Iterative Combinatorial Auction for Carrier Collaboration in Logistic Services

In collaborative logistics, multiple carriers form a network to share their transportation capacities. Collaboration among carriers results in improved resource utilization and, therefore, reduced costs. In this thesis, we propose an auction-based model for carrier collaboration in transportation services. The model achieves carrier collaboration through facilitating the negotiation among carriers over a group of shipping orders required by one or a group of shippers. The negotiation is conducted through a combinatorial iterative auction mechanism with the objective of minimizing the carriers’ overall costs. We first present a centralized carrier collaboration problem model in which a central entity has all required information to compute an optimal solution. We then consider a more realistic game theoretic setting where auction-based mechanism is applied to deal with self-interests of carriers. Compared with one-shot auctions, the proposed iterative bidding framework has the properties of reducing carriers’ information revelation and accommodating dynamic changes during the bidding process. Experimental results show that the procurement cost performance and the quality of solutions computed using the proposed iterative auction model is close to that of the optimal solutions

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Sustainable supply chain management towards disruption and organizational ambidexterity:A data driven analysis

Balancing sustainability and disruption of supply chains requires organizational ambidexterity. Sustainable supply chains prioritize efficiency and economies of scale and may not have sufficient redundancy to withstand disruptive events. There is a developing body of literature that attempts to reconcile these two aspects. This study gives a data-driven literature review of sustainable supply chain management trends toward ambidexterity and disruption. The critical review reveals temporal trends and geographic distribution of literature. A hybrid of data-driven analysis approach based on content and bibliometric analyses, fuzzy Delphi method, entropy weight method, and fuzzy decision-making trial and evaluation laboratory is used on 273 keywords and 22 indicators obtained based on the experts’ evaluation. The most important indicators are identified as supply chain agility, supply chain coordination, supply chain finance, supply chain flexibility, supply chain resilience, and sustainability. The regions show different tendencies compared with others. Asia and Oceania, Latin America and the Caribbean, and Africa are the regions needs improvement, while Europe and North America show distinct apprehensions on supply chain network design. The main contribution of this review is the identification of the knowledge frontier, which then leads to a discussion of prospects for future studies and practical industry implementation

Learning-Based Matheuristic Solution Methods for Stochastic Network Design

Cette dissertation consiste en trois études, chacune constituant un article de recherche. Dans tous les trois articles, nous considérons le problème de conception de réseaux multiproduits, avec coût fixe, capacité et des demandes stochastiques en tant que programmes stochastiques en deux étapes. Dans un tel contexte, les décisions de conception sont prises dans la première étape avant que la demande réelle ne soit réalisée, tandis que les décisions de flux de la deuxième étape ajustent la solution de la première étape à la réalisation de la demande observée. Nous considérons l’incertitude de la demande comme un nombre fini de scénarios discrets, ce qui est une approche courante dans la littérature. En utilisant l’ensemble de scénarios, le problème mixte en nombre entier (MIP) résultant, appelé formulation étendue (FE), est extrêmement difficile à résoudre, sauf dans des cas triviaux. Cette thèse vise à faire progresser le corpus de connaissances en développant des algorithmes efficaces intégrant des mécanismes d’apprentissage en matheuristique, capables de traiter efficacement des problèmes stochastiques de conception pour des réseaux de grande taille. Le premier article, s’intitulé "A Learning-Based Matheuristc for Stochastic Multicommodity Network Design". Nous introduisons et décrivons formellement un nouveau mécanisme d’apprentissage basé sur l’optimisation pour extraire des informations concernant la structure de la solution du problème stochastique à partir de solutions obtenues avec des combinaisons particulières de scénarios. Nous proposons ensuite une matheuristique "Learn&Optimize", qui utilise les méthodes d’apprentissage pour déduire un ensemble de variables de conception prometteuses, en conjonction avec un solveur MIP de pointe pour résoudre un problème réduit. Le deuxième article, s’intitulé "A Reduced-Cost-Based Restriction and Refinement Matheuristic for Stochastic Network Design". Nous étudions comment concevoir efficacement des mécanismes d’apprentissage basés sur l’information duale afin de guider la détermination des variables dans le contexte de la conception de réseaux stochastiques. Ce travail examine les coûts réduits associés aux variables hors base dans les solutions déterministes pour guider la sélection des variables dans la formulation stochastique. Nous proposons plusieurs stratégies pour extraire des informations sur les coûts réduits afin de fixer un ensemble approprié de variables dans le modèle restreint. Nous proposons ensuite une approche matheuristique utilisant des techniques itératives de réduction des problèmes. Le troisième article, s’intitulé "An Integrated Learning and Progressive Hedging Method to Solve Stochastic Network Design". Ici, notre objectif principal est de concevoir une méthode de résolution capable de gérer un grand nombre de scénarios. Nous nous appuyons sur l’algorithme Progressive Hedging (PHA), ou les scénarios sont regroupés en sous-problèmes. Nous intégrons des methodes d’apprentissage au sein de PHA pour traiter une grand nombre de scénarios. Dans notre approche, les mécanismes d’apprentissage developpés dans le premier article de cette thèse sont adaptés pour résoudre les sous-problèmes multi-scénarios. Nous introduisons une nouvelle solution de référence à chaque étape d’agrégation de notre ILPH en exploitant les informations collectées à partir des sous problèmes et nous utilisons ces informations pour mettre à jour les pénalités dans PHA. Par conséquent, PHA est guidé par les informations locales fournies par la procédure d’apprentissage, résultant en une approche intégrée capable de traiter des instances complexes et de grande taille. Dans les trois articles, nous montrons, au moyen de campagnes expérimentales approfondies, l’intérêt des approches proposées en termes de temps de calcul et de qualité des solutions produites, en particulier pour traiter des cas très difficiles avec un grand nombre de scénarios.This dissertation consists of three studies, each of which constitutes a self-contained research article. In all of the three articles, we consider the multi-commodity capacitated fixed-charge network design problem with uncertain demands as a two-stage stochastic program. In such setting, design decisions are made in the first stage before the actual demand is realized, while second-stage flow-routing decisions adjust the first-stage solution to the observed demand realization. We consider the demand uncertainty as a finite number of discrete scenarios, which is a common approach in the literature. By using the scenario set, the resulting large-scale mixed integer program (MIP) problem, referred to as the extensive form (EF), is extremely hard to solve exactly in all but trivial cases. This dissertation is aimed at advancing the body of knowledge by developing efficient algorithms incorporating learning mechanisms in matheuristics, which are able to handle large scale instances of stochastic network design problems efficiently. In the first article, we propose a novel Learning-Based Matheuristic for Stochastic Network Design Problems. We introduce and formally describe a new optimizationbased learning mechanism to extract information regarding the solution structure of a stochastic problem out of the solutions of particular combinations of scenarios. We subsequently propose the Learn&Optimize matheuristic, which makes use of the learning methods in inferring a set of promising design variables, in conjunction with a state-ofthe- art MIP solver to address a reduced problem. In the second article, we introduce a Reduced-Cost-Based Restriction and Refinement Matheuristic. We study on how to efficiently design learning mechanisms based on dual information as a means of guiding variable fixing in the context of stochastic network design. The present work investigates how the reduced cost associated with non-basic variables in deterministic solutions can be leveraged to guide variable selection within stochastic formulations. We specifically propose several strategies to extract reduced cost information so as to effectively identify an appropriate set of fixed variables within a restricted model. We then propose a matheuristic approach using problem reduction techniques iteratively (i.e., defining and exploring restricted region of global solutions, as guided by applicable dual information). Finally, in the third article, our main goal is to design a solution method that is able to manage a large number of scenarios. We rely on the progressive hedging algorithm (PHA) where the scenarios are grouped in subproblems. We propose a two phase integrated learning and progressive hedging (ILPH) approach to deal with a large number of scenarios. Within our proposed approach, the learning mechanisms from the first study of this dissertation have been adapted as an efficient heuristic method to address the multi-scenario subproblems within each iteration of PHA.We introduce a new reference point within each aggregation step of our proposed ILPH by exploiting the information garnered from subproblems, and using this information to update the penalties. Consequently, the ILPH is governed and guided by the local information provided by the learning procedure, resulting in an integrated approach capable of handling very large and complex instances. In all of the three mentioned articles, we show, by means of extensive experimental campaigns, the interest of the proposed approaches in terms of computation time and solution quality, especially in dealing with very difficult instances with a large number of scenarios

Big data optimization in electric power systems: a review

There are different definitions of big data, and among them, the most common definition refers to three or five characteristics, called volume, velocity, variety, value, and veracity from (Laney (2001)). Volume could include Tera Byte, Peta Byte, Exa Byte, and Zetta Byte. Velocity describes how fast the data are retrieved and processed ‘‘Batch or streaming”. Variety describes structured, semi-structured, and unstructured data (Laney, 2001, Zikopoulos and Eaton, 2011). Veracity explains the integrity and disorderliness of data, while value refers to how good is the “value” we derive from analyzing data? (Zicari et al., 2016). Electrical power systems are networks of components arrayed to supply, transfer, and use electric power. In power system since models are used to predict and characterize operations. However, there is a necessity for powerful optimization algorithms for information processing to learn models as the size increase of data is becoming a global problem to solve large-scale optimization problems. Any optimization problem includes a real function to be maximized or minimized by systematically determination of input values from an allowed set of values. Richness and quantity of large data sets provide the potential to enhance statistical learning performance but require smart models that use the latent low-dimensional structure for effective 2 data separation. This chapter reviews the most recent scientific articles related to large and big data optimization in power systems. Optimization issues such as logistics in power systems and techniques including nonsmooth, nonconvex, and unconstrained large-scale optimization are presented. After a brief review of big data, scientometric analysis has been applied using keywords of “big data” and “power system.” Besides, keywords analysis, network visualization, journal map, and bibliographic coupling analysis have been done to draw a path on big data works in power system problems. Also, the most common useful techniques in large-scale optimization in power system have been reviewed. At the end of this chapter, metaheuristic techniques in big data optimization are reviewed to show that many efforts have been involved in big data optimization in power system and systematically highlight some perspectives on big data optimization

Centralized Cooperative Control for Route Surveillance with Constant Communication

The route surveillance mission is a new application of unmanned aircraft systems (UASs) to meet the reconnaissance and surveillance requirements of combatant commanders. The new mission intends to field a UAS consisting of unmanned aerial vehicles (UAVs) that can provide day and night surveillance of convoy routes. This research focuses on developing a solution strategy for the mission based on the application of optimal control and cooperative control theory. The route surveillance controller uses the UAS team size to divide the route into individual sectors for each entity. A specifically designed cost function and path constraints are used to formulate an optimal control problem that minimizes the revisit time to the route and the overall control energy of the UAS. The problem complexity makes an analytical solution difficult, so a numerical technique based on the Gauss pseudo-spectral method is used to solve for the optimal solution. The output trajectories describe a path that each entity could fly to provide surveillance on the route. Simulated and real-world routes containing likely urban and rural characteristics were used to test the controller and show that the developed system provides feasible surveillance solutions under certain conditions. These results represent baseline statistics for future studies in this research area