Tsan Sheng Ng

An Application of the EM Algorithm to Degradation Modeling

We consider a class of degradation processes that can consist of distinct phases of behavior. In particular, the degradation rates could possibly increase or decrease in a non-smooth manner at some point in time when the underlying degradation process changes phase. To model the degradation path of a given device, we use an independent-increments stochastic process with a single unobserved change-point. Furthermore, we assume that the change- point varies randomly from device-to-device. The likelihood functions for such a model are analytically intractable, so in this paper we develop an EM algorithm for this model to obtain the maximum likelihood estimators efficiently. We demonstrate the applicability of the method using two different models, and present some computational results of our implementation.

Robust multi-market newsvendor models with interval demand data

We present a robust model for determining the optimal order quantity and market selection for short-life-cycle products in a single period, newsvendor setting. Due to limited information about demand distribution in particular for short-life-cycle products, stochastic modeling approaches may not be suitable. We propose the minimax regret multi-market newsvendor model, where the demands are only known to be bounded within some given interval. In the basic version of the problem, a linear time solution method is developed. For the capacitated case, we establish some structural results to reduce the problem size, and then propose an approximation solution algorithm based on integer programming. Finally, we compare the performance of the proposed minimax regret model against the typical average-case and worst-case models. Our test results demonstrate that the proposed minimax regret model outperformed the average-case and worst-case models in terms of risk-related criteria and mean profit, respectively.

Semiconductor lot allocation using robust optimization

In this work, the problem of allocating a set of production lots to satisfy customer orders is considered. This research is of relevance to lot-to-order matching problems in semiconductor supply chain settings. We consider that lot-splitting is not allowed during the allocation process due to standard practices. Furthermore, lot-sizes are regarded as uncertain planning data when making the allocation decisions due to potential yield loss. In order to minimize the total penalties of demand un-fulfillment and over-fulfillment, a robust mixed-integer optimization approach is adopted to model is proposed the problem of allocating a set of work-in-process lots to customer orders, where lot-sizes are modeled using ellipsoidal uncertainty sets. To solve the optimization problem efficiently we apply the techniques of branch-and-price and Benders decomposition. The advantages of our model are that it can represent uncertainty in a straightforward manner with little distributional assumptions, and it can produce solutions that effectively hedge against the uncertainty in the lot-sizes using very reasonable amounts of computational effort.

Managing Complex Mechatronics R&D: A Systems Design Approach

To compress research and development (R&D) cycle times of high-tech mechatronic products with conformance performance metrics, managing R&D projects to allow engineers from electrical, mechanical, and manufacturing disciplines receive real-time design feedback and assessment are essential. In this paper, we propose a systems design procedure to integrate mechanical design, structure prototyping, and servo evaluation through careful comprehension of the servo-mechanical-prototype production cycle commonly employed in mechatronic industries. Our approach focuses on the Modal Parametric Identification of key feedback parameters for fast exchange of design specifications and information. This enables efficient conduct of product design evaluations, and supports schedule compression of the R&D project life cycle in the highly competitive consumer electronics industry. Using the commercial hard disk drive as a case example, we demonstrate how our approach allow inter-disciplinary specifications to be communicated among engineers from different backgrounds to speed up the R&D process for the next generation of intelligent manufacturing. This provides the management of technology team with powerful decision-making tools for project strategy formulation, and improvements in project outcome are potentially massive because of the low costs of change.

An approach for analyzing and managing flexibility in engineering systems design based on decision rules and multistage stochastic programming

ABSTRACT This article introduces an approach to assess the value and manage flexibility in engineering systems design based on decision rules and stochastic programming. The approach differs from standard Real Options Analysis (ROA) that relies on dynamic programming in that it parameterizes the decision variables used to design and manage the flexible system in operations. Decision rules are based on heuristic-triggering mechanisms that are used by Decision Makers (DMs) to determine when it is appropriate to exercise the flexibility. They can be treated similarly as, and combined with, physical design variables, and optimal values can be determined using multistage stochastic programming techniques. The proposed approach is applied as demonstration to the analysis of a flexible hybrid waste-to-energy system with two independent flexibility strategies under two independent uncertainty drivers in an urban environment subject to growing waste generation. Results show that the proposed approach recognizes the value of flexibility to a similar extent as the standard ROA. The form of the solution provides intuitive guidelines to DMs for exercising the flexibility in operations. The demonstration shows that the method is suitable to analyze complex systems and problems when multiple uncertainty sources and different flexibility strategies are considered simultaneously.

Robust demand service achievement for the co-production newsvendor

The co-production newsvendor problem is motivated by two-stage production processes that simultaneously yield a set of output products of different grades from the same input stocks. Co-production is a characteristic feature of processes such as semiconductor manufacturing and crude oil distillation. In the first stage, the newsvendor executes the order quantities for the input stocks prior to learning the actual demands and grading fractions of the products. In the second stage, the available production is allocated to satisfy the realized demands. Downward substitution is allowed in the allocation; i.e., demands for lower grades can always be filled by higher grades but not vice versa. The co-production newsvendor seeks to achieve maximum demand service level, subject to resource or budget constraints. This article proposes the use of the aspiration level approach to model the decision problem. Furthermore, it is assumed that only the means and supports of the uncertain demands and grading fractions are available, and the model is extended using robust optimization techniques. The resulting model is a linear program and can be solved very efficiently. Computational tests show that the proposed model performs favorably compared to other stochastic optimization approaches for the same problem.

Production planning with approved vendor matrices for a hard-disk drive manufacturer

We develop an optimal production schedule for a manufacturer of hard-disk drives that offers its customers the approved vendor matrix (AVM) as a competitive advantage. An AVM allows each customer to pick and choose the various product component vendors for individual or pairs of components constituting their product. The production planning problem faced by the manufacturer is to meet customer demand as precisely as possible while observing the matrix restrictions and also the limited availability of production resources. We formulate this problem as a linear programming model with a large number of variables, and present a solution procedure based on the column generation technique. A special class of the problem is then studied, whereby the number of production setups in each period is limited and discrete. We modify our formulation into a mixed-integer problem, and proceed to develop procedures that can obtain good feasible solutions using linear programming rounding techniques.

The Inquiry of Labor Market Dynamics in Knowledge Economies: An Agent-Based Approach

Elucidating the dynamics of national labor trends is a highly complex problem, as the labor market system is an interaction and interrelation of many variables and subsystems. To understand firm behavior and hence enable effective policy making, one must adopt a systems thinking and evolutionary approach to study the interplay of these forces and systems. This paper advocates the use of agent-based modeling to study labor market dynamics in a knowledge-based economy, in particular, the emergence of firm strategies and macroeconomic phenomena. An agent-based model is developed to simulate a labor market where firms post job offers to fill vacancies and decide how to choose and remunerate employees. Employees are initialized with a certain skill level and select jobs by comparing job offers. We then allow evolutionary forces to “select” firms that best survive in a given scenario and explore the strategies that emerge from interconnected systems and processes.

A resilience optimization approach for workforce-inventory control dynamics under uncertainty

The presence of uncertainties in manufacturing systems and supply chains can cause undesirable behavior. Failure to account for these in the design phase can further impair the capability of systems to respond to changes effectively. In this work, we consider a dynamic workforce-inventory control problem wherein inventory planning, production releases, and workforce hiring decisions need to be made. The objective is to develop planning rules to achieve important requirements related to dynamic transient behavior when system parameters are imprecisely known. To this end, we propose a resilience optimization model for the problem and develop a novel local search procedure that combines the strengths of recent developments in robust optimization technology and small signal stability analysis of dynamic systems. A numerical case study of the problem demonstrates significant improvements of the proposed solution in controlling fluctuations and high variability found in the system’s inventory, work-in-process, and workforce levels. Overall, the proposed model is shown to be computationally efficient and effective in hedging against model uncertainties.

Robust regret for uncertain linear programs with application to co-production models

This paper considers the regret optimization criterion for linear programming problems with uncertainty in the data inputs. The problems of study are more challenging than those considered in previous works that address only interval objective coefficients, and furthermore the uncertainties are allowed to arise from arbitrarily specified polyhedral sets. To this end a safe approximation of the regret function is developed so that the maximum regret can be evaluated reasonably efficiently by leveraging on previous established results and solution algorithms. The proposed approach is then applied to a two-stage co-production newsvendor problem that contains uncertainties in both supplies and demands. Computational experiments demonstrate that the proposed regret approximation is reasonably accurate, and the corresponding regret optimization model performs competitively well against other optimization approaches such as worst-case and sample average optimization across different performance measures.