Fuzzy Optimization
Malihe Niksirat
Abstract
Purpose: During the Corona virus epidemic and in order to comply with the rules of social distancing, public transport operators have to operate with less capacity. Because demand may be overcapacity in different areas at different times of the day, drivers are forced to refrain from serving passengers ...
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Purpose: During the Corona virus epidemic and in order to comply with the rules of social distancing, public transport operators have to operate with less capacity. Because demand may be overcapacity in different areas at different times of the day, drivers are forced to refrain from serving passengers at certain stations to avoid overcrowding.Methodology: The purpose of this paper is to develop decision support tools to prevent congestion of vehicles. Also, in order to consider the real conditions, two types of fuzzy and scenario-based uncertainty are considered. A dynamic nonlinear integer programming model is introduced to obtain the optimal service pattern for vehicles that are ready to be dispatched. To overcome the combined uncertainty of the problem, possibility theory has been proposed as a new fuzzy stochastic programming approach that has significant advantages.Findings: The model is clearly strikes a balance between observing social distancing by reducing the capacity of vehicles and reducing the waiting time of passengers who lose services. Numerical examples are provided to illustrate the proposed concepts and model and to compare the results.Originality/Value: The proposed decision support model can suggest service patterns for different lines service and can assess public transport operators to evaluate the advantages and disadvantages of implementing epidemic-based service patterns due to operational advances and demand level of travelers.