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Abstract
Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions.
Highlights
We focus on the optimization of minimizing the total cost, which includes fuel consumption, different driver wages in different time slots, and CO2 emissions, in time-varying road conditions
This paper investigated the joint optimization problem of the green vehicle scheduling and routing problem, taking time-varying vehicles speeds and different wages in different time slots into considerations
The objective of the proposed model aims at minimizing transportation related costs and fixed cost of vehicles, and considers the cost of carbon emission
Summary
The optimization objectives of this study are shown as follows: to determine the optimal visiting route for each vehicle and to choose the optimal departure time for each vehicle from several available time slots
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