Stochastic profit maximization and pricing in hub location problems with elastic demand: Mathematical formulations and exact algorithms
研究了在需求不确定且随价格变化的情况下，如何同时决定枢纽位置、节点分配、定价和路径以最大化利润，并提出了求解大规模问题的精确算法。
This paper addresses profit maximization and pricing in capacitated and uncapacitated single allocation hub location problems taking into account the uncertain and price-elastic demand. We formulate the problem as a two-stage stochastic program in which a finite set of discrete price values are used to derive the demand. The models aim to determine simultaneously the location of hubs, the allocation of nodes to the hubs, the pricing decisions and the routing of demand within the network in order to maximize profit. The mathematical formulations for small uncapacitated and capacitated problems can be solved to optimality by commercial solvers. To solve large instances, we develop a Benders decomposition algorithm and an enhanced Lagrangian relaxation technique. We conduct extensive computational experiments using two well-known hub location datasets and present numerical results and analysis along with managerial insights.