The Vehicle Routing Problem with Service Level Constraints

We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates transportation services for a limited number of partners and should respect contractual obligations on service levels. The problem also generalizes several important classes of vehicle routing problems with profits. To solve it, we propose a compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management, which relies on problem-tailored solution representation, crossover and local search operators, as well as an adaptive penalization mechanism establishing a good balance between service levels and costs. Our computational experiments show that the proposed heuristic returns very high-quality solutions for this difficult problem, matches all optimal solutions found for small and medium-scale benchmark instances, and improves upon existing algorithms for two important special cases: the vehicle routing problem with private fleet and common carrier, and the capacitated profitable tour problem. The branch-and-price algorithm also produces new optimal solutions for all three problems

IoT analytics and agile optimization for solving dynamic team orienteering problems with mandatory visits

Transport activities and citizen mobility have a deep impact on enlarged smart cities. By analyzing Big Data streams generated through Internet of Things (IoT) devices, this paper aims to show the efficiency of using IoT analytics, as an agile optimization input for solving real-time problems in smart cities. IoT analytics has become the main core of large-scale Internet applications, however, its utilization in optimization approaches for real-time configuration and dynamic conditions of a smart city has been less discussed. The challenging research topic is how to reach real-time IoT analytics for use in optimization approaches. In this paper, we consider integrating IoT analytics into agile optimization problems. A realistic waste collection problem is modeled as a dynamic team orienteering problem with mandatory visits. Open data repositories from smart cities are used for extracting the IoT analytics to achieve maximum advantage under the city environment condition. Our developed methodology allows us to process real-time information gathered from IoT systems in order to optimize the vehicle routing decision under dynamic changes of the traffic environments. A series of computational experiments is provided in order to illustrate our approach and discuss its effectiveness. In these experiments, a traditional static approach is compared against a dynamic one. In the former, the solution is calculated only once at the beginning, while in the latter, the solution is re-calculated periodically as new data are obtained. The results of the experiments clearly show that our proposed dynamic approach outperforms the static one in terms of rewardsThis project has received the support of the Ajuntament of Barcelona and the Fundació “la Caixa” under the framework of the Barcelona Science Plan 2020-2023 (grant 21S09355-001)Peer ReviewedPostprint (published version

Spatial coverage in routing and path planning problems

Routing and path planning problems that involve spatial coverage have received increasing attention in recent years in different application areas. Spatial coverage refers to the possibility of considering nodes that are not directly served by a vehicle as visited for the purpose of the objective function or constraints. Despite similarities between the underlying problems, solution approaches have been developed in different disciplines independently, leading to different terminologies and solution techniques. This paper proposes a unified view of the approaches: Based on a formal introduction of the concept of spatial coverage in vehicle routing, it presents a classification scheme for core problem features and summarizes problem variants and solution concepts developed in the domains of operations research and robotics. The connections between these related problem classes offer insights into common underlying structures and open possibilities for developing new applications and algorithms

Orienteering Problem: A survey of recent variants, solution approaches and applications

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

A Systematic Review of Approximability Results for Traveling Salesman Problems leveraging the TSP-T3CO Definition Scheme

The traveling salesman (or salesperson) problem, short TSP, is a problem of strong interest to many researchers from mathematics, economics, and computer science. Manifold TSP variants occur in nearly every scientific field and application domain: engineering, physics, biology, life sciences, and manufacturing just to name a few. Several thousand papers are published on theoretical research or application-oriented results each year. This paper provides the first systematic survey on the best currently known approximability and inapproximability results for well-known TSP variants such as the "standard" TSP, Path TSP, Bottleneck TSP, Maximum Scatter TSP, Generalized TSP, Clustered TSP, Traveling Purchaser Problem, Profitable Tour Problem, Quota TSP, Prize-Collecting TSP, Orienteering Problem, Time-dependent TSP, TSP with Time Windows, and the Orienteering Problem with Time Windows. The foundation of our survey is the definition scheme T3CO, which we propose as a uniform, easy-to-use and extensible means for the formal and precise definition of TSP variants. Applying T3CO to formally define the variant studied by a paper reveals subtle differences within the same named variant and also brings out the differences between the variants more clearly. We achieve the first comprehensive, concise, and compact representation of approximability results by using T3CO definitions. This makes it easier to understand the approximability landscape and the assumptions under which certain results hold. Open gaps become more evident and results can be compared more easily

An updated annotated bibliography on arc routing problems

The number of arc routing publications has increased significantly in the last decade. Such an increase justifies a second annotated bibliography, a sequel to Corberán and Prins (Networks 56 (2010), 50–69), discussing arc routing studies from 2010 onwards. These studies are grouped into three main sections: single vehicle problems, multiple vehicle problems and applications. Each main section catalogs problems according to their specifics. Section 2 is therefore composed of four subsections, namely: the Chinese Postman Problem, the Rural Postman Problem, the General Routing Problem (GRP) and Arc Routing Problems (ARPs) with profits. Section 3, devoted to the multiple vehicle case, begins with three subsections on the Capacitated Arc Routing Problem (CARP) and then delves into several variants of multiple ARPs, ending with GRPs and problems with profits. Section 4 is devoted to applications, including distribution and collection routes, outdoor activities, post-disaster operations, road cleaning and marking. As new applications emerge and existing applications continue to be used and adapted, the future of arc routing research looks promising.info:eu-repo/semantics/publishedVersio

The bid construction problem for truckload transportation services procurement in combinatorial auctions : new formulations and solution methods

De nos jours, l'évolution du commerce électronique ainsi que des niveaux de la consommation requièrent des acteurs de la chaine logistique et en particulier les transporteurs de gérer efficacement leurs opérations. Afin de rester concurrentiels et maximiser leurs profits, ils doivent optimiser leurs opérations de transport. Dans cette thèse de doctorat, nous nous focalisons sur les enchères combinatoires en tant que mécanisme de négociation pour les marchés d'approvisionnement des services de transport routier par camions permettant à un expéditeur d'externaliser ses opérations de transport et aux transporteurs d'acquérir des contrats de transport. Les mises combinatoires permettent à un transporteur participant à l'enchère d'exprimer ses intérêts pour une combinaison de contrats mis à l'enchère dans une même mise. Si la mise gagne, tous les contrats qui la forment seront alloués au transporteur au tarif exigé. Les défis majeurs pour le transporteur sont de déterminer les contrats de transport sur lesquels miser, les regrouper dans plusieurs mises combinatoires, s'il y a lieu, et décider des prix à soumettre pour chaque mise générée. Ces défis décisionnels définissent le problème de construction de mises combinatoires (BCP pour Bid Construction Problem). Chaque transporteur doit résoudre le BCP tout en respectant ses engagements préexistants et ses capacités de transport et en tenant compte des offres des compétiteurs, ce qui rend le problème difficile à résoudre. Dans la pratique, la majorité des transporteurs se basent sur leur connaissance du marché et leur historique pour fixer leurs prix des mises. Dans la littérature, la majorité des travaux sur le BCP considèrent des modèles déterministes où les paramètres sont connus et se limitent à un contexte de flotte homogène. En plus, nous notons qu'un seul travail à considérer une variante stochastique du BCP. Dans cette thèse de doctorat, nous visons à faire avancer les connaissances dans ce domaine en introduisant de nouvelles formulations et méthodes de résolution pour le BCP Le premier chapitre de cette thèse introduit une nouvelle variante du BCP avec une flotte hétérogène. En partant d'une comparaison des similitudes et des différences entre le BCP et les problèmes classiques de de tournées de véhicules, nous proposons une nouvelle formulation basée sur les arcs avec de nouvelles contraintes de bris de symétrie pour accélérer la résolution. Ensuite, nous proposons une approche heuristique et une autre exacte pour résoudre ce problème. L'heuristique développée est une recherche adaptative à grands voisinages (ALNS pour Adaptive Large Neighborhood Search) et se base sur le principe de destruction puis réparation de la solution à l'aide d'opérateurs conçus spécifiquement pour le BCP traité. La méthode exacte utilise la meilleure solution heuristique pour résoudre notre modèle mathématique avec le solveur CPLEX. Les résultats obtenus montrent la pertinence de nos méthodes en termes de qualités des solutions et des temps de calculs et ce pour des instances de grande taille. Dans le deuxième chapitre, nous nous attaquons à un cas particulier du BCP où le transporteur n'a pas d'engagements existants et vise à déterminer un ensemble de contrats mis à l'enchère profitables à miser dessus. Cette problématique correspond à un problème de tournées de véhicules avec profits (TOP pour Team Orienteering Problem). Nous proposons pour le TOP une heuristique ALNS hybride avec de nouveaux opérateurs ainsi que de nouvelles fonctionnalités tenant compte de la nature du problème. Ensuite, nous comparons les performances de notre méthode avec toutes les méthodes déjà publiées dans la littérature traitant du TOP. Les résultats montrent que notre méthode surpasse généralement toutes les approches existantes en termes de qualité des solutions et/ou temps de calculs quand elle est testée sur toutes les instances de la littérature. Notre méthode améliore la solution d'une instance de grande taille, ce qui surligne sa performance. Dans le troisième chapitre, nous nous focalisons sur l'incertitude associée aux prix de cessions des contrats mis à l'enchère et sur les offres des transporteurs concurrents. Il n'existe qu'un seul article qui traite de l'incertitude dans le BCP cependant il ne permet pas de générer des mises multiples. Ainsi, nous proposons une nouvelle formulation pour le BCP avec des prix stochastiques permettant de générer des mises combinatoires et disjointes. Nous présentons deux méthodes pour résoudre ce problème. La première méthode est hybride et à deux étapes. Dans un premier temps, elle résout un problème de sélection pour déterminer un ensemble de contrats profitables. Dans un second temps, elle résout simultanément un problème de sélection de contrats et de détermination de prix des mises (CSPP pour Contracts Selection and Pricing Problem) en ne considérant que les contrats sélectionnés dans la première étape. Notre méthode exacte résout, avec l'algorithme de branch-and-cut, le CSPP sans présélectionner des contrats. Les résultats expérimentaux et de simulations que nous rapportons soulignent la performance de nos deux méthodes et évaluent l'impact de certains paramètres sur le profit réel du transporteur. Dans le quatrième chapitre, nous nous focalisons sur l'incertitude liée au succès des mises et à la non-matérialisation des contrats. Généralement, le transporteur souhaite avoir la garantie que si certaines des mises ne sont pas gagnées ou un contrat ne se matérialise pas, il n'encourra pas de perte en servant le sous-ensemble de contrats gagnés. Dans cette recherche, nous adressons le BCP avec prix stochastiques et développons une méthode exacte qui garantit un profit non négatif pour le transporteur peu importe le résultat des enchères. Nos simulations des solutions optimales démontrent, qu'en moyenne, notre approche permet au transporteur d'augmenter son profit en plus de garantir qu'il reste non-négatif peu importe les mises gagnées ou la matérialisation des contrats suivant l'enchère.Nowadays, the evolution of e-commerce and consumption levels require supply chain actors, in particular carriers, to efficiently manage their operations. In order to remain competitive and to maximize their profits, they must optimize their transport operations. In this doctoral thesis, we focus on Combinatorial Auctions (CA) as a negotiation mechanism for truckload (TL) transportation services procurement allowing a shipper to outsource its transportation operations and for a carrier to serve new transportation contracts. Combinatorial bids offer a carrier the possibility to express his valuation for a combination of contracts simultaneously. If the bid is successful, all the contracts forming it will be allocated to the carrier at the submitted price. The major challenges for a carrier are to select the transportation contracts to bid on, formulate combinatorial bids and associated prices. These decision-making challenges define the Bid Construction Problem (BCP). Each carrier must solve a BCP while respecting its pre-existing commitments and transportation capacity and considering unknown competitors' offers, which makes the problem difficult to solve. In practice, the majority of carriers rely on their historical data and market knowledge to set their prices. In the literature, the majority of works on the BCP propose deterministic models with known parameters and are limited to the problem with a homogeneous fleet. In addition, we found a single work addressing a stochastic BCP. In this thesis, we aim to advance knowledge in this field by introducing new formulations and solution methods for the BCP. The first chapter of this thesis introduces the BCP with a heterogeneous fleet. Starting from a comparison between the BCP and classical Vehicle Routing Problems (VRPs), we propose a new arc-based formulation with new symmetry-breaking constraints for the BCP. Next, we propose exact and heuristic approaches to solve this problem. Our Adaptive Large Neighborhood Search (ALNS) heuristic is based on a destroy-repair principle using operators designed for this problem. Our exact method starts from the heuristic solution and solves our mathematical model with CPLEX. The results we obtained revealed the relevance of our methods in terms of solutions quality and computational times for large instances with up to 500 contracts and 50 vehicles. In the second chapter, we tackle a particular case of the BCP where the carrier has no pre-existing commitments and aims to select a set of profitable auctioned contracts to bid on. This problem corresponds to a Team Orienteering Problem (TOP). We propose a hybrid ALNS heuristic for the TOP with new operators as well as new features taking into account the nature of the problem. Then, we compare the performance of our algorithm against the best solutions from the literature. The results show that our method generally outperforms all the existing ones in terms of solutions quality and/or computational times on benchmark instances. Our method improves one large instance solution, which highlights its performance. In the third chapter, we focus on the uncertainty associated with the auctioned contracts clearing prices and competing carriers offers. Only one article dealing with uncertainty in the BCP existed but it does not allow to generate multiple bids. Thus, we propose a new formulation for the BCP with stochastic prices allowing to generate non-overlapping combinatorial bids. We present two methods to solve this problem. The first one is a two-step hybrid heuristic. First, it solves a Contracts Selection Problem to determine a set of profitable contracts to bid on. Secondly, it simultaneously solves a Contracts Selection and Pricing Problem (CSPP) by considering only the set of auctioned contracts selected in the first stage. Our exact method solves a CSPP by branch-and-cut without pre-selecting contracts. The experimental and simulation results underline the performance of our two methods and evaluate the impact of certain parameters on the carrier's real profit. In the fourth chapter, we focus on the uncertainty associated with bids success and contracts non-materialization. Generally, the carrier seeks to be assured that if some of the submitted bids are not won or a contract does not materialize, it will not incur a loss by serving the remaining contracts. In this research, we address the BCP with stochastic prices and develop an exact method that ensures a non-negative profit for the carrier regardless of the auction outcomes and contracts materialization. Our simulations of the optimal solutions show that, on average, our approach increases the carrier's profit in addition to guaranteeing its non-negativity regardless of the bids won or the contracts materialization

MODEL OPTIMISASI UNTUK MASALAH MINIMISASI WAKTU PERJALANAN WISATA TUR-TUNGGAL DI DAERAH KEPULAUAN

Penelitian ini bertujuan untuk mengembangkan model optimasi penyelesaian masalah minimisasi waktu perjalanan wisata tur-tunggal di daerah kepulauan. Model dikembangkan dengan menggunakan pendekatan integer programming dan diformulasikan dalam bentuk non-linear. Faktor-faktor yang dipertimbangkan meliputi klaster pulau, rute dan jadwal keberangkatan di setiap titik koneksi, dan seleksi titik akomodasi. Validasi model dilakukan melalui percobaan numerikal untuk menguji konsistensi dan adaptabilitas nilai keluaran model terhadap perubahan parameter yang diberikan. Skenario percobaan direncanakan berdasarkan variasi hari keberangkatan dan maksimum waktu kover titik akomodasi. Hasil menunjukkan bahwa model memiliki adaptabilitas dan konsisten terhadap perubahan parameter berdasarkan empat belas skenario yang diberikan