arXiv subjects

Balazs Kulcsar

Publications and source records attributed to Balazs Kulcsar.

10 recordsLinked to original sources

arXiv2026

A user-driven pricing and scheduling framework for public electric vehicle charging

Public electric vehicle (EV) charging infrastructure has expanded rapidly, yet utilization across charging stations remains uneven and often inefficient. Existing operator-determined pricing schemes offer limited flexibility to coordinate heterogeneous user demand within constrained capacity. This study proposes a user-driven pricing and scheduling framework for public EV charging. Users submit advance bids specifying acceptable time slots, bid prices, and quantity bounds. Based on these bids, the charging operator determines prices and charging slot assignments. After observing the outcomes, users decide whether to accept the resulting prices and allocations. The operator's decision problem incorporates profit objectives, user participation requirements, and capacity constraints across charging levels and time slots. The framework captures a three-stage interaction involving user bidding, operator decisions, and user acceptance. Numerical case studies reveal trade-offs among user acceptance, operator revenue, and charging capacity utilization under different charging levels. The findings provide practical guidance and insights into designing flexible pricing schemes that better accommodate heterogeneous user preferences while improving system efficiency.

math.OC↗

arXiv2025

Learning for routing: A guided review of recent developments and future directions

This paper reviews the current progress in applying machine learning (ML) tools to solve NP-hard combinatorial optimization problems, with a focus on routing problems such as the traveling salesman problem (TSP) and the vehicle routing problem (VRP). Due to the inherent complexity of these problems, exact algorithms often require excessive computational time to find optimal solutions, while heuristics can only provide approximate solutions without guaranteeing optimality. With the recent success of machine learning models, there is a growing trend in proposing and implementing diverse ML techniques to enhance the resolution of these challenging routing problems. We propose a taxonomy categorizing ML-based routing methods into construction-based and improvement-based approaches, highlighting their applicability to various problem characteristics. This review aims to integrate traditional OR methods with state-of-the-art ML techniques, providing a structured framework to guide future research and address emerging VRP variants.

cs.AI↗

arXiv2025

Collaborative Charging Scheduling via Balanced Bounding Box Methods

Electric mobility faces several challenges, most notably the high cost of infrastructure development and the underutilization of charging stations. The concept of shared charging offers a promising solution. The paper explores sustainable urban logistics through horizontal collaboration between two fleet operators and addresses a scheduling problem for the shared use of charging stations. To tackle this, the study formulates a collaborative scheduling problem as a bi-objective nonlinear integer programming model, in which each company aims to minimize its own costs, creating inherent conflicts that require trade-offs. The Balanced Bounding Box Methods (B3Ms) are introduced in order to efficiently derive the efficient frontier, identifying a reduced set of representative solutions. These methods enhance computational efficiency by selectively disregarding closely positioned and competing solutions, preserving the diversity and representativeness of the solutions over the efficient frontier. To determine the final solution and ensure balanced collaboration, cooperative bargaining methods are applied. Numerical case studies demonstrate the viability and scalability of the developed methods, showing that the B3Ms can significantly reduce computational time while maintaining the integrity of the frontier. These methods, along with cooperative bargaining, provide an effective framework for solving various bi-objective optimization problems, extending beyond the collaborative scheduling problem presented here.

math.OC↗

arXiv2025

The large-scale charging scheduling problem for fleet batteries: Lagrangian decomposition with time-block reformulations

There is a rise in the need for efficient battery charging methods due to the high penetration of electromobility solutions. Battery swapping, a technique in which fully or partially depleted batteries are exchanged and then transported to a central facility for charging, introduces a unique scheduling problem. For scenarios involving a large number of batteries, commercial solvers and existing methods do not yield optimal or near-optimal solutions in a reasonable time due to high computational complexity. Our study presents a novel approach that combines variable layering with Lagrangian decomposition. We develop a new, tighter time-block reformulation for one of the Lagrangian sub-problems, enhancing convergence rates when used with our partial-variable fixing Lagrangian heuristic. We also propose an ergodic-iterate-based local search method to further improve the solution quality. Lower bounds are improved by learning the relation between Lagrangian multipliers and electricity cost. Our extensive benchmarks show superior computational performance against commercial solvers. We achieved, on average, a 43% lower objective value compared to state-of-the-art methods. In 71% of the instances, we obtained near-optimal solutions (optimality gap less than 6%), and 93% of the instances were below 10%. We obtained feasible solutions for all instances, compared to only 65% feasibility using incumbent methods. The developed exact method aims to support future research on charging scheduling, especially important for micromobility industry, vehicle-to-grid (V2G) applications, and second-life utilization of batteries. Furthermore, the developed polyhedral insights can be useful in other scheduling problems with a common underlying mathematical structure.

math.OC↗

arXiv2024

Minimum-Delay Opportunity Charging Scheduling for Electric Buses

Transit agencies that operate battery-electric buses must carefully manage fast-charging infrastructure to extend daily bus range without degrading on-time performance. To support this need, we propose a mixed-integer linear programming model to schedule opportunity charging that minimizes the amount of departure delay in all trips served by electric buses. Our novel approach directly tracks queuing at chargers in order to set and propagate departure delays. Allowing but minimizing delays makes it possible to optimize performance when delays due to traffic conditions and charging needs are inevitable, in contrast with existing methods that require charging to occur during scheduled layover time. To solve the model, we develop two algorithms based on decomposition. The first is an exact solution method based on Combinatorial Benders (CB) decomposition, which avoids directly enumerating the model's logic-based "big M" constraints and their inevitable computational challenges. The second, inspired by the CB approach but more efficient, is a polynomial-time heuristic based on linear programming that we call 3S. Computational experiments on both a simple notional transit network and the real bus system of King County, Washington, USA demonstrate the performance of both methods. The 3S method appears particularly promising for creating good charging schedules quickly at real-world scale.

math.OC↗

arXiv2024

Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach

With the increasing number of fast-electric vehicle charging stations (fast-EVCSs) and the popularization of information technology, electricity price competition between fast-EVCSs is highly expected, in which the utilization of public and/or privacy-preserved information will play a crucial role. Self-interest electric vehicle (EV) users, on the other hand, try to select a fast-EVCS for charging in a way to maximize their utilities based on electricity price, estimated waiting time, and their state of charge. While existing studies have largely focused on finding equilibrium prices, this study proposes a personalized dynamic pricing policy (PeDP) for a fast-EVCS to maximize revenue using a reinforcement learning (RL) approach. We first propose a multiple fast-EVCSs competing simulation environment to model the selfish behavior of EV users using a game-based charging station selection model with a monetary utility function. In the environment, we propose a Q-learning-based PeDP to maximize fast-EVCS' revenue. Through numerical simulations based on the environment: (1) we identify the importance of waiting time in the EV charging market by comparing the classic Bertrand competition model with the proposed PeDP for fast-EVCSs (from the system perspective); (2) we evaluate the performance of the proposed PeDP and analyze the effects of the information on the policy (from the service provider perspective); and (3) it can be seen that privacy-preserved information sharing can be misused by artificial intelligence-based PeDP in a certain situation in the EV charging market (from the customer perspective).

eess.SY↗

arXiv2023

Linear Lyapunov Functions for Nonlinear Compartmental Systems

This technical note examines exponential stability of the null solution to a large class of compartmental systems governed by ordinary differential equations. Sufficient conditions under which these systems admit a linear Lyapunov function are provided. The coefficients of the Lyapunov functions and the exponential decay rate they yield are obtained from an eigenvalue problem. For a special case of the system class considered, we derive an equivalence between attractivity of the null solution and the existence of a linear Lyapunov function.

eess.SY↗

arXiv2023

Collaborative electric vehicle routing with meet points

In this paper, we develop a profit-sharing-based optimal routing mechanism to incentivize horizontal collaboration among urban goods distributors. This paper investigates a collaborative routing problem for urban logistics, in which the exchange of goods at meet points is optimally planned en route. We show that collaboration does not only reduce the total cost but also increases the profit of each company by sharing some customers and the related profit. Hence, we focus on solving a collaborative electric vehicle routing problem under constraints such as customer-specific time windows, opportunity charging, vehicle capacity, and meet-point synchronization. The proposed Collaborative Electric Vehicle Routing Problem with Meet Point (CoEVRPMP) is modeled as a mixed-integer nonlinear programming problem. We first present an exact method for optimal benchmarks via decomposition. To handle real-world problems, we suggest using a metaheuristic method: adaptive large neighborhood search with linear programming. The viability and scalability of the collaborative method are demonstrated via numerical case studies: (i) a real-world case of two grocery stores in the city of Gothenburg, Sweden, and (ii) a large-scale experiment with 500 customers. The results underline the importance of horizontal collaboration among delivery companies. Collaboration helps to reduce the environmental footprint (total energy consumed) and to increase the individual company's profit at the same time.

math.OC↗

arXiv2022

Deep Q-learning: a robust control approach

In this paper, we place deep Q-learning into a control-oriented perspective and study its learning dynamics with well-established techniques from robust control. We formulate an uncertain linear time-invariant model by means of the neural tangent kernel to describe learning. We show the instability of learning and analyze the agent's behavior in frequency-domain. Then, we ensure convergence via robust controllers acting as dynamical rewards in the loss function. We synthesize three controllers: state-feedback gain scheduling H2, dynamic Hinf, and constant gain Hinf controllers. Setting up the learning agent with a control-oriented tuning methodology is more transparent and has well-established literature compared to the heuristics in reinforcement learning. In addition, our approach does not use a target network and randomized replay memory. The role of the target network is overtaken by the control input, which also exploits the temporal dependency of samples (opposed to a randomized memory buffer). Numerical simulations in different OpenAI Gym environments suggest that the Hinf controlled learning performs slightly better than Double deep Q-learning.

cs.LG↗

arXiv2022

On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity

Most complex machine learning and modelling techniques are prone to over-fitting and may subsequently generalise poorly to future data. Artificial neural networks are no different in this regard and, despite having a level of implicit regularisation when trained with gradient descent, often require the aid of explicit regularisers. We introduce a new framework, Model Gradient Similarity (MGS), that (1) serves as a metric of regularisation, which can be used to monitor neural network training, (2) adds insight into how explicit regularisers, while derived from widely different principles, operate via the same mechanism underneath by increasing MGS, and (3) provides the basis for a new regularisation scheme which exhibits excellent performance, especially in challenging settings such as high levels of label noise or limited sample sizes.

stat.ML↗