A variable-reduction technique for the fixed-route vehicle-refueling problem

The electric vehicle routing problem with nonlinear charging function

“…The FRVCP is a variant of the well-known fixed-route vehicle-refueling problem (FRVRP). The FRVRP seeks the minimum-cost refueling policy (which fuel stations to visit and the refueling quantity at each station) for a given origin-destination route (Suzuki 2014). Most of the research into the FRVRP and its variants applies only to internal combustion vehicles (which have negligible refueling times), but a few extensions to EVs have been reported.…”

DSS of vehicle refueling: A new enhanced approach with fuel weight considerations

“…Second, while in theory P2 can be solved to optimality by using a dynamic programming method, it is difficult to do so in practice (partly because the state variable, which reflects a vehicle's fuel level at various points along the route, is not discrete; see Suzuki [10]). Third, although it is possible to develop a metaheuristic that solves P2 to near optimality, this requires rather long solution time, especially for large instances (solving FRVRPs quickly is crucial for many carriers, as they must solve thousands of FRVRPs each day; see Suzuki [13]). …”

“…Our approach, for example, can be combined with the preprocessing technique proposed by Suzuki [13] to cut the CPU time of solving large instances. Our approach can also be used jointly with the "total cost minimization" concept developed by Suzuki [9] by slightly modifying the functional forms of the objective function and selected constraints.…”

Decision Support System of Truck Routing and Refueling: A Dual‐Objective Approach

“…We solve the above lexicographic optimization model to optimality by using the method (algorithm) developed specifically for this study (it is difficult to solve our model by using the standard solution technique, as it combines the resource (time)‐constrained SRP and the FRVRP, both of which are known to be NP‐hard (e.g., Zhu & Wilhelm, ; Suzuki, ). This algorithm, which resembles the branch‐and‐bound enumeration technique, seeks the optimal solution by finding the best refueling policy for all the possible routes between s and d , except for those whose distances are substantially longer than that of the shortest route (such that they cannot theoretically be the route chosen under the optimal solution).…”

“…It is known that this model generates only the Pareto-optimal (i.e., nondominated) solutions for every possible weight vector [w 1 , w 2 ], because if there exist multiple optimal solutions that minimize Equation (18), the model selects only the solution which achieves the minimal value of U 1 or U 2 , whichever is nonbinding in Equations (19) or (20) (Ogryczak, 1994). We solve the above lexicographic optimization model to optimality by using the method (algorithm) developed specifically for this study (it is difficult to solve our model by using the standard solution technique, as it combines the resource (time)-constrained SRP and the FRVRP, both of which are known to be NP-hard (e.g., Zhu & Wilhelm, 2007;Suzuki, 2012). This algorithm, which resembles the branch-and-bound enumeration technique, seeks the optimal solution by finding the best refueling policy for all the possible routes between s and d, except for those whose distances are substantially longer than that of the shortest route (such that they cannot theoretically be the route chosen under the optimal solution).…”