The Optimal Ordering Periods for Internet Shopping under Time Dependent Consumer Demand

This study attempts to determine the optimal goods ordering periods for internet stores by considering time-dependent consumer demands and close demand-supply interactions. In order to capture dynamic and time-sensitive consumers, the entire study period is divided into a number of ordering periods with various duration. In the demand side, the study formulates a consumer utility function to construct a binary logit model, which determines consumers’ choice probabilities between internet shopping and conventional in-store shopping. The expected choice probability of choosing internet shopping is aggregated by a transformation probability density function of individual income based on the logit model. Then, the study further aggregates individual consumer choice probability to estimate the total demand for internet stores by considering variations in access time to retail stores, and delay time of receiving ordered goods. In the supply side, the study formulates transportation costs considering extra labor cost due to 24 hours business hours of internet stores, and constructs inventory costs reflecting the relationship between the batch ordering of goods, which are made by internet store operators to their suppliers and continuous ordering of goods, which are made by their consumers. Finally, a case study and sensitivity analysis are provided by R-company in Taiwan to illustrate the application of the models. The results show how the operators of internet stores should determine the number and duration of ordering periods in response to time-dependent consumer demand, thereby maximizing their profits

A continuous approximation model for the optimal design of public bike-sharing systems

During the last decade, public bike-sharing systems have gained momentum and popularity. Many cities worldwide have put their trust in bike-sharing to promote bicycle use and move towards more sustainable mobility. This paper presents a parsimonious model from which to derive the optimal strategical design variables for bike-sharing systems (i.e. the number of bicycles, the number of stations and the required intensity of rebalancing operations). This requires an integrated view of the system, allowing the optimization of the trade-off between the costs incurred by the operating agency and the level of service offered to users. The approach is based on the modelling technique of continuous approximations, which requires strong simplifications but allows obtaining very clear trade-offs and insights. The model has been validated using data from Bicing in Barcelona, and the results prove, for example, the existence of economies of scale in bike-sharing systems. Also, station-based and free-floating system configurations are compared, showing that free-floating systems achieve a better average level of service for the same agency costs. In spite of this, the performance of free-floating systems will tend to deteriorate in the absence of a strong regulation. Furthermore, if electrical bikes are used, results show that battery recharging will not imply an active restriction in station-based configurations. In conclusion, the proposed modeling approach represents a tool for strategic design in the planning phase and provides a better understanding of bike-sharing systemsPeer ReviewedPostprint (author's final draft

Locating and Protecting Facilities Subject to Random Disruptions and Attacks

Recent events such as the 2011 Tohoku earthquake and tsunami in Japan have revealed the vulnerability of networks such as supply chains to disruptive events. In particular, it has become apparent that the failure of a few elements of an infrastructure system can cause a system-wide disruption. Thus, it is important to learn more about which elements of infrastructure systems are most critical and how to protect an infrastructure system from the effects of a disruption. This dissertation seeks to enhance the understanding of how to design and protect networked infrastructure systems from disruptions by developing new mathematical models and solution techniques and using them to help decision-makers by discovering new decision-making insights. Several gaps exist in the body of knowledge concerning how to design and protect networks that are subject to disruptions. First, there is a lack of insights on how to make equitable decisions related to designing networks subject to disruptions. This is important in public-sector decision-making where it is important to generate solutions that are equitable across multiple stakeholders. Second, there is a lack of models that integrate system design and system protection decisions. These models are needed so that we can understand the benefit of integrating design and protection decisions. Finally, most of the literature makes several key assumptions: 1) protection of infrastructure elements is perfect, 2) an element is either fully protected or fully unprotected, and 3) after a disruption facilities are either completely operational or completely failed. While these may be reasonable assumptions in some contexts, there may exist contexts in which these assumptions are limiting. There are several difficulties with filling these gaps in the literature. This dissertation describes the discovery of mathematical formulations needed to fill these gaps as well as the identification of appropriate solution strategies

Economic and Environmental Impacts of Drone Delivery

Motivated by the potentially huge economic and environmental benefits of drone delivery, this dissertation developed mathematical models using the continuous approximation methodology to quantify the cost and emissions savings that drone delivery can provide relative to conventional truck delivery on multi-stop routes for a range of operating characteristics, delivery environments, and carbon intensities of power generation. This research considers two types of drone delivery: drone-only delivery and truck-drone delivery. In drone-only delivery, drones travel out-and-back from a depot to make each delivery. In truck-drone delivery, a truck and drone tandem make deliveries in parallel with the drone being launched and recovered at the truck. The research suggests that the delivery cost and emissions savings relative to conventional truck delivery can be substantial, but strongly depend on drone operating cost and emissions rates and their interrelationship. Because drone emissions depend on both the drone energy consumption rate and the electricity generation, Chapter 3 classifies five fundamental drone energy consumption models, and documents wide variability in the published drone energy consumption rates, due to different drone types, operating conditions and fundamental modeling assumptions. Chapters 4 and 5 provide continuous approximation models for the cost and the emissions with truck-only delivery and the two drone delivery services (drone-only and truck-drone), and show how the savings with drones depend on key characteristics of the drone and the operational setting. Chapter 6 examines the cost and emissions tradeoffs with optimal use of drone-only delivery and truck-drone delivery and shows the importance of the drone operating cost and energy consumption rates, as well as the delivery density and truck capacity. Results show that replacing truck-only delivery with drones can provide both cost and environmental benefits, with drone-only delivery preferred when drone operating cost and emissions rates and/or delivery density are very low and truck-drone delivery preferred when drone operating cost and emissions rates, truck-drone capacity, and/or delivery density are not very low. Results also show there can be a large tradeoff between cost and emissions when the ratio of drone operating cost rate to drone emissions rate differs from the ratio for trucks

Um modelo de logistica de suprimento de algodão para a industria textil de Santa Catarina

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro TecnologicoSão abordados temas relacionados com logística, uma disciplina ou ciência jovem, de origens pós-segunda guerra mundial. Os industriais analisaram os resultados obtidos pelos militares durante a guerra nas suas operações. Compreendeu-se então, que os problemas logísticos militares e industriais eram muito similares, quando estes abordados pela teoria de sistemas. Nesse momento pode-se dizer que nasceu a logística industrial. Este trabalho foi desenvolvido nesse ambiente logístico, para as indústrias têxteis de Santa Catarina, e para apoiar o gerenciamento das operações de suprimentos de algodão. O algodão constitui a matéria-prima básica destas indústrias. Estas indústrias, constituem um pólo têxtil, de fundamental importância, principalmente para o município de Blumenau e contribuem para o desenvolvimento sócio-econômico do Estado de Santa Catarina

Gestion optimale des pièces de rechange dans un réseau logistique multi-échelon flexible

Ce mémoire aborde la problématique de planification et contrôle des inventaires des pièces de rechange pour des systèmes assujettis à des défaillances aléatoires. Nous décrivons une série de modèles de décision pour gérer une gamme de pièces de rechange pour des réseaux constitués de plusieurs équipements en opération. Chaque équipement est composé d’une ou de plusieurs pièces qui sont nécessaires à son bon fonctionnement. Lorsqu’une pièce tombe en panne, elle est remplacée par une rechange, si disponible en stock, elle sera ensuite acheminée vers l’atelier de réparation pour la remettre en état de fonctionnement. Les modèles étudiés dans ce travail sont adaptés à une organisation disposant d’un réseau d’équipements, de canaux de transport, de stocks de pièces de rechange et de plusieurs stations de réparation. Les stocks et les stations sont déployés pour desservir un territoire, une zone ou une région, afin de garantir un niveau de service requis. Les modèles mathématiques proposés, décrivant les processus de défaillance, de réparation et de transport, utilisent la théorie des files d’attente. Celle-ci traduit fidèlement le phénomène de défaillance et de réparation provoqué par la contrainte de capacité des stations de réparation et des canaux de transport. Le processus stochastique qui engendre les arrivées des pièces défaillantes aux stations de réparation est supposé être un processus de Poisson. Les délais de traitement et de transit d’une pièce ne sont pas connus avec certitude, ils sont considérés comme des variables aléatoires qui suivent des distributions générales. Le système de gestion de stock des pièces de rechanges adopte une politique de transaction continue à réapprovisionnement unitaire (S-1, S). Dans ce travail, nous nous intéressons à l’analyse de l’évolution du système dans un régime permanent afin de trouver une politique de contrôle optimale qui dépend essentiellement de la quantité de pièces de rechange à garder en stock et de la capacité de traitement des stations de réparation. Nous développons aussi des modèles approximatifs pour traiter des configurations logistiques multi-échelon, ainsi que des demandes en urgence dans un échelon supérieur, et des transferts latéraux entre magasins de même échelon. Ces modèles sont ajustés pour faire face à des mesures de services différentes et pour traiter plusieurs références de pièces de rechange. Plusieurs algorithmes ont été proposés et implémentés, ils ont donné lieu à des résultats numériques dégageant des courbes d’efficiences (Coût, Niveau de service, Capacité) permettant aux gestionnaires de prendre des décisions éclairées le long du cycle de vie du système. En outre, une étude comparative très poussée a permis de démontrer l’exactitude des résultats obtenus par nos algorithmes avec les meilleures contributions publiées dans la littérature. Mots clés : pièces de rechange, gestion des stocks, politiques de maintenance, politiques de contrôle des stocks, fonction de renouvellement, files d’attente, chaînes logistiques, systèmes multi-échelon.This paper deals with the problem of planning and control of spare parts inventory for systems subject to random failures. We describe a series of decision models to manage a range of spare parts for networks made up of several equipment in operation. Each equipment is composed of one or more parts that are necessary for its proper functioning. When part breaks down, it is replaced by another, if available in stock., it will then be taken to the repair station to restore it to its working condition. The models studied in this work are adapted to an organization with a network of equipment, transport channels, stocks of spare parts and several repair stations. Inventories and stations are deployed to serve a territory, zone or region to ensure a required level of service. The proposed mathematical models, describing the failure, the repair and the transport processes, use the queuing theory. It accurately reflects the phenomenon of failure and repair caused by the capacity constraints of repair stations and transport channels. The stochastic process that generates the arrival of failure parts at repair stations is assumed to be a Poisson process. The processing and transit times of a part are not known with certainty, they are considered as random variables, which follow general distributions. The spare parts inventory management system adopts a continuous transaction policy with unit replenishment (S-1, S). In this thesis, we are interested in analyzing the evolution of the system in a permanent regime, in order to find an optimal control policy which depends essentially on the number of spare parts to be kept in stock and the processing capacity of repair stations. We also develop approximate models to deal with multi-echelon logistic configurations, as well as emergency requests in a higher echelon, and lateral transfers between stores of the same echelon level. These models are adjusted to deal with different service measures and to handle several spare parts references. Several algorithms have been proposed and implemented, giving rise to numerical results yielding efficiency curves (Cost, Service Level, Capacity) allowing managers to make informed decisions throughout the life cycle of the system. In addition, a very detailed comparative study has been conducted to demonstrate the accuracy of the results obtained by our algorithms with the best contributions published in the literature. Keywords: Spare parts inventory management, maintenance policies, inventory control policies, renewal function, queuing system, supply chains, multi-echelon networks

Sustainable and reliable design of large-scale complex logistics systems under competition and uncertainties

Logistics systems generally involve multiple interacting stakeholders who endogenously make decisions based on their individual, sometimes conflicting, objectives. Meanwhile, many of such systems may be disrupted from time to time under extreme threats (e.g., natural or human-induced disasters). These endogenous and exogenous factors often adversely impact system performance and result in significant societal disutility. My dissertation research focuses on developing mathematical models for design and analysis of large-scale logistics systems, especially those under competition and uncertainties. It holistically captures interactions and joint impacts of various objectives in large-scale supply chains, including supply reliability (against disruptions), service competition (against competitors), as well as demand uncertainties.Built upon a general analysis framework, we seek applications and extensions to address concerns of the current renewable energy sector. Starting from a logistics angle, i.e., the biofuel supply chain design, we investigate its profound economic and societal impact. First, We develop game-theoretical models based on Continuum Approximation (CA) to study a reliable competitive location problem where facilities are simultaneously subject to (i) symmetric or leader-follower types of competitions, and (ii) location-dependent probabilistic failures. An optimization model is formulated to capture the symmetric Nash competition between two companies. The goal is to maximize the expected profit (service revenue minus the sum of initial facility construction costs and the expected customer transportation costs) under normal and failure scenarios. Building upon this result, we build a bilevel leader-follower Stackelberg competition model to derive the optimal facility location design when one of the companies has the first-mover advantage over its competitor. Our CA approach is able to effectively solve the models. For special cases, closed-form analytical solutions can be obtained. Numerical experiments with hypothetical data and a case study for competitive biofuel supply chain design in the State of Illinois are conducted. The results revealed managerial insights on how competing companies should optimally plan their facility locations. Then, we propose a systematic optimization framework to analyze how biofuel supply chain decisions are affected by (i) crop yield/supply uncertainty, (ii) refinery disruption risks, and (iii) competition against existing food supply chains. The interactions among the biofuel industry, farmers and food industry are captured by a Stackelberg-Nash game, formulated under a CA scheme. The expected profits of both the farmers and the biofuel industry are evaluated based on probability distributions of crop yield and refinery disruption risks over space. Functional optimization, e.g., variational calculus, is used to derive the equilibrium conditions and suggest numerical algorithms. A series of numerical experiments are conducted for both hypothetical test cases and a Midwest case study to (i) show computational performance and robustness of the modeling approach, (ii) analyze the impacts of system parameters, as well as (iii) draw managerial insights in realistic settings. In addition, we propose a heuristic modeling framework to overcome the challenge that applying CA in solving dynamic facility location problems. First, we formulate a continuous model for the dynamic version by augmenting the time dimension, while relaxing the location consistency constraints. To translate the CA output into a set of discrete facility locations, we extend the disk model (for one static time period) to a tube model (for multiple time periods). Then, the location consistency constraints are enforced through a nonlinear optimization model with penalty terms. Lastly, we propose an iterative tube regulation algorithm to solve the penalty-based optimization problem. We analyze the accuracy and convergence of our modeling framework and conduct numerical experiments to verify its performance. The model and the solution procedure we proposed are very generic and flexible; thus, it can be extended to variants (e.g., incorporating existing facilities at the beginning of the horizon). Finally, we investigate a difficult trilemma: with limited farmland, how does the government stimulate the growth of the biofuel industry while, at the same time, protect food security and preserve environmental sustainability? Our framework is applied to address such multiple cross-interacting systems associating with the biofuel industry development in a broader context. We aim to provide policy guidelines on governmental mandates to induce socially favorable farmland use configurations to support a sustainable bio-economy