Intermediate service facility planning in a stochastic and competitive market: Incorporating agent-infrastructure interactions over networks
Intermediate service facility planning in a stochastic and competitive market: Incorporating agent-infrastructure interactions over networks
复制标题
DOI：
10.1016/j.trc.2023.104242
复制
发表时间：
2023-04
期刊：
影响因子：
--
通讯作者：
Sina Baghali;Zhaomiao Guo;Julio Deride;Yueyue Fan
中科院分区：
文献类型：
--
作者：
Sina Baghali;Zhaomiao Guo;Julio Deride;Yueyue Fan
本文提出了一个基于网络的多智能体优化模型，用于随机竞争市场中服务设施的战略规划。我们专注于中间性质的服务设施类型，即，用户可能需要偏离最短路径以在用户的计划起点和目的地之间接收/提供服务。这个问题在新兴的交通流动性中有许多应用，包括动态共乘枢纽设计和替代燃料汽车加油站的竞争性设施选址和分配问题。本文的主要贡献是建立了一个新的多智能体优化框架，考虑分散的决策设施投资者和用户在交通网络，并提供了严格的分析，其数学性质，如系统平衡的唯一性和存在性。此外，我们开发了一个精确的凸重新制定原来的多智能体优化问题，以克服非凸性带来的计算挑战。对案例研究的广泛分析表明，所提出的模型可以在不确定的环境中捕捉不同利益相关者之间的复杂互动。此外，我们的模型允许量化的随机建模和信息可用性的价值，通过探索随机度量，包括值的随机解（VSS）和期望值的完美信息（EVPI），在多智能体框架。
This paper presents a network-based multi-agent optimization model for the strategic planning of service facilities in a stochastic and competitive market. We focus on the type of service facilities that are of intermediate nature, i.e., users may need to deviate from the shortest path to receive/provide services in between the users’ planned origins and destinations. This problem has many applications in emerging transportation mobility, including dynamic ride-sharing hub design and competitive facility location and allocation problems for alternative fuel vehicle refueling stations. The main contribution of this paper is establishing a new multi-agent optimization framework considering decentralized decision makings of facility investors and users over a transportation network and providing rigorous analyses of its mathematical properties, such as uniqueness and existence of system equilibrium. In addition, we develop an exact convex reformulation of the original multi-agent optimization problems to overcome computational challenges brought by non-convexity. Extensive analysis on case studies showed how the proposed model can capture the complex interaction between different stakeholders in an uncertain environment. Additionally, our model allowed quantifying the value of stochastic modeling and information availability by exploring stochastic metrics, including value of stochastic solution (VSS) and expected value of perfect information (EVPI), in a multi-agent framework.