Fuzzy-logic controlled genetic algorithm for the rail-freight crew-scheduling problem

AbstractThis article presents a fuzzy-logic controlled genetic algorithm designed for the solution of the crew-scheduling problem in the rail-freight industry. This problem refers to the assignment of train drivers to a number of train trips in accordance with complex industrial and governmental regulations. In practice, it is a challenging task due to the massive quantity of train trips, large geographical span and significant number of restrictions. While genetic algorithms are capable of handling large data sets, they are prone to stalled evolution and premature convergence on a local optimum, thereby obstructing further search. In order to tackle these problems, the proposed genetic algorithm contains an embedded fuzzy-logic controller that adjusts the mutation and crossover probabilities in accordance with the genetic algorithm’s performance. The computational results demonstrate a 10% reduction in the cost of the schedule generated by this hybrid technique when compared with a genetic algorithm with fixed crossover and mutation rates

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Evolutionary algorithms for scheduling operations

While business process automation is proliferating through industries and processes, operations such as job and crew scheduling are still performed manually in the majority of workplaces. The linear programming techniques are not capable of automated production of a job or crew schedule within a reasonable computation time due to the massive sizes of real-life scheduling problems. For this reason, AI solutions are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic operator and conclusion about their effectiveness. Secondly, the practical efficiency of such algorithms is unknown due to the lack of comparison with manually produced schedules. Finally, the implications of real-life implementation of the algorithm are rarely considered. This research aims at addressing all three limitations. Collaborations with DBSchenker,the rail freight carrier, and Garnett-Dickinson, the printing company,have been established. Multi-disciplinary research methods including document analysis, focus group evaluations, and interviews with managers from different levels have been carried out. A standard EA has been enhanced with developed within research intelligent operators to efficiently solve the problems. Assessment of the developed algorithm in the context of real life crew scheduling problem showed that the automated schedule outperformed the manual one by 3.7% in terms of its operating efficiency. In addition, the automatically produced schedule required less staff to complete all the jobs and might provide an additional revenue opportunity of £500 000. The research has also revealed a positive attitude expressed by the operational and IT managers towards the developed system. Investment analysis demonstrated a 41% return rate on investment in the automated scheduling system, while the strategic analysis suggests that this system can enable attainment of strategic priorities. The end users of the system, on the other hand, expressed some degree of scepticism and would prefer manual methods

Comparative Study of Interval Type-2 and Type-1 Fuzzy Genetic and Flower Pollination Algorithms in Optimization of Fuzzy Fractional Order <em>PI^λD^μ </em> Controllers

In this chapter, a comparison between fuzzy genetic optimization algorithm (FGOA) and fuzzy flower pollination optimization algorithm (FFPOA) is bestowed. In extension, the prime parameters of each algorithm adapted using interval type-2 and type-1 fuzzy logic system (FLS) are presented. The key feature of type-2 fuzzy system is alimenting the modeling uncertainty to the algorithms, and hence it is a prime motivation of using interval type-2 fuzzy systems for dynamic parameter adaption. These fuzzy algorithms (type-1 and type-2 fuzzy system versions) are compared with the design of fuzzy control systems used for controlling the dihybrid level control process subject to system component (leak) fault. Simulation results reveal that interval type-2 fuzzy-based FPO algorithm outperforms the results of the type-1 and type-2 fuzzy GO algorithm

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

Sem resumo disponível.publishe

The impact of artificial intelligence in the rail industry

Artificial Intelligence (AI) is being introduced in enterprise systems due to the promising benefits that such a disruptive technology can present, even when taking into account its risks and challenges. In the rail industry, AI is being implemented to help improve train delays, reduce infrastructure and rolling stock maintenance costs, and to improve customer’s experience. In intermodal terminals, this technology helps improve passenger flow through hubs, avoids freight cargo losses and improves cargo monitoring inside the terminals. The aim of this investigation is to study the impact of Artificial Intelligence in the rail industry, and, in order to conduct this investigation, a quantitative methodology approach was used to answer three research questions. Initially, an analysis of the differences of sociodemographic factors on the knowledge about AI occurred. Posteriorly, an analysis of the influence of the benefits, risks and trust on the implementation of AI in the rail industry was conducted and, in order to expand the scope of the study outside the rail industry, an analysis of the impact of AI in the intermodal transportation systems was completed. The results show that sociodemographic differences among the respondent’s knowledge about AI exist, along with the confirmation that the factors of benefits, risks and trust influence the implementation of AI in the rail industry. Regarding intermodal transport systems, the same effects and additionally the awareness of AI were proved to influence the implementation of these kinds of systems.A Inteligência Artificial (IA) está a ser implementada em sistemas empresariais devido aos benefícios que esta tecnologia disruptiva pode apresentar, mesmo tendo em conta os riscos e desafios associados. Na indústria ferroviária, a IA está a ser aplicada para melhorar os atrasos na chegada de comboios, para reduzir os custos de manutenção tradicionais de comboios e da infraestrutura, e para melhorar a experiência do cliente. No que diz respeito ao transporte intermodal, esta tecnologia pode melhorar o fluxo de passageiros dentro dos hubs, evitar perdas de mercadoria e melhorar a sua monitorização dentro dos terminais logísticos. O objetivo desta investigação é o estudo do impacto da Inteligência Artificial na indústria ferroviária e, para tal, foi usada uma metodologia quantitativa para responder a três questões de pesquisa. Primeiramente, ocorreu uma análise das diferenças de fatores sociodemográficos no conhecimento de IA. Posteriormente, ocorreu também uma análise da influência dos benefícios, riscos e confiança na implementação da tecnologia de IA nos sistemas desta indústria e, adicionalmente, para expandir esta investigação para além da indústria ferroviária, ocorreu uma análise do impacto da IA nos sistemas de transporte intermodais. Os resultados obtidos permitem demostrar que existem diferenças sociodemográficas entre os inquiridos e existe também a confirmação da influência que os benefícios, riscos e confiança podem trazer para a implementação de IA nesta indústria. No que diz respeito aos sistemas de transporte intermodal, os mesmos efeitos já referidos e adicionalmente da noção da IA na implementação destes sistemas, são confirmados

Operations Research Modeling of Cyclic Train Timetabling, Cyclic Train Platforming, and Bus Routing Problems

Public transportation or mass transit involves the movement of large numbers of people between a given numbers of locations. The services provided by this system can be classified into three groups: (i) short haul: a low-speed service within small areas with high population; (ii) city transit: transporting people within a city; and (iii) long haul: a service with long trips, few stops, and high speed (Khisty and Lall, 2003). It can be also classified based on local and express services. The public transportation planning includes five consecutive steps: (i) the network design and route design; (ii) the setting frequencies or line plan; (iii) the timetabling; (iv) the vehicle scheduling; and (v) the crew scheduling and rostering (Guihaire and Hao, 2008; Schöbel, 2012). The first part of this dissertation considers three problems in passenger railway transportation. It has been observed that the demand for rail travel has grown rapidly over the last decades and it is expected that the growth continues in the future. High quality railway services are needed to accommodate increasing numbers of passengers and goods. This is one of the key factors for economic growth. The high costs of railway infrastructure ask for an increased utilization of the existing infrastructure. Attractive railway services can only be offered with more reliable rolling stock and a more reliable infrastructure. However, to keep a high quality standard of operations, smarter methods of timetable construction are indispensable, since existing methods have major shortcomings. The first part of this dissertation, comprising Chapters 1-6, aims at developing a cyclic (or periodic) timetable for a passenger railway system. Three different scenarios are considered and three mixed integer linear programs, combined with heuristics for calculating upper and lower bounds on the optimal value for each scenario, will be developed. The reason of considering a periodic timetable is that it is easy to remember for passengers. The main inputs are the line plan and travel time between and minimum dwell time at each station. The output of each model is an optimal periodic timetable. We try to optimize the quality of service for the railway system by minimizing the length of cycle by which trains are dispatched from their origin. Hence, we consider the cycle length as the primary objective function. Since minimizing travel time is a key factor in measuring service quality, another criterion--total dwell time of the trains--is considered and added to the objective function. The first problem, presented in Chapter 3, has already been published in a scholarly journal (Heydar et al., 2013). This chapter is an extension of the work of Bergmann (1975) and is the simplest part of this research. In this problem, we consider a single-track unidirectional railway line between two major stations with a number of stations in between. Two train types--express and local--are dispatched from the first station in an alternate fashion. The express train stops at no intermediate station, while the local train should make a stop at every intermediate station for a minimum amount of dwell time. A mixed integer linear program is developed in order to minimize the length of the dispatching cycle and minimize the total dwell time of the local train at all stations combined. Constraints include a minimum dwell time for the local train at each station, a maximum total dwell time for the local train, and headway considerations on the main line an in stations. Hundreds of randomly generated problem instances with up to 70 stations are considered and solved to optimality in a reasonable amount of time. Instances of this problem typically have multiple optimal solutions, so we develop a procedure for finding all optimal solutions of this problem. In the second problem, presented in Chapter 4, we present the literature\u27s first mixed integer linear programming model of a cyclic, combined train timetabling and platforming problem which is an extension of the model presented in Chapter 3 and Heydar et al. (2013). The work on this problem has been submitted to a leading transportation journal (Petering et al., 2012). From another perspective, this work can be seen as investigating the capacity of a single track, unidirectional rail line that adheres to a cyclic timetable. In this problem, a set of intermediate stations lies between an origin and destination with one or more parallel sidings at each station. A total of T train types--each with a given starting and finishing point and a set of known intermediate station stops--are dispatched from their respective starting points in cyclic fashion, with one train of each type dispatched per cycle. A mixed integer linear program is developed in order to schedule the train arrivals and departures at the stations and assign trains to tracks (platforms) in the stations so as to minimize the length of the dispatching cycle and/or minimize the total stopping (dwell) time of all train types at all stations combined. Constraints include a minimum dwell time for each train type in each of the stations in which it stops, a maximum total dwell time for each train type, and headway considerations on the main line and on the tracks in the stations. This problem belongs to the class of NP-hard problems. Hundreds of randomly generated and real-world problem instances with 4-35 intermediate stations and 2-11 train types are considered and solved to optimality in a reasonable amount of time using IBM ILOG CPLEX. Chapter 5 expands upon the work in Chapter 4. Here, we present a mixed integer linear program for cyclic train timetabling and routing on a single track, bi-directional rail line. There are T train types and one train of each type is dispatched per cycle. The decisions include the sequencing of the train types on the main line and the assignment of train types to station platforms. Two conflicting objectives--(1) minimizing cycle length (primary objective) and (2) minimizing total train journey time (secondary objective)--are combined into a single weighted sum objective to generate Pareto optimal solutions. Constraints include a minimum stopping time for each train type in each station, a maximum allowed journey time for each train type, and a minimum headway on the main line and on platforms in stations. The MILP considers five aspects of the railway system: (1) bi-directional train travel between stations, (2) trains moving at different speeds on the main line, (3) trains having the option to stop at stations even if they are not required to, (4) more than one siding or platform at a station, and (5) any number of train types. In order to solve large scale instances, various heuristics and exact methods are employed for computing secondary parameters and for finding lower and upper bounds on the primary objective. These heuristics and exact methods are combined with the math model to allow CPLEX 12.4 to find optimal solutions to large problem instances in a reasonable amount of time. The results show that it is sometimes necessary for (1) a train type to stop at a station where stopping is not required or (2) a train type to travel slower than its normal speed in order to minimize timetable cycle time. In the second part of this dissertation, comprising Chapters 7-9, we study a transit-based evacuation problem which is an extension of bus routing problem. This work has been already submitted to a leading transportation journal (Heydar et al., 2014). This paper presents a mathematical model to plan emergencies in a highly populated urban zone where a certain numbers of pedestrians depend on transit for evacuation. The proposed model features a two-level operational framework. The first level operation guides evacuees through urban streets and crosswalks (referred to as the pedestrian network ) to designated pick-up points (e.g., bus stops), and the second level operation properly dispatches and routes a fleet of buses at different depots to those pick-up points and transports evacuees to their destinations or safe places. In this level, the buses are routed through the so-called vehicular network. An integrated mixed integer linear program that can effectively take into account the interactions between the aforementioned two networks is formulated to find the maximal evacuation efficiency in the two networks. Since the large instances of the proposed model are mathematically difficult to solve to optimality, a two-stage heuristic is developed to solve larger instances of the model. Over one hundred numerical examples and runs solved by the heuristic illustrate the effectiveness of the proposed solution method in handling large-scale real-world instances

AIRO 2016. 46th Annual Conference of the Italian Operational Research Society. Emerging Advances in Logistics Systems Trieste, September 6-9, 2016 - Abstracts Book

The AIRO 2016 book of abstract collects the contributions from the conference participants. The AIRO 2016 Conference is a special occasion for the Italian Operations Research community, as AIRO annual conferences turn 46th edition in 2016. To reflect this special occasion, the Programme and Organizing Committee, chaired by Walter Ukovich, prepared a high quality Scientific Programme including the first initiative of AIRO Young, the new AIRO poster section that aims to promote the work of students, PhD students, and Postdocs with an interest in Operations Research. The Scientific Programme of the Conference offers a broad spectrum of contributions covering the variety of OR topics and research areas with an emphasis on “Emerging Advances in Logistics Systems”. The event aims at stimulating integration of existing methods and systems, fostering communication amongst different research groups, and laying the foundations for OR integrated research projects in the next decade. Distinct thematic sections follow the AIRO 2016 days starting by initial presentation of the objectives and features of the Conference. In addition three invited internationally known speakers will present Plenary Lectures, by Gianni Di Pillo, Frédéric Semet e Stefan Nickel, gathering AIRO 2016 participants together to offer key presentations on the latest advances and developments in OR’s research

Modelling methodologies for railway asset management

Management of railway assets incurs significant expenditure. Railway asset management modelling can predict the cost and efficacy of an asset management plan, and thus support the asset management planning process. Modelling frameworks can be used to facilitate the development of large, multi-asset, whole life cycle models which can be used to represent large sections of rail track and associated assets. This is achieved with libraries of models and tools with a high level of inter-compatibility. This research set out to support the development of modelling frameworks for railway asset management. It sought to determine the state of the art of railway asset management modelling in order to find which assets require further modelling development before they can be suitably represented in a framework’s model library. It also sought to determine the most accurate and suitable modelling methodology to base the framework upon. These aims were met by first carrying out a literature review to determine the state of the art of asset management modelling for major railway asset types. This review found Petri net models solved via Monte Carlo methods to be the most suitable modelling methodology for asset management. The level crossing asset class was chosen for the development of several models to explore the different types of Petri net model, concentrating on the computational resources required. This asset class was chosen as no asset management model was found in literature, and the diversity of the asset interactions. Literature review found several asset classes in need of further development, and some where asset management modelling may not be possible without other advances. The level crossing Petri net models developed demonstrated that computational requirements differ between the various types of Petri net. Stochastic Petri nets were found to simulate quickly, but had a high memory requirement. Coloured Petri nets were found to have the opposite requirements. A novel Petri net type, the Simple Coloured Petri net was developed to create a balance in computational cost. It was further found that complex processes such as scheduling and resource allocation can only be carried out using Coloured Petri nets due to their enhanced feature set. This work has found that further research on modelling specific asset classes is required to enable the development of a complete asset modelling library for use in a framework. If large models are to be developed, it is recommended that the Simple Coloured Petri net be used to balance computational requirements. Any models requiring complex functions should be developed using the Coloured Petri net methodology