Sharing network features analysis and dispatching strategy design
| DOI | https://doi.org/10.1108/IMDS-01-2022-0019 |
| Published date | 06 October 2022 |
| Date | 06 October 2022 |
| Pages | 2371-2392 |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
| Author | Tong Lv,Shi Lefeng,Weijun He |
Sharing network features analysis
and dispatching strategy design
Tong Lv
China Three Gorges University, Yichang, China
Shi Lefeng
National Center for Applied Mathematics in Chongqing,
Chongqing Normal University, Chongqing, China, and
Weijun He
China Three Gorges University, Yichang, China
Abstract
Purpose –A vital job for one sharing business is dynamically dispatching shared items to balance the
demand-supply of different sharing points in one sharing network. In order to construct a highly efficient
dispatch strategy, this paper proposes a new dispatching algorithm based on the findings of sharing network
characteristics.
Design/methodology/approach –To that end, in this paper, the profit-changing process of single sharing
points is modeled and analyzed first. And then, the characteristics of the whole sharing network are
investigated. Subsequently, some interesting propositions are obtained, based on which an algorithm (named
the Two-step random forest reinforcement learning algorithm) is proposed.
Findings –The authors discover that the sharing points of a common sharing network could be
categorized into 6 types according to their profit dynamics; a sharing network that is made up of various
combinations of sharing stations would exhibit distinct profit characteristics. Accounting for the
characteristics, a specific method for guiding thedynamicdispatchofsharedproductsisdevelopedand
validated.
Originality/value –Because the suggested method considers the interaction features between sharing points
in a sharing network, its computation speeds and the convergence efficacy to the global optimum scheme are
better than similar studies. It suits better to the sharing business requiring a higher time-efficiency.
Keywords Sharing business, Sharing point, Dispatching strategy, Multi-step random forest reinforcement
learning algorithm
Paper type Research paper
1. Introduction
The continual advancement of Internet technology is transforming many sectors’business
models, making them more intelligent and convenient (Perren and Kozinets, 2017;Sestino
et al., 2020), within which the sharing economy is the most prominent one. Thanks to the
assistance of demand-supply matching platforms and other auxiliary devices such as long-
distance monitoring and controlling devices, some derivative sharing models, such as
sharing bikes, sharing cars, and the likes, are stepping into our everyday lives, bringing
about lots of convenience to citizens and thus gaining more and more popularity (Fan et al.,
2019;Ritter and Schanz, 2019). On the operational aspect, sharing economies transfer the
right to use goods or services to the demander for a short period of time without any transfer
of ownership, which not only brings utility to the demander but also creates value for the
supplier (Pasimeni, 2021). Particularly in the fields with high investment costs and low
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This paper is fully funded by the National Social Science Fund of China (grant number: 19BJY077), the
Humanities and Social Sciences Projects of the Chongqing Municipal Education Commission of China
(grant number: 20SKGH036), and the Natural Science Foundation of Chongqing (grant number:
cstc2020jcyj-msxmX0808).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0263-5577.htm
Received 10 January 2022
Revised 7 May 2022
29 June 2022
1 September 2022
Accepted 3 September 2022
Industrial Management & Data
Systems
Vol. 122 No. 10, 2022
pp. 2371-2392
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-01-2022-0019
utilization rates of the invested items, such as in the fields of vehicle trips and lodging (Zhou
et al., 2020;Song et al., 2020;Sutherland and Jarrahi, 2018), the separation of the usage rights
of shared products from their owning rights could allow the usage rate to increase and,
meanwhile, amortize the investment cost for each usage, thereby lowering the participating
threshold of the corresponding business (Shi et al., 2020).
Because of these merits, many experts predict that the sharing model will be one of the
most essential trip models in future smart cities, especially when smart vehicles are wildly
populated and can be monitored and managed by a cloud-based platform (Ma et al., 2021;
Manogaran et al., 2022;Yang et al., 2021a,b;Bernardi and Diamantini, 2018;Minttu and Nina,
2020;Curtis and Mont, 2020). From the operation perspective, the realization of the sharing
model requires the collective participation of both the sharing network and the sharing
platform. The sharing network provides a physical foundation for the sharing business,
serving as a physical link between the sharing platform’s online virtual transactions and the
real use of shared items (Jalali et al., 2022), while the sharing platform is in charge of
dispatching shared products throughout the sharing network. It is noteworthy that the
dispatching efficiency of the sharing platform depends closely on the dispatching algorithm.
A highly efficient algorithm not only promotes the utilization rate of shared items but could
even be favorable to reducing the layout scale of the sharing network (Boyac et al., 2015).
Therefore, it is preferable to consider the sharing network typology and the dynamic
dispatching strategy of a sharing platform together when designing one of them or both.
Unfortunately, as we know, existing studies in the domain of sharing network design or
dispatching strategy design seldom treat them as a whole to discuss (Jingyi et al., 2022;Ge
et al., 2022;Chen et al., 2021). To close the gap, this study first examines the connection
relationship between the sharing network architecture and the dispatching strategy of
shared products, and then designs a method to guide the dispatching of shared products in
the sharing network at the end.
The remaining contents of this article are arranged as follows: section 2 presents the basic
notions and the basic setting related with this article; following that, in section 3, the
operational characteristics of sharing points and sharing networks are analyzed respectively,
based on which some propositions are obtained; according to the propositions, an algorithm
for dynamically dispatching shared products in sharing networks is put forth in section 4;
subsequently the algorithm is then validated using a mathematical example in section 5;in
the end, a conclusion is given in section 6.
Compared with current relevant researches, the main contributions of this article could be
summarized as follows: (1) combining the characteristics analysis of sharing networks with
the dispatching strategy design of shared products to reveal their relationships; (2)
corresponding optimization algorithm is suggested.
2. Sharing business description
2.1 Preliminary
2.1.1 Basic notions. As mentioned in the above introduction, the implementation of a sharing
business heavily relies on the layout of the sharing network and the dynamic dispatchment of
the sharing platform to shared items. The root reason is an imbalance in the supply and
demand for shared products at each sharing point. In order to balance the supply-demand
and achieve the profit maximum, not only should the layout design of the sharing network be
reasonable, but the dispatching efficiency of the sharing platform also ought to be improved
(Cho et al., 2020;He et al., 2021;Beojone and Geroliminis, 2021).
To demonstrate the influence of the supply-demand imbalance, Figure 1 is drawn.
Figure 1 shows a sharing network in which the supply-demand states of different sharing
points differ due to the stochasticity of demand and the liquidity of shared products among
sharing points. As shown in Figure 1, the supply of points 1 and 3 is more than demand, while
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