but used heuristic algorithms to solve their problem.…”

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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“…We have implemented the acceptance criterion of simulated annealing, and the ALNS heuristics stops after 1000 iterations. For more details we refer the readers to Hemmati et al (2014).…”

Section: Algorithm 35 Adaptive Large Neighborhood Searchmentioning

confidence: 99%

An effective heuristic for solving a combined cargo and inventory routing problem in tramp shipping

Hemmati

Stålhane

Hvattum

et al. 2015

Computers & Operations Research

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In this paper a vendor managed inventory (VMI) service in tramp shipping is considered. VMI takes advantage of introducing flexibility in delivery time and cargo quantities by transferring inventory management and ordering responsibilities to the vendor which in this case is a shipping company. A two-phase heuristic is proposed to determine routes and schedules for the shipping company. The heuristic first converts inventories into cargoes, thus turning the problem into a classic ship routing and scheduling problem. It then uses adaptive large neighborhood search to solve the resulting cargo routing and scheduling problem. The heuristic iteratively changes the cargoes generated to handle the customer's inventories, based on the information obtained from an initial solution. Computational results are presented, discussed and compared with exact solutions on large realistic instances. The results reveal the potential savings from converting traditional contracts of affreightment to an integrated VMI service. The factors that influence the benefits obtainable through VMI are also analyzed.

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“…Privé et al (2006) investigate a problem arising in the soft drink distribution, where all delivered goods originate from a unique distribution center and all collected goods end up at a unique destination as well. A related problem for maritime transportation is presented by Hemmati et al (2014). They consider optional spot cargoes and solve it as routing and scheduling problem.…”

Section: Literature Reviewmentioning

confidence: 99%

The multi-vehicle profitable pickup and delivery problem

2016

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The transportation industry expanded rapidly in a highly competitive environment. Logistics companies with insufficient volume of transport capacities are forced to make a selection of customers that they can integrate efficiently into their tours. This is of particular relevance in the pickup and delivery market, where shipments from several different customers can be moved on the same vehicle. In the literature, however, the problem of customer selection has not been applied for the given class of pickup and delivery problems so far. We want to fill this gap by introducing the multi-vehicle profitable pickup and delivery problem (MVPPDP), where multiple carriers transport goods from a selection of pickup customers to the corresponding delivery customers within given travel time limits. For this problem, we propose a method based on general variable neighborhood search (GVNS). We conduct experiments with two different variants of this method, namely a sequential (GVNSseq) and a self-adaptive (GVNSsa) version. Additionally, we compare it to an algorithm based on Guided Local Search (GLS), which is known to find good solutions for related problems very fast. The performance of these methods is examined on the basis of data instances with up to 1000 customer requests. In an experimental study, we observe that both variants of GVNS with 11 neighborhoods outperform GLS with regard to solution quality for all sizes of test instances. However, for medium sized and large instances, GLS shows an advantage in average runtimes.

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Benchmark Suite for Industrial and Tramp Ship Routing and Scheduling Problems

Cited by 20 publications

References 25 publications

Shipping Optimisation Systems (SOS): tramp optimisation perspective

Shipping Optimisation Systems (SOS): tramp optimisation perspective

An effective heuristic for solving a combined cargo and inventory routing problem in tramp shipping

The multi-vehicle profitable pickup and delivery problem

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