#Scheduling electric vehicles (EVs) in a #Mobility-#on-#Demand (#MoD) scheme involves determining the optimal assignment of EVs to customer requests while minimizing the overall cost or maximizing the revenue. The problem can be formulated as a mixed-integer programming (#MIP) problem as follows:
Decision Variables:
*x_ij: binary variable that takes value 1 if customer i is assigned to EV j, 0 otherwise
*y_j: binary variable that takes value 1 if EV j is in use, 0 otherwise
Objective Function:
*Minimize the total cost or maximize the total revenue:
Min/Max Σ(i,j) c_ij * x_ij - Σ(j) r_j * y_j
where c_ij is the cost (or revenue) of assigning customer i to EV j, and r_j is the cost (or revenue) of keeping EV j in use.
Constraints:
*Each customer is assigned to exactly one EV:
Σ(j) x_ij = 1, for all i
*Each EV can serve at most one customer at a time:
Σ(i) x_ij ≤ y_j, for all j
*Capacity constraints:
Σ(i) a_i * x_ij ≤ b_j * y_j, for all j
where a_i is the demand of customer i and b_j is the capacity of #EV j
Non-negativity constraints:
x_ij ≥ 0, for all i,j
y_j ≥ 0, for all j
The above MIP formulation can be solved using a range of #optimization software tools, such as #Gurobi, #CPLEX, or #SCIP. The solution to the MIP problem provides an optimal schedule of #EVs in the #MoD scheme, which can be used to minimize the #cost or maximize the revenue of the system while meeting the #demand of #customers.
#electricvehicles #software #programming
No comments:
Post a Comment