Managing Advanced Synchronization Aspects in Logistics Systems

In this thesis, we model various complex logistics problems and develop appropriate techniques to solve them. We improve industrial practices by introducing synchronized solutions to problems that were previously solved independently. The first part of this thesis focuses on cross-docks. We simultaneously optimize supplier orders and cross-docking operations to either reduce the storage space required or evenly distribute workload over the week. The second part of this thesis is devoted to transport problems in which two types of vehicles are synchronized, one of which can be transported by the other. The areas of application range from home services to parcel delivery to customers. After analyzing the complexity associated with these synchronized solutions (i.e., largescale problems for which the decisions depend on each other), we design algorithms based on the "destroy-and-repair" principle to find efficient solutions. We also introduce mathematical programs for all the considered problems. The problems under study arose directly from collaborations with various industrial partners. In this respect, our achieved solutions have been benchmarked with current industrial practice. Depending on the problem, we have been able to reduce the environmental impact generated by the industrial activities, the overall cost, or the social impact. The achieved gains compared to current industrial practice range from 10 to 70%, depending on the application. -- Dans cette thèse, nous modélisons divers problèmes logistiques complexes et développons des techniques appropriées pour les résoudre. Nous cherchons à améliorer certaines pratiques industrielles en introduisant des solutions synchronisées à des problèmes qui étaient auparavant résolus indépendamment. La première partie de cette thèse porte sur les cross-docks. Nous optimisons simultanément les commandes fournisseurs et les opérations au sein de la plateforme de logistique pour réduire l’espace de stockage requis ou répartir uniformément la charge de travail sur la semaine. La deuxième partie de cette thèse est consacrée aux problèmes de transport dans lesquels deux types de véhicules sont synchronisés, l’un pouvant être transporté par l’autre. Les domaines d’application vont du service à domicile à la livraison de colis chez des clients. Après avoir analysé la complexité des solutions synchronisées (c’est-à-dire des problèmes de grandes dimensions pour lesquels les décisions dépendent les unes des autres), nous concevons des algorithmes basés sur le principe de "destruction / reconstruction" pour trouver des solutions efficaces. Nous modélisons également les problèmes considérés avec la programmation mathématique. Les problèmes à l’étude viennent de collaborations avec divers partenaires industriels. A cet égard, les solutions que nous présentons sont comparées aux pratiques industrielles actuelles. En fonction du problème, nous avons pu réduire l’impact environnemental généré par les activités industrielles, le coût global, ou l’impact social des solutions. Les gains obtenus par rapport aux pratiques industrielles actuelles varient de 10 à 70%, selon l’application. Mot-clefs: Logistique, Synchronisation, Problème de transport, Tournée de véhicules, Plateforme de Cross-dock (transbordement), Programmation Mathématiques, Métaheuristiques, Matheuristiques, Instances Réelle

Towards Sustainable Freight Energy Management - Development of a Strategic Decision Support Tool

Freight transportation, in its current shape and form, is on a highly unsustainable trajectory. Global demand for freight is ever increasing, while this demand is predominantly serviced by inefficient, fossil fuel dependent transportation options. The management of energy use in freight transportation has been identified as a significant opportunity to improve the sustainability of the freight sector. Given the vast amount of energy mitigation measures and policies to choose from to attempt this, decision-makers need support and guidance in terms of selecting which policies to adopt – they are faced with a complex and demanding problem. These complexities result, in part, from the vast range, scope and extent of measures to be considered by decision-makers. The tool developed needs to encompass a suitable methodology for comparing proverbial apples to oranges in a fair and unbiased manner, despite the development of one consistent assessment metric that can accommodate this level of diversity being problematic. Further to this, decision-makers need insight into the extent of implementation that is required for each measure. Because the level of implementation of each measure is variable and the extent to which each adopted measure will be implemented in the network needs to be specified, the number of potential measure implementation combinations that decision-makers need to consider is infinite, adding further complexity to the problem. Freight energy management measures cannot, and should not, be evaluated in isolation. The knock-on effects of measure adoption on the performance of other measures need to be considered. Measures are not all independent and decision-makers need to take these dependencies and their ramifications into account. In addition, there is dimensionality to be accounted for in terms of each measure, because one measure can be applied in a variable manner across different components of the freight network. A unique and independent decision needs to be made on the application of a measure for each of these network components (for example for each mode). Decisions on freight transportation impact all three traditional pillars of sustainability: social, environmental and economic. Measure impacts, thus, need to be assessed over multiple criteria. Decisions will affect a variety of stakeholders and outcomes must be acceptable to a range of interested parties. Sustainability criteria are often in conflict with one another, implying that there are trade-offs to be negotiated by the decision-makers. Decision-makers, thus, need to propose system alterations, or a portfolio of system alterations, that achieve improvements in some sustainability respects, whilst maintaining a balance between all other sustainability aspects. Moreover, the magnitude of impacts (be it positive or negative) of a measure on the sustainability criteria is variable, adding additional dimensionality to the problem. The aim of the research presented in this dissertation was to develop a decision support tool which addresses the complexities involved in the formulation of freight transport energy management strategies on behalf of the decision-makers, facilitating the development of holistic, sustainable and comprehensive freight management policy by government level decision-makers. The Freight Transport Energy Management Tool (FTEMT) was developed in response to this research objective, using a standardised operations research approach as a roadmap for its development. Following a standardised operations research approach to model development provides a structure where stakeholder participation can be encouraged at all the key stages in the decision-making process; it offers a logical basis for proposing solutions and for assessing any proposed suggestions by others; it ensures that the appraisal of alternative solutions is conducted in a logical, consistent and comprehensive manner against the full set of objectives; and it provides a means for assessing whether the implemented instruments have performed as predicted, enabling the improvement of the model being developed. The FTEMT can be classified as a simulation optimisation model, which is a combination between multi-objective optimisation and simulation. The simulation component provides a suitably accurate representation of the freight system and affords the ability to approximate the effect that measure implementation will have on the sustainability objectives, whilst the optimisation component provides the ability to effectively explore the decision space and reduces the number of alternative options (and, therefore, the complexity) that decision-makers need to consider. It is this simulation optimisation backbone of the FTEMT that enables the tool to address all the complexities surrounding the problem, enabling the decision support produced by the FTEMT to provide the information necessary for decision-makers to steer the freight transport sector towards true sustainability. Although this problem originates from the domain of sustainable transportation planning, the combination of operations research and transport modelling knowledge applied proved essential in developing a decision support tool that is able to generate adequate decision support on the problem. To demonstrate the use and usefulness of the decision support system developed, a fictitious case study version of the FTEMT was modelled and is discussed throughout this dissertation. Results from the case study implementation were used to verify and validate the tool, to demonstrate the decision support generated and to illustrate how this decision support can be interpreted and incorporated into a decision-making process. Outputs from the case study FTEMT proved the tool to be operationally valid, as it successfully achieved its stated objectives (the FTEMT unearths a Pareto set of solutions close to the true efficient frontier through the exploration of different energy management measure combinations). Explained in short, the value of using the FTEMT to generate decision support is that it explores the decision space and reduces the number of decision alternatives that decision-makers need to consider to a manageable number of solutions, all of which represent harmonic measure combinations geared toward optimal performance in terms of the entire spectrum of the problem objectives. These solutions are developed taking all the complexity issues surrounding the problem into account. Decision-makers can, thus, have confidence that the acceptance of any one of the solutions proposed by the FTEMT will be a responsible and sound decision. As an additional benefit, preferences and strategic priorities of the decision-makers can be factored in when selecting a preferred decision alternative for implementation. Decision-makers must debate the trade-offs between solutions and need to determine what they are willing to sacrifice to realise what gain, but they are afforded the opportunity to select solutions that show the greatest alignment with their official mandates. The structure of the FTEMT developed and described in this dissertation presents a practical methodology for producing decision support on the development of sound freight energy management policy. This work serves as a basis to stimulate further scholarship and expands upon the collective knowledge on the topic, by proposing an approach that is able to address the full scale of complexities involved in the production of such decision support

Vehicle Routing Problem in Cold Chain Logistics: a Joint Distribution Model with Carbon Trading Mechanisms

Fierce competition and the mandate for green development have driven cold chain logistics companies to minimize total distribution costs and carbon emissions to gain a competitive advantage and achieve sustainable development. However, the cold chain logistics literature considers carbon trading mechanisms in sharing economy, namely the joint distribution, is limited. Our research builds a Joint Distribution-Green Vehicle Routing Problem (JD-GVRP) model, in which cold chain logistics companies collaborate among each other to deliver cold chain commodities by considering carbon tax policy. Based on the real business data from four cold chain companies and 28 customers, a simulated annealing (SA) algorithm is applied to optimize the model. The results indicate that joint distribution is an effective way to reduce total costs and carbon emissions when compared with the single distribution. The total cost is positively correlated with the carbon price, while the carbon emissions vary differently when the carbon price increases. In addition, carbon quotas have no effect on the delivery path. This research expands cold chain logistics literature by linking it with joint distribution and carbon trading mechanisms. Moreover, this research suggests that cold chain logistics companies could enhance delivery efficiency, reduce the business cost, and improve competitiveness by reinforcing the collaboration at the industry level. Furthermore, the government should advocate the mode of joint distribution and formulate an effective carbon trading policy to better utilize social and industrial resources to achieve the balanced economic and environmental benefits

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges

A multi-objective, hub-and-spoke model to design and manage biofuel supply chains

In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants relies on a hub-and-spoke structure which optimizes transportation costs of biomass. The model proposed optimizes the CO2 style= position: relative; tabindex= 0 id= MathJax-Element-1-Frame \u3eCO2 emissions due to transportation-related activities in the supply chain. The model also optimizes the social impact of biofuels. The social impacts are evaluated by the number of jobs created. The multi-objective optimization model is solved using an augmented ϵ style= position: relative; tabindex= 0 id= MathJax-Element-2-Frame \u3eϵ-constraint method. The method provides a set of Pareto optimal solutions. We develop a case study using data from the Midwest region of the USA. The numerical analyses estimates the quantity and cost of cellulosic ethanol delivered under different scenarios generated. The insights we provide will help policy makers design policies which encourage and support renewable energy production

Ordonnancement de camions dans une plateforme logistique : complexité, méthodes de résolution et incertitudes

La problématique dite de crossdocking a été source de beaucoup d'attention ces dernières années dans la littérature. Un crossdock est une plateforme logistique favorisant, par une synchronisation efficace des camions entrants et sortants, une rotation rapide des produits, le volume de produits stockés devant être le plus faible possible. Le crossdocking soulève de nombreux problèmes logistiques, dont notamment celui de l'ordonnancement des camions entrants et sortants sur les quais de la plateforme. L'objectif classiquement considéré dans la littérature pour ce problème est la minimisation du makespan, critère très répandu en d'ordonnancement. Pour le crossdocking néanmoins, minimiser la date de départ du dernier camion ne garantie pas nécessairement une bonne synchronisation des camions et le makespan ne semble donc pas être l'objectif le plus pertinent. Pour répondre au besoin de synchronisation et favoriser les rotations rapides, notre travail propose alternativement de minimiser la somme des temps de séjour des palettes dans le stock. Nous étudions d'abord la version déterministe de ce problème d'ordonnancement. Sa complexité est détaillée selon différentes hypothèses pour identifier les éléments menant à sa NP-difficulté. Différentes méthodes de résolutions sont proposées. Une méthode classique de programmation linéaire en nombres entiers utilisant des variables de décision indexées par le temps. Une famille d'inégalités valides est également proposée et exploitée dans un algorithme avec ajout itératif de coupes. Des méthodes basées sur la programmation par contraintes sont enfin proposées. Une analyse comparative de ces différentes méthodes est proposée. Dans un deuxième temps, nous étudions une version non-déterministe de notre problème d'ordonnancement dans laquelle des incertitudes sur les dates d'arrivée des camions sont introduites sous la forme d'intervalles de temps équiprobables. Une méthode d'ordonnancement proactive-réactive utilisant le concept de groupes d'opérations permutables est proposée pour faire face aux incertitudes. Des groupes de camions permutables sont séquencés et affectés aux quais puis, durant l'exécution d'ordonnancement, en fonction de la réalisation des dates d'arrivée, un ordre est choisi dans chaque groupe à l'aide d'un algorithme réactif.Crossdocking has received a lot of attention in the literature in recent years. A crossdock is a logistic platform that promotes rapid product turnover through efficient synchronization of incoming and outgoing trucks, with the volume of products stored being kept as low as possible. Crossdocking raises many logistical problems, including the scheduling of incoming and outgoing trucks on the platform's docks. The classical objective considered in the literature for this problem is the minimization of the makespan, a very common criterion in scheduling. However, for crossdocking, minimizing the departure date of the last truck does not necessarily guarantee a good synchronization of the trucks and the makespan does not seem to be the most relevant objective. In order to meet the need for synchronization and to help fast rotations, our work proposes alternatively to minimize the sum of the pallets' sojourn times in the warehouse. We first study the deterministic version of this scheduling problem. Its complexity is detailed under different assumptions to identify the elements leading to its NP-hardness. Different solution methods are proposed. A classical integer linear programming method using time-indexed decision variables. A family of valid inequalities is also proposed and exploited in an algorithm with iterative addition of cuts. Finally, methods based on constraint programming are proposed. A comparative analysis of these different methods is proposed. In a second step, we study a non-deterministic version of our scheduling problem in which uncertainties on truck arrival dates are introduced in the form of equiprobable time intervals. A proactive-reactive scheduling method using the concept of permutable operation groups is proposed to cope with the uncertainties. Groups of permutable trucks are sequenced and assigned to the docks and then, during the scheduling run, based on the realization of arrival dates, an order is chosen in each group using a reactive algorithm

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Modeling and Solution Methodologies for Mixed-Model Sequencing in Automobile Industry

The global competitive environment leads companies to consider how to produce high-quality products at a lower cost. Mixed-model assembly lines are often designed such that average station work satisfies the time allocated to each station, but some models with work-intensive options require more than the allocated time. Sequencing varying models in a mixed-model assembly line, mixed-model sequencing (MMS), is a short-term decision problem that has the objective of preventing line stoppage resulting from a station work overload. Accordingly, a good allocation of models is necessary to avoid work overload. The car sequencing problem (CSP) is a specific version of the MMS that minimizes work overload by controlling the sequence of models. In order to do that, CSP restricts the number of work-intensive options by applying capacity rules. Consequently, the objective is to find the sequence with the minimum number of capacity rule violations. In this dissertation, we provide exact and heuristic solution approaches to solve different variants of MMS and CSP. First, we provide five improved lower bounds for benchmark CSP instances by solving problems optimally with a subset of options. We present four local search metaheuristics adapting efficient transformation operators to solve CSP. The computational experiments show that the Adaptive Local Search provides a significant advantage by not requiring tuning on the operator weights due to its adaptive control mechanism. Additionally, we propose a two-stage stochastic program for the mixed-model sequencing (MMS) problem with stochastic product failures, and provide improvements to the second-stage problem. To tackle the exponential number of scenarios, we employ the sample average approximation approach and two solution methodologies. On one hand, we develop an L-shaped decomposition-based algorithm, where the computational experiments show its superiority over solving the deterministic equivalent formulation with an off-the-shelf solver. We also provide a tabu search algorithm in addition to a greedy heuristic to tackle case study instances inspired by our car manufacturer partner. Numerical experiments show that the proposed solution methodologies generate high-quality solutions by utilizing a sample of scenarios. Particularly, a robust sequence that is generated by considering car failures can decrease the expected work overload by more than 20\% for both small- and large-sized instances. To the best of our knowledge, this is the first study that considers stochastic failures of products in MMS. Moreover, we propose a two-stage stochastic program and formulation improvements for a mixed-model sequencing problem with stochastic product failures and integrated reinsertion process. We present a bi-objective evolutionary optimization algorithm, a two-stage bi-objective local search algorithm, and a hybrid local search integrated evolutionary optimization algorithm to tackle the proposed problem. Numerical experiments over a case study show that while the hybrid algorithm provides a better exploration of the Pareto front representation and more reliable solutions in terms of waiting time of failed vehicles, the local search algorithm provides more reliable solutions in terms of work overload objective. Finally, dynamic reinsertion simulations are executed over industry-inspired instances to assess the quality of the solutions. The results show that integrating the reinsertion process in addition to considering vehicle failures can keep reducing the work overload by around 20\% while significantly decreasing the waiting time of the failed vehicles