The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

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

Modeling, Control and Optimisation of Hybrid Systems in a Manufacturing Setting

This study comprises a body of work that investigates the performance of hybrid manufacturing systems. And we have provided a valuable insight into the development of the optimisation techniques for hybrid manufacturing system. With the primary objective of developing prac-tical mathematical algorithms that balance trade-o? cost between product quality and completion time. For sta-bility criterion, a sliding mode control was deployed

Dynamic scheduling of manufacturing systems with setups and random disruptions

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 249-256).Manufacturing systems are often composed of machines that can produce a variety of items but that most undergo time-consuming (and possibly costly) setups when switching between product types. Scheduling these setups efficiently can have important economic effects on the performance of the plant and involves a tradeoff between throughput, inventory, and operating costs. In addition, the schedule must be robust to random disruptions such as failures or raw material shortages, which are common in production environments. In this thesis, we study policies that address the setup scheduling problem dynamically, in response to current conditions in the system. A new heuristic, called the Hedging Zone Policy (HZP), is introduced and developed. It is a dynamic-sequence policy that always produces the current part type at its maximum production rate until a fixed base stock level is reached. Then, before switching setups, the policy might produce the current part type at its demand rate for some additional time. When selecting changeovers, the HZP implements two types of decision rules. If the difference between base stock and surplus level is small for all part types, the item with the largest weighted difference is selected. Otherwise, the policy uses a fixed priority ranking to select between items that are far from their base stock value. In order to demonstrate the benefits of our policy, we also adapt and implement several other heuristics that have been proposed in the literature for related models. The policies are first analyzed in a purely deterministic setting. The stability of the HZP is addressed and it is shown that a poor selection of its parameters leads to a condition in which some low-priority parts are ignored, resulting in an unstable system. Using Lyapunov's direct method, we obtain an easy-to-evaluate and not-too-conservative condition that ensures production of all part types with bounded surplus. We then compare, through a series of extensive numerical experiments with three-part-type systems, the deterministic performance of the policies in both make-to-order and make-to-stock settings. We show that the HZP outperforms other policies within its class in both cases, a fact that is mainly attributed to its priority-based decisions. When compared to the approximate optimal cost of the problem, our policy performs very well in the make-to-order case, while the simplicity of its base stock structure makes it less competitive in the deterministic make-to-stock problem. The results are then leveraged for the study of a stochastic model, where we consider the effect of random disruptions in the form of machine failures. We prove that our model converges to a fluid limit under an appropriate scaling. This fact allows us to employ our deterministic stability conditions to verify the stochastic (rate) stability of the failure-prone system. We also extend our previous numerical experiments by characterizing the performance of the policies in the stochastic setting. The results show that the HZP still outperforms other policies in the same class. Furthermore, we find that except for cases where failures occur much less or much more frequently than changeovers, the HZP outperforms a fixed-sequence policy that is designed to track a pre-determined, near-optimal deterministic schedule.by Fernando Tubilla.Ph.D

Production and maintenance planning of deteriorating manufacturing systems taking into account the quality of products

The research work presented in this thesis addresses the integration of quality aspects in the development of stochastic dynamic programming models. The goal is to determine the joint optimal production planning, and several maintenance strategies for an unreliable and deteriorating manufacturing system. In particular, we conjecture that deterioration has a severe influence on various aspects of the machine, thus this leads to divide our research work in three (3) phases. In the first one, we analyze the simultaneous production planning and quality control problem for an unreliable manufacturing system. The machine is subject to deterioration whose effect is observed mainly on the quality throughput. The quality related decisions involves a major overhaul strategy that counters the effect of deterioration. A simulation optimization approach is applied to determine the optimal control policy, providing a better understanding about the influence of quality deterioration on such system. The second phase of the research analyzes the fact where the deterioration of the production system is originated by a combination of several factors. We consider that the system deteriorates by the combined effect of the wear of the machine and imperfect repairs. Multiple operational states are implemented to model variations on the rate of defectives. Furthermore at failure, either a repair or a major overhaul can be conducted; however the machine deteriorates even more following repairs. We use a Semi-arkov decision model, since the rate of defectives is depended of the machine’s history denoted by the number of repairs and the set of multiple operational states. Then the simultaneous production plan, and repair/overhaul switching strategy are determined through numerical methods. The third phase complements the previous models by considering that the deterioration of the production systems has a twofold effect that decreases the quality of the parts produced and also increases the failure intensity. We employ the age of the machine to denote the progressive deterioration. At failure it is conducted a minimal repair that leaves the machine at the same level of deterioration before failure. To counter completely the effect of deterioration it can be performed a major overhaul. Moreover, this phase introduces preventive maintenance strategies to reduce partially the level of deterioration. This set of characteristics yields to formulate a Semi-Markov model that thorough numerical methods, we determine the joint optimal production plan and the overhaul and preventive maintenance strategies. This model clarifies the role of quality aspects on the optimal control policy. In this way our research deepens the effects of quality aspects and deterioration on the optimal control policy, and provides interesting contributions to the domain of stochastic control of manufacturing systems. Additionally, a number of numerical examples are conducted as illustration, and extensive sensitivity analyses are presented with the purpose to confirm the structure and validity of the obtained control policies. The models developed in this thesis provide further insights into the relations between the production policy and quality aspects in the context of deterioration, and also contribute to a better understanding about the behavior of stochastic manufacturing systems