Simultaneous Planning of Liner Ship Speed Optimization, Fleet Deployment, Scheduling and Cargo Allocation with Container Transshipment

Due to a substantial growth in the world waterborne trade volumes and drastic changes in the global climate accounted for CO2 emissions, the shipping companies need to escalate their operational and energy efficiency. Therefore, a multi-objective mixed-integer non-linear programming (MINLP) model is proposed in this study to simultaneously determine the optimal service schedule, number of vessels in a fleet serving each route, vessel speed between two ports of call, and flow of cargo considering transshipment operations for each pair of origin-destination. This MINLP model presents a trade-off between economic and environmental aspects considering total shipping time and overall shipping cost as the two conflicting objectives. The shipping cost comprises of CO2 emission, fuel consumption and several operational costs where fuel consumption is determined using speed and load. Two efficient evolutionary algorithms: Nondominated Sorting Genetic Algorithm II (NSGA-II) and Online Clustering-based Evolutionary Algorithm (OCEA) are applied to attain the near-optimal solution of the proposed problem. Furthermore, six problem instances of different sizes are solved using these algorithms to validate the proposed model.Comment: 28 pages, 10 figure

Fleet deployment and demand fulfillment for container shipping liners

This paper models and solves a fleet deployment and demand fulfillment problem for container shipping liners with consideration of the potential overload risk of containers. Given the stochastic weights of transported containers, chance constraints are embedded in the model at the strategic level. Several realistic limiting factors such as the fleet size and the available berth and yard resources at the ports are also considered. A non-linear mixed integer programming (MIP) model is suggested to optimally determine the transportation demand fulfillment scale for each origin-destination pair, as well as the ship deployment plan along each route, with an objective incorporating revenue, fixed operation cost, fuel consumption cost, holding cost for transhipped containers, and extra berth and yard costs. Two efficient algorithms are then developed to solve the non-linear MIP model for different instance sizes. Numerical experiments based on real-world data are conducted to validate the effectiveness of the model and the algorithms. The results indicate the proposed methodology yields solutions with an optimality gap less than about 0.5%, and can solve realistic instances with 19 ports and four routes within about one hour.</p

Fleet deployment and demand fulfillment for container shipping liners

This paper models and solves a fleet deployment and demand fulfillment problem for container shipping liners with consideration of the potential overload risk of containers. Given the stochastic weights of transported containers, chance constraints are embedded in the model at the strategic level. Several realistic limiting factors such as the fleet size and the available berth and yard resources at the ports are also considered. A non-linear mixed integer programming (MIP) model is suggested to optimally determine the transportation demand fulfillment scale for each origin-destination pair, as well as the ship deployment plan along each route, with an objective incorporating revenue, fixed operation cost, fuel consumption cost, holding cost for transhipped containers, and extra berth and yard costs. Two efficient algorithms are then developed to solve the non-linear MIP model for different instance sizes. Numerical experiments based on real-world data are conducted to validate the effectiveness of the model and the algorithms. The results indicate the proposed methodology yields solutions with an optimality gap less than about 0.5%, and can solve realistic instances with 19 ports and four routes within about one hour.</p

Methods for strategic liner shipping network design

In this paper the combined fleet-design, ship-scheduling and cargo-routing problem with limited availability of ships in liner shipping is considered. A composite solution approach is proposed in which the ports are first aggregated into port clusters to reduce the problem size. When the cargo flows are disaggregated, a feeder service network is introduced to ship the cargo within a port cluster. The solution method is tested on a problem instance containing 58 ports on the Asia-Europe trade lane of Maersk. The best obtained profit gives an improvement of more than 10% compared to the reference network based on the Maersk network

Optimization Models for Collaborative Vessel Allocation : A Computational Study of How Collaboration Between Shipping Companies Can Reduce Fuel Costs and CO2 Emissions

Transportation by sea entails costs for shipping companies as well as emissions that contributes to the challenges regarding global warming. A variety of approaches can be implemented in order to facilitate reductions of these measures. In our thesis, we study how collaboration between shipping companies that carries out a sequence of deliveries with time windows can be a way of reducing fuel costs and CO2 emissions. To explore this, we formulate two optimization models in terms of mixed integer linear problems that minimizes the fuel costs resulting from the sequence of deliveries. The main decisions to be made in these models are the vessel allocation and the choice of speed levels. Fuel consumption forms the basis for the fuel costs and the CO2 emissions. Because the relationship between speed and fuel consumption is nonlinear, the relationship is linearized to formulate linear models. Collaboration is defined in terms of a collaborative decision of vessel allocation and speed levels where the shipping companies join their fleets of vessels and the deliveries that are requested to be carried out. In our computational study, the models are implemented using a dataset obtained from the company Signal Ocean. In addition, data regarding fuel consumption is collected from the Clarksons Research Portal. A variety of time window scenarios are implemented in order to explore the effects of collaboration when the underlying assumptions changes. The results show that joining the fleets of vessels and the requested deliveries in the decision of vessel allocation and choice of speed levels implies considerable reductions in both fuel costs and CO2 emissions.nhhma

Liner ship fleet planning with uncertain container shipment demand

Ph.DDOCTOR OF PHILOSOPH