Collaborative capacity sharing in liner shipping operations

Abstract

This paper studies collaborative capacity sharing between two liner shipping companies by investigating the impacts of collaboration on the sharing of fuel-consumption. The proposed model is extended from the vehicle routing problem with time windows (VRPTW) through the inclusion of slow-steaming decision variables and constraints to manage the sharing of fuel consumption. Two cases differing in size are developed using Indonesian archipelago for the data background and three policies on fuel-consumption sharing are investigated: open policy, proportionate-sharing policy, and equal-sharing policy. The application of the collaborative model in Indonesian archipelago contributes to the scant literature in maritime logistics collaboration. The optimisation results from generated instances show that the open policy leads to minimum total fuel consumption but the unclear pattern in fuel-consumption sharing between carriers makes it impractical for planning purposes. Moreover, the fuel consumptions of the proportionate-sharing and equal-sharing policies are not significantly different but the smaller variance in the results of the proportionate-sharing policy indicates more predictability. The proportionate-sharing policy is therefore considered the most suitable for route planning.

Bibliometric Review of Route Optimization in Maritime Transportation: Environmental Sustainability and Operational Efficiency

“…Bittante et al ( 52 ), on the other hand, pay regard to different variables such as fuel price, time horizon, and berthing time for minimizing cost on the Liquefied Natural Gas (LNG) supply chain network. Another paper Wibisono and Jittamai ( 53 ), aimed to solve the capacity sharing problem in liner shipping companies and found that proportionate sharing on fuel consumption is preferable to equal sharing.…”

Improving delivery routes using combined heuristic and optimization in a consumer goods distribution company

“…Constraints (7) set the lower and upper time windows for a vehicle at a node, while constraints (8) manage the arrival times similar to the explanation in the heuristic section. Sub-tour breaking constraints are not required due to constraints (8). Lastly, constraints (9)-(11) state the nature of decision variables involved: , are binary and are continuous, thus the model falls in the category MILP problem.…”

“…More recent VRP studies include, e.g. VRPTW with probabilistic travel times [6], green VRP [7], or VRP in maritime logistics [8].…”

Multi-objective evolutionary algorithm for a ship routing problem in maritime logistics collaboration

“…The deviation between the targeted and actual total costs of each carrier is calculated, summed for all carriers, and subject to minimisation. Wibisono and Jittamai (2015) proposed the idea of this sharing policy and named it proportionate-sharing policy. The authors also showed that the policy leads to a smaller variance compared to the other sharing policies, thus is more reliable for planning.…”