A Robust Optimization Framework for eBus Charging Infrastructure Planning

Heuristics based on large neighborhood search have recently shown outstanding results in solving various transportation and scheduling problems. Large neighborhood search methods explore a complex neighborhood by use of heuristics. Using large neighborhoods makes it possible to find better candidate solutions in each iteration and hence traverse a more promising search path. Starting from the large neighborhood search method, we give an overview of very large scale neighborhood search methods and discuss recent variants and extensions like variable depth search and adaptive large neighborhood search.

Heuristics based on large neighborhood search have recently shown outstanding results in solving various transportation and scheduling problems. Large neighborhood search methods explore a complex neighborhood by use of heuristics. Using large neighborhoods makes it possible to find better candidate solutions in each iteration and hence traverse a more promising search path. Starting from the large neighborhood search method, we give an overview of very large scale neighborhood search methods and discuss recent variants and extensions like variable depth search and adaptive large neighborhood search.

An optimization model for electric vehicle charging infrastructure planning considering queuing behavior with finite queue length

The implementation of a sustainable and efficient electric bus (eBus) transportation network requires addressing multiple concerns, such as limited driving range and battery charging/discharging time. Currently, eBuses can travel between 200 to 300 km on a single charge, and fast charging stations can fully recharge a battery in a matter of minutes. However, a failure in a charging station might negatively impact the operation of the system with unnecessary delays for the users. Taking this into account, we propose and implement a model for the Robust eBuses Charging Location problem that takes into account potential vulnerabilities of the transportation system. Our model incorporates a protection mechanism that allows eBuses to reach a backup charging station in case the regular one is down. We propose a MIP model to tackle this problem with minimal disruptions in the regular operation of the eBuses. Furthermore, we also present a Large Neighbourhood Search framework to efficiently tackle the problem. Our empirical evaluation suggests that our framework can operate a robust service with a small number of charging stations for three Irish cities and our Large Neighbourhood Search approach largely outperforms a popular commercial MIP solver.

In this paper, we address the vehicle routing problem with mixed fleet of conventional and heterogenous electric vehicles, denoted VRP-MFHEV. This problem is motivated by a real-life industrial application and it is defined by a mixed fleet of heterogenous Electric Vehicles (EVs) having distinct battery capacities and operating costs, and identical Conventional Vehicles (CVs) that could be used to serve a set of geographically scattered customers. The EVs could be charged during their trips at the depot and in the available charging stations, which offer charging with a given technology of chargers and propose different charging costs. EVs are subject to the compatibility constraints with the available charging technologies and they could be partially charged. The objective is to minimize the number of employed vehicles and to minimize the total travel and charging costs. To solve the VRP-MFHEV, we propose a Multi-Start Iterated Tabu Search (ITS) based on Large Neighborhood Search (LNS). The LNS is used in the tabu search of the intensification phase and the diversification phase of the ITS. Different implementation schemes of the proposed method including best-improvement and first-improvement strategies, are tested on generalized benchmark instances. The computational results show that ITS produces competitive results, with respect to results obtained in previous studies, while the computational time remains reasonable for each instance. Moreover, using LNS in the intensification phase of ITS seems improving the generated solutions compared to using other neighborhood search procedures such as 2opt.KeywordsElectric vehicle routing problemElectric vehicle chargingMeta-heuristicsIterated Tabu SearchLarge Neighborhood SearchOptimization

Optimal charging management and infrastructure planning for free-floating shared electric vehicles

Urban logistics face complexity due to traffic congestion, fleet heterogeneity, warehouse constraints, and driver workload balancing, especially in the Heterogeneous Multi-Trip Vehicle Routing Problem with Time Windows and Time-Varying Networks (HMTVRPTW-TVN). We develop a mixed-integer linear programming (MILP) model with dual-peak time discretization and exact linearization for heterogeneous fleet coordination. Given the NP-hard nature, we propose a Hyper-Heuristic based on Cumulative Reward Q-Learning (HHCRQL), integrating reinforcement learning with heuristic operators in a Markov Decision Process (MDP). The algorithm dynamically selects operators using a four-dimensional state space and a cumulative reward function combining timestep and fitness. Experiments show that, for small instances, HHCRQL achieves solutions within 3% of Gurobi’s optimum when customer nodes exceed 15, outperforming Large Neighborhood Search (LNS) and LNS with Simulated Annealing (LNSSA) with stable, shorter runtime. For large-scale instances, HHCRQL reduces gaps by up to 9.17% versus Iterated Local Search (ILS), 6.74% versus LNS, and 5.95% versus LNSSA, while maintaining relatively stable runtime. Real-world validation using Shanghai logistics data reduces waiting times by 35.36% and total transportation times by 24.68%, confirming HHCRQL’s effectiveness, robustness, and scalability.

The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by diverse variables. This paper introduces the Estimating the Required Density of EV Charging (ERDEC) stations model, which is an analytical approach to estimating the optimal density of charging stations for certain urban areas, which are subsequently aggregated to city level planning. The optimal charging station’s density is derived to minimize the total cost. A numerical study is conducted to obtain the correlations among the various parameters in the proposed model, such as regional parameters, technological parameters and coefficient factors. To investigate the effect of technological advances, the corresponding changes in the optimal density and total cost are also examined by various combinations of technological parameters. Daejeon city in South Korea is selected for the case study to examine the applicability of the model to real-world problems. With real taxi trajectory data, the optimal density map of charging stations is generated. These results can provide the optimal number of chargers for driving without driving-range anxiety. In the initial planning phase of installing charging infrastructure, the proposed model can be applied to a relatively extensive area to encourage the usage of electric vehicles, especially areas that lack information, such as exact candidate sites for charging stations and other data related with electric vehicles. The methods and results of this paper can serve as a planning guideline to facilitate the extensive adoption of electric vehicles.

Current trends suggest that there is a substantial increase in the overall usage of electric vehicles (EVs). This, in turn, is causing drastic changes in the transportation industry and, more broadly, in business, policy making, and society. One concrete challenge brought by the increase in the number of EVs is a higher demand for charging stations. This paper presents a methodology to address the challenge of EV charging station deployment. The proposed methodology combines multiple sources of heterogeneous real?world data for the sake of deriving insights that can be of a great value to decision makers in the field, such as EV charging infrastructure providers and/or local governments. Our starting point is the business data, ie, data describing charging infrastructure, historical data about charging transactions, and information about competitors in the market. Another type of data used are geographical data, such as places of interest located around chargers (eg, hospitals, restaurants, and shops) and driving distances between available chargers. The merged data from different sources are used to predict charging station utilization when EV charging infrastructure and/or contextual data change, eg, when another charging station or a place of interest is created. On the basis of such predictions, we suggest where to deploy new charging stations. We foresee that the proposed methodology can be used by EV charging infrastructure providers and/or local governments as a decision support tool that prescribes an optimal area to place a new charging station while keeping a desired level of utilization of the charging stations. We showcase the proposed methodology with an illustrative example involving the Dutch EV charging infrastructure through the period from 2013 to 2016. Specifically, we prescribe the optimal location for new ELaadNL charging stations based on different objectives such as maximizing the overall charging network utilization and/or increasing the number of chargers in scarcely populated areas.

This paper investigates the joint scheduling problem of battery electric bus fleets and plug-in charging infrastructure in an urban transit system. The operation of an electric bus network is inherently a multi-component system, where vehicle assignment, battery energy management, and charger capacity decisions interact and jointly determine system performance and cost efficiency. To capture these interdependencies, we propose a system-level integrated scheduling framework that simultaneously determines bus trip assignments, charging event timing and duration, and charger utilization plans. The problem is formulated as a continuous-time mixed-integer linear programming model that minimizes the total system cost, subject to operational feasibility, battery state-of-charge dynamics, and charger capacity constraints. To enhance computational tractability, a Lagrangian relaxation-based decomposition approach is developed, coupled with a linear programming-based diving heuristic. Computational experiments on benchmark instances demonstrate that the proposed framework produces high-quality system-level schedules with substantially reduced solution time compared with directly using a commercial solver. A real-world case study based on a large charging station in Beijing shows that the optimized joint schedules reduce the required fleet size from 22 to 13 buses and the number of chargers from five to two, leading to a 38.3% reduction in total system cost. These results highlight the effectiveness and practical value of the proposed approach for the planning and operation of urban electric bus transit systems.

Scheduling is a decision-making process, which is employed to allocate resources to tasks in a given time. Scheduling problems are in general NP-hard. In order to solve scheduling problems, three common types of methods have been used: exact methods (e.g., branch & bound and dynamic programming), population based metaheuristics (e.g., genetic algorithm and ant colony optimisation), and local search (LS) algorithms (e.g., simulated annealing and iterated local search). Exact methods are not able to address the practical-sized problems effectively with regard to both CPU times and solution quality. LS algorithms have recently attracted much more attention because of their simplicity, being easy to implement, robustness, and high effectiveness. However, the available LS algorithms in the literature typically use a generic structure for speci fic problems. In other words, the biggest disadvantage of those methods is the lack of problem speci fic components into their algorithmic structures. To ll in this gap, in this thesis, we consider constraint-based local search (CBLS) algorithms to solve scheduling problems because of their effectiveness and also because they are not used much in the scheduling literature. The key difference of CBLS with other LS algorithms is in the use of the problem specifi c information in the search process. CBLS helps the search focus more on areas where efforts will bring more effect, and thus increase the scalability of the search. In other words, CBLS attempts to exploit the essence of the problem and, based on the speci ficities of the problem, defi nes the procedures that will guide the search towards better local optima. The effectiveness of our proposed CBLS techniques is shown throughout this thesis by solving several scheduling problems, such as flowshops with blocking constraints, aircraft operations, and customer order problems. The first scheduling problem is permutation flowshop scheduling problem (PFSP). It is one of the most thoroughly studied scheduling problems. However, mixed blocking PFSP (MBPFSP) is a generalised and more realistic version of PFSP with real-life applications such as cider industry. MBPFSP is an important branch of `zero capacity buffer' scheduling problems. The second scheduling problem is aircraft scheduling problem (ASP). ASP involves allocation of aircraft to runways for arrival and departure flights, minimising total delays. In this thesis, we focus on both single-runway and multiple-runway ASP cases. The third scheduling problem is customer order scheduling problem (COSP), which has many applications including the pharmaceutical industries and the paper industries. All of the three above-mentioned scheduling problems are NP-hard. They have made signi ficant progress in recent years. However, within practical time limits, existing algorithms still either find low quality solutions or struggle with practical-sized problems. In this thesis, we aim to advance their search by better exploiting the problem speci fic structural knowledge, extracted from the constraints and the objective functions. We run our experiments on a range of respective standard benchmark problem instances. Experimental results and comprehensive analyses show that our new algorithms signi ficantly outperform respective state-of-the-art scheduling algorithms.

This paper deals with a real world application that consists in the vehicle routing problem with mixed fleet of conventional and heterogenous electric vehicles including new constraints, denoted VRP-HFCC. This problem is defined by a set of customers that have to be served by a mixed fleet of vehicles composed of heterogenous fleet of Electric Vehicles (EVs) with distinct battery capacities and operating costs, and a set of identical Conventional Vehicles (CVs). The EVs could be charged during their trips in the available charging stations, which offer charging with a given technology of chargers and time dependent charging costs. Charging stations are also subject to operating time windows constraints. EVs are subject to the compatibility constraints with the available charging technologies and they could be partially charged. Intermittent charging at the depot is also allowed provided that constraints related to the electricity grid are satisfied. The objective is to minimize the number of employed vehicles and to minimize the total travel and charging costs. The developed multi-start algorithm is based on the Iterated Local Search metaheuristic which uses a Large Neighborhood Search with two different insertion strategies in the Local Search procedure. Different implementation schemes of the proposed method are tested on a set of real data instances with up to 550 customers as well as on generalized benchmark instances.

Abstract: With the rapid adoption of electric vehicles (EVs) worldwide, the demand for efficient and accessible charging infrastructure has become increasingly significant. Electric Vehicle Charging Station Sites (EVCSS) play a crucial role in supporting the widespread deployment and usability of EVs. This introduction abstract provides a concise overview of the key aspects and considerations surrounding the establishment of EVCSS. The abstract begins by highlighting the exponential growth of the electric vehicle market and the consequent need for a reliable charging network. It explores the various types of charging stations, including slow charging, fast charging, and ultra-fast charging, each catering to different charging requirements and time constraints. Moreover, the abstract delves into the importance of strategically locating charging stations to maximize convenience for EV owners, such as near residential areas, commercial centers, and major transportation hubs. Furthermore, the abstract addresses the critical elements that contribute to an effective EVCSS design. It emphasizes the significance of infrastructure scalability to accommodate the projected increase in EV adoption, ensuring the availability of charging stations for all EV users. The integration of renewable energy sources, such as solar panels or wind turbines, is also highlighted as a sustainable approach to powering EVCSS. The abstract briefly discusses the importance of interoperability and standardization in charging infrastructure to facilitate seamless charging experiences for EV owners, irrespective of the vehicle brand or model. It emphasizes the need for universally compatible charging connectors and protocols to eliminate barriers and promote widespread EV adoption. Finally, the abstract touches upon the emerging technologies in the EV charging landscape, such as wireless charging and vehicle-to-grid (V2G) integration. It acknowledges the potential benefits and challenges associated with these advancements, highlighting the need for further research and development to optimize their implementation in EVCSS. The research on Electric Vehicle Charging Station Sites (EVCSS) holds significant importance in addressing the challenges and opportunities associated with the widespread adoption of electric vehicles (EVs). Electric vehicles have gained considerable momentum as a promising solution to reduce greenhouse gas emissions and mitigate climate change. However, the successful transition to sustainable transportation heavily relies on the availability of an efficient and reliable charging infrastructure.

Site selection for shared charging and swapping stations using the SECA and TRUST methods

Sequential construction planning of electric taxi charging stations considering the development of charging demand