Fleet co-deployment for liner shipping alliance: Vessel pool operation with uncertain demand

2021 ◽  
Vol 214 ◽  
pp. 105923
Author(s):  
Jihong Chen ◽  
Chenglin Zhuang ◽  
Chen Yang ◽  
Zheng Wan ◽  
Xin Zeng ◽  
...  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jingxu Chen ◽  
Yiran Wang ◽  
Xinlian Yu ◽  
Zhiyuan Liu

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.


1992 ◽  
Vol 65 (4) ◽  
pp. 593 ◽  
Author(s):  
Yu-Min Chen ◽  
Dipak C. Jain

2021 ◽  
pp. 1-13
Author(s):  
Sun Jianzhu ◽  
Zhang Qingshan ◽  
Yu Yinyun

Multi-site selection is a hot research issue for equipment manufacturing enterprises. With the development of smart industry, equipment manufacturing enterprises have entered the era of personalized and small batch manufacturing. Enterprises want to better meet customer needs and win competition, they must carry out scientific factory planning and site selection, so as to ensure quick response to the market. Based on this, this paper proposes a two-stage location selection model. Firstly, the method uses fuzzy numbers to express the demand size of demand points. Secondly, the distance factor is used as a criterion to select the candidate manufacturing bases with sufficient available resources. Next, the location model of enterprise manufacturing base is established which the goal of maximizing service efficiency and the constraints of time, cost and demand. Finally, a random numerical example is used to simulate the model, and lingo is used to solve it.


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