An intelligent simulation platform for train traffic control under disturbance

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Railway disturbance management is inherently a multi-objective optimization problem that concerns both the operators’ cost and passenger’s service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions. Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first

Impact of Iterated Local Search Heuristic Hybridization on Vehicle Routing Problems: Application to the Capacitated Profitable Tour Problem

International audienceThe present paper highlights the impact of heuristic hybridization on Vehicle Routing Problems (VRPs). More specifically, we focus on the hybridization of the Iterated Local Search heuristic (ILS). We propose different hybridization levels for ILS with two other heuristics, namely a Variable Neighborhood Descent with Random neighborhood ordering (RVND) and a Large Neighborhood Search heuristic (LNS). To evaluate the proposed approaches, we test them on a variant of VRPs called the Capacitated Profitable Tour Problem (CPTP). In a CPTP, the visit of all customers is no longer required and the visit of each customer generates a specific profit. The available fleet of vehicle is limited and capacitated. The aim of the CPTP is to choose which set of customers to visit and in which order to maximize the difference between collected profits and routing costs. Our experiments show that the more ILS is hybridized the better are the results. To bring out the effectiveness of the proposed hybrid approach combining ILS, RVND and LNS, a comparison is made between that proposed approach and three local search heuristics from the literature of the CPTP. The obtained results are competitive