The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

International audienceDue to new regulations and further technological progress in the field of electric vehicles, the research community faces the new challenge of incorporating the electric energy based restrictions into vehicle routing problems. One of these restrictions is the limited battery capacity which makes detours to recharging stations necessary, thus requiring efficient tour planning mechanisms in order to sustain the competitiveness of electric vehicles compared to conventional vehicles. We introduce the Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and recharging stations (E-FSMFTW) to model decisions to be made with regards to fleet composition and the actual vehicle routes including the choice of recharging times and locations. The available vehicle types differ in their transport capacity, battery size and acquisition cost. Furthermore, we consider time windows at customer locations, which is a common and important constraint in real-world routing and planning problems. We solve this problem by means of branch-and-price as well as proposing a hybrid heuristic, which combines an Adaptive Large Neighbourhood Search with an embedded local search and labelling procedure for intensification. By solving a newly created set of benchmark instances for the E-FSMFTW and the existing single vehicle type benchmark using an exact method as well, we show the effectiveness of the proposed approach

Metaheuristics for solving a multimodal home-healthcare scheduling problem

Abstract We present a general framework for solving a real-world multimodal home-healthcare scheduling (MHS) problem from a major Austrian home-healthcare provider. The goal of MHS is to assign home-care staff to customers and determine efficient multimodal tours while considering staff and customer satisfaction. Our approach is designed to be as problem-independent as possible, such that the resulting methods can be easily adapted to MHS setups of other home-healthcare providers. We chose a two-stage approach: in the first stage, we generate initial solutions either via constraint programming techniques or by a random procedure. During the second stage, the initial solutions are (iteratively) improved by applying one of four metaheuristics: variable neighborhood search, a memetic algorithm, scatter search and a simulated annealing hyper-heuristic. An extensive computational comparison shows that the approach is capable of solving real-world instances in reasonable time and produces valid solutions within only a few seconds

Tour planning with a hybrid heterogeneous electric fleet

National audienceIn recent years, the research in sustainable and energy efficient mobility increased substantially with the rising concerns about climate change. One of the major contributors to this change are green house gas emissions created by the industry, during energy production, agriculture, and transportation. Electric powered mobility (e-mobility) is one of the directions currently taken in order to address those concerns in the transportation sector. Nowadays there exist several classes of electric vehicles to replace classical internal combustion engine vehicles (ICEV). Two prominent examples are the pure battery electric vehicle (BEV) and the plug-in hybrid electric vehicle (PHEV). BEVs use an electric power-train using the energy stored in a rechargeable battery. Its limited capacity and the relative long recharging times imposes significant operational challenges to the planner. However, the price for electricity is far lower than fossil fuel, which can lead to more cost-efficient tours compared to ICEVs. PHEVs do have both engines embedded and thus avoiding the range limitations while benefiting from the cheap electric engine for short distances. This comes at the expense of smaller batteries and higher consumption rates for both energy and fuel due to the additional weight of an extra engine. In addition, the initial costs are quite large for both BEVs and PHEVs, which requires requires cost-efficient tour plans to render them an economically sound choice. In the literature, several definitions for heterogeneous fleets of ICEVs have been investigated [1]. The operational routing problem for BEVs has been studied with increased interest in recent years, starting with the work of [2]. However, a formulation and solution method to tackle the combined problem considering different ICEV, PHEV and BEV types is still missing in the literature. 2 Hybrid heterogeneous electric fleet planning The hybrid heterogeneous electric fleet routing problem with time windows and recharging stations (H 2 EFTW) is a rich vehicle routing problem considering demand and time windows at customers, an energy resource for PHEVs and BEVs and different cost metrics based on the engine used to travel between pairs of nodes. Time-window bounds are modelled as hard constraints and waiting times prior to serving customers are not penalized. The energy resource can be replenished using optional recharging stations. As part of the problem, the amount o

Routing a Mix of Conventional, Plug-in Hybrid, and Electric Vehicles

International audienceWe introduce an electric vehicle routing problem combining conventional, plug-in hybrid, and electric vehicles. Electric vehicles are constrained in their service range by their battery capacity, and may require time-consuming recharging operations at some specific locations. Plug-in hybrid vehicles have two engines, an internal combustion engine and an electric engine using a built-in rechargeable battery. These vehicles can avoid visits to recharging stations by switching to fossil fuel. However, this flexibility comes at the price of a generally higher consumption rate and utility cost. To solve this complex problem variant, we design a sophisticated metaheuristic which combines a genetic algorithm with local and large neighborhood search. All route evaluations, within the approach, are based on a layered optimization algorithm which combines labeling techniques and greedy evaluation policies to optimally insert recharging stations visits in a fixed trip and to select the fuel types. The metaheuristic is finally hybridized with an integer programming solver, over a set partitioning formulation, so as to recombine high-quality routes from the past search into better solutions. Extensive experimental analyses are conducted, highlighting the good performance of the algorithm and the contribution of each of its main components. Finally, we investigate the impact of fuel and energy cost on fleet composition decisions. Our experiments show that a careful use of a mixed fleet can significantly reduce operational costs in a large variety of price scenarios, in comparison with the use of a fleet composed of a single vehicle class

Hybrid Heuristics for Multimodal Homecare Scheduling

Abstract. We focus on hybrid solution methods for a large-scale realworld multimodal homecare scheduling (MHS) problem, where the objective is to find an optimal roster for nurses who travel in tours from patient to patient, using different modes of transport. In a first step, we generate a valid initial solution using Constraint Programming (CP). In a second step, we improve the solution using one of the following metaheuristic approaches: (1) variable neighborhood descent, (2) variable neighborhood search, (3) an evolutionary algorithm, (4) scatter search and (5) a simulated annealing hyper heuristic. Our evaluation, based on computational experiments, demonstrates how hybrid approaches are particularly strong in finding promising solutions for large real-world MHS problem instances.

Metaheuristics for solving a multimodal home-healthcare scheduling problem

International audienceWe present a general framework for solving a real-world multi-modal home-healthcare scheduling (MHS) problem from a major Austrian home-healthcare provider. The goal of MHS is to assign home-care staff to customers and determine efficient multimodal tours while considering staff and customer satisfaction. Our approach is designed to be as problem-independent as possible, such that the resulting methods can be easily adapted to MHS setups of other home-healthcare providers. We chose a two-stage approach: in the first stage, we generate initial solutions either via constraint programming techniques or by a random procedure. During the second stage, the initial solutions are (iteratively) improved by applying one of four metaheuristics: variable neighborhood search, a memetic algorithm, scatter search and a simulated annealing hyper-heuristic. An extensive computational comparison shows that the approach is capable of solving real-world instances in reasonable time and produces valid solutions within only a few seconds