Shih-Cheng Horng

Evolutionary algorithm for stochastic job shop scheduling with random processing time

In this paper, an evolutionary algorithm of embedding evolutionary strategy (ES) in ordinal optimization (OO), abbreviated as ESOO, is proposed to solve for a good enough schedule of stochastic job shop scheduling problem (SJSSP) with the objective of minimizing the expected sum of storage expenses and tardiness penalties using limited computation time. First, a rough model using stochastic simulation with short simulation length will be used as a fitness approximation in ES to select N roughly good schedules from search space. Next, starting from the selected N roughly good schedules we proceed with goal softening procedure to search for a good enough schedule. Finally, the proposed ESOO algorithm is applied to a SJSSP comprising 8 jobs on 8 machines with random processing time in truncated normal, uniform, and exponential distributions. The simulation test results obtained by the proposed approach were compared with five typical dispatching rules, and the results demonstrated that the obtaining good enough schedule is successful in the aspects of solution quality and computational efficiency.

Pulsed high-intensity focused ultrasound enhances the relative permeability of the blood–tumor barrier in a glioma-bearing rat model

The use of pulsed high-intensity focused ultrasound (HIFU) with an ultrasound contrast agent (UCA) has been shown to disrupt the blood-brain barrier (BBB) noninvasively and reversibly in the targeted regions. This study evaluated the relative permeability of the blood-tumor barrier (BTB) after sonication by pulsed HIFU. Entry into the brain of chemotherapeutic agents is impeded by the BBB even though the permeability of this barrier may be partially reduced in the presence of a brain tumor. F98 glioma-bearing rats were injected intravenously with Evans blue (EB) with or without BTB disruption induced by pulsed HIFU. Sonication was applied at an ultrasound frequency of 1 MHz with a 5% duty cycle, and a repetition frequency of 1 Hz. The accumulation of EB in brain tumor and the tumor-to-contralateral brain ratio of EB were highest after pulsed HIFU exposure. Sonication followed by EB injection showed a tumor-to-contralateral brain ratio in the target tumors which was about 2 times that of the control tumors. This research demonstrates that pulsed HIFU enhances the relative permeability of the BTB in glioma- bearing rats. The results of this pilot study support the idea that further evaluation of other treatment strategies, such as HIFU exposure in addition to combined chemotherapy or repeated pulsed HIFU exposure to increase delivery of drugs into brain tumors, might be useful.

Association between contrast-enhanced MR images and blood-brain barrier disruption following transcranial focused ultrasound.

To investigate the correlation between the contrast‐enhanced magnetic resonance imaging (MRI) signal and the duration of blood–brain barrier (BBB) disruption induced by focused ultrasound (FUS).

An ordinal optimization theory-based algorithm for a class of simulation optimization problems and application

In this paper, we have proposed an ordinal optimization theory-based two-stage algorithm to solve for a good enough solution of the stochastic simulation optimization problem with huge input-variable space @Q. In the first stage, we construct a crude but effective model for the considered problem based on an artificial neural network. This crude model will then be used as a fitness function evaluation tool in a genetic algorithm to select N excellent settings from @Q. In the second stage, starting from the selected N excellent settings we proceed with the existing goal softening searching procedures to search for a good enough solution of the considered problem. We applied the proposed algorithm to the reduction of overkills and retests in a wafer probe testing process, which is formulated as a stochastic simulation optimization problem that consists of a huge input-variable space formed by the vector of threshold values in the testing process. The vector of good enough threshold values obtained by the proposed algorithm is promising in the aspects of solution quality and computational efficiency. We have also justified the performance of the proposed algorithm in a wafer probe testing process based on the ordinal optimization theory.

Evolutionary algorithm assisted by surrogate model in the framework of ordinal optimization and optimal computing budget allocation

This work proposes an evolutionary algorithm (EA) that is assisted by a surrogate model in the framework of ordinal optimization (OO) and optimal computing budget allocation (OCBA) for use in solving the real-time combinatorial stochastic simulation optimization problem with a huge discrete solution space. For real-time applications, an off-line trained artificial neural network (ANN) is utilized as the surrogate model. EA, assisted by the trained ANN, is applied to the problem of interest to obtain a subset of good enough solutions, S. Also for real-time application, the OCBA technique is used to find the best solution in S, and this is the obtained good enough solution. Most importantly, a systematic procedure is provided for evaluating the performance of the proposed algorithm by estimating the distance of the obtained good enough solution from the optimal solution. The proposed algorithm is applied to a hotel booking limit (HBL) problem, which is a combinatorial stochastic simulation optimization problem. Extensive simulations are performed to demonstrate the computational efficiency of the proposed algorithm and the systematic performance evaluation procedure is applied to the HBL problem to quantify the goodness of the obtained good enough solution.

Memetic Algorithm for Real-Time Combinatorial Stochastic Simulation Optimization Problems With Performance Analysis

A three-phase memetic algorithm (MA) is proposed to find a suboptimal solution for real-time combinatorial stochastic simulation optimization (CSSO) problems with large discrete solution space. In phase 1, a genetic algorithm assisted by an offline global surrogate model is applied to find N good diversified solutions. In phase 2, a probabilistic local search method integrated with an online surrogate model is used to search for the approximate corresponding local optimum of each of the N solutions resulted from phase 1. In phase 3, the optimal computing budget allocation technique is employed to simulate and identify the best solution among the N local optima from phase 2. The proposed MA is applied to an assemble-to-order problem, which is a real-world CSSO problem. Extensive simulations were performed to demonstrate its superior performance, and results showed that the obtained solution is within 1% of the true optimum with a probability of 99%. We also provide a rigorous analysis to evaluate the performance of the proposed MA.

Combining Artificial Bee Colony With Ordinal Optimization for Stochastic Economic Lot Scheduling Problem

The stochastic economic lot scheduling problem (SELSP) considers the make-to-stock production of multiple standardized products on a single machine with limited capacity and set-up costs under random demands, random set-up times, and random production times. The SELSP is an NP-hard inventory problem. Current solutions for the SELSP can be classified as analytic or heuristic. In both approaches, however, the computation time needed to obtain an optimal solution is still unsatisfactory. In this paper, the SELSP is first formulated as a fixed-sequence base-stock (FSBS) system with quantity-limited lot-sizing policy. An algorithm combining artificial bee colony (ABC) approach and ordinal optimization (OO) theory, abbreviated as ABCOO, is then proposed to find a good enough base-stock level of the FSBS system using reasonable computation time. The proposed algorithm combines the advantage of multidirectional search in ABC with the advantage of goal softening in OO. Finally, the ABCOO algorithm is used to solve an SELSP involving 12 products and three queuing models. Test results obtained by the ABCOO algorithm are compared with four lot-sizing policies and three meta-heuristic methods. The base-stock level obtained by the ABCOO algorithm is excellent in terms of solution quality and computational efficiency. Furthermore, a time series forecasting technique is used to predict the variant demand rates needed to resolve time-lag problems of the ABCOO algorithm. Tests of the forecasting technique confirm that it considerably improves the performance and enables the proposed algorithm real-time applications.

Applying PSO and OCBA to Minimize the Overkills and Re-Probes in Wafer Probe Testing

In this paper, the problem of minimizing overkills and re-probes in wafer probe testing is formulated as a multiobjective optimization problem. Overkill is a measure of good dies that were considered bad and re-probe is an additional manual probe testing to save overkills. The goal is to provide an optimal setting of threshold values for engineers to decide whether to carry out a re-probe after the two times of automatic probe testing. A two-stage algorithm is proposed to take advantage of particle swarm optimization (PSO) and optimal computing budget allocation (OCBA) for solving a good enough setting that minimizes overkills and re-probes within a reasonable computational time. A crude model based on a shorter stochastic simulation with a small number of test wafers is used as a fitness evaluation in a PSO algorithm to select N good enough settings. Then, we proceed with the refined OCBA to search for a good enough setting. The two-stage algorithm is applied to a real semiconductor product, and the threshold values obtained by the proposed algorithm are promising in the aspects of solution quality and computational efficiency. We have also demonstrated the computational efficiency of our algorithm by comparing with the genetic algorithm and evolution strategy.

Ordinal optimization based approach to the optimal resource allocation of grid computing system

In this paper, we have formulated the resource allocation optimization problem for expanding service and increasing reliability of a grid computing system. The formulated problem is a combinational optimization as well as an NP-hard problem. We firstly decompose this problem into a minimizing budget problem and a maximizing reliability problem. An approximate model is proposed to estimate the service reliability of a resource allocation design within a tolerable computation time. Secondly, we employ an ordinal optimization (OO) based approach to solve the maximizing reliability problem and a bisection method to solve the minimizing budget problem. The proposed OO based approach consists of two stages. A binary particle swarm optimization (BPSO) algorithm is employed in the first stage using the approximate model for fitness evaluation and selects a subset of good enough solutions. Then we proceed with the goal softening searching procedure in the second stage using more refined approximate models to search for a good enough solution from the subset obtained in the first stage. We have demonstrated the test results by the simulation on a 16-node and 25-link large grid computing system including two resource-managing nodes. We used 8 bisection iterations that consumed 20 minutes to obtain a good enough resource allocation design and the corresponding minimum budget. To test the optimality of the solutions obtained by our approach, we also solved the maximizing reliability problem using two competing methods, the GA and classical BPSO algorithm. The good enough solution obtained by the proposed approach is promising in the aspects of solution quality and computational efficiency.

Prenatal Exposure to Diagnostic Ultrasound Impacts Blood-Brain Barrier Permeability in Rats

The central nervous system vasculature consists of a tightly sealed endothelium that forms the blood-brain barrier (BBB); these blood vessels are impermeable to large-molecular-size agents. The aim of this study was to determine the influence of prenatal ultrasound exposure on blood-brain barrier (BBB) integrity as measured by the permeation of Evans blue (EB) through the BBB during the postnatal development of the rat. Diagnostic levels of ultrasound (2.89 MHz, mechanical index = 1.1, acoustic output power = 70.5 mW) for 1 h and 2 h per day, for 9 consecutive days were used on Sprague-Dawley rats. Offspring were assessed postnatally on days 10, 17, 24 and 38. Our analysis of over 139 animals reveals that, when exposed to diagnostic levels of ultrasound during embryonic development, a statistically significant amount of EB extravasation into the cerebrum and cerebellum could be detected on postnatal day 10 but not later. In addition, small changes in pup body weight, cerebrum weight and cerebellum weight were observed after relatively prolonged ultrasound exposure on all postnatal days. Taken together, these results emphasize the need for further investigation of the effects of ultrasound exposure during the potentially vulnerable period of intense BBB development in the human fetus.