Open source solution approaches to a class of stochastic supply chain problems

This research proposes a variety of solution approaches to a class of stochastic supply chain problems, with normally distributed demand in a certain period of time in the future. These problems aim to provide the decisions regarding the production levels; supplier selection for raw materials; and optimal order quantity. The typical problem could be formulated as a mixed integer nonlinear program model, and the objective function for maximizing the expected profit is expressed in an integral format. In order to solve the problem, an open source solution package BONMIN is first employed to get the exact optimum result for small scale instances; then according to the specific feature of the problem a tailored nonlinear branch and bound framework is developed for larger scale problems through the introduction of triangular approximation approach and an iterative algorithm. Both open source solvers and commercial solvers are employed to solve the inner problem, and the results to larger scale problems demonstrate the competency of introduced approaches. In addition, two small heuristics are also introduced and the selected results are reported

Hyperconnected fulfillment and inventory allocation and deployment models

Consumption patterns have been changed dramatically over the past decades, notably by the growth of e-commerce. With the prevalence of e-commerce and home delivery, customer expectations for a faster, punctual, and cheap delivery are increasing. In fact, many customers are expecting for same-day or x-hour deliveries now and offering fast delivery becomes more and more critical for e-retailers to survive in a fierce market competition. However, many companies are simply lacking financial, physical, and/or operational resources to increase their responsiveness. Focusing on solving the challenges in the perspective of fulfillment and inventory, we aim to find a breakthrough from a recently emerging logistics innovation movement induced by the introduction of the Physical Internet (PI). PI can potentially enable responsive yet affordable fulfillment for companies of any size through open asset utilization and multi-player operations. The key of PI innovation is transforming asset-driven logistics operations to service-driven logistics operations. This thesis provides an academic foundation for hyperconnected fulfillment to effectively satisfy the growing customer expectations on responsive deliveries. We first present a comprehensive design and evaluation of a hyperconnected fulfillment system. Then, we focus on providing inventory operations models, inventory allocation and deployment respectively, which maximally utilize the key features of hyperconnected fulfillment system: connectivity, flexibility, and decentralization. In Chapter 2, a hyperconnected fulfillment and delivery system is designed in the context of the last-mile operations in urban areas. A comprehensive system and decision architecture of the hyperconnected system is modeled. We carefully design the scenarios to show a gradual transformation from dedicated to hyperconnected system in each thread of delivery and fulfillment so as to reveal the marginal impact of each step of transformation. We conduct a scenario analysis using a simulation platform built upon the system and decision architecture where autonomous agents are optimizing their decisions and interact with the environment. The experimental results clearly demonstrate the potential benefit of hyperconnected urban fulfillment and delivery system by concurrently improving often opposing performance criteria: economic efficiency, service capability and sustainability. Chapter 3 tackles an optimal inventory allocation problem among multiple sales outlets. Specifically, we analyze a case where a dropshipper allocates availability to multiple e-retailers via availability promising e-contracts (APCs). Under the APC, the e-retailers do not observe actual availability and this information asymmetry leads them to pose a promised availability threshold (PAT). PAT is a threshold on remaining promised availability set by an e-retailer for a product of a dropshipper, below which the e-retailer unlists the product and thus does not accept any more orders from customers, until the promised availability is climbed above the threshold by the dropshipper. The dropshipper's APC problem with PAT is modeled as 2-stage stochastic program with two stochastic parameters: demand and PAT. We design and evaluate three contract policies differentiated by the allowance level for overpromising: guaranteed fulfillment, controlled fillrate, and penalty-driven fillrate policies. We also present a modeling approach to convert the endogenous demands, per-retailer-distribution of which is affected by the APCs, to exogenous demands with linear substitution constraints. The numerical results show the penalty-driven fillrate policy is the dominating strategy for dropshippers especially under a lean availability. Chapter 4 tackles an inventory deployment problem under the context of open asset utilization and responsive fulfillment. When it comes to very responsive deliveries, such as X-hour deliveries, the physical availability of inventories near the delivery locations becomes necessary, which requires a broad and dense fulfillment network. The open asset utilization and service-driven fulfillment operations of the PI can enable affordable access to such decentralized fulfillment network comprised of the open fulfillment centers. We evaluate the benefit of such decentralized fulfillment network for a responsive fulfillment and develop an appropriate inventory deployment model, which possesses a partially pooled demand and inventory structure induced by responsiveness requirements, as a variant of Newsvendor. We derive a pragmatic heuristic inventory solution, W-solution, and present an efficient binary search based solution heuristic, W-heuristic. Then, via numerical experiments over both theoretical and empirical demand distributions, we demonstrate the advantage of decentralized network and w-solution over centralized network and allocation-based inventory model, pre-allocation model, respectively. We also report rather counter-intuitive observations that the w-solution which accounts for pooling leads to more inventory than pre-allocation model which does not account for pooling under low sales margin.Ph.D

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

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

Algorithms for Scheduling Problems

This edited book presents new results in the area of algorithm development for different types of scheduling problems. In eleven chapters, algorithms for single machine problems, flow-shop and job-shop scheduling problems (including their hybrid (flexible) variants), the resource-constrained project scheduling problem, scheduling problems in complex manufacturing systems and supply chains, and workflow scheduling problems are given. The chapters address such subjects as insertion heuristics for energy-efficient scheduling, the re-scheduling of train traffic in real time, control algorithms for short-term scheduling in manufacturing systems, bi-objective optimization of tortilla production, scheduling problems with uncertain (interval) processing times, workflow scheduling for digital signal processor (DSP) clusters, and many more

Multiobjective metaheuristic approaches for mean-risk combinatorial optimisation with applications to capacity expansion

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Evaluation of sales and operations planning in a process industry

Cette thèse porte sur la planification des ventes et des opérations (S±&OP) dans une chaîne d'approvisionnements axée sur la demande. L'objectif de la S±&OP, dans un tel contexte, est de tirer profit de l'alignement de la demande des clients avec la capacité de la chaîne d'approvisionnement par la coordination de la planification des ventes, de la production, de la distribution et de l'approvisionnement. Un tel processus de planification exige une collaboration multifonctionnelle profonde ainsi que l'intégration de la planification. Le but étant d'anticiper l'impact des décisions de vente sur les performances de la chaîne logistique , alors que l'influence de la dynamique des marchés est prise en compte pour les décisions concernant la production, la distribution et l'approvisionnement. La recherche a été menée dans un environnement logistique manufacturier multi-site et multi-produit, avec un approvisionnement et des ventes régis par des contrats ou le marché. Cette thèse examine deux approches de S±&OP et fournit un support à la décision pour l'implantation de ces méthodes dans une chaîne logistique multi-site de fabrication sur commande. Dans cette thèse, une planification traditionnelle des ventes et de la production basée sur la S±feOP et une planification S±fcOP plus avancée de la chaîne logistique sont tout d'abord caractérisées. Dans le système de chaîne logistique manufacturière multi-site, nous définissons la S±&OP traditionnelle comme un système dans lequel la planification des ventes et de la production est effectuée conjointement et centralement, tandis que la planification de la distribution et de l'approvisionnement est effectuée séparément et localement à chaque emplacement. D'autre part, la S±fcOP avancée de la chaîne logistique consiste en la planification des ventes, de la production, de la distribution et de l'approvisionnement d'une chaîne d'approvisionnement effectuée conjointement et centralement. Basés sur cette classification, des modèles de programmation en nombres entiers et des modèles de simulation sur un horizon roulant sont développés, représentant, respectivement, les approches de S±&OP traditionnelle et avancée, et également, une planification découplée traditionnelle, dans laquelle la planification des ventes est effectuée centralement et la planification de la production, la distribution et l'approvisionnement est effectuée séparément et localement par les unités d'affaires. La validation des modèles et l'évaluation pré-implantation sont effectuées à l'aide d'un cas industriel réel utilisant les données d'une compagnie de panneaux de lamelles orientées. Les résultats obtenus démontrent que les deux méthodes de S±feOP (traditionnelle et avancée) offrent une performance significativement supérieure à celle de la planification découplée, avec des bénéfices prévus supérieurs de 3,5% et 4,5%, respectivement. Les résultats sont très sensibles aux conditions de marché. Lorsque les prix du marché descendent ou que la demande augmente, de plus grands bénéfices peuvent être réalisés. Dans le cadre de cette recherche, les décisions de vente impliquent des ventes régies par des contrats et le marché. Les décisions de contrat non optimales affectent non seulement les revenus, mais également la performance manufacturière et logistique et les décisions de contrats d'approvisionnement en matière première. Le grand défi est de concevoir et d'offrir les bonnes politiques de contrat aux bons clients de sorte que la satisfaction des clients soit garantie et que l'attribution de la capacité de la compagnie soit optimisée. Également, il faut choisir les bons contrats des bons fournisseurs, de sorte que les approvisionnements en matière première soient garantis et que les objectifs financiers de la compagnie soient atteints. Dans cette thèse, un modèle coordonné d'aide à la décision pour les contrats e développé afin de fournir une aide à l'intégration de la conception de contrats, de l'attribution de capacité et des décisions de contrats d'approvisionnement pour une chaîne logistique multi-site à trois niveaux. En utilisant la programmation stochastique à deux étapes avec recours, les incertitudes liées à l'environnement et au système sont anticipées et des décisions robustes peuvent être obtenues. Les résultats informatiques montrent que l'approche de modélisation proposée fournit des solutions de contrats plus réalistes et plus robustes, avec une performance prévue supérieure d'environ 12% aux solutions fournies par un modèle déterministe

Developing lean and responsive supply chains : a robust model for alternative risk mitigation strategies in supply chain designs

This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC’s network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet

Extensions to Newsvendor Problems

Newsvendor problems (NVPs) form an important and much-studied family of inventory control problems. Although the use of the term varies somewhat, in most situations the term NVP refers to a single-period stochastic inventory control problem involving a single product. Assuming that the demand comes from a known probability distribution, this classic problem can be solved easily with calculus (Arrow et al., 1951), and the solution appears in nearly all inventory management textbooks. In this thesis, we expand the literature in four directions. In Chapter 2, we consider an integrated approach, in which the NVP order quantities are determined directly from the data. Though the topic of integrated approaches has already been studied in the literature, the idea of constructing a robust approach that deals with nonlinear NVPs is novel. In this chapter, we introduce such an approach, and we perform extensive simulation experiments to examine the performance of the approach in different settings, including situations when the true model is known and when the underlying model is mis-specified. In Chapter 3, we consider the effect that small changes in NVP parameters would have on the optimal solution, which is commonly referred to as sensitivity analysis. We show that one can perform sensitivity analysis for NVP using techniques from stochastic programming and discrete approximation. Our method is very general and can handle changes in prices and costs, changes in demand distributions, and cross-price elasticities of demand. Moreover, computational results show that our method yields accurate estimates with very reasonable computing effort. In Chapter 4, we examine the effect of judgemental adjustments in an NVP context. Several attempts have been made to quantify the outcomes of such adjustments. However, much of this literature assumes that accurate demand forecasts are available. We consider the (more realistic) case in which the forecasts may be inaccurate, due for example to insufficient data or model mis-specification. Computational results indicate that, in some cases, judgemental adjustment can lead to an increase in profit rather than a decrease. We discuss conditions under which the adjustments are beneficial and the situations when they are not. We also propose a heuristic algorithm for “tuning” the adjustment parameters in practice. In Chapter 5, we propose an alternative non-parametric approach to the variant of the NVP in which the goal is to minimise the conditional value at risk (CVaR). Given the difficulties with treating observations with extreme values, the existing parametric methods often underestimate the downside risk and lead to a significant loss in extreme cases. The existing non-parametric methods, on the other hand, are extremely computationally expensive with large instances and depend heavily on the form of the profit function. Using both simulation and real-life case studies, we show that our proposed method can be very useful in practice, allowing decisionmakers to suffer far less downside loss in extreme cases while requiring reasonable computing effort