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Minimum-delay opportunity charging scheduling for electricbuses

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Title: Minimum-delay opportunity charging scheduling for electricbuses
Authors: McCabe, Dan, Ban, Xuegang (Jeff), Kulcsár, Balázs Adam, 1975
Source: Communications in Transportation Research. 5
Subject Terms: Opportunity charging, Combinatorial Benders decomposition, Layover charging, Battery-electric bus, Heuristics
Description: Transit agencies that operate battery-electric buses must carefully manage fast-charging infrastructure to extend daily bus range without degrading on-time performance. To support this need, we propose a mixed-integer linear programming model to schedule opportunity charging that minimizes the amount of departure delay in all trips served by electric buses. Our novel approach directly tracks queuing at chargers in order to set and propagate departure delays. Allowing but minimizing delays makes it possible to optimize performance when delays due to traffic conditions and charging needs are inevitable, in contrast with existing methods that require charging to occur during scheduled layover time. To solve the model, we develop two algorithms based on decomposition. The first is an exact solution method based on combinatorial Benders (CB) decomposition, which avoids directly enumerating the model's logic-based “big M” constraints and their inevitable computational challenges. The second, inspired by the CB approach but more efficient, is a polynomial-time heuristic based on linear programming that we call Select–Sequence–Schedule (3S). Computational experiments on both a simple notional transit network and the real bus system of King County, Washington, USA demonstrate the performance of both methods. The 3S method appears particularly promising for creating good charging schedules quickly at real-world scale.
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  Data: Minimum-delay opportunity charging scheduling for electricbuses
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  Data: <searchLink fieldCode="AR" term="%22McCabe%2C+Dan%22">McCabe, Dan</searchLink><br /><searchLink fieldCode="AR" term="%22Ban%2C+Xuegang+%28Jeff%29%22">Ban, Xuegang (Jeff)</searchLink><br /><searchLink fieldCode="AR" term="%22Kulcsár%2C+Balázs+Adam%22">Kulcsár, Balázs Adam</searchLink>, 1975
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  Data: <i>Communications in Transportation Research</i>. 5
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  Data: <searchLink fieldCode="DE" term="%22Opportunity+charging%22">Opportunity charging</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+Benders+decomposition%22">Combinatorial Benders decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Layover+charging%22">Layover charging</searchLink><br /><searchLink fieldCode="DE" term="%22Battery-electric+bus%22">Battery-electric bus</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristics%22">Heuristics</searchLink>
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  Label: Description
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  Data: Transit agencies that operate battery-electric buses must carefully manage fast-charging infrastructure to extend daily bus range without degrading on-time performance. To support this need, we propose a mixed-integer linear programming model to schedule opportunity charging that minimizes the amount of departure delay in all trips served by electric buses. Our novel approach directly tracks queuing at chargers in order to set and propagate departure delays. Allowing but minimizing delays makes it possible to optimize performance when delays due to traffic conditions and charging needs are inevitable, in contrast with existing methods that require charging to occur during scheduled layover time. To solve the model, we develop two algorithms based on decomposition. The first is an exact solution method based on combinatorial Benders (CB) decomposition, which avoids directly enumerating the model's logic-based “big M” constraints and their inevitable computational challenges. The second, inspired by the CB approach but more efficient, is a polynomial-time heuristic based on linear programming that we call Select–Sequence–Schedule (3S). Computational experiments on both a simple notional transit network and the real bus system of King County, Washington, USA demonstrate the performance of both methods. The 3S method appears particularly promising for creating good charging schedules quickly at real-world scale.
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      – SubjectFull: Combinatorial Benders decomposition
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