An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem

Modern manufacturing systems build on an effective scheduling scheme that makes full use of the system resource to increase the production, in which an important aspect is how to minimize the makespan for a certain production task (i.e., the time that elapses from the start of work to the end) in order to achieve the economic profit. This can be a difficult problem, especially when the production flow is complicated and production tasks may suddenly change. As a consequence, exact approaches are not able to schedule the production in a short time. In this paper, an adaptive scheduling algorithm is proposed to address the makespan minimization in the dynamic job shop scheduling problem. Instead of a linear order, the directed acyclic graph is used to represent the complex precedence constraints among operations in jobs. Inspired by the heterogeneous earliest finish time (HEFT) algorithm, the adaptive scheduling algorithm can make some fast adaptations on the fly to accommodate new jobs which continuously arrive in a manufacturing system. The performance of the proposed adaptive HEFT algorithm is compared with other state-of-the-art algorithms and further heuristic methods for minimizing the makespan. Extensive experimental results demonstrate the high efficiency of the proposed approach

Selective on/off switching at room temperature of a magnetic bistable {Fe2Co2} complex with single crystal-to-single crystal transformation via intramolecular electron transfer

National Nature Science Foundation of China [21172084, 20802022]; Self-determined research funds of CCNU from the colleges' basic research and operation of MOE [CCNU13F006, CCNU11C01002]A cyano-bridged {Fe2Co2} complex shows reversible single crystal-to-single crystal transformation between diamagnetic and paramagnetic states switched specifically by losing and absorbing methanol at room temperature in the solid state. And the solvent loss form presents temperature- and pressure-induced intramolecular electron transfer behaviour

Landau level splitting in Cd3As2 under high magnetic fields

Three-dimensional topological Dirac semimetals (TDSs) are a new kind of Dirac materials that exhibit linear energy dispersion in the bulk and can be viewed as three-dimensional graphene. It has been proposed that TDSs can be driven to other exotic phases like Weyl semimetals, topological insulators and topological superconductors by breaking certain symmetries. Here we report the first transport experiment on Landau level splitting in TDS Cd3As2 single crystals under high magnetic fields, suggesting the removal of spin degeneracy by breaking time reversal symmetry. The detected Berry phase develops an evident angular dependence and possesses a crossover from nontrivial to trivial state under high magnetic fields, a strong hint for a fierce competition between the orbit-coupled field strength and the field-generated mass term. Our results unveil the important role of symmetry breaking in TDSs and further demonstrate a feasible path to generate a Weyl semimetal phase by breaking time reversal symmetry.Comment: 31 page

a drum-buffer-rope based scheduling method for semiconductor manufacturing system

Scheduling in semiconductor manufacturing system is an important task for the industries faced with a large amount of resource competitions. Effective scheduling can improve the overall system performance and customer satisfaction. In this paper, a scheduling method with the core of bottleneck equipment control is designed referring to Drum-Buffer-Rope (DBR) theory. In order to identify the main-bottleneck, the relative load which takes the feature of reentrance into consideration is applied. For the avoidance of local blocking, the management of sub-bottleneck is presented. As both releasing and dispatching are taken into account, a scheduling method based on the compound priority is formed. Finally, HP-24 semiconductor wafer fabrication is used as an example to demonstrate the effectiveness of the proposed method. © 2011 IEEE.Ansaldo Sistemi Industriali - Results to the Power of ThreeScheduling in semiconductor manufacturing system is an important task for the industries faced with a large amount of resource competitions. Effective scheduling can improve the overall system performance and customer satisfaction. In this paper, a scheduling method with the core of bottleneck equipment control is designed referring to Drum-Buffer-Rope (DBR) theory. In order to identify the main-bottleneck, the relative load which takes the feature of reentrance into consideration is applied. For the avoidance of local blocking, the management of sub-bottleneck is presented. As both releasing and dispatching are taken into account, a scheduling method based on the compound priority is formed. Finally, HP-24 semiconductor wafer fabrication is used as an example to demonstrate the effectiveness of the proposed method. © 2011 IEEE

ANFIS and SA Based Approach to Prediction, Scheduling, and Performance Evaluation for Semiconductor Wafer Fabrication

In this paper, an optimized mechanism for Semiconductor wafer fabrication (SWF) by integrating the Adaptive neuro-fuzzy inference system (ANFIS) with Simulated annealing (SA) algorithm is proposed. In this approach, aiming to solve the rush order problem which significantly affect the cycle time and impact the Work in process (WIP) of lots due to the high priority, we build an ANFIS based prediction model which will be embedded into releasing to forecast product codes and quantities of the contingent rush orders. Then a scheduler based on SA algorithm is constructed, the coding of which represents a combination of scheduling policies, including lot releasing policies, dispatching rules, batching rules and setting-up rules. When the SA finished its optimization process, an optimal scheduling policy is produced. By using the proposed approach, we will find that the system can be optimized to a large extent and give a better performance.In this paper, an optimized mechanism for Semiconductor wafer fabrication (SWF) by integrating the Adaptive neuro-fuzzy inference system (ANFIS) with Simulated annealing (SA) algorithm is proposed. In this approach, aiming to solve the rush order problem which significantly affect the cycle time and impact the Work in process (WIP) of lots due to the high priority, we build an ANFIS based prediction model which will be embedded into releasing to forecast product codes and quantities of the contingent rush orders. Then a scheduler based on SA algorithm is constructed, the coding of which represents a combination of scheduling policies, including lot releasing policies, dispatching rules, batching rules and setting-up rules. When the SA finished its optimization process, an optimal scheduling policy is produced. By using the proposed approach, we will find that the system can be optimized to a large extent and give a better performance

bottleneck prediction method based on improved adaptive network-based fuzzy inference system (anfis) in semiconductor manufacturing system

Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method. © 2012 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press (CIP).Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method. © 2012 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press (CIP)

Adaptive Path Following and Locomotion Optimization of Snake-Like Robot Controlled by the Central Pattern Generator

This work investigates locomotion efficiency optimization and adaptive path following of snake-like robots in a complex environment. To optimize the locomotion efficiency, it takes energy consumption and forward velocity into account to investigate the optimal locomotion parameters of snake-like robots controlled by a central pattern generator (CPG) controller. A cuckoo search (CS) algorithm is applied to optimize locomotion parameters of the robot for environments with variable fractions and obstacle distribution. An adaptive path following method is proposed to steer the snake-like robot forward and along a desired path. The efficiency and accuracy of the proposed path following method is researched. In addition, a control framework that includes a CPG network, a locomotion efficiency optimization algorithm, and an adaptive path following method is designed to control snake-like robots move in different environments. Simulation and experimental results are presented to illustrate the performance of the proposed locomotion optimization method and adaptive path following controller for snake-like robots in complexity terrains