1,721,058 research outputs found
Hybrid Algorithms Service, Computing and Manufacturing Systems: Routing and Scheduling Solutions
No full textLayered Graph Models for the Electric Vehicle Routing Problem With Nonlinear Charging Functions
Full text linkElectric vehicle routing problems (EVRPs) involve the routing of a fleet of electric vehicles (EVs) to visit a set of customers while typically minimizing the total travel and charging time. Due to their limited autonomy, EVs may need to recharge their batteries en-route at charging stations (CSs). Thus, routing decisions also include which CSs to visit, and how much energy to charge during those visits. These decisions are compounded by the fact that charging times follow a nonlinear charging function with respect to the EV's state of charge (SoC). We propose a layered graph representation for the EVRP with nonlinear charging functions (EVRP-NL). Specifically, the layers correspond to discretized SoC values. Therefore, the arcs' energy consumption is approximated to match those values. We develop two compact formulations based on the layered graph. Furthermore, we introduced two charging policies that facilitate aligning charging duration with practical considerations. Computational results demonstrate the effectiveness of our formulations. Our best formulation effectively handles instances with up to 40 customers. On those instances, compared to the state-of-the-art compact formulation, our formulation solves 13 more instances to optimality with less than half of the computational time. Considering instances solved by both formulations to optimally, the approximation entailed by our formulation yields a 0.94% deviation on average. Since our best performing formulation is compact, it may be readily used by a broad audience. Furthermore, as the majority of algorithms for the EVPR and its variants are heuristics, our formulation could be beneficial in evaluating the performance of these methods
Charge scheduling for electric freight vehicles
Full text linkWe consider a fleet of electric freight vehicles (EFVs) that must deliver goods to a set of customers over the course of multiple days. In an urban environment, EFVs are typically charged at a central depot and rarely use public charging stations during delivery routes. Therefore, the charging schedule at the depot must be planned ahead of time so as to allow the vehicles to complete their routes at minimal cost. Vehicle fleet operators are subject to commercial electricity rate plans, which should be accounted for in order to provide an accurate estimation of the energy-related costs and restrictions. In addition, high vehicle utilization rates can accelerate battery aging, thereby requiring degradation mitigation considerations. We develop and solve a comprehensive mathematical model that incorporates a large variety of features associated with the use of EFVs. These include a realistic charging process, time-dependent energy costs, battery degradation, grid restrictions, and facility-related demand charges. Extensive numerical experiments are conducted in order to draw managerial insights regarding the impact of such features on the charging schedules of EFVs
Exact solution of the evasive flow capturing problem
Full text linkThe Evasive Flow Capturing Problem is defined as the problem of locating a set of law enforcement facilities on the arcs of a road network to intercept unlawful vehicle flows traveling between origin-destination pairs, who in turn deviate from their route to avoid any encounter with such facilities. Such deviations are bounded by a given tolerance. We first propose a bilevel program that, in contrast to previous studies, does not require a priori route generation. We then transform this bilevel model into a single-stage equivalent model using duality theory to yield a compact formulation. We finally reformulate the problem by describing the extreme rays of the polyhedral cone of the compact formulation and by projecting out the auxiliary variables, which leads to facet-defining inequalities and a cut formulation with an exponential number of constraints. We develop a branch-and-cut algorithm for the resulting model, as well as two separation algorithms to solve the cut formulation. Through extensive experiments on real and randomly generated networks, we demonstrate that our best model and algorithm accelerate the solution process by at least two orders of magnitude compared with the best published algorithm. Furthermore, our best model significantly increases the size of the instances that can be solved optimally
50th Anniversary Invited Article—Future research directions in stochastic vehicle routing
No full textStochastic vehicle routing, which deals with routing problems in which some of the key problem parameters are not known with certainty, has been an active, but fairly small research area for almost 50 years. However, over the past 15 years we have witnessed a steady increase in the number of papers targeting stochastic versions of the vehicle routing problem (VRP). This increase may be explained by the larger amount of data available to better analyze and understand various stochastic phenomena at hand, coupled with methodological advances that have yielded solution tools capable of handling some of the computational challenges involved in such problems. In this paper, we first briefly sketch the state-of-The-Art in stochastic vehicle routing by examining the main classes of stochastic VRPs (problems with stochastic demands, with stochastic customers, and with stochastic travel or service times), the modeling paradigms that have been used to formulate them, and existing exact and approximate solution methods that have been proposed to tackle them. We then identify and discuss two groups of critical issues and challenges that need to be addressed to advance research in this area. These revolve around the expression of stochastic phenomena and the development of new recourse strategies. Based on this discussion, we conclude the paper by proposing a number of promising research directions
A continuous approximation model for the fleet composition problem
No full textThis paper presents a continuous approximation model to determine the long-term vehicle fleet composition needed to perform distribution activities. The problem is a realistic variant of the vehicle routing problem, in which the fleet size and mix are also decision variables. The types of vehicles differ in terms of their capacities, fixed costs and variable costs. The objective is to minimize the total cost, subject to capacity and route duration constraints. We assume customers are distributed over a circular service region partitioned into zones, each of which is serviced by a single vehicle. The routing costs are assessed through a continuous approximation model. We present a mixed integer non-linear formulation for the problem, followed by computationally efficient upper and lower bounding procedures. The performance of the model and of its bounds is assessed on several test instances. © 2012 Elsevier Ltd
A Threshold Recourse Policy for the Electric Vehicle Routing Problem with Stochastic Energy Consumption
No full text50th Anniversary Invited Article—Goods distribution with electric vehicles: Review and research perspectives
No full textSince the mid-2000s, electric vehicles have gained popularity in several countries even though their market share is still relatively low. However, most gains have been made in the area of passenger vehicles and most technical and scientific studies have been devoted to this case. By contrast, the potential of electric vehicular technology for goods distribution has received less attention. The issues related to the use of electric vehicles for goods distribution reveal a wide range of relevant research problems. The aims of this survey paper are to provide transportation researchers an overview of the technological and marketing background needed to conduct research in this area, to present a survey of the existing research in this field, and to offer perspectives for future research
Long-haul vehicle routing and scheduling with idling options
No full textThis paper introduces the vehicle routing and truck driver scheduling problem with idling options, an extension of the long-haul vehicle routing and truck driver scheduling problem with a more comprehensive objective function that accounts for routing cost, driver cost and idling cost, i.e., the cost associated with energy supply used to maintain drivers' comfort when the vehicle is not moving. For the idling cost, we consider Electrified Parking Space (EPS) and Auxiliary Power Unit (APU) usage costs. The use of EPSs or APUs avoids keeping the engine running while the vehicle is not moving. We develop a multi-start matheuristic algorithm that combines adaptive large neighborhood search and mixed integer linear programming. We present extensive computational results on instances derived from the Solomon test bed
An exact algorithm to solve the vehicle routing problem with stochastic demands under an optimal restocking policy
Full text linkThis paper examines the Vehicle Routing Problem with Stochastic Demands (VRPSD), in which the actual demand of customers can only be realized upon arriving at the customer location. Under demand uncertainty, a planned route may fail at a specific customer when the observed demand exceeds the residual capacity. There are two ways to face such failure events, a vehicle can either execute a return trip to the depot at the failure location and refill the capacity and complete the split service, or in anticipation of potential failures perform a preventive return to the depot whenever the residual capacity falls below a threshold; overall, these return trips are called recourse actions. In the context of VRPSD, a recourse policy which schedules various recourse actions based on the visits planned beforehand on the route must be designed. An optimal recourse policy prescribes the cost-effective returns based on a set of optimal customer-specific thresholds. We propose an exact solution method to solve the multi-VRPSD under an optimal restocking policy. The Integer L-shaped algorithm is adapted to solve the VRPSD in a branch-and-cut framework. To enhance the efficiency of the presented algorithm, several lower bounding schemes are developed to approximate the expected recourse cost
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