Governments in many counties are taking measures to promote electric vehicles. An important strategy is to build enough charging infrastructures so as to allevi- ate drivers' range anxieties. To help the governmen...
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Governments in many counties are taking measures to promote electric vehicles. An important strategy is to build enough charging infrastructures so as to allevi- ate drivers' range anxieties. To help the governments make plans about the public charging network, we propose a multi-stage stochastic integer programming model to determine the locations and capacities of charging facilities over finite planning horizons. We use the logit choice model to estimate drivers' random choices towards different charging stations nearby. The objective of the model is to minimize the expected total cost of installing and operating the charging facilities. Two simple al- gorithms are designed to solve this model, an approximation algorithm and a heuristic algorithm. A branch-and-price algorithm is also designed for this model, and some implementation details and improvement methods are explained. We do some nu- merical experiments to test the efficiency of these algorithms. Each algorithm has advantages over the CPLEX MIP solver in terms of solution time or solution qual- ity. A case study of Oakville is presented to demonstrate the process of designing an electric vehicle public charging network using this model in Canada.
The COVID-19 lockdown has reduced public transportation service to the disadvantaged and disabled people who urgently need adequate mobility to obtain essential suppliers. This paper aims to improve the life quality o...
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The COVID-19 lockdown has reduced public transportation service to the disadvantaged and disabled people who urgently need adequate mobility to obtain essential suppliers. This paper aims to improve the life quality of people with disabilities and elderly people by addressing social exclusion, accessibility, and mobility issues. Demand responsive transport services are frequently offered in the context of door-to-door transportation of the elderly and persons with disabilities. We study and compare two frameworks. We apply both Sample average approximation (SAA) and Rolling Horizon (RH) to optimize a car sharing system for the total cost, including initiation cost and operation cost after fleet size is determined. The model is implemented with given geographic conditions and other local information to be tailored for specific applications for local communities. Given that no historical data is available, random sample data is generated to simulate expected demands. We consider three types of probability distributions for daily demand data, and the results generated using three different distributions are being examined and compared. The research shows that the demand data following a normal distribution results in the minimum total cost. Additionally, we study the impact of several factors on total cost, including demand fulfillment rates and operation hours. Our results suggest that the impact of fulfillment rate on fleet size is exponential after a threshold under all three types of daily demand data, and extended operation hours can significantly reduce the total cost. Finally, the paper provides applicable frameworks for city planners, NPOs, and policymakers to better allocate limited resources to implement the carsharing system when little to no historical travel information is available for low-density population areas. It is anticipated that the outcome from this research would benefit disadvantaged and disabled travelers during COVID-19 or similar difficult
In this paper, we consider multi-period single resource stochastic capacity expansion problems with lost sales. We study two models. The first model does not consider fixed-charge for capacity purchases, while the sec...
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In this paper, we consider multi-period single resource stochastic capacity expansion problems with lost sales. We study two models. The first model does not consider fixed-charge for capacity purchases, while the second one incorporates fixed-charges. We use multi-stagestochasticinteger programs to present both models and show how adding the fixed-charge cost changes the structure of the mathematical models. For both models, we study their structures and design polynomial-time algorithms to solve them. We present computational results to show the performance of the designed algorithms.
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