arXiv subjects

Markó Horváth

Publications and source records attributed to Markó Horváth.

3 recordsLinked to original sources

arXiv2026

A unified modeling framework and improved formulations for single-hoist cyclic scheduling

The cyclic hoist scheduling problem originates in electroplating lines, where a single or multiple hoists transport parts between processing tanks subject to technological constraints. The objective is typically to determine a cyclic sequence of hoist movements that minimizes the cycle time while satisfying travel and processing constraints. Although the problem has been widely studied for several decades, the literature contains a puzzling phenomenon: different studies often report different optimal cycle times for the same benchmark instances, which limits the comparability and reproducibility of computational results. In this paper, we revisit the modeling of cyclic hoist scheduling problems from a unified perspective. We introduce a consistent modeling approach for single-hoist problems and analyze several mixed-integer linear programming (MIP) formulations proposed in the literature. Our analysis identifies modeling inconsistencies and clarifies the relationships between existing formulations. Based on these observations, we propose straightforward constraint programming (CP) models that can serve as baseline approaches, and we also derive improved MIP~formulations. Extensive computational experiments compare the strength and performance of the investigated formulations. To support reproducible research, we also provide a publicly available library containing benchmark instances and implementations of several CP and MIP~formulations for single-hoist cyclic hoist scheduling.

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arXiv2024

A general modeling and simulation framework for dynamic vehicle routing

In dynamic vehicle routing problems (DVRPs), some part of the information is revealed or changed on the fly, and the decision maker has the opportunity to re-plan the vehicle routes during their execution, reflecting on the changes. Accordingly, the solution to a DVRP is a flexible policy rather than a set of fixed routes. A policy is basically a problem-specific algorithm that is invoked at various decision points in the planning horizon and returns a decision according to the current state. Since DVRPs involve dynamic decision making, a simulator is an essential tool for dynamically testing and evaluating the policies. Despite this, there are few tools available that are specifically designed for this purpose. To fill this gap, we have developed a simulation framework that is suitable for a wide range of dynamic vehicle routing problems and allows to dynamically test different policies for the given problem. In this paper, we present the background of this simulation tool, for which we proposed a general modeling framework suitable for formalizing DVRPs independently of simulation purposes. Our open source simulation tool is already available, easy to use, and easily customizable, making it a useful tool for the research community.

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arXiv2024

A cost function approximation method for dynamic vehicle routing with docking and LIFO constraints

In this paper, we study a dynamic pickup and delivery problem with docking constraints. There is a homogeneous fleet of vehicles to serve pickup-and-delivery requests at given locations. The vehicles can be loaded up to their capacity, while unloading has to follow the last-in-first-out (LIFO) rule. The locations have a limited number of docking ports for loading and unloading, which may force the vehicles to wait. The problem is dynamic since the transportation requests arrive real-time, over the day. Accordingly, the routes of the vehicles are to be determined dynamically. The goal is to satisfy all the requests such that a combination of tardiness penalties and traveling costs is minimized. We propose a cost function approximation based solution method. In each decision epoch, we solve the respective optimization problem with a perturbed objective function to ensure the solutions remain adaptable to accommodate new requests. We penalize waiting times and idle vehicles. We propose a variable neighborhood search based method for solving the optimization problems, and we apply two existing local search operators, and we also introduce a new one. We evaluate our method using a widely adopted benchmark dataset, and the results demonstrate that our approach significantly surpasses the current state-of-the-art methods.

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