Stochastic Programming for Liner Ship Routing and Scheduling under Uncertain Sea Ice Conditions

It is anticipated that in the foreseeable future the Northern Sea Route (NSR) will be able to serve commercial shipping as an alternative transportation shortcut between East Asia and Europe, especially in the summer season. The sailing time, however, is heavily subject to the variation of sea ice conditions along this route. Any participating shipping company must consider how to mitigate the ill effects on itinerary planning caused by sailing time and cost uncertainty. Finding a good trade-off between the benefit from a tight schedule and the risk caused by an unexpected delay is a key element in relevant routing and scheduling decisions, and may be beyond the reach of traditional deterministic planning models. With the aim of maximizing profit over all possible shipping environment scenarios, this article proposes a two-stage stochastic nonlinear integer programming model for liner ship routing and scheduling with uncertain shipping time and cost, the nonlinearity of which arises from the coexistence of schedule-sensitive shipping demand and uncertain arrival time variables in the objective function. The model is converted into an equivalent linear integer programming counterpart by introducing a set of nominal delay variables, and Benders decomposition is applied to solve the linearized problem. Numerical experiments and sensitivity analyses are conducted to validate the efficacy and effectiveness of the model, the results of which suggest several managerial insights that can be used to guide liner ship route and schedule planning under uncertain shipping conditions.

A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry

This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO2 equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.

Ship routing and scheduling systems: forecasting, upscaling and viability

Purpose The purpose of this paper is to resolve three problems in ship routing and scheduling systems. Problem 1 is the anticipation of the future cargo transport demand when the shipping models are stochastic based on this demand. Problem 2 is the capacity of these models in processing large number of ships and cargoes within a reasonable time. Problem 3 is the viability of tramp shipping when it comes to real problems. Design/methodology/approach A commodity-trade forecasting system is developed, an information technology platform is designed and new shipping elements are added to the models to resolve tramp problems of en-route ship bunkering, low-tide port calls and hold-cleaning cost caused by carrying incompatible cargoes. Findings More realistic stochastic cargo quantity and freight can now be anticipated, larger number of ships and cargoes are now processed in time and shipping systems are becoming more viable. Practical implications More support goes to ship owners to make better shipping decisions. Originality/value New norms are established in forecasting, upscaling and viability in ship routing and scheduling systems.