ﻻ يوجد ملخص باللغة العربية
This paper considers the vehicle routing problem of a fleet operator to serve a set of transportation requests with flexible time windows. That is, the operator presents discounted transportation costs to customers to exchange the time flexibility of pickup or delivery. A win-win routing schedule can be achieved via such a process. Different from previous research, we propose a novel bi-level optimization framework, to fully characterize the interaction and negotiation between the fleet operator and customers. In addition, by utilizing the property of strong duality, and the KKT optimality condition of customer optimization problem, the bi-level vehicle routing problem can be equivalently reformulated as a mixed integer nonlinear programming (MINLP) problem. Besides, an efficient algorithm combining the merits of Lagrangian dual decomposition method and Benders decomposition method, is devised to solve the resultant MINLP problem. Finally, extensive numerical experiments are conducted, which validates the effectiveness of proposed bi-level model on the operation cost saving, and the efficacy of proposed solution algorithm on computation speed.
A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand respon
Lithium-ion battery packs are usually composed of hundreds of cells arranged in series and parallel connections. The proper functioning of these complex devices requires suitable Battery Management Systems (BMSs). Advanced BMSs rely on mathematical m
Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency. Most of the existing research on unsignalized intersection cooperation c
The increased uptake of electric vehicles (EVs) leads to increased demand for electricity, and sometime pressure to power grids. Uncoordinated charging of EVs may result in putting pressure on distribution networks, and often some form of optimisatio
Quantum annealing (QA) is a quantum computing algorithm that works on the principle of Adiabatic Quantum Computation (AQC), and it has shown significant computational advantages in solving combinatorial optimization problems such as vehicle routing p