Shi-Chung Chang

Dispatching-driven deadlock avoidance controller synthesis for flexible manufacturing systems

This paper develops a new method for synthesizing deadlock avoidance controllers (DACs) that realize job and machine dispatching policies of a flexible manufacturing system (FMS) into deadlock free control actions. Such controllers not only keep the FMS capable of repeating any of its operations, but also achieve a high resource utilization under any given dispatching policy. Our methodology is based on an untimed Petri net formalism. It consists of four ingredients: 1) a bottom-up approach for synthesizing a controlled production Petri net (CPPN) model of a FMS; 2) a necessary and sufficient liveness condition based on decomposition of the CPPN into controlled production subnets and the concept of minimal resource requirements; 3) a sufficient procedure to test whether the liveness condition is kept after a control action is executed; and 4) an algorithm that combines the test procedure with the given dispatching policy to generate valid and utilization maximizing control actions. We assess that this method is of polynomial time complexity and show that it results in a much larger class of controls than that of an existing deadlock avoidance scheme. >

Scheduling semiconductor wafer fabrication by using ordinal optimization-based simulation

Computational efficiency is one of the major challenges of applying simulation to short-term operation scheduling of semiconductor wafer fabrication factories (fabs), which are characterized by re-entrant process flows, stringent production control requirements and fast changing technology and business environments. The paper explores the application of the ordinal optimization (OO)-based simulation technique to efficiently selecting good rules for scheduling wafer fabrications. An efficient simulation tool, which makes use of OO and optimal computing budget allocation techniques, is developed. Experiments with the OO-based simulation tool are conducted for static selection of good rules under different factors such as initial state, performance index and time horizon. Results indicate that one to two orders of computation time reduction over traditional simulations can be achieved and that what rules are good varies with factors of initial state, performance index and time horizon. These results motivate our further investigation about applications to dynamic selection of dispatching rules upon the occurrence of two significant uncertain events: holding of significant amount of wafers-in-process due to engineering causes and major machine failures. Results demonstrate the value of dynamic rule selection for uncertainty handling, the insightful selection of good rules and the needs for future research.

Payment cost minimization auction for deregulated electricity markets using surrogate optimization

Deregulated electricity markets use an auction mechanism to select offers and their power levels for energy and ancillary services. A settlement mechanism is then used to determine the payments resulting from the selected offers. Currently, most independent system operators (ISOs) in the United States use an auction mechanism that minimizes the total offer costs but determine payment costs using a settlement mechanism that pays uniform market clearing prices (MCPs) to all selected offers. Under this setup, the auction and settlement mechanisms are inconsistent since minimized costs are different from payment costs. Illustrative examples in the literature have shown that for a given set of offers, if an auction mechanism that directly minimizes the payment costs is used, then payment costs can be significantly reduced as compared to minimizing offer costs. This observation has led to discussions among stakeholders and policymakers in the electricity markets as to which of the two auction mechanisms is more appropriate for ISOs to use. While methods for minimizing offer costs abound, limited approaches for minimization of payment costs have been reported. This paper presents an effective method for directly minimizing payment costs. In view of the specific features of the problem including the nonseparability of its objective function, the discontinuity of offer curves, and the maximum term in defining MCPs, our key idea is to use augmented Lagrangian relaxation and to form and solve offer and MCP subproblems by using the surrogate optimization framework. Numerical testing results demonstrate that the method is effective, and the resulting payment costs are significantly lower than what are obtained by minimizing the offer costs for a given set of offers.

Scheduling flexible flow shops with no setup effects

This paper presents an efficient, optimization model-based approach for scheduling the production of discrete-part, make-to-order type of flexible flow shops, where setup effects are negligible. A nominal scheduling algorithm based on Lagrangian relaxation and minimum cost linear network flow is first developed for scheduling under nominal conditions. Fast rescheduling algorithms that exploit the economic interpretation of the Lagrange multipliers and the network structure of production flows are then proposed for timely adjusting the nominal schedule to cope with disturbances. Numerical results on realistic examples demonstrate that our methodology is quite effective; it generates near-optimal schedules, provides relatively smooth adjustment for small disturbances, and is computationally efficient. >

Payment Cost Minimization Auction for Deregulated Electricity Markets With Transmission Capacity Constraints

Deregulated electricity markets in the U.S. currently use an auction mechanism that minimizes total supply bid costs to select bids and their levels. Payments are then settled based on market-clearing prices. Under this setup, the consumer payments could be significantly higher than the minimized bid costs obtained from auctions. This gives rise to ldquopayment cost minimization,rdquo an alternative auction mechanism that minimizes consumer payments. We previously presented an augmented Lagrangian and surrogate optimization framework to solve payment cost minimization problems without considering transmission. This paper extends that approach to incorporate transmission capacity constraints. The consideration of transmission constraints complicates the problem by entailing power flow and introducing locational marginal prices (LMPs). DC power flow is used for simplicity and LMPs are defined by ldquoeconomic dispatchrdquo for the selected supply bids. To characterize LMPs that appear in the payment cost objective function, Karush-Kuhn-Tucker (KKT) conditions of economic dispatch are established and embedded as constraints. The reformulated problem is difficult in view of the complex role of LMPs and the violation of constraint qualifications caused by the complementarity constraints of KKT conditions. Our key idea is to extend the surrogate optimization framework and use a regularization technique. Specific methods to satisfy the ldquosurrogate optimization conditionrdquo in the presence of transmission capacity constraints are highlighted. Numerical testing results of small examples and the IEEE Reliability Test System with randomly generated supply bids demonstrate the quality, effectiveness, and scalability of the method.

Group Elevator Scheduling With Advance Information for Normal and Emergency Modes

Group elevator scheduling has long been recognized as an important problem for building transportation efficiency, since unsatisfactory elevator service is one of the major complaints of building tenants. It now has a new significance driven by homeland security concerns. The problem, however, is difficult because of complicated elevator dynamics, uncertain traffic in various patterns, and the combinatorial nature of discrete optimization. With the advent of technologies, one important trend is to use advance information collected from devices such as destination entry, radio frequency identification, and sensor networks to reduce uncertainties and improve efficiency. How to effectively utilize such information remains an open and challenging issue. This paper presents the optimized scheduling of a group of elevators with destination entry and future traffic information for normal operations and coordinated emergency evacuation. Key problem characteristics are abstracted to establish a two-level separable formulation. A decomposition and coordination approach is then developed, where subproblems are solved by ordinal optimization-based local search, and top ranked nodes are selectively optimized by using dynamic programming. The approach is then extended to handle up-peak with little or no future traffic information, elevator parking for low intensity traffic, and coordinated emergency evacuation. Numerical testing results demonstrate near-optimal solution quality, computational efficiency, the value of future traffic information, and the potential of using elevators for emergency evacuation.

Design of a Lagrangian relaxation-based hierarchical production scheduling environment for semiconductor wafer fabrication

This paper describes the design of a two-level hierarchical production scheduling engine, which captures the industrial practice of mass production semiconductor fabrication factories (fabs). The two levels of the hierarchy consist of a mid-term scheduler and a short-term scheduler, and are aimed at achieving coordination between the fab-wide objectives and local shop-floor operations. The mid-term scheduler maximizes weighted production flow to reduce the fab-wide cycle time and ensure on-time delivery by properly setting daily production target volumes and reference work-in-process (WIP) levels for individual part types and stages. Mid-term scheduling results are further broken down into more detailed schedules by the short-term scheduler. In addition to the same set of operational constraints in mid-term scheduling, the short-term scheduler includes the consideration of batching effects. It maximizes weighted production flow while tracking the daily production targets and the reference WIP levels specified by mid-term scheduling. The schedulers adopt a solution methodology with three ingredients; the Lagrange relaxation approach, network flow optimization, and Frank-Wolfe method. The scheduling tool is reasonably efficient in computation.

Efficient Simulation-Based Composition of Scheduling Policies by Integrating Ordinal Optimization With Design of Experiment

Semiconductor wafer fab operations are characterized by complex and reentrant production processes over many heterogeneous machine groups with stringent performance requirements. Efficient composition of good scheduling policies from combinatorial options of wafer release and machine dispatching rules has posed a significant challenge to competitive fab operations. In this paper, we design a fast simulation-based methodology by an innovative integration of ordinal optimization (OO) and design of experiments (DOEs) to efficiently select a good scheduling policy for fab operations. Instead of finding the exact performance among scheduling policies, our approach compares their relative orders of performance to a specified level of confidence. Our new approach consists of three stages: performance estimation model construction using DOE, policy option screening process, and final simulation evaluation with intelligent computing budget allocation. The exponential convergence of OO is integrated into all the three stages to significantly improve computational efficiency. Simulation results of applications to scheduling wafer fabrications not only screen out good scheduling policies but also provide insights about how factors such as wafer release and the dispatching of each machine group may affect production cycle times and smoothness under a reentrant process flow. Most of the OO-based DOE simulations require 2-3 orders of magnitude less computation time than those of a traditional approach. Such a high speedup enables decision makers to explore much larger problems. Note to Practitioners - This paper designs a fast simulation-based methodology to compose a good scheduling policy from various dispatching rules of fab operations. The methodology innovatively applies DOE to estimate performance of dispatching rule combinations (policies) over various machines groups in a fab, screens out good enough policy options by using OO over the performance estimation, and allocates computation time intelligently to simulate potentially good options. Our study shows that OO-based DOE simulations require 2-3 orders of magnitude less computation time than those of a traditional approach. The high speedup enables fab managers to identify good scheduling policies from the many combinations of wafer release and dispatching rules.

SHEWMA: an end-of-line SPC scheme using wafer acceptance test data

In this paper, an end-of-line quality control scheme based on wafer acceptance test (WAT) data is presented. Due to the multiple-stream and sequence-disorder effects typically present in the WAT data, an abnormal process shift caused by one machine at an in-line step may become vague for detection using end-of-line WAT data. A methodology for generating robust design parameters for the simultaneous application of Shewhart and EWMA control charts to WAT data is proposed. This SHEWMA scheme is implemented in a foundry environment and its detection and diagnosis-enhancing capabilities are validated using both numerical derivations and fab data. Results show that the SHEWMA scheme is superior to the current practices in detection speed. Its use is complementary to the existing in-line SPC for process integration.

Fast fab scheduling rule selection by ordinal comparison-based simulation

In this paper, an ordinal comparison (OC)-based simulation tool is designed and applied to achieve fast selection of wafer release and lot dispatching rule combination for fab operations. By comparing relative orders of performance among scheduling rules to a specified level of confidence, the OC approach reduces simulation time significantly. The tool consists of (1) a discrete event simulator, (2) a fab model database, (3) a library of scheduling rules, (4) a library of performance indices, (5) an ordinal comparator, and (6) a computation budget allocation. Rule selections from a set of prominent fab scheduling rules under frequently considered fab performance indices such as mean and variance of cycle time and smoothness are studied over various time horizons by using a benchmark fab model. Results demonstrate a potential improvement in efficiency over traditional simulation by two orders of magnitude. In addition to insights about static selection of rules over various objectives and time horizons, our simulation studies also indicate the necessity of dynamic selection.