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This paper presents a network-based multi-agent optimization model for thestrategic planning of service facilities in a stochastic and competitivemarket. We focus on the type of service facilities that are of intermediatenature, i.e., users may need to deviate from the shortest path toreceive/provide services in between the users' planned origins anddestinations. This problem has many applications in emerging transportationmobility, including dynamic ride-sharing hub design and competitive facilitylocation and allocation problems for alternative fuel vehicle refuelingstations. The main contribution of this paper is establishing a new multi-agentoptimization framework considering decentralized decision makings of facilityinvestors and users over a transportation network and providing rigorousanalyses of its mathematical properties, such as uniqueness and existence ofsystem equilibrium. In addition, we develop an exact convex reformulation ofthe original multi-agent optimization problems to overcome computationalchallenges brought by non-convexity. Extensive analysis on case studies showedhow the proposed model can capture the complex interaction between differentstakeholders in an uncertain environment. Additionally, our model allowedquantifying the value of stochastic modeling and information availability byexploring stochastic metrics, including value of stochastic solution (VSS) andexpected value of perfect information (EVPI), in a multi-agent framework.

Intermediate Service Facility Planning in a Stochastic and Competitive Market: Incorporating Agent-infrastructure Interactions over Networks