In this study, we propose a mixed-integer linear programming model for a Heterogeneous Fixed Fleet Drone Routing problem (HFFDRP) that minimizes the post-disaster inspection cost of a disaster-affected area by accounting a number of drone trajectory-specific factors into consideration such as battery recharging costs, servicing costs, drone hovering, turning, acceleration, constant, and deceleration costs, and many others. The trajectories between each pair of nodes are constructed using a path construction model. Two heuristic algorithms are proposed, namely, Adaptive Large Neighborhood Search (ALNS) algorithm and Modified Backtracking Adaptive Threshold Accepting (MBATA) algorithm, to solve the largest instances of our proposed optimization model. Computational results indicate that the proposed MBATA algorithm is capable of producing high-quality solutions consistently within a reasonable amount of time. Finally, a real-life case study is used to visualize and validate the modeling.

Nessa dissertação apresentamos um problema de programação da produção, motivado por uma indústria alimentícia caracterizada pela perecibilidade dos produtos, sequenciamento da produção dos lotes e pela necessidade de sincronização de recursos escassos para operação das linhas de produção. Em indústrias desse ramo, existem altos custos associados a estocagem dos produtos, a fim de evitar sua perda, de modo que é essencial a boa gestão dos processos industriais e do estoque. Modelos matemáticos de programação inteira mista foram desenvolvidos para tratar o problema, bem como o estudo da inclusão de diversas restrições da literatura para o tratamento da perecibilidade. Testes computacionais foram realizados para as validações dos modelos matemáticos, entretanto, devido à dificuldade de determinar soluções de boa qualidade pelo solver de otimização, foram propostos métodos heurísticos baseados na formulação matemática. Com o objetivo de mostrar o desempenho das heurísticas, comparamos as suas performances na resolução de instâncias da literatura e exemplares baseados no cenário produtivo da indústria com os resultados do solver. / In this dissertation we present a lot sizing and scheduling problem motivated by a food industry characterized by the perishability of the products, sequencing the production of the lots and by the need of synchronization of scarce resources for the operation of the production lines. In this type of industry, there are high costs associated with stocking the products in order to avoid their loss, so that good management of industrial processes and inventory is essential. Mathematical models of mixed integer programming were developed to treat the problem, as well as the study of the inclusion of several restrictions of the literature for the treatment of perishability. Computational tests were performed for the validations of the mathematical models, however, due to the difficulty of determining solutions of good quality by the optimization solver, heuristic methods based on the mathematical formulation were proposed. In order to show the performance of the heuristics, we compare their performances in solving instances of the literature and exemplars based on the productive scenario of the industry with the results of solver.

Nessa dissertação apresentamos um problema de programação da produção, motivado por uma indústria alimentícia caracterizada pela perecibilidade dos produtos, sequenciamento da produção dos lotes e pela necessidade de sincronização de recursos escassos para operação das linhas de produção. Em indústrias desse ramo, existem altos custos associados a estocagem dos produtos, a fim de evitar sua perda, de modo que é essencial a boa gestão dos processos industriais e do estoque. Modelos matemáticos de programação inteira mista foram desenvolvidos para tratar o problema, bem como o estudo da inclusão de diversas restrições da literatura para o tratamento da perecibilidade. Testes computacionais foram realizados para as validações dos modelos matemáticos, entretanto, devido à dificuldade de determinar soluções de boa qualidade pelo solver de otimização, foram propostos métodos heurísticos baseados na formulação matemática. Com o objetivo de mostrar o desempenho das heurísticas, comparamos as suas performances na resolução de instâncias da literatura e exemplares baseados no cenário produtivo da indústria com os resultados do solver. / In this dissertation we present a lot sizing and scheduling problem motivated by a food industry characterized by the perishability of the products, sequencing the production of the lots and by the need of synchronization of scarce resources for the operation of the production lines. In this type of industry, there are high costs associated with stocking the products in order to avoid their loss, so that good management of industrial processes and inventory is essential. Mathematical models of mixed integer programming were developed to treat the problem, as well as the study of the inclusion of several restrictions of the literature for the treatment of perishability. Computational tests were performed for the validations of the mathematical models, however, due to the difficulty of determining solutions of good quality by the optimization solver, heuristic methods based on the mathematical formulation were proposed. In order to show the performance of the heuristics, we compare their performances in solving instances of the literature and exemplars based on the productive scenario of the industry with the results of solver.

Avec plus de 345 millions de tonnes de déchets produits en France en 2012, la performance de la chaîne logistique de collecte, transport et traitement de ces produits et matériaux est devenue un enjeu économique et écologique majeur dans notre société. Dans cette thèse, nous nous intéressons à l’optimisation de la chaîne de collecte et transport des déchets sur le plan tactique et opérationnel. Nous modélisons dans un premier temps un nouveau problème tactique d’optimisation de flux de déchets avec sites de transfert et de traitement sur un horizon mono-périodique puis multi-périodique, afin d’exploiter un réseau logistique existant de manière optimale. Nous résolvons différentes variantes de ce problème linéaire mixte à l’aide d’un solveur. Nous étudions dans un second temps la planification opérationnelle de la collecte de conteneurs d’apport volontaire et des tournées de véhicules associées en résolvant un problème riche de tournées avec gestion de stocks et plateformes de vidage intermédiaires. Nous proposons un modèle d’optimisation de ce nouveau problème et le résolvons par un algorithme à voisinages larges (ALNS) dans un cadre déterministe puis stochastique, dans lequel le remplissage des conteneurs est aléatoire et plus conforme à la réalité. Nous obtenons des résultats compétitifs en évaluant notre approche sur des instances de la littérature proches de notre problème riche. En réalisant un logiciel d’optimisation à destination d’une entreprise de collecte et transport de déchets, nous améliorons également de manière significative les tournées de véhicules en application réelle. / With more than 345 million tons produced in France in2012, waste supply chain management is an important economical and ecological issue for our society. In this thesis, we focus on optimizing waste supply chain on both the tactical and operational decision levels. In order to optimize an existing waste logistic network in medium term, we first solve a multimodal flow problem where products are transferred and transformed in sites of various size, in a mono-periodic then multi-periodic horizon. At an operational level, we study the planning and routing of vehicles used for voluntary drop-off waste container collection by solving a complex inventory routing problem with intermediate facilities. We use a large neighborhoods search metaheuristic to solve both the deterministic and stochastic approaches, where waste supply quantity is also subject to uncertainty. We obtain competitive results on instances coming from the literature on classical routing problems close to our rich case. We also develop an optimization software used by a French waste management company and significantly improve routes in a real application.

Pickup and delivery problems (PDPs) have been studied extensively in past decades. A wide variety of research exits on both exact algorithms and heuristics for generic variations of the problem as well as real-life applications, which continue to spark new challenges and open up new opportunities for researchers. In this dissertation, we study two variations of pickup and delivery problem that arise in industry and develop new computational methods that are shown to be effective with respect to existing algorithms and scheduling procedures found in practice. The first problem is the pickup and delivery problem with transshipment (PDPT). The work presented here was inspired by a daily route planning problem at a regional air carrier. In structuring the analysis, we describe a unique way to model the transshipment option on a directed graph. With the graph as the foundation, we implemented a branch and price algorithm. Preliminary results showed that it has difficulty in solving large instances. As an alternative, we developed a greedy randomized adaptive search procedure (GRASP) with several novel features. In the construction phase, shipment requests are inserted into routes until all demand is satisfied or no feasible insertion exists. In the improvement phase, an adaptive large neighborhood search algorithm is used to reconstruct portions of the feasible routes. Specialized removal and insertion heuristics were designed for this purpose. We also developed a procedure for generating problem instances in the absence of any in the literature. Testing was done on existing PDP data sets and generated PDPT data set. For the former, the performance and solution quality of the GRASP were comparable to the best known heuristics. For the latter, GRASP found the near optimal solution in most test cases. In the second part of the dissertation, we focus on a new version of the heterogeneous PDP in which the capacity of each vehicle can be modified by reconfiguring its interior to satisfy different types of customer demands. The work was motivated by a daily route planning problem arising at a senior activity center. A fleet of configurable vans is available each day to transport participants to and from the center as well as to secondary facilities for rehabilitative and medical treatment. To find solutions, we developed a two-phase heuristic that makes use of ideas from greedy randomized adaptive search procedures with multiple starts. In phase I, a set of good feasible solutions is constructed using a series of randomized procedures. A representative subset of those solutions is selected as candidates for improvement by solving a max diversity problem. In phase II, an adaptive large neighborhood search (ALNS) heuristic is used to find local optima by reconstructing portions of the feasible routes. Also, a specialized route feasibility check with vehicle type reassignment is introduced to take full advantage of the heterogeneous nature of vehicles. The effectiveness of the proposed methodology is demonstrated by comparing the solutions it provided for the equivalent of several weeks with those that were used in practice and derived manually. The analysis indicates that anywhere from 30% to 40% savings can be achieved with the multi-start ALNS heuristic. An exact method is introduced based on branch and price and cut for settings with more restricted time windows. In the procedure, the master problem at each node in the search tree is solved by column generation to find a lower bound. To improve the bound, subset-row inequalities are applied to the variables of the master problem. Columns are generated by solving the pricing subproblems with a labeling algorithm enhanced by new dominance conditions. Local search on the columns is used to quickly find promising alternatives. Implementation details and ways to improve the performance of the overall procedure are discussed. Testing was done on a set of real instances as well as a set of randomly generated instances with up to 50 customer requests. The results show that optimal solutions are obtained in majority of cases. / text

Adaptive Large Neighborhood Search (ALNS) is a fairly recent yet popular single-solution heuristic for solving discrete optimization problems. Even though the heuristic has been a popular choice for researchers in recent times, the parallelization of this algorithm is not widely studied in the literature compared to the other classical metaheuristics. To extend the existing literature, this study proposes several different parallel schemes to parallelize the basic/sequential ALNS algorithm. More specifically, seven different parallel schemes are employed to target different characteristics of the ALNS algorithm and the capability of the local computers. The schemes of this study are implemented in a master-slave architecture to manage and assign loads in processors of the local computers. The overall goal is to simultaneously explore different areas of the search space in an attempt to escape the local minima, taking effective steps toward the optimal solution and, to the end, accelerating the convergence of the ALNS algorithm. The performance of the schemes is tested by solving a capacitated vehicle routing problem (CVRP) with available wellknown test instances. Our computational results indicate that all the parallel schemes are capable of providing a competitive optimality gap in solving CVRP within our investigated test instances. However, the parallel scheme (scheme 1), which runs the ALNS algorithm independently within different slave processors (e.g., without sharing any information with other slave processors) until the synchronization occurs only when one of the processors meets its predefined termination criteria and reports the solution to the master processor, provides the best running time with solving the instances approximately 10.5 times faster than the basic/sequential ALNS algorithm. These findings are applied in a real-life fulfillment process using mixed-mode delivery with trucks and drones. Complex but optimized routes are generated in a short time that is applicable to perform last-mile delivery to customers.