Minimax Regret Model for Liner Shipping Fleet Deployment with Uncertain Demand

2016 ◽  
Vol 2549 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Shuaian Wang ◽  
Zhiyuan Liu ◽  
Xiaobo Qu
Keyword(s):  
Liner Shipping ◽  
Uncertain Demand ◽  
Minimax Regret ◽  
Fleet Deployment

scholarly journals Integrated Optimization of Port Rotation Direction and Fleet Deployment for Container Liner Shipping Routes

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jingxu Chen ◽  
Yiran Wang ◽  
Xinlian Yu ◽  
Zhiyuan Liu
Keyword(s):  
Demand Uncertainty ◽  
Liner Shipping ◽  
Uncertain Demand ◽  
Multicommodity Flow ◽  
Rotation Direction ◽  
Container Liner Shipping ◽  
Planning Methodology ◽  
Total Network ◽  
Selection Of ◽  
Fleet Deployment

This paper provides an integrated planning methodology for the optimization of port rotation direction and fleet deployment for container liner shipping routes with consideration of demand uncertainty. We first consider a special case that demand is deterministic. A multicommodity flow network model is developed via minimizing the total network-wide cost. Its decisions are the selection of port rotation direction and fleet deployment and container routings in the shipping network. Afterward, we address the generic case that uncertain demand is considered, which is represented by potentially realizable demand scenarios. We develop a minimax regret model to procure the least maximum regret across all the demand scenarios. The proposed models are applied to an Asia-Europe-Oceania liner shipping network with 46 ports and 12 ship routes. Results could provide the liner company with a comprehensive decision tool to simultaneously determine port rotation direction and fleet deployment when tackling uncertain demand.

Fleet deployment in liner shipping: a case study

2009 ◽  
Vol 36 (5) ◽  
pp. 397-409 ◽  
Author(s):  
Kjetil Fagerholt ◽  
Trond A. V. Johnsen ◽  
Haakon Lindstad
Keyword(s):  
Liner Shipping ◽  
Fleet Deployment

Routing and fleet deployment in liner shipping with spot voyages

2015 ◽  
Vol 57 ◽  
pp. 188-205 ◽  
Author(s):  
Rodrigo Moretti Branchini ◽  
Vinícius Amaral Armentano ◽  
Reinaldo Morabito
Keyword(s):  
Liner Shipping ◽  
Fleet Deployment

Liner shipping cycle cost modelling, fleet deployment optimization and what-if analysis

2011 ◽  
Vol 13 (3) ◽  
pp. 278-297 ◽  
Author(s):  
Panayotis G Zacharioudakis ◽  
Stylianos Iordanis ◽  
Dimitrios V Lyridis ◽  
Harilaos N Psaraftis
Keyword(s):  
Liner Shipping ◽  
Cost Modelling ◽  
Deployment Optimization ◽  
Fleet Deployment

Liner shipping fleet deployment considering decision makers’ psychological behavior

2015 ◽  
Vol 2015 (1) ◽  
pp. 10694
Author(s):  
Shuaian Wang ◽  
Zhiyuan Liu ◽  
Xiaobo Qu ◽  
Ying Yang
Keyword(s):  
Liner Shipping ◽  
Decision Makers ◽  
Psychological Behavior ◽  
Fleet Deployment

Joint Planning of Fleet Deployment, Speed Optimization, and Cargo Allocation for Liner Shipping

2015 ◽  
Vol 49 (4) ◽  
pp. 922-938 ◽  
Author(s):  
Jun Xia ◽  
Kevin X. Li ◽  
Hong Ma ◽  
Zhou Xu
Keyword(s):  
Liner Shipping ◽  
Speed Optimization ◽  
Joint Planning ◽  
Fleet Deployment

scholarly journals A Holistic Optimization Model for Integrated Tactical Level Planning in Liner Shipping

2020 ◽  
Author(s):  
Junayed Pasha
Keyword(s):  
Mathematical Model ◽  
Liner Shipping ◽  
Operational Cost ◽  
Inventory Cost ◽  
Frequency Determination ◽  
Vessel Scheduling ◽  
Late Arrival ◽  
Speed Optimization ◽  
Service Frequency ◽  
Fleet Deployment

Supply chain management plays an important role in ensuring an efficient merchandise trade. Freight transportation is an integral part of supply chain management. A significant part of freight transportation is covered by maritime transportation, as the largest portion of the global merchandise trade, in terms of volume, is carried out by maritime transportation. Liner shipping, which runs on fixed routes and schedules, plays a colossal role for the global seaborne trade. Liner shipping companies deal with three decision levels, namely strategic level, tactical level, and operational level. The strategic-level decisions are taken for more than six months to several years. The tactical-level decisions are effective for three months to six months. Moreover, the operational level decisions are taken for a couple of weeks to less than three months.This dissertation involves the tactical-level decisions in liner shipping, which include: (1) service frequency determination; (2) fleet deployment; (3) sailing speed optimization; and (4) vessel scheduling. The service frequency determination problem deals with determining the time headway between consecutive vessels along a liner shipping route. The fleet deployment problem assigns vessels from the liner shipping company’s fleet (and sometimes, from other liner shipping companies’ fleets) to liner shipping routes. The sailing speed optimization problem deals with selecting sailing speeds along different voyage legs of a given port rotation. The vessel scheduling problem lists the schedules (e.g., arrival time, handling time, departure time) at different ports.A comprehensive review of the liner shipping literature revealed that the existing literature on the tactical-level decisions focused on these problems individually. Solutions from different solution methodologies for the separate problems may have compatibility problems. Moreover, they are not attractive to the liner shipping companies, who look for integrated solutions. Hence, this research aimed to develop a combined mathematical model that comprises the four tactical-level decisions in liner shipping (i.e., service frequency determination, fleet deployment, sailing speed optimization, and vessel scheduling). This mathematical model is named the Holistic Optimization Model for Tactical-Level Planning in Liner Shipping (HOMTLP).The objective of the HOMTLP mathematical model is to maximize of the total profit from transport of cargo. The major route service cost components, found from the literature, are covered by the model, which include: (I) total late arrival cost; (II) total port handling cost; (III) total fuel consumption cost; (IV) total vessel operational cost; (V) total vessel chartering cost; (VI) total container inventory cost in sea; (VII) total container inventory cost at ports of call; (VIII) total emission cost in sea; and (IX) total emission cost at ports of call. Along with the integration of all four tactical-level decisions, the mathematical model has a number of key advantages. First, the model provides flexibility to both the liner shipping company and the marine container terminal operators, as it offers multiple time windows and handling rates at each port of call. Second, the payload carried by the vessels is considered while estimating fuel consumption. Third, the preference of customers is reflected by modification of the container demand at different sailing speeds. Fourth, container inventory is accounted for at ports of call and in sea. Fifth, emissions of different harmful substances are captured in order to preserve the environment.This dissertation carried out a set of numerical experiments to test the performance of the HOMTLP model, where BARON was used as the solution approach. It was revealed that when there was an increase in the unit fuel cost, the unit emission cost, vessel availability, the unit late arrival cost, and the unit freight rate, the sailing speed was reduced. On the other hand, when there was an increase in the unit inventory cost, the unit operational cost, as well as the unit chartering cost, the sailing speed was increased. Moreover, the total required number of vessels was increased, when there as an increase in the unit fuel cost, the unit emission cost, vessel availability, the unit late arrival cost, and the unit freight rate. On the contrary, the total required number of vessels was decreased, when there was an increase in the unit inventory cost, the unit operational cost as well as the unit chartering cost. It was also revealed that the total profit was increased, when more choices were available for time windows and/or container handling rates. The numerical experiments highlighted several other findings. Most importantly, it was found that the HOMTLP model can provide effective tactical-level decisions. Hence, the mathematical model can assist liner shipping companies to take tactical-level decisions, which are effective and profitable.

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