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In recent years, microgrids have developed as important parts of power systems and have provided affordable, reliable, and sustainable supplies of electricity. Each microgrid is managed as a single controllable entity with respect to the existing power system but demands for joint operation and sharing the benefits between a microgrid and its hosting utility. This paper is focused on the joint operation of a microgrid and its hosting utility, which cooperatively minimize daily generation costs through energy exchange, and presents a payment calculation scheme for power transactions based on a fair allocation of reduced generation costs. To fairly compensate for energy exchange between the micro- and utility grids, we adopt the cooperative game theoretic solution concept of Shapley value. We design a case study for a fictitious interconnection model between the Mueller microgrid in Austin, Texas and the utility grid in Taiwan. Our case study shows that when compared to standalone generations, both the micro- and utility grids are better off when they collaborate in power exchange regardless of their individual contributions to the power exchange coalition. [ABSTRACT FROM AUTHOR]

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 methodology is optimization-based, exploits the separability of the scheduling problem formulations, and facilitates quick answers to "what if" analysis. Test results using field data indicate that the two-level scheduling tool is reasonably efficient in computation and leads to throughput increase and reduction of output variations. The scheduling results can further be utilized in variability analysis for cycle time reduction. [ABSTRACT FROM AUTHOR]

Presents a Lagrangian relaxation-based approach for production scheduling of flexible flow shops where sequence-dependent setup effects are significant. Mathematical formulation of the scheduling problem; Solution methodology and feasibility adjustment heuristic; Analysis of numerical results and algorithmic properties; Application of algorithm to scheduling circuit probing.

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. Note to Practitioners—This paper studies group elevator scheduling with destination entry and future traffic information for normal operations, as well as for coordinated emergency evacuation. By exploiting the separable problem structure, a two-level formulation is established capable of modeling advance information. An approach is then developed by incorporating several innovative ideas into a decomposition and coordination framework, aiming to achieve near-optimal performance. The approach has also been extended for cases with little or no future traffic information and coordinated emergency evacuation. Numerical testing results are encouraging and further improvement is needed to reduce CPU time for online implementation. [ABSTRACT FROM AUTHOR]

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 (00) 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. [ABSTRACT FROM AUTHOR]

A job shop is a typical environment for manufacturing low-volume and high-variety discrete parts, where parts are of various due dates, priorities, and sequences of production operations. Good scheduling of when to do what using which resource is critical and challenging for the competitiveness of job shops. The Lagrangian relaxation neural network (LRNN) presented by Luh et al. provides an effective solution to this problem. To further speed up the scheduling of large problems, the parallelism of the LRNN approach is exploited in this paper for hardware implementation. A parallel processor based on the single-instruction multiple-data-stream architecture and its associated instruction set are designed. The architecture is implemented in a single-poly quadruple-metal 035-µm CMOS technology. Test results shows that the fabricated chip achieves 10 and 30 times speed-up when compared with several commercial digital signal processor chips and a 600-MHz PC, respectively. [ABSTRACT FROM AUTHOR]

Focuses on the efficiency of scheduling semiconductor wafer fabrication using ordinal optimization-based simulation. Development of an efficient simulation tool; Achievement of orders of computation time reduction over traditional simulations; Applications to dynamic selection of dispatching rules.

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-clearingprices. Under this setup, the consumer payments could be significantly higher than the minimized bid costs obtained from auctions. This gives rise to "payment cost minimization," 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 "economic dispatch" 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 "surrogate optimization condition" 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. [ABSTRACT FROM AUTHOR]

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 deter- mine 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. [ABSTRACT FROM AUTHOR]