Maritime container shipping fleet deployment considering demand uncertainty

In this paper, under the consideration of two carbon emissions policies, the issues of optimizing ship speed and fleet deployment for container shipping were addressed. A mixed-integer nonlinear programming model of ship speed and fleet deployment was established with the objective of minimising total weekly operating costs. A simulated annealing algorithm was proposed to solve the problem. An empirical analysis was conducted with the data selected from the benchmark suite. The applicability and effectiveness of the established model and its algorithm are verified by the results. According to the results, two policies of the cap-and-trade programme and the carbon tax can better optimize the results of the ship speed and fleet deployment problem to achieve the goal of reducing carbon emissions. The research remarks in this paper will provide a solution for container shipping companies to make optimized decisions under carbon emissions policies.

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Optimal fleet deployment for electric vehicle sharing systems with the consideration of demand uncertainty

Computers & Operations Research ◽

10.1016/j.cor.2021.105437 ◽

2021 ◽

pp. 105437

Author(s):

Chung-Cheng Lu ◽

Shangyao Yan ◽

Hui-Chieh Li ◽

Ali Diabat ◽

Hsiao-Tung Wang

Keyword(s):

Electric Vehicle ◽

Demand Uncertainty ◽

Fleet Deployment

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

Discrete Dynamics in Nature and Society ◽

10.1155/2021/3592460 ◽

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.

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Optimal Fleet Deployment Strategy: Model the Effect of Shared E-Bikes on Bike-Sharing System

Journal of Advanced Transportation ◽

10.1155/2021/6678637 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Siying Zhu

Keyword(s):

Demand Uncertainty ◽

Fleet Size ◽

Deployment Strategy ◽

System A ◽

Strategy Model ◽

Trip Distance ◽

Bike Sharing ◽

Optimisation Model ◽

The Impact ◽

Fleet Deployment

Following the bike-sharing system, the shared e-bike becomes increasingly popular due to the advantage in speed, trip distance, and so forth. However, limited research has investigated the impact of the introduction of shared e-bikes on the existing bike-sharing systems. This paper aims to study the effect of shared e-bikes on the traditional bike-sharing system and determine the optimal fleet deployment strategy under a bimodal transportation system. A stochastic multiperiod optimisation model is formulated to capture the demand uncertainty of travelers. The branch-and-bound algorithm is applied to solve problem. A 15-station numerical example is applied to examine the validity of the model and the effectiveness of the solution algorithm. The performance of integrated e-bike and bike-sharing system has been compared with the traditional bike-sharing system. The impacts of the charging efficiency, fleet size, and pricing strategy of e-bike-sharing system on the traditional bike-sharing system have been examined.

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