Transportation Optimization in Tactical and Operational Wood Procurement Planning

RÉSUMÉ : L'économie canadienne est dépendante du secteur forestier. Cependant, depuis quelques années, ce secteur fait face à de nouveaux défis, tels que la récession mondiale, un dollar canadien plus fort et une baisse significative de la demande de papier journal. Dans ce nouveau contexte, une planification plus efficace de la chaîne d'approvisionnement est devenue un élément essentiel pour assurer le succès et la pérennité du secteur. Les coûts de transport représentent une dépense importante pour les entreprises forestières. Ceci est dû aux grands volumes de produits qui doivent être transportés sur de grandes distances, en particulier dans le contexte géographique d'un grand pays comme le Canada. Même si les problèmes de tournée de véhicules sont bien couverts dans la littérature, le secteur forestier a beaucoup de caractéristiques uniques qui nécessitent de nouvelles formulations des problèmes et des algorithmes de résolution. À titre d’exemple, les volumes à transporter sont importants comparés à d’autres secteurs et il existe aussi des contraintes de synchronisation à prendre en compte pour planifier l'équipement qui effectue le chargement et le déchargement des véhicules. Cette thèse traite des problèmes de planification de la chaîne logistique d'approvisionnement en bois: récolter diverses variétés de bois en forêt et les transporter par camion aux usines et aux zones de stockage intermédiaire en respectant la demande pour les différents produits forestiers. Elle propose trois nouvelles formulations de ces problèmes. Ces problèmes sont différents les uns des autres dans des aspects tel que l'horizon de planification et des contraintes industrielles variées. Une autre contribution de cette thèse sont les méthodologies développées pour résoudre ces problèmes dans le but d’obtenir des calendriers d’approvisionnement applicables par l’industrie et qui minimisent les coûts de transport. Cette minimisation est le résultat d’allocations plus intelligentes des points d'approvisionnement aux points de demande, d’une tournée de véhicules qui minimise la distance parcourue à vide et de décisions d'ordonnancement de véhicules qui minimisent les files d’attentes des camions pour le chargement et le déchargement. Dans le chapitre 3 on considère un modèle de planification tactique de la récolte. Dans ce problème, on détermine la séquence de récolte pour un ensemble de sites forestiers, et on attribue des équipes de récolte à ces sites. La formulation en programme linéaire en nombres entiers (PLNE) de ce problème gère les décisions d'inventaire et alloue les flux de bois à des entrepreneurs de transport routier sur un horizon de planification annuel. La nouveauté de notre approche est d'intégrer les décisions de tournée des véhicules dans la PLNE. Cette méthode profite de la flexibilité du plan de récolte pour satisfaire les horaires des conducteurs dans le but de conserver une flotte constante de conducteurs permanents et également pour minimiser les coûts de transport. Une heuristique de génération de colonnes est créée pour résoudre ce problème avec un sous-problème qui consiste en un problème du plus court chemin avec capacités (PCCC) avec une solution qui représente une tournée de véhicule. Dans le chapitre 4, on suppose que le plan de récolte est fixé et on doit déterminer les allocations et les inventaires du modèle tactique précédent, avec aussi des décisions de tournée et d'ordonnancement de véhicules. On synchronise les véhicules avec les chargeuses dans les forêts et dans les usines. Les contraintes de synchronisation rendent le problème plus difficile. L’objectif est de déterminer la taille de la flotte de véhicules dans un modèle tactique et de satisfaire la demande des usines avec un coût minimum. Le PLNE est résolu par une heuristique de génération de colonnes. Le sous-problème consiste en un PCCC avec une solution qui représente une tournée et un horaire quotidien d'un véhicule. Dans le chapitre 5, on considère un PLNE du problème similaire à celui étudié dans le chapitre 4, mais dans un contexte plus opérationnel: un horizon de planification d'un mois. Contrairement aux horaires quotidiens de véhicules du problème précédent, on doit planifier les conducteurs par semaine pour gérer les situations dans lesquelles le déchargement d’un camion s’effectue le lendemain de la journée où le chargement a eu lieu. Cette situation se présente quand les conducteurs travaillent la nuit ou quand ils travaillent après les heures de fermeture de l'usine et doivent décharger leur camion au début de la journée suivante. Ceci permet aussi une gestion plus directe des exigences des horaires hebdomadaires. Les contraintes de synchronisation entre les véhicules et les chargeuses qui sont présentes dans le PLNE permettent de créer un horaire pour chaque opérateur de chargeuse. Les coûts de transport sont alors minimisés. On résout le problème à l’aide d’une heuristique de génération de colonnes. Le sous-problème consiste en un PCCC avec une solution qui représente une tournée et un horaire hebdomadaire d’un véhicule.----------ABSTRACT : The Canadian economy is heavily dependent on the forestry industry; however in recent years, this industry has been adapting to new challenges including a worldwide economic downturn, a strengthening Canadian dollar relative to key competing nations, and a significant decline in newsprint demand. Therefore efficiency in supply chain planning is key for the industry to succeed in the future. Transportation costs in particular represent a significant expense to forestry companies. This is due to large volumes of product that must be transported over very large distances, especially in the geographic context of a country the size of Canada. While the field of vehicle routing problems has been heavily studied and applied to many industries for decades, the forestry industry has many unique attributes that necessitate new problem formulations and solution methodologies. These include, but are not limited to, very large (significantly higher than vehicle capacity) volumes to be transported and synchronization constraints to schedule the equipment that load and unload the vehicles. This thesis is set in the wood procurement supply chain of harvesting various assortments of wood in the forest, transporting by truck to mills and intermediate storage locations, while meeting mill demands of the multiple harvested products, and contributes three new problem formulations. These problems differ with respect to planning horizon and varied industrial constraints. Another contribution is the methodologies developed to resolve these problems to yield industrially applicable schedules that minimize vehicle costs: from smarter allocations of supply points to demand points, vehicle routing decisions that optimize the occurrence of backhaul savings, and vehicle scheduling decisions that minimize queues of trucks waiting for loading and unloading equipment. In Chapter 3, we consider a tactical harvest planning model. In this problem we determine the sequence of the harvest of various forest sites, and assign harvest teams to these sites. The MILP formulation of this problem makes inventory decisions and allocates wood flow to trucking contractors over the annual planning horizon, subject to demand constraints and trucking capacities. The novel aspect of our approach is to incorporate vehicle routing decisions into our MILP formulation. This takes advantage of the relatively higher flexibility of the harvest plan to ensure driver shifts of desired characteristics, which is important to retain a permanent driver fleet, and also prioritize the creation of backhaul opportunities in the schedule. A branch-and-price heuristic is developed to resolve this problem, with the subproblem being a vehicle routing problem that represents a geographical shift for a vehicle. In Chapter 4, we assume the harvest plan to be an input, and integrate the allocation and inventory variables of the previous tactical model with vehicle routing and scheduling decisions, synchronizing the vehicles with loaders in the forests and at the mills. The synchronization constraints make a considerably more difficult problem. We use this as a tactical planning model, with no specific driver constraints but a goal of determining vehicle fleet size to maximize their utilization. The objective is to meet mill demands over the planning horizon while minimizing transportation and inventory costs, subject to capacity, wood freshness, fleet balancing, and other industrial constraints. The MILP formulation of the problem is resolved via a column generation algorithm, with the subproblem being a daily vehicle routing and scheduling problem. In Chapter 5, we consider a similar problem formulation to that studied in Chapter 4, but set in a more operational context over a planning horizon of approximately one month. Unlike the daily vehicle schedules of the previous problem, we must schedule drivers by week to manage situations of picking up a load on one day and delivering on another day, which is necessary when drivers work overnight shifts or when they work later than mill closing hours and must unload their truck on the next day's shift. This also allows for more direct management of weekly schedule requirements. Loader synchronization constraints are present in the model which derives a schedule for each loader operator. Given mill demands, transportation costs are then minimized. We resolve the problem via a branch-and-price heuristic, with a subproblem of a weekly vehicle routing and scheduling problem. We also measure the benefits of applying interior point stabilization to the resource synchronization constraints in order to improve the column generation, a new application of the technique

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Rolling Stock Rescheduling in Passenger Railways: Applications in short-term planning and in disruption management

Modern society is highly dependent on a reliable railway system for workforce mobility and easy access to the cities. However, the daily operations of a large passenger railway system are subject to unexpected disruptions such as rolling stock breakdowns or malfunctioning infrastructure. In a disrupted situation, the railway operator must adapt the timetable, rolling stock and crew to the modified conditions. This adaptation of resource allocations requires the solution of complex combinatorial problems in very short time and thus represents a major challenge for the involved dispatchers. In this thesis we develop models and solution methods for the rescheduling of the rolling stock during disruptions. The models incorporate service aspects (such as seat capacity), efficiency aspects (such as number of kilometers driven by the rolling stock), and process related aspects (such as the need for night-time relocation of rolling stock). The thesis contains applications of the developed models in three different contexts. First, we present a framework for applying the rescheduling models in the highly uncertain environment of railway disruption management, and we demonstrate the trade-off between computation time and solution quality. Second, we embed the rolling stock rescheduling models in a simulation framework to account for the dynamic passenger behavior during disruptions. This framework allows us to significantly decrease the delays experienced by passengers. Third, we apply the rescheduling models to real-life planning problems from the short-term planning department of the Netherlands Railways. The models lead to a considerable speed-up of the process and significant savings

Evolutionary Algorithms for Resource Constrained Project Scheduling Problems

The resource constrained project scheduling problems (RCPSPs) are well-known challenging research problems that require efficient solutions to meet the planning need of many practical high-value projects. RCPSPs are usually solved using optimization problem-solving approaches. In recent years, evolutionary algorithms (EAs) have been extensively employed to solve optimization problems, including RCPSPs. Despite that numerous EAs have been developed for solving various RCPSPs, there is no single algorithm that is consistently effective across a wide range of problems. In this context, this thesis aims to propose a few new algorithms for solving different RCPSPs that include singular-resource and multiple-resource problems with single and multiple objectives. In general, RCPSPs are solved with an assumption that its activities are homogeneous, where all activities require all resource types. However, many activities are often singular, requiring only a single resource to complete an activity. Even though the existing algorithms that were developed for multi-resource problems, can solve this RCPSP variant with minor modifications, they are computationally expensive because they include some unnecessary resource constraints in the optimization process. In this thesis, at first, a problem with singular resource and single objective is considered. A heuristic-embedded genetic algorithm (GA) has been proposed for solving this problem, and it's effectiveness has been investigated. To enhance the performance of this algorithm, three heuristics are proposed and integrated with it. As there are no test problems available for singular resource problems, new benchmark problems are generated by modifying the existing multi-resource RCPSPs test set. As compared with experimental results of one of the modified algorithms and an exact solver, it was shown that the proposed algorithm achieved a better quality of solution while requiring a significantly smaller computational budget. The proposed algorithm is then extended to make it suitable for solving multi-resource cases with a single objective, which are known as traditional RCPSPs. A self-adaptive GA is developed for this problem. The proposed self-adaptive component of the algorithm selects an appropriate genetic operator based on their performance as the evolution progresses and increases. To judge the performance of this algorithm, small to large-scale problem instances have been solved from the PSP Library and the results are compared with state-of-the-art algorithms. Based on the experimental results, it was found that the proposed algorithm was able to obtain much better solutions than the non-self-adaptive GA. Furthermore, the proposed approach outperformed the state-of-the-art algorithms. In practice, cost of some resources varies with the day of the week or specific days in the month or year. To consider these day dependent costs, a new cost function is developed that is integrated with the usual cost fitness function in a multi-objective version of RCPSPs. Completion time is considered as the second objective. A heuristic-embedded self-adaptive multi-objective GA is proposed for both singular and multi-resource problems. In this algorithm, the selection mechanism is based on crowding distance and a reference point. A customized mutation operator is also introduced. The experimental results show that the proposed variant, with reference points-based selection, outperformed the variant, with crowding distance-based selection. In many situations, resource availability varies with time, such as time of the day and in some particular days. A dynamic multi-operators-based GA is proposed to deal with this variant. Along with the genetic operators, two local search methods are also included in the self-adaptive mechanism. The proposed approach has been validated using both large-scale singular and multi-resource problem instances with a single objective. Its experimental results demonstrate the efficiency of the proposed dynamic multi-operator-based approach. In summary, the proposed algorithms can solve different variants of RCPSPs that cover a broad spectrum of project scheduling problems, with significantly less computational tim

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Integrated planning and budget allocation for highway maintenance, rehabilitation, and capital construction projects

Highway infrastructure is one of the critical components of the infrastructure network needed for the socio-economic development of a country. However, increased urbanization, limited funds, the need to consider sustainability continue to challenge the planning process for developing and maintaining highway infrastructure. Accordingly, decision-makers are tasked with making optimal decisions while achieving the strategic goals set by federal, state, district, and/or local highway agencies. Pivotal to making such resource allocation decisions, is the availability and accuracy of asset-related data and planning constraints which can guide data-driven decisions to be made by State Highway agencies (SHAs). Currently, several decision-makers still depend significantly on subjective engineering judgment to make decisions on funds allocation. Hence, there is a need for more formal and logical approaches to resource allocation as well as evaluation metrics for conducting alternatives analysis. This notwithstanding, the development of multiple incompatible legacy systems and the presence of several funding categories with stringent project eligibility requirements underpins a “siloed” approach to planning for highway infrastructure. There are often multiple functional groups working on the same asset network but with heterogeneous information systems and distinct decision-making practices. This “siloed” approach can create inefficiencies in projects selection and lead to inter-project conflicts in the highway projects proposed by these different functional groups. When left unaddressed, these spatial-temporal conflicts among projects can result in the misuse of limited taxpayer dollars and ultimately, a lower performance of the network. To address these issues with budget allocation and integrated highway planning, this study contributes to the body of knowledge in three primary ways. First, the study provides a synthesized analysis of budget allocation methods and provides a comprehensive approach to evaluating the performance of different methods employed for M&R decision-making. Secondly, this study formulates and accounts for the impact of multiple funding categories and project eligibility restrictions in budget allocation models. The inclusion of this pragmatic characteristic of M&R decision-making demonstrates the inefficiencies that can result from having increasing restrictions on multiple funding categories. Thirdly, a shared ontology is developed to enable a dynamic link between planning information and projects information. The resulting formalized representation (ontology) was validated by using multiple approaches including automated consistency checking, task-based evaluation, and data-driven evaluation. An implementation tool was also developed and applied to an actual case study problem. The tool was validated by using a Charrette test and feedback from subject-matter experts.Civil, Architectural, and Environmental Engineerin

Interdependent Security and Compliance in Service Selection

Application development today is characterized by ever shorter release cycles and more frequent change requests. Hence development methods such as service composition are increasingly arousing interest as viable alternative approaches. While employing web services as building blocks rapidly reduces development times, it raises new challenges regarding security and compliance since their implementation remains a black box which usually cannot be controlled. Security in particular gets even more challenging since some applications require domainspecific security objectives such as location privacy. Another important aspect is that security objectives are in general no singletons but subject to interdependence. Hence this thesis addresses the question of how to consider interdependent security and compliance in service composition. Current approaches for service composition do neither consider interdependent security nor compliance. Selecting suiting services for a composition is a combinatorial problem which is known to be NP-hard. Often this problem is solved utilizing genetic algorithms in order to obtain near-optimal solutions in reasonable time. This is particularly the case if multiple objectives have to be optimized simultaneously such as price, runtime and data encryption strength. Security properties of compositions are usually verified using formal methods. However, none of the available methods supports interdependence effects or defining arbitrary security objectives. Similarly, no current approach ensures compliance of service compositions during service selection. Instead, compliance is verified afterwards which might necessitate repeating the selection process in case of a non-compliant solution. In this thesis, novel approaches for considering interdependent security and compliance in service composition are being presented and discussed. Since no formal methods exist covering interdependence effects for security, this aspect is covered in terms of a security assessment. An assessment method is developed which builds upon the notion of structural decomposition in order to assess the fulfillment of arbitrary security objectives in terms of a utility function. Interdependence effects are being modeled as dependencies between utility functions. In order to enable compliance-awareness, an approach is presented which checks compliance of compositions during service selection and marks non-compliant parts. This enables to repair the corresponding parts during the selection process by replacing the current services and hence avoids the necessity to repeat the selection process. It is demonstrated how to embed the presented approaches into a genetic algorithm in order to ease integration with existing approaches for service composition. The developed approaches are being compared to state-of-the-art genetic algorithms using simulations