The problem addresses the expected cost minimization of meeting the uncertain demand of a product during a discrete time planning horizon. The product is supplied by selecting fixed quantity shipments that have lead t...
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(纸本)9789897583520
The problem addresses the expected cost minimization of meeting the uncertain demand of a product during a discrete time planning horizon. The product is supplied by selecting fixed quantity shipments that have lead times. Due to the uncertainty of demand, corrective actions, such as shipment cancellations and postponements, must be taken with associated costs and delays. The problem is modeled as an extension of the discrete lot-sizing problem with different capacities and uncertain demand, which belongs to the NP-hard class. To improve the resolution of the problem by tightening its formulation, valid inequalities based on the (l, S) inequalities approach are used. Given that the inequalities are highly dominated for most experimental instances, a scheme is established to determine undominated ones. Computational experiments are performed on the resolution of the model and variants that include subsets of undominated and representative valid inequalities for instances of several information structures of uncertainty. The experimental results allow to conclude that the inclusion of undominated and representative derived (l, S) valid inequalities enable a more efficient resolution of the model.
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
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