fleet allocation
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2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Jianhua Cao ◽  
Weixiang Xu ◽  
Wenzheng Wang

In Bike-Sharing System (BSS), the initial number of bikes at station will affect the time interval and the amount of rebalancing, which is usually empirically determined and does not reflect the characteristics of consumer demand in finer time granularity, thus possibly leading to biased conclusions. In this paper, a fleet allocation method considering demand gap is first proposed to calculate the initial number of bikes at each station. Then, taking the number of demand gap periods as the decision variable, an optimization model is built to minimize the total rebalancing amount. Furthermore, the research periods are divided into multiple subcycles, the single-cycle and multicycle rebalancing strategies are presented, and the additional subcycle rebalancing method is introduced to amend the number of bikes between subcycles to decrease the rebalancing amount of the next subcycle. Finally, our methods are verified in effectively decreasing the rebalancing amount in a long-term rebalancing problem.


2019 ◽  
Vol 53 (3) ◽  
pp. 623-641 ◽  
Author(s):  
Yixiao Huang ◽  
Lei Zhao ◽  
Warren B. Powell ◽  
Yue Tong ◽  
Ilya O. Ryzhov

In a two-tiered city logistics system, an urban logistics company usually partitions the urban area into regions and allocates its delivery fleet (e.g., vehicles, couriers) to these regions. On a daily basis, the delivery station in each region receives the delivery packages from the city distribution centers and delivers them to customers within the region, using its allocated delivery vehicles. A tactical decision in such a city logistics system is the allocation of its delivery fleet to the regions to minimize the expected operational cost of the entire system. However, because of the complexity of the urban delivery operations and the day-to-day variance of the customer demand, an accurate evaluation of the expected operational cost associated with an allocation decision can be very expensive. We propose a learning policy that adaptively selects the fleet allocation to learn the underlying expected operational cost function by incorporating the value of information. Specifically, we exploit the monotonicity of the expected operational cost in the number of allocated delivery vehicles in a region and extend the idea of knowledge gradient with discrete priors with resampling and regeneration (KGDP-R&R). Our numerical results demonstrate the effectiveness of KGDP-R&R against other learning policies as well as its managerial implications compared with heuristics in practice. The online appendix is available at https://doi.org/10.1287/trsc.2018.0861 .


2018 ◽  
Vol 10 (11) ◽  
pp. 4120 ◽  
Author(s):  
Xiuqiao Sun ◽  
Jian Wang ◽  
Weitiao Wu ◽  
Wenjia Liu

The freeway service patrol problem involves patrol routing design and fleet allocation on freeways that would help transportation agency decision-makers when developing a freeway service patrols program and/or altering existing route coverage and fleet allocation. Based on the actual patrol process, our model presents an overlapping patrol model and addresses patrol routing design and fleet allocation in a single integrated model. The objective is to minimize the overall average incident response time. Two strategies—overlapping patrol and non-overlapping patrol—are compared in our paper. Matrix encoding is applied in the genetic algorithm (GA), and to maintain population diversity and avoid premature convergence, a niche strategy is incorporated into the traditional genetic algorithm. Meanwhile, an elitist strategy is employed to speed up the convergence. Using numerical experiments conducted based on data from the Sioux Falls network, we clearly show that: overlapping patrol strategy is superior to non-overlapping patrol strategy; the GA outperforms the simulated annealing (SA) algorithm; and the computational efficiency can be improved when LINGO software is used to solve the problem of fleet allocation.


2016 ◽  
Vol 53 (5) ◽  
pp. 1485-1504 ◽  
Author(s):  
Peter W. Jansen ◽  
Ruben E. Perez

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