Non-stationary demand forecasting by cross-sectional aggregation

In this paper the relative effectiveness of top-down (TD) versus bottom-up (BU) approaches is compared for cross-sectionally forecasting aggregate and sub-aggregate demand. We assume that the sub-aggregate demand follows a non-stationary Integrated Moving Average (IMA) process of order one and a Single Exponential Smoothing (SES) procedure is used to extrapolate future requirements. Such demand processes are often encountered in practice and SES is one of the standard estimators used in industry (in addition to being the optimal estimator for an IMA process). Theoretical variances of forecast error are derived for the BU and TD approach in order to contrast the relevant forecasting performances. The theoretical analysis is supported by an extensive numerical investigation at both the aggregate and sub-aggregate level, in addition to empirically validating our findings on a real dataset from a European superstore. The results demonstrate the increased benefit resulting from cross-sectional forecasting in a non-stationary environment than in a stationary one. Valuable insights are offered to demand planners and the paper closes with an agenda for further research in this area. © 2015 Elsevier B.V. All rights reserved

Models for Retail Inventory Management with Demand Learning

Matching supply with demand is key to success in the volatile and competitive retail business. To this end, retailers seek to improve their inventory decisions by learning demand from various sources. More interestingly, retailers' inventory decisions may in turn obscure the demand information they observe. This dissertation examines three problems in retail contexts that involve interactions between inventory management and demand learning. First, motivated by the unprecedented adverse impact of the 2008 financial crisis on retailers, we consider the inventory control problem of a firm experiencing potential demand shifts whose timings are known but whose impacts are not known. We establish structural results about the optimal policies, construct novel cost lower bounds based on particular information relaxations, and propose near-optimal heuristic policies derived from those bounds. We then consider the optimal allocation of a limited inventory for fashion retailers to conduct "merchandise tests" prior to the main selling season as a demand learning approach. We identity a key tradeoff between the quantity and quality of demand observations. We also find that the visibility into the timing of each sales transaction has a pivotal impact on the optimal allocation decisions and the value of merchandise tests. Finally, we consider a retailer selling an experiential product to consumers who learn product quality from reviews generated by previous buyers. The retailer maximizes profit by choosing whether to offer the product for sale to each arriving customer. We characterize the optimal product offering policies to be of threshold type. Interestingly, we find that it can be optimal for the firm to withhold inventory and not to offer the product even if an arriving customer is willing to buy for sure. We numerically demonstrate that personalized offering is most valuable when the price is high and customers are optimistic but uncertain about product quality.Doctor of Philosoph

Sistemas granulares evolutivos

Orientador: Fernando Antonio Campos GomideTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Recentemente tem-se observado um crescente interesse em abordagens de modelagem computacional para lidar com fluxos de dados do mundo real. Métodos e algoritmos têm sido propostos para obtenção de conhecimento a partir de conjuntos de dados muito grandes e, a princípio, sem valor aparente. Este trabalho apresenta uma plataforma computacional para modelagem granular evolutiva de fluxos de dados incertos. Sistemas granulares evolutivos abrangem uma variedade de abordagens para modelagem on-line inspiradas na forma com que os humanos lidam com a complexidade. Esses sistemas exploram o fluxo de informação em ambiente dinâmico e extrai disso modelos que podem ser linguisticamente entendidos. Particularmente, a granulação da informação é uma técnica natural para dispensar atenção a detalhes desnecessários e enfatizar transparência, interpretabilidade e escalabilidade de sistemas de informação. Dados incertos (granulares) surgem a partir de percepções ou descrições imprecisas do valor de uma variável. De maneira geral, vários fatores podem afetar a escolha da representação dos dados tal que o objeto representativo reflita o significado do conceito que ele está sendo usado para representar. Neste trabalho são considerados dados numéricos, intervalares e fuzzy; e modelos intervalares, fuzzy e neuro-fuzzy. A aprendizagem de sistemas granulares é baseada em algoritmos incrementais que constroem a estrutura do modelo sem conhecimento anterior sobre o processo e adapta os parâmetros do modelo sempre que necessário. Este paradigma de aprendizagem é particularmente importante uma vez que ele evita a reconstrução e o retreinamento do modelo quando o ambiente muda. Exemplos de aplicação em classificação, aproximação de função, predição de séries temporais e controle usando dados sintéticos e reais ilustram a utilidade das abordagens de modelagem granular propostas. O comportamento de fluxos de dados não-estacionários com mudanças graduais e abruptas de regime é também analisado dentro do paradigma de computação granular evolutiva. Realçamos o papel da computação intervalar, fuzzy e neuro-fuzzy em processar dados incertos e prover soluções aproximadas de alta qualidade e sumário de regras de conjuntos de dados de entrada e saída. As abordagens e o paradigma introduzidos constituem uma extensão natural de sistemas inteligentes evolutivos para processamento de dados numéricos a sistemas granulares evolutivos para processamento de dados granularesAbstract: In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of uncertain data streams. Evolving granular systems comprise an array of online modeling approaches inspired by the way in which humans deal with complexity. These systems explore the information flow in dynamic environments and derive from it models that can be linguistically understood. Particularly, information granulation is a natural technique to dispense unnecessary details and emphasize transparency, interpretability and scalability of information systems. Uncertain (granular) data arise from imprecise perception or description of the value of a variable. Broadly stated, various factors can affect one's choice of data representation such that the representing object conveys the meaning of the concept it is being used to represent. Of particular concern to this work are numerical, interval, and fuzzy types of granular data; and interval, fuzzy, and neurofuzzy modeling frameworks. Learning in evolving granular systems is based on incremental algorithms that build model structure from scratch on a per-sample basis and adapt model parameters whenever necessary. This learning paradigm is meaningful once it avoids redesigning and retraining models all along if the system changes. Application examples in classification, function approximation, time-series prediction and control using real and synthetic data illustrate the usefulness of the granular approaches and framework proposed. The behavior of nonstationary data streams with gradual and abrupt regime shifts is also analyzed in the realm of evolving granular computing. We shed light upon the role of interval, fuzzy, and neurofuzzy computing in processing uncertain data and providing high-quality approximate solutions and rule summary of input-output data sets. The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streamsDoutoradoAutomaçãoDoutor em Engenharia Elétric

Data-driven reconfigurable supply chain design and inventory control

In this dissertation, we examine resource mobility in a supply chain that attempts to satisfy geographically distributed demand through resource sharing, where the resources can be inventory and manufacturing capacity. Our objective is to examine how resource mobility, coupled with data-driven analytics, can result in supply chains that without customer service level reduction blend the advantages of distributed production-inventory systems (e.g., fast fulfillment) and centralized systems (e.g., economies of scale, less total buffer inventory, and reduced capital expenditures). We present efficient and effective solution methods for logistics management of multi-location production-inventory systems with transportable production capacity. We present a novel, generalized representation of demand uncertainty and propose data-driven responses to the manage a single location inventory system under such demands.Ph.D

DECISION ANALYTICAL METHODS FOR ROBUST WATER INFRASTRUCTURE PLANNING UNDER DEEP UNCERTAINTY

Deep uncertainties resulting from climate change, demographic pressures, and rapidly evolving socioeconomic conditions are challenging the way that water planners design and operate large-scale infrastructure systems. Conventionally, water infrastructures have been developed using stationary methods, assuming that the underlying uncertainties can be derived from historical data or experience. However, these methods are less useful under deeply uncertain climate and socioeconomic conditions, in which the future can be substantially different from the past and cannot be expressed by well-defined probability distributions. The recognition of deep uncertainties in long-term water resources planning has led to the development of “decision-analytical” frameworks that do not require predictions or prior probabilistic inference about the future. Instead, these approaches seek for alternatives that perform well across a broad range of conditions (robust) and can adapt to changing conditions (flexible). This dissertation aims to develop three new decision-analytical frameworks that build upon the previous work. The first study presents a generalized framework for water infrastructure design under climate change using regret-based robustness criterion and compares the findings to more conventional, predict-then-act based analyses of infrastructure design. The method is demonstrated for the design of a run-of-the-river hydropower system in Malawi. The second study further develops the framework by considering multiple climatic, demographic, and socioeconomic uncertainties in the context of a water supply design project in the Coastal Kenya. This improved framework incorporates a Bayesian belief network to blend multiple sources of subjective information from model projections and expert opinions elicited from stakeholder workshops. The third framework develops a decision-analytical approach for flexible river basin planning under climate change and applies to the problem of long-term water supply and irrigation planning in the Niger River Basin. The framework makes use of a stochastic programming model to search for optimal planning pathways under a wide range of scenarios that represent both natural climate variability and climate changes. In this process, the framework explores uncertain beliefs associated with the probability weights assigned to each scenario and identifies “belief dominant” pathways that are insensitive to underlying probabilistic assumptions and are more promising based on climate projections

Scheduling Allocation and Inventory Replenishment Problems Under Uncertainty: Applications in Managing Electric Vehicle and Drone Battery Swap Stations

In this dissertation, motivated by electric vehicle (EV) and drone application growth, we propose novel optimization problems and solution techniques for managing the operations at EV and drone battery swap stations. In Chapter 2, we introduce a novel class of stochastic scheduling allocation and inventory replenishment problems (SAIRP), which determines the recharging, discharging, and replacement decisions at a swap station over time to maximize the expected total profit. We use Markov Decision Process (MDP) to model SAIRPs facing uncertain demands, varying costs, and battery degradation. Considering battery degradation is crucial as it relaxes the assumption that charging/discharging batteries do not deteriorate their quality (capacity). Besides, it ensures customers receive high-quality batteries as we prevent recharging/discharging and swapping when the average capacity of batteries is lower than a predefined threshold. Our MDP has high complexity and dimensions regarding the state space, action space, and transition probabilities; therefore, we can not provide the optimal decision rules (exact solutions) for SAIRPs of increasing size. Thus, we propose high-quality approximate solutions, heuristic and reinforcement learning (RL) methods, for stochastic SAIRPs that provide near-optimal policies for the stations. In Chapter 3, we explore the structure and theoretical findings related to the optimal solution of SAIRP. Notably, we prove the monotonicity properties to develop fast and intelligent algorithms to provide approximate solutions and overcome the curses of dimensionality. We show the existence of monotone optimal decision rules when there is an upper bound on the number of batteries replaced in each period. We demonstrate the monotone structure for the MDP value function when considering the first, second, and both dimensions of the state. We utilize data analytics and regression techniques to provide an intelligent initialization for our monotone approximate dynamic programming (ADP) algorithm. Finally, we provide insights from solving realistic-sized SAIRPs. In Chapter 4, we consider the problem of optimizing the distribution operations of a hub using drones to deliver medical supplies to different geographic regions. Drones are an innovative method with many benefits including low-contact delivery thereby reducing the spread of pandemic and vaccine-preventable diseases. While we focus on medical supply delivery for this work, it is applicable to drone delivery for many other applications, including food, postal items, and e-commerce delivery. In this chapter, our goal is to address drone delivery challenges by optimizing the distribution operations at a drone hub that dispatch drones to different geographic locations generating stochastic demands for medical supplies. By considering different geographic locations, we consider different classes of demand that require different flight ranges, which is directly related to the amount of charge held in a drone battery. We classify the stochastic demands based on their distance from the drone hub, use a Markov decision process to model the problem, and perform computational tests using realistic data representing a prominent drone delivery company. We solve the problem using a reinforcement learning method and show its high performance compared with the exact solution found using dynamic programming. Finally, we analyze the results and provide insights for managing the drone hub operations

Learning with bounded memory.

The paper studies infinite repetition of finite strategic form games. Players use a learning behavior and face bounds on their cognitive capacities. We show that for any given beliefprobability over the set of possible outcomes where players have no experience. games can be payoff classified and there always exists a stationary state in the space of action profiles. In particular, if the belief-probability assumes all possible outcomes without experience to be equally likely, in one class of Prisoners' Dilemmas where the average defecting payoff is higher than the cooperative payoff and the average cooperative payoff is lower than the defecting payoff, play converges in the long run to the static Nash equilibrium while in the other class of Prisoners' Dilemmas where the reserve holds, play converges to cooperation. Results are applied to a large class of 2 x 2 games.Cognitive complexity; Bounded logistic quantal response learning; Long run outcomes;