The timely transportation of goods to customers is an essential component of
economic activities. However, heavy-duty diesel trucks used for goods delivery
significantly contribute to greenhouse gas emissions within many large
metropolitan areas, including Los Angeles, New York, and San Francisco. To
reduce GHG emissions by facilitating freight electrification, this paper
proposes Joint Routing and Charging scheduling for electric trucks. The
objective of the associated optimization problem is to minimize the cost of
transportation, charging, and tardiness. A large number of possible
combinations of road segments as well as a large number of combinations of
charging decisions and charging durations leads to a combinatorial explosion in
the possible decisions electric trucks can make. The resulting mixed-integer
linear programming problem is thus extremely challenging because of the
combinatorial complexity even in the deterministic case. Therefore, a Surrogate
Level-Based Lagrangian Relaxation (SLBLR) method is employed to decompose the
overall problem into significantly less complex truck subproblems. In the
coordination aspect, each truck subproblem is solved independently of other
subproblems based on the values of Lagrangian multipliers. In addition to
serving as a means of guiding and coordinating trucks, multipliers can also
serve as a basis for transparent and explanatory decision-making by trucks.
Testing results demonstrate that even small instances cannot be solved using
the off-the-shelf solver CPLEX after several days of solving. The SLBLR method,
on the other hand, can obtain near-optimal solutions within a few minutes for
small cases, and within 30 minutes for large ones. Furthermore, it has been
demonstrated that as battery capacity increases, the total cost decreases
significantly; moreover, as the charging power increases, the number of trucks
required decreases as well