Genetic programming hyper-heuristic with vehicle collaboration for uncertain capacitated arc routing problem

Due to its direct relevance to post-disaster operations, meter reading and civil refuse collection, the Uncertain Capacitated Arc Routing Problem (UCARP) is an important optimisation problem. Stochastic models are critical to study as they more accurately represent the real world than their deterministic counterparts. Although there have been extensive studies in solving routing problems under uncertainty, very few have considered UCARP, and none consider collaboration between vehicles to handle the negative effects of uncertainty. This article proposes a novel Solution Construction Procedure (SCP) that generates solutions to UCARP within a collaborative, multi-vehicle framework. It consists of two types of collaborative activities: one when a vehicle unexpectedly expends capacity (route failure), and the other during the refill process. Then, we propose a Genetic Programming Hyper-Heuristic (GPHH) algorithm to evolve the routing policy used within the collaborative framework. The experimental studies show that the new heuristic with vehicle collaboration and GP-evolved routing policy significantly outperforms the compared state-of-the-art algorithms on commonly studied test problems. This is shown to be especially true on instances with larger numbers of tasks and vehicles. This clearly shows the advantage of vehicle collaboration in handling the uncertain environment, and the effectiveness of the newly proposed algorithm

Arc routing problems: A review of the past, present, and future

[EN] Arc routing problems (ARPs) are defined and introduced. Following a brief history of developments in this area of research, different types of ARPs are described that are currently relevant for study. In addition, particular features of ARPs that are important from a theoretical or practical point of view are discussed. A section on applications describes some of the changes that have occurred from early applications of ARP models to the present day and points the way to emerging topics for study. A final section provides information on libraries and instance repositories for ARPs. The review concludes with some perspectives on future research developments and opportunities for emerging applicationsThis research was supported by the Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional, Grant/Award Number: PGC2018-099428-B-I00. The Research Council of Norway, Grant/Award Numbers: 246825/O70 (DynamITe), 263031/O70 (AXIOM).Corberán, Á.; Eglese, R.; Hasle, G.; Plana, I.; Sanchís Llopis, JM. (2021). Arc routing problems: A review of the past, present, and future. Networks. 77(1):88-115. https://doi.org/10.1002/net.21965S8811577

A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching

An efficient team is essential for the company to successfully complete new projects. To solve the team formation problem considering person-job matching (TFP-PJM), a 0-1 integer programming model is constructed, which considers both person-job matching and team members' willingness to communicate on team efficiency, with the person-job matching score calculated using intuitionistic fuzzy numbers. Then, a reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The RL-GP adopts the ensemble population strategies. Before the population evolution at each generation, the agent selects one from four population search modes according to the information obtained, thus realizing a sound balance of exploration and exploitation. In addition, surrogate models are used in the algorithm to evaluate the formation plans generated by individuals, which speeds up the algorithm learning process. Afterward, a series of comparison experiments are conducted to verify the overall performance of RL-GP and the effectiveness of the improved strategies within the algorithm. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams. This study reveals the advantages of reinforcement learning methods, ensemble strategies, and the surrogate model applied to the GP framework. The diversity and intelligent selection of search patterns along with fast adaptation evaluation, are distinct features that enable RL-GP to be deployed in real-world enterprise environments.Comment: 16 page

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

Problèmes de tournées en viabilité hivernale utilisant la prévision des volumes d’épandage

RÉSUMÉ : Cette thèse combine deux domaines de recherche différents appliqués au déneigement : la recherche opérationnelle et la science des données. La science des données a été utilisée pour développer un modèle de prédiction de quantité de sel et d’abrasif avec une méthodologie d’apprentissage machine; par la suite, ce modèle est pris en compte pour la confection des tournées de véhicules. La confection des tournées a été élaborée en utilisant des outils de la recherche opérationnelle, qui servent à optimiser les tournées en considérant plusieurs contraintes et en intégrant les données réelles. La thèse est le fruit d’une collaboration avec deux villes québécoises, Granby et Saint-Jean-sur- Richelieu. Elle traite une application réelle en viabilité hivernale, qui est l’opération d’épandage. Cette opération est une activité nécessaire, dont le but est d’assurer une meilleure circulation routière. Cependant, cela se réalise avec un coût économique et environnemental important. Par conséquent, la réduction de ce coût devient une grande préoccupation. Cette thèse contribue significativement aux opérations d’épandage : premièrement, nous prédisons la quantité nécessaire de sel et d’abrasif à épandre afin d’éviter le surépandage; deuxièmement, nous optimisons les tournées des opérations d’épandage en considérant la variation de la quantité. La première contribution de cette thèse consiste en un modèle de prédiction des quantités de sel et d’abrasif pour chaque segment de rue et pour chaque heure, en utilisant des algorithmes d’apprentissage machine. L’importance de cette contribution réside d’une part dans l’intégration des données géomatiques avec les données météo-routières, et d’autre part dans l’extraction des variables importantes (feature engineering) pour le modèle de prédiction. Plusieurs algorithmes d’apprentissage machine ont été évalués : (les forêts aléatoires, les arbres extrêmement aléatoires, les réseaux de neurones artificiels, Adaboost, Gradient Boosting Machine et XGBoost). Le modèle élaboré par XGBoost a réalisé une meilleure performance. Le modèle de prédiction permet non seulement de prédire les quantités de sel et d’abrasif nécessaires à épandre mais aussi, d’identifier les variables les plus importantes pour la prédiction. Cette information représente un outil de décision intéressant pour les gestionnaires. L’identification des variables importantes pourrait améliorer les opérations de déneigement. D’après les résultats trouvés, le facteur humain (conducteur) influence significativement la quantité d’épandage; donc, le contrôle de ce facteur peut améliorer considérablement ces opérations. La deuxième contribution introduit un nouveau problème dans la littérature : le problème de tournées de véhicules générales avec capacité dont la quantité de sel et d’abrasif dépend du temps. Le problème est basé sur l’hypothèse que le modèle de prédiction est capable de fournir la quantité d’épandage pour chaque segment et pour chaque heure avec une bonne précision. Le fait d’avoir cette information pour chaque heure et pour chaque segment de rue, introduit la notion du temps dépendant. Le nouveau problème est modélisé à l’aide d’une formulation mathématique sur le graphe original, ce qui présente un défi de modélisation. En effet, il est difficile d’associer des temps de début et de fin uniques à un arc ou à une arête. Une métaheuristique basée sur la stratégie de destruction et construction a été développée pour résoudre les grandes instances. La métaheuristique est inspirée de SISRs (Slack Induction by String Removals). Elle considère la demande dépendante du temps et la présence des arêtes par la méthode d’évaluation basée sur la programmation dynamique. De nouvelles instances ont été créées à partir des instances des problèmes de tournées de véhicules générales avec contrainte de capacité avec demande fixe. Elles ont été générées à partir de différents types de fonction dont la demande dépend du temps. La troisième contribution propose une nouvelle approche, dans le but de présenter le niveau de priorité des rues (la hiérarchie de service) sous forme d’une fonction linéaire dépendante du temps. Le problème présenté dans cette contribution concerne des tournées de véhicules générales hiérarchiques avec contrainte de capacité sous l’incertitude de la demande. Lorsque les données collectées ne permettent pas de développer un bon modèle de prédiction, la notion de demande dépendante du temps n’est plus valide. L’approche robuste a démontré une grande réussite pour traiter et résoudre les problèmes avec incertitude. Une métaheuristique robuste a été proposée pour résoudre les deux cas réels de Granby et de Saint-Jean-sur-Richelieu. La métaheuristique a été validée par un modèle mathématique sur les petites instances générées à partir des cas réels. La simulation de Monte Carlo a été utilisée pour évaluer les différentes solutions proposées. En outre, elle permet d’offrir aux gestionnaires un outil de décision pour comparer les différentes solutions robustes, et aussi pour comprendre le compromis entre le niveau de robustesse souhaité et d’autres mesures de performances (coût, risque, niveau de service).----------ABSTRACT : This thesis combines two different fields applied to winter road maintenance : operational research and data science. Data science was used to develop a prediction model for the quantity of salt and abrasive with a machine learning methodology, later this model is considered for building vehicles routing. This route planning was developed using operational research which seeks to optimize routes by looking at several constraints and by integrating real data. The thesis which is the fruit of a collaboration with two Canadian cities Granby and Saint-Jean-sur-Richelieu, deals with a real application in winter road maintenance which is the spreading operation. The spreading operation presents an activity necessary for winter road maintenance, in order to ensure better road traffic. However, this road safety comes with a significant economic and environmental cost, which creates a great concern in order to reduce the economic and environmental impact. This thesis contributes significantly in the spreading operations : firstly, predicting the necessary quantity of salt and abrasive to be spread in order to avoid over-spreading, secondly optimizing the spreading operations routes considering quantity variations. The first contribution of this thesis is to develop a prediction model for the quantities of salt and abrasive using machine learning algorithms, for each street segment and for each hour. The importance of this contribution lies in the integration of geomatic data with weather-road data, and also the feature engineering. Several machine learning algorithms were evaluated (Random Forest, Extremely Random Trees, Artificial Neural Networks, Adaboost, Gradient Boosting Machine and XGBoost); ultimately XGBoost performed better. The prediction model not only predicts the amounts of salt and abrasive needed to spread, but also identifies the most important variables in the model. This information presents an interesting decision-making tool for managers. The identification of important variables could improve snow removal operations. According to the results, the human factor (driver) significantly influences the amount of spreading, so controlling this factor can significantly improve the spreading operations.The second contribution introduces a new problem in the literature : the mixed capacitated general routing problem with time-dependent demand; the problem is based on the assumption that the prediction model is able to provide the amount of spreading for each segment and for each hour with good accuracy. Having this information for each hour and for each street segment introduces the concept of time dependency. The new problem was modeled using a mathematical formulation on the original graph, which presents a modeling challenge since it is difficult to associate a unique starting and ending time to an arc or edge. A meta-heuristic based on the destruction and construction strategy has been developed to solve large-scale instances. The meta-heuristic is inspired by SISRs considers time-dependent demand and the presence of edges by an evaluation method based on dynamic programming. New instances were created from the instances of the mixed capacitated general routing problem with fixed demand; the new instances were generated from different types of function where the demand varies with time. The third contribution proposes a new approach to present the service hierarchy or the priority level of streets, as a time-dependent linear function. The problem addressed in this contribution concerns the hierarchical mixed capacitated general routing problems under demand uncertainty. When the collected data does not allow the development of a good prediction model, the concept of time-dependent demand is no longer valid. The robust approach has demonstrated great success in resolving and dealing with problems with uncertainty. A robust meta-heuristic was proposed to solve the two real cases Granby and Saint-Jean-sur-Richelieu, the meta-heuristic was validated by a mathematical model on small instances generated from the real cases. The Monte Carlo simulation was used, on the one hand, to evaluate the different solutions proposed, and, on the other hand, to offer managers a decision tool to compare the different robust solutions and also to understand the trade-off between the desired level of robustness, and other performance measures (cost, risk, level of service)

Design and Analysis of Efficient Freight Transportation Networks in a Collaborative Logistics Environment

The increase in total freight volumes, reducing volume per freight unit, and delivery deadlines have increased the burden on freight transportation systems of today. With the evolution of freight demand trends, there also needs to be an evolution in the freight distribution processes. Today\u27s freight transportation processes have a lot of inefficiencies that could be streamlined, thus preventing concerns like increased operational costs, road congestion, and environmental degradation. Collaborative logistics is one of the approaches where supply chain partners collaborate horizontally or/and vertically to create a centralized network that is more efficient and serves towards a common goal or objective. In this dissertation, we study intermodal transportation, and cross-docking, two major pillars of efficient, cheap, and faster freight transportation in a collaborative environment. We design an intermodal network from a centralized network perspective where all the participants intermodal operators, shippers, carriers, and customers strive towards a synchronized and cost-efficient freight network. Also, a cross-dock scheduling problem is presented for competitive shippers using a centralized cross-dock facility. The problem develops a fast heuristic and meta-heuristic approach to solve large-scale real-world problems and draws key insights from a cross-dock operator and inbound carrier\u27s perspectives

Optimisation for Large-scale Maintenance, Scheduling and Vehicle Routing Problems

Solving real-world combinatorial problems is involved in many industry fields to minimise operational cost or to maximise profit, or both. Along with continuous growth in computing power, many asset management decision-making processes that were originally solved by hand now tend to be based on big data analysis. Larger scale problem can be solved and more detailed operation instructions can be delivered. In this thesis, we investigate models and algorithms to solve large scale Geographically Distributed asset Maintenance Problems (GDMP). Our study of the problem was motivated by our business partner, Gaist solutions Ltd., to optimise scheduling of maintenance actions for a drainage system in an urban area. The models and solution methods proposed in the thesis can be applied to many similar issues arising in other industry fields. The thesis contains three parts. We firstly built a risk driven model considering vehicle routing problems and the asset degradation information. A hyperheuristic method embedded with customised low-level heuristics is employed to solve our real-world drainage maintenance problem in Blackpool. Computational results show that our hyperheuristic approach can, within reasonable CPU time, produce much higher quality solutions than the scheduling strategy currently implemented by Blackpool council. We then attempt to develop more efficient solution approaches to tackle our GDMP. We study various hyperheuristics and propose efficient local search strategies in part II. We present computational results on standard periodic vehicle routing problem instances and our GDMP instances. Based on manifold experimental evidences, we summarise the principles of designing heuristic based solution approaches to solve combinatorial problems. Last bu not least, we investigate a related decision making problem from highway maintenance, that is again of interest to Gaist solutions Ltd. We aim to make a strategical decision to choose a cost effective method of delivering the road inspection at a national scale. We build the analysis based on the Chinese Postman Problem and theoretically proof the modelling feasibility in real-world road inspection situations. We also propose a novel graph reduction process to allow effective computation over very large data sets

The Pickup and Multiple Delivery Problem

This thesis presents my work on the pickup and multiple delivery problem, a real-world vehicle routing and scheduling problem with soft time windows, working time and last-in-first-out constraints, developed in collaboration with Transfaction Ltd., who conduct logistics analysis for several large retailers in the UK. A summary of relevant background literature is presented highlighting where my research fits into and contributes to the broader academic landscape. I present a detailed model of the problem and thoroughly analyse a case-study data set, obtaining distributions used for further research. A new variable neighbourhood descent with memory hyper-heuristic is presented and shown to be an effective technique for solving instances of the real-world problem. I analyse strategies for cooperation and competition amongst haulage companies and quantify their effectiveness. The value of time and timely information for planning pickup and delivery requests is investigated. The insights gained are of real industrial relevance, highlighting how a variety of business decisions can produce significant cost savings