Route Scheduling for Centralized Electric Bus Depots

Abstract

Route scheduling is crucial for uninterrupted operation of modern bus fleets consisting of electric buses. This paper proposes an exact route scheduling optimization model for centralized bus depots based on mixed integer linear programming. In order to adjust to the current situation at many electric bus depots, the model considers a heterogeneous fleet consisting of multiple types of electric buses with different battery capacities. With additional charging scheduling the model can minimize the number of buses charging simultaneously which directly leads to load peak reduction. This allows considering further parameters, such as for example the grid capacity limit. The model can be used to minimize the necessary number of buses, to define the optimum composition of the fleet as well as to minimize the total cost of the fleet. The results show a clear cost advantage of operating a heterogeneous fleet as well as the benefits of combined route-and charging scheduling. Timetables from five real depots from the city of Hamburg in Germany were used as examples in this paper. Analysis of the proposed model using real data can provide a valuable input to other transportation companies preparing for the electrification of their fleet.

Optimizing the Scheduling of Electrified Public Transport System in Malta

“…Although this does not necessarily remove the NP-hardness, based on the relaxation applied, the problem can be solved in terms of a mixed-integer linear programming problem, which is a result of applying LP relaxation to the mixed-integer programming (MILP). A case of scheduling electric buses with the help of MILP was proposed in [19,20]. Notably, in [20], the MILPbased approach allows arbitrage, especially during off-peak hours, to charge the electric bus batteries, which satisfies the objective of minimizing the number of buses and the cost of the bus operation, thereby making the problem a mixed-integer one.…”

“…A case of scheduling electric buses with the help of MILP was proposed in [19,20]. Notably, in [20], the MILPbased approach allows arbitrage, especially during off-peak hours, to charge the electric bus batteries, which satisfies the objective of minimizing the number of buses and the cost of the bus operation, thereby making the problem a mixed-integer one. A similar sequential planning process was described in [21], where a line planning and a vehicle scheduling problem were solved concurrently by converting the sequential problem into a MILP.…”

“…Heuristics [7,10] Combined auction NP-hard Classical Optimizers [20][21][22] Exactness in the solution Polynomial combinations Evolutionary Computation [23][24][25] Accommodative to non-differentiable heuristics Global optimality not gauranteed…”

“…Time and SOC variables are limited in (17) and (18), respectively. The binary variables for trips and buses are defined in (19) and (20), respectively.…”

Optimization of e-Mobility Service for Disabled People Using a Multistep Integrated Methodology

“…In the literature, many studies analyze e-mobility facing the vehicle routing problem, fundamental for delivery and collection of goods or people, integrated with the EVs charging needs [13][14][15][16][17][18][19][20][21]. In Ref.…”