Neural Bee Colony Optimization: A Case Study in Public Transit Network Design

In this work we explore the combination of metaheuristics and learned neural network solvers for combinatorial optimization. We do this in the context of the transit network design problem, a uniquely challenging combinatorial optimization problem with real-world importance. We train a neural network policy to perform single-shot planning of individual transit routes, and then incorporate it as one of several sub-heuristics in a modified Bee Colony Optimization (BCO) metaheuristic algorithm. Our experimental results demonstrate that this hybrid algorithm outperforms the learned policy alone by up to 20% and the original BCO algorithm by up to 53% on realistic problem instances. We perform a set of ablations to study the impact of each component of the modified algorithm.Comment: 9 pages. 1 figure with six sub-figure

Differential evolution for urban transit routing problem

The urban transit routing problem (UTRP) involves the construction of route sets on existing road networks to cater for the transit demand efficiently. This is an NP-hard problem, where the generation of candidate route sets can lead to a number of potential routes being discarded on the grounds of infeasibility. This paper presents a new repair mechanism to complement the existing terminal repair and the make-small-change operators in dealing with the infeasibility of the candidate route set. When solving the UTRP, the general aim is to determine a set of transit route networks that achieves a minimum total cost for both the passenger and the operator. With this in mind, we propose a differential evolution (DE) algorithm for solving the UTRP with a specific objective of minimizing the average travel time of all served passengers. Computational experiments are performed on the basis of benchmark Mandl’s Swiss network. Computational results from the proposed repair mechanism are comparable with the existing repair mechanisms. Furthermore, the combined repair mechanisms of all three operators produced very promising results. In addition, the proposed DE algorithm outperformed most of the published results in the literature

Efficient heuristic algorithms for location of charging stations in electric vehicle routing problems

Indexación: Scopus.This work has been partially supported by CONICYT FONDECYT by grant 11150370, FONDEF IT17M10012 and the “Grupo de Logística y Transporte” at the Universidad del Bío-Bío.. This support is gratefully acknowledged.Eco-responsible transportation contributes at making a difference for companies devoted to product delivery operations. Two specific problems related to operations are the location of charging stations and the routing of electric vehicles. The first one involves locating new facilities on potential sites to minimise an objective function related to fixed and operational opening costs. The other one, electric vehicle routing problem, involves the consolidation of an electric-type fleet in order to meet a particular demand and some guidelines to optimise costs. It is determined by the distance travelled, considering the limited autonomy of the fleet, and can be restored by recharging its battery. The literature provides several solutions for locating and routing problems and contemplates restrictions that are closer to reality. However, there is an evident lack of techniques that addresses both issues simultaneously. The present article offers four solution strategies for the location of charging stations and a heuristic solution for fleet routing. The best results were obtained by applying the location strategy at the site of the client (relaxation of the VRP) to address the routing problem, but it must be considered that there are no displacements towards the recharges. Of all the other three proposals, K-means showed the best performance when locating the charging stations at the centroid of the cluster. © 2012-2018. National Institute for R and D in Informatics.https://sic.ici.ro/wp-content/uploads/2018/03/Art.-8-Issue-1-2018-SIC.pd

Data-Driven Optimization Models for Feeder Bus Network Design

Urbanization is not a modern phenomenon. However, it is worthwhile to note that the world urban population growth curve has up till recently followed a quadratic-hyperbolic pattern (Korotayey and Khaltourina, 2006). As cities become larger and their population expand, large and growing metropolises have to face the enormous traffic demand. To alleviate the increasing traffic congestion, public transit has been considered as the ideal solution to such troubles and problems restricting urban development. The metro is a type of efficient, dependable and high-capacity public transport adapted in metropolises worldwide. At the same time, the residents from crowded cities migrated to the suburban since 1950s. Such sub-urbanization brings more decentralized travel demands and has challenged to the public transit system. Even the metro lines are extended from inner city to outer city, the commuters living in suburban still have difficulty to get to the rail station due to the limited transportation resources. It is becoming inevitable to develop the regional transit network such as feeder bus that picks up the passengers from various locations and transfer them to the metro stations or transportation hubs. The feeder bus will greatly improve the efficiency of metro stations whose service area in the suburban area is usually limited. Therefore, how to develop a well-integrated feeder system is becoming an important task to planners and engineers. Realizing the above critical issues, the dissertation focus on the feeder bus network design problem (FBNDP) and contributes to three main parts: 1. Develop a data-mining strategy to retrieve OD pair from the large scale of the cellphone data. The OD pairs are able to present the users’ daily behaver including the location of residence, workplace with the timestamp of each trip. The spatial distribution of urban rail transit user demand from the OD pair will help to support the establishment and optimization of the feeder bus network. The dissertation details the procedure of data acquisition and utilization. The machine leaning is applied to predict the travel demand in the future. 2. Present a mathematical model to design the appropriate service area and routing plans for a flexible feeder transit. The proposed model features in utilizing the real-world data input and simultaneously selecting bus stops and designing the route from those targeted stops to urban rail stops. 3. Propose an improved feeder bus network design model to provide precise service to the commuters. Considering the commuters are time-sensitive during the peak hours, the time-windows of each demand is taken in to account when generating the routes and the schedule of feeder bus system. The model aims to pick up the demand within the time-windows of the commuters’ departure time and drop off them within the reasonable time. The commuters will benefit from the shorter waiting time, shorter walking distance and efficient transfer timetable

Urban Transit Network Design Problems: A Review of Population-based Metaheuristics

The urban transit network design problem (UTNDP) involves the development of a transit route set and associated schedules for an urban public transit system. The design of efficient public transit systems is widely considered as a viable option for the economic, social, and physical structure of an urban setting. This paper reviews four well-known population-based metaheuristics that have been employed and deemed potentially viable for tackling the UTNDP. The aim is to give a thorough review of the algorithms and identify the gaps for future research directions

A Tabu Search Based Metaheuristic for Dynamic Carpooling Optimization

International audienceThe carpooling problem consists in matching a set of riders' requests with a set of drivers' offers by synchronizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process between users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complexity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal decisions automatically. To increase users' satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated solutions , while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France

Optimal Alignments for Designing Urban Transport Systems: Application to Seville

The achievement of some of the Sustainable Development Goals (SDGs) from the recent 2030 Agenda for Sustainable Development has drawn the attention of many countries towards urban transport networks. Mathematical modeling constitutes an analytical tool for the formal description of a transportation system whereby it facilitates the introduction of variables and the definition of objectives to be optimized. One of the stages of the methodology followed in the design of urban transit systems starts with the determination of corridors to optimize the population covered by the system whilst taking into account the mobility patterns of potential users and the time saved when the public network is used instead of private means of transport. Since the capture of users occurs at stations, it seems reasonable to consider an extensive and homogeneous set of candidate sites evaluated according to the parameters considered (such as pedestrian population captured and destination preferences) and to select subsets of stations so that alignments can take place. The application of optimization procedures that decide the sequence of nodes composing the alignment can produce zigzagging corridors, which are less appropriate for the design of a single line. The main aim of this work is to include a new criterion to avoid the zigzag effect when the alignment is about to be determined. For this purpose, a curvature concept for polygonal lines is introduced, and its performance is analyzed when criteria of maximizing coverage and minimizing curvature are combined in the same design algorithm. The results show the application of the mathematical model presented for a real case in the city of Seville in Spain.Ministerio de Economía y Competitividad MTM2015-67706-

Metaheuristic approaches for urban transit scheduling problem: a review

Urban Transit Network Design Problem (UTNDP) focuses on deriving useful set of routes, manageable timetabling for each transit route and transit scheduling based on available resources. UTNDP is commonly subdivided into Urban Transit Routing Problem (UTRP) and Urban Transit Scheduling Problem (UTSP), respectively. There are various approaches applied to solve the UTSP. The aim of this paper is to give a comprehensive list of studies on UTSP that deals with metaheuristic approaches such as Tabu Search, Simulated Annealing, Genetic Algorithm and their hybrid methods. This review also addressed possible gaps of the approaches and the limitations of the overall problem. It can be concluded that only some of the metaheuristic approaches and sub-problems are highly studied in UTSP. This review will be useful for researchers who are interested in expanding their knowledge and conduct research in UTSP using metaheuristic approaches

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown