Optimization of mobile facility operations under time-varying traffic congestion and stochastic demand
A two-stage stochastic planning model is developed to optimize the operation of Mobile Facility(MF).The model considers the stochastic nature of both time-varying traffic conditions and user demands,providing a powerful tool for decision-makers.In the first stage,the model decides the number of MFs,schedules,and routes.The second stage focuses on the allocation of user demands and determines the unsatisfied service volume.To solve this model efficiently,this study combines time-dependent shortest-path algorithms with an L-shaped algorithm.To solve the time-dependent shortest-path problems for the movement paths of MFs and user arrivals at service points,the vary-ing speeds on the road links are discretized into piecewise functions.This makes the travel time a continuous linear function that satisfies the first-in-first-out network property,thereby enabling the modification of existing shortest-path algorithms for solving the time-dependent shortest-path prob-lems.In the L-shaped algorithm,the first-stage model is treated as the master problem,whereas the second-stage model serves as a subproblem.The algorithm first solves the master problem to obtain the first-stage decision variables,which are then used to solve the subproblem to generate optimal cuts for the master problem.Through iterative interactions between the master problem and subprob-lem,convergence to the global optimal solution of the model is achieved.In addition,the algorithm converges rapidly by incorporating valid inequalities.The proposed model and algorithm are empiri-cally studied using the case of MF instances in the COVID-19 nucleic acid testing service in Jiading District,Shanghai.The results indicate that the multi-cut L-shaped algorithm combined with valid in-equalities significantly improves the solution efficiency.Furthermore,as the scenarios of the user de-mand distribution increase,both the expected value of perfect information and the value of stochastic solutions significantly increase.These results underscore the importance of obtaining accurate infor-mation and considering the stochastic nature of traffic conditions and user demands during decision-making.
万方数据
维普
