Restless bandit marginal productivity indices II: multiproject case and scheduling a multiclass make-to-order/-stock M/G/1 queue

This paper develops a framework based on convex optimization and economic ideas to formulate and solve approximately a rich class of dynamic and stochastic resource allocation problems, fitting in a generic discrete-state multi-project restless bandit problem (RBP). It draws on the single-project framework in the author's companion paper "Restless bandit marginal productivity indices I: Single-project case and optimal control of a make-to-stock M/G/1 queue", based on characterization of a project's marginal productivity index (MPI). Our framework significantly expands the scope of Whittle (1988)'s seminal approach to the RBP. Contributions include: (i) Formulation of a generic multi-project RBP, and algorithmic solution via single-project MPIs of a relaxed problem, giving a lower bound on optimal cost performance; (ii) a heuristic MPI-based hedging point and index policy; (iii) application of the MPI policy and bound to the problem of dynamic scheduling for a multiclass combined MTO/MTS M/G/1 queue with convex backorder and stock holding cost rates, under the LRA criterion; and (iv) results of a computational study on the MPI bound and policy, showing the latter's near-optimality across the cases investigated

Essays on the effective integration of risk management with operations management decisions

textIn today's marketplace, firms' exposure to business uncertainties and risks are continuously increasing as they strive to meet dynamically changing customer needs under intensifying competitive pressures. Consequently, modern supply chains are continuously evolving to effectively manage these uncertainties and the allied risks through both operational and financial hedging strategies. In practice, firms extensively use operational hedging strategies such as operational flexibility, capacity flexibility, postponement, multi-sourcing, supplier diversification, component commonality, substitutability, transshipments and holding excess stocks as operational means for risk management. On the other hand, financial hedging which involves buying and selling financial instruments, carrying large cash reserves or adopting conservative financial policies, changes the cash flow stream of the firms and may help to reduce the firms exposure to business risks and uncertainties. Overall, in this dissertation we explore how risk management can be integrated with operating decisions so as to improve the firm value creating more wealth for the shareholders. In the first essay, we focus on capacity flexibility as a means of operational hedging for risk management in an MTO production environment under demand uncertainty. We demonstrate that capacity flexibility may not only be used to hedge against the demand uncertainty, but may also be employed to effectively protect against possible suboptimal operating decisions in the future. In the second essay, we focus on operational hedging in financially constrained startup firms when making short-term production and long-term investment decisions. We provide an analytical characterization of the optimal investment and operating decisions and analyze the impact of market parameters on the operations of the firm. Our findings highlight an interesting operational hedging behavior between the process investment decisions and the short-term production commitments of the firm when they are faced with financial constraints. Our third essay focuses on the value of integrated financial risk management activities by publicly traded established firms under the risk of incurring financial distress cost. Different from the existing operations management literature, we study the risk management by a public corporation within the value framework of finance; hence our findings do not require any specific assumptions about the investors' utility functions. Moreover, we contribute to the operations management research by examining the impact of the costs of financial distress on hedging and operating plans of the firm. Overall, in this dissertation, we examine the effective integration of operational and financial risk management so as to improve the firm value creating more wealth for the shareholders.Information, Risk, and Operations Management (IROM

Optimal Policy for Production Systems with Two Flexible Resources and TwoProducts

Manufacturing companies are facing increasing volatility in demand. As a result, there has been an emerging need for a flexible multi-period manufacturing system that uses multiple resources to produce multiple products with stochastic demands. To manage such multi-product, multi-resource systems, manufacturers need to make two decisions simultaneously: setting a production quantity for each product and allocating the limited resources dynamically among the products. Unfortunately, although the flexibility design and investment have been extensively studied, the literature has been muted on how to make production and allocation decisions optimally from an operational perspective. This article attempts to fill this literature gap by investigating a multi-period system using multiple flexible resources to produce two products. We identify the structural property of the cost functions, namely ρ-differential monotone. Based on this property, the optimal production and allocation policy can be characterized by switching curves, which divide the state space into eight or nine sub-regions based on the segmentation of decision rules. We analyze different cases in terms of production costs and resource utilization ratios, and show how they affect the optimal production and allocation decisions. Finally, we compare three heuristic policies to the optimal one to display the advantage of resource flexibility and the effectiveness of a heuristic policy. Supplementary materials are available for this article. Go to the publisher’s online edition of IISE Transaction, datasets, additional tables, detailed proofs, etc

Models for Flexible Supply Chain Network Design

Arguably Supply Chain Management (SCM) is one of the central problems in Operations Research and Management Science (OR/MS). Supply Chain Network Design (SCND) is one of the most crucial strategic problems in the context of SCM. SCND involves decisions on the number, location, and capacity, of production/distribution facilities of a manufacturing company and/or its suppliers operating in an uncertain environment. Specifically, in the automotive industry, manufacturing companies constantly need to examine and improve their supply chain strategies due to uncertainty in the parameters that impact the design of supply chains. The rise of the Asian markets, introduction of new technologies (hybrid and electric cars), fluctuations in exchange rates, and volatile fuel costs are a few examples of these uncertainties. Therefore, our goal in this dissertation is to investigate the need for accurate quantitative decision support methods for decision makers and to show different applications of OR/MS models in the SCND realm. In the first technical chapter of the dissertation, we proposed a framework that enables the decision makers to systematically incorporate uncertainty in their designs, plan for many plausible future scenarios, and assess the quality of service and robustness of their decisions. Further, we discuss the details of the implementation of our framework for a case study in the automotive industry. Our analysis related to the uncertainty quantification, and network's design performance illustrates the benefits of using our framework in different settings of uncertainty. Although this chapter is focused on our case study in the automotive industry, it can be generalized to the SCND problem in any industry. We have outline the shortcomings of the current literature in incorporating the correlation among design parameters of the supply chains in the second technical chapter. In this chapter, we relax the traditional assumption of knowing the distribution of the uncertain parameters. We develop a methodology based on Distributionally Robust Optimization (DRO) with marginal uncertainty sets to incorporate the correlation among uncertain parameters into the designing process. Further, we propose a delayed generation constraint algorithm to solve the NP-hard correlated model in significantly less time than that required by commercial solvers. Further, we show that the price of ignoring this correlation in the parameters increases when we have less information about the uncertain parameters and that the correlated model gives higher profit when exchange rates are high compared to the stochastic model (with the independence assumption). We extended our models in previous chapters by presenting capacity options as a mechanism to hedge against uncertainty in the input parameters. The concept of capacity options similar to financial options constitute the right, but not the obligation, to buy more commodities from suppliers with a predetermined price, if necessary. In capital-intensive industries like the automotive industry, the lost capital investment for excess capacity and the opportunity costs of underutilized capacity have been important drivers for improving flexibility in supply contracts. Our proposed mechanism for high tooling cost parts decreases the total costs of the SCND and creates flexibility within the structure of the designed SCNs. Moreover, we draw several insights from our numerical analyses and discuss the possibility of price negotiations between suppliers and manufacturers over the hedging fixed costs and variable costs. Overall, the findings from this dissertation contribute to improve the flexibility, reliability, and robustness of the SCNs for a wide-ranging set of industries.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145819/1/nsalehi_1.pd

Stratégies de production manufacturière dans un environnement de chaîne d'approvisionnement : approche dynamique stochastique

Ce projet porte sur le controle des activites operationnelles de la production dans un environnement de chaine d'approvisionnement. Nous nous adressons aux problemes de controle des rythmes de production, des actions de mise en course ainsi que des strategies de maintenance preventive de systemes manufacturiers contraints par un environnement interne et/ou exteme non fiables. A cet egard, nous cherchons a determiner des stratégies integrees de production et d'approvisionnement, en presence de plusieurs foumisseurs potentiels. Nous proposons une approche sequentielle de resolution basee sur la modelisation mathematique et la resolution numerique ainsi que la simulation, les plans d'experiences et les algorithmes genetiques. La premiere partie de l'approche basee sur la theorie de commande optimale et/ou impulsionnelle est indispensable pour avoir une base solide permettant de proposer des strategies de controle qui s'approchent de I'optimum. Quant a la deuxieme partie de I'approche, elle vient completer la premiere afin de developper des processus decisionnels des activites manufacturieres bases sur les politiques developpees. De plus, elle permet d'etendre les dites strategies pour couvrir des systemes plus complexes. A un niveau opérationnel de decision, nous demontrons la grande utilite de la combinaison des deux approches susmentionnees qui peut s'averer incontoumable pour amener des solutions a des problemes A^P-difficiles. L'application de 1'approche aux systemes etudies nous a permis de proposer des strategies de production plus realistes et plus économiques. La prise en consideration du systeme manufacturier dans son environnement exteme, nous a permis de mettre en evidence I'importance d'une gestion integree des fonctions de production et d'approvisionnement. A cet egard, les politiques de controle proposees nous ont permis de reduire jusqu'a 10 % le coiit total, encouru quand il s'agit d'une gestion dissociee. Cette these amene des solutions a une classe de problemes de modelisation dynamique stochastique de systeme manufacturier et ce, a plus qu'un niveau de la hierarchie de déecision. De plus, elle met en application une approche globale de resolution permettant de developper des processus decisionnels de gestion. Cette approche peut surmonter les problemes lies a la resolution des modeles mathematiques quand il s'agit de systemes de faille reelle

Supply chain network design under uncertainty and risk

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.We consider the research problem of quantitative support for decision making in supply chain network design (SCND). We first identify the requirements for a comprehensive SCND as (i) a methodology to select uncertainties, (ii) a stochastic optimisation model, and (iii) an appropriate solution algorithm. We propose a process to select a manageable number of uncertainties to be included in a stochastic program for SCND. We develop a comprehensive two-stage stochastic program for SCND that includes uncertainty in demand, currency exchange rates, labour costs, productivity, supplier costs, and transport costs. Also, we consider conditional value at risk (CV@R) to explore the trade-off between risk and return. We use a scenario generator based on moment matching to represent the multivariate uncertainty. The resulting stochastic integer program is computationally challenging and we propose a novel iterative solution algorithm called adaptive scenario refinement (ASR) to process the problem. We describe the rationale underlying ASR, validate it for a set of benchmark problems, and discuss the benefits of the algorithm applied to our SCND problem. Finally, we demonstrate the benefits of the proposed model in a case study and show that multiple sources of uncertainty and risk are important to consider in the SCND. Whereas in the literature most research is on demand uncertainty, our study suggests that exchange rate uncertainty is more important for the choice of optimal supply chain strategies in international production networks. The SCND model and the use of the coherent downside risk measure in the stochastic program are innovative and novel; these and the ASR solution algorithm taken together make contributions to knowledge

Commande à seuils critiques de la production dans un atelier de fabrication avec machines en tandem non fiables

Éléments de gestion optimale de la production dans les ateliers de fabrication -- Vision de décomposition hiérarchisée des ateliers de fabrication -- Particularités des processus de production en tandem -- État de l'art sur la gestion sous-optimale de la production en tandem -- Optimisation d'une classe de politiques décentralisées de production, à seuils critiques, pour deux machines non fiables en tandem -- Optimisation d'une classe de politiques décentralisées de la production, à seuils critiques, pour M machines en tandem -- Exploration numérique des propriétés des lois de commande décentralisées de la production à seuils critiques

On Production and Subcontracting Strategies for Manufacturers with Limited Capacity and Backlog-Dependent Demand

We study a manufacturing firm that builds a product to stock to meet a random demand. If there is a positive surplus of finished goods, the customers make their purchases without delay and leave. If there is a backlog, the customers are sensitive to the quoted lead time and some choose not to order if they feel that the lead time is excessive. A set of subcontractors, who have different costs and capacities, are available to supplement the firm's own production capacity. We derive a feedback policy that determines the production rate and the rate at which the subcontractors are requested to deliver products. The performance of the system when it is managed according to this policy is evaluated. The subcontractors represent a set of capacity options, and we calculate the values of these options

Commande optimale stochastique appliquée aux systèmes manufacturiers avec des sauts semi-Markoviens

Les travaux de ce mémoire sont constitués de deux parties principales. La première partie tente de formuler un nouveau modèle du problème de commande optimale stochastique de systèmes sur un horizon fini. Les systèmes considérés sont soumis à des phénomènes aléatoires dits sauts de perturbation qui sont modélisés par un processus semi-Markovien. Ces sauts de perturbation traduits par des taux de transition dépendent de l’état du système et du temps. Par conséquent, le problème de commande est formulé comme un problème d’optimisation dans un environnement stochastique. La deuxième partie vise à modéliser des systèmes de production flexible (SPF). Dans ce mémoire, ces SPF se composent de plusieurs machines en parallèles, ou en série, ou d’une station de travail (une machine représentative). Ces machines sont sujettes à des pannes et à des réparations aléatoires. L’objectif de la modélisation est de déterminer les taux de production u(t) de ces machines en satisfaisant les fluctuations de demande d(t) sur un horizon fini. Dans ce mémoire, nous avons : (a) proposé un nouveau modèle du problème d’optimisation dans un environnement stochastique sur un horizon fini pour deux cas; avec taux d’actualisation (ρ > 0) et sans taux d’actualisation (ρ = 0); (b) modélisé des SPF en déterminant une stratégie de commande plus réaliste incluant stratégie de production; (c) présenté des exemples numériques à l’aide d’une méthode de Kushner et Dupuis (2001)