Papers by Shi-Chung Chang
CIRP Annals, 1999
By combining neural network optimization ideas with "Lagrangian relaxation" for constraint handli... more By combining neural network optimization ideas with "Lagrangian relaxation" for constraint handling, a novel Lagrangian relaxation neural network (LRNN) has recently been developed for job shop scheduling. This paper is to explore architectural design issues for the hardware implementation of such neural networks. A digital circuitry with a micro-controller and an optimization chip is designed, where a parallel architecture and a pipeline architecture are explored for the optimization chip. Simulation results show that the LRNN hardware will provide near-optimal solutions for practical job shop scheduling problems. It is estimated that the parallel architecture will obtain one order of magnitude speed gain, and the pipeline architecture will obtain two orders speed gain as compared with the currently used method.
IEEE International Conference on Automation Science and Engineering, 2005.
Configuration and operation of building transportation systems, e.g., elevators and stairs for of... more Configuration and operation of building transportation systems, e.g., elevators and stairs for offices, hotels, and apartments, are important, and have profound societal impact such as in improving efficiency, reducing costs, and saving lives. Establishing methodologies that are effective and coherent across configuration and operation phases while covering both normal and emergency modes, however, is difficult. In this paper, coherent configuration and operation of building transportation systems for both normal and emergency modes are studied through a synergistic integration of optimization, formal semantics, and constraint satisfaction. Based on a formal semantics, a statistical configuration method using a coarse-grain model and an optimizationbased operation method using a fine-grain model are developed. These methods are integrated by using constraint programming to efficiently select high quality configurations with performance coherent across to the operation phase for both normal and emergency modes.
IEEE Transactions on Automation Science and Engineering, 2008
Group elevator scheduling has long been recognized as an important problem for building transport... more 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.
Automatica, 1989
A hierarchical, time decomposition and coordination scheme for long horizon optimal control probl... more A hierarchical, time decomposition and coordination scheme for long horizon optimal control problems is suitable for parallel processing and adds a new dimension to results on large-scale dynamic optimization.
IEEE Transactions on Automation Science and Engineering, 2000
Group elevator scheduling has long been recognized as an important problem for building transport... more 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.
On the computation of optimal strategies for large scale control problems
1986 25th IEEE Conference on Decision and Control, 1986
This paper studies the numerical feasibility of the decomposition method of large scale optimal c... more This paper studies the numerical feasibility of the decomposition method of large scale optimal control problems proposed in [3]. A parallel algorithm based on the second order method is developed and compared with the first order gradient method. Since the convergence speed of low level sub-problems plays an important role in the computation time of the overall problem, a heuristics is proposed to improve the low level convergence speed. A numerical example is used to compare the second order method with the first order gradient method as well as the one level gradient method. It is also used to demonstrate the improvement of the computational efficiency for the low level subproblems.
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Papers by Shi-Chung Chang