A hybrid push/pull-dispatching rule for a photobay in a 300 mm wafer fab

In this paper, the simulation analysis of an automated material handling system (AMHS) for a photobay in a 300 mm wafer fab was analyzed, considering the effects of the dispatching rules. Discrete-event simulation models were developed in an e-M Plant to study this system. Currently, the combination of the shortest distance with the nearest vehicle (SD_NV) and the first-encounter first-served (FEFS) dispatching rule was used in this system. In order to improve the system performance, a hybrid push/pull (PP) dispatching rule was proposed. The simulation results reveal a substantial improvement of the AMHS performance and reduced the WIP and cycle time as a consequence of implementing a PP dispatching rule.     

Meta-heuristics for a complex push–pull production system

Hybrid push/pull production systems have received recent attention in the literature. This paper investigates a hybrid push/pull system originating from a foundry. The aim is to determine safety stock and replenishment levels for a large inventory situated at the junction point between component production and assembly operations. Components are produced according to a make-to-stock policy and are received into inventory when completed. Assembled goods are made-to-order, pulling components from the inventory when required. Classical techniques cannot be used in such a complex environment because they are based on invalid assumptions. This study proposes heuristically controlled simulations for attaining good solutions to the problem. Experimental results demonstrate and compare the proposed methods.     

Optimum policy in hybrid manufacturing/remanufacturing system

In this study, a hybrid manufacturing/remanufacturing system for a kind of short life cycle product with stochastic demand and stochastic returned products was investigated. The objective is to minimize the total cost of the hybrid system. The impacts of the amount of products manufactured and the proportion of the remanufactured part to the returned products on the total cost of the system were analyzed. Through theoretic analyses and numerical experiments, the relationship between the total cost of the system and the two influential factors were identified. The total cost of the hybrid system could be reduced significantly by setting optimum values of the amount of products manufactured and the proportion of the remanufactured part to the returned products. The sensitivity of the optimum values of the decision variables and the minimum cost with respect to the system parameters were also investigated.     

Approximations of lead-time distributions in an assemble-to-order system under a base-stock policy

In this paper, we consider an assemble-to-order production system in which a product with multiple components is produced under a base-stock policy. The demand follows a Poisson arrival process, and the production time at each facility is exponentially distributed.     

Selection of a pull production control policy under different demand situations for a manufacturing system by AHP-algorithm

A multistage serial production system is considered in the present paper. A generalized model has been developed with the use of probabilistic demand situations for the end product. The demand situations considered are binomial, exponential, lognormal and Poisson. These demand patterns are used as input parameter for various production control policies. The output values for performance parameters are obtained by simulation. The production policies analyzed are Kanban, CONWIP and Hybrid as alternatives for controlling the engineering manufacture. In the presence of several performance measures, it often becomes difficult for the management to select the most appropriate policy. Analytic hierarchy process (AHP) has been implemented for the purpose of selection. Computational results have been reported along with the sensitivity analysis after designing and conducting various experiments.     

An application of a real-time scheduling system for turbulent manufacturing environments

Classical scheduling approaches are not effective in real plants since they assume highly unrealistic assumptions that are seldom found in practice. These assumptions mainly deal with the consideration of the manufacturing environment as static. But in most real manufacturing installations some unexpected events take place. Ignoring these events may lead to schedules of unacceptable performance. Moreover, a quick reaction to changes is necessary, taking into account the significant amount of indirect costs involved in the derived interruptions. Reactivity and flexibility have to become key requirements at high control levels since they can contribute to shorten throughput times, improve equipment utilisation rates and service to customers. There is a need to extend classical scheduling models, so that they are able to better represent the factory. Genetic algorithms stand for one of the most promising alternative techniques to the classical ones. They show non-linearity, domain independence, robustness and flexibility. In this article, an evolutionary genetic algorithm has been used for sequencing job-shop problems in a real-time environment. Untoward events such as lack of materials or tools, change in production orders or unavailability of machines have been represented in the model and a solution using genetic algorithms has been designed and implemented. In the context of a European project belonging to the Competitive and Sustainable Growth programme, a prototype of a dynamic scheduler using genetic algorithms is described and some conclusions are drawn regarding the feasibility and quality of the solution.     

Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

This paper discusses the implementation of RFID technologies, which enable the shop floor visibility and reduce uncertainties in the real-time scheduling for hybrid flowshop (HFS) production. In the real-time HFS environment, the arriving of new jobs is dynamic, while the processes in work stages are not continuous. The decision makers in shop floor level and stage level have different objectives. Therefore, classical off-line HFS scheduling approaches cannot be used under these situations. In this research, two major measures are taken to deal with these specific real-time features. Firstly, a ubiquitous manufacturing (UM) environment is created by deploying advanced wireless devices into value-adding points for the collection and synchronization of real-time shop floor data. Secondly, a multi-period hierarchical scheduling (MPHS) mechanism is developed to divide the planning time horizon into multiple shorter periods. The shop floor manager and stage managers can hierarchically make decisions for their own objectives. Finally, the proposed MPHS mechanism is illustrated by a numerical case study.     

A genetic-based methodology for hybrid electric vehicles sizing

 As private transport concerns, the global challenge of this millennium is the reduction of carbon dioxide emissions from passenger cars by improving fuel economy without sacrificing the vehicle performance. Hybrid electric vehicles powertrain, combining electric motor with an auxiliary power unit, can improve effectively the vehicle performance and fuel economy, reducing at the same time the effects of the use of private cars on the air quality of the cities. These advantages can be achieved only if the design of the powertrain is inspired to the minimisation of the main figures of merit holding in consideration many general aspects and variables. As supporting methodology in developing this difficult activity, a genetic-based sizing methodology will be presented. It will be aimed to minimise a function objective which takes into account not only technical specifications but also environmental, social, and economic aspects. Some interesting simulation results will be reported to prove the validity of the methodology, which will contribute to a substantial reduction of the pollutant emissions from hybrid electric vehicles.     

A two-stage hybrid manufacturing model with controllable make-to-order production rates

As an effective strategy to facilitate delivering customized products within short lead time, hybrid manufacturing via a two-stage process has received attention from academia and industry. In this paper, we study a two-stage hybrid manufacturing system in which semifinished products are manufactured in a make-to-stock fashion in the first stage and end-products are produced from semifinished goods in a make-to-order (MTO) mode in the second stage. The rate of MTO production can be controlled within given limits, depending on the status of the system. The primary goal of this paper is to study a policy for coordinating order admission, MTO production rate, and inventory replenishment controls. Formulating the problem as a Markov decision process model, we characterize the structure of optimal control policies to maximize the long-run average profit. Using a numerical experiment, we study how the flexibility in MTO production rate affects the optimal policy and the optimal profit. We also examine the effect of the number of alternative MTO production rates on the optimal profit. We propose three heuristic policies implementable for general cases. The first heuristic describes two linear switching functions for admission and production controls and a selection rule for MTO production rate control. The second heuristic specifies fixed thresholds for the control decisions using the local information. The third heuristic presents linear switching functions that approximate the optimal threshold curves. Unlike second and third heuristics, the first heuristic does not require a grid search to determine the control parameters. We implement numerical studies to examine the marginal impact of system parameters and the effect of the number of alternative MTO production rates on the performance of the heuristics. Compared to the optimal policy, the average percentage performance of the first and third heuristics is less than 1% for both numerical studies. On the other hand, the average percentage performance of the second heuristic is larger than 3%, and it exceeds 10% for a set of particular problem examples.     

A decision support system for production scheduling in an ion plating cell

Production scheduling is one of the major issues in production planning and control of individual production units which lies on the heart of the performance of manufacturing organizations. Traditionally, production planning decision, especially scheduling, was resolved through intuition, experience, and judgment. Machine loading is one of the process planning and scheduling problems that involves a set of part types and a set of tools needed for processing the parts on a set of machines. It provides solution on assigning parts and allocating tools to optimize some predefined measures of productivity. In this study, Ion Plating industry requires similar approaches on allocating customer's order, i.e. grouping production jobs into batches and arrangement of machine loading sequencing for (i) producing products with better quality products; and (ii) enabling to meet due date to satisfy customers. The aim of this research is to develop a Machine Loading Sequencing Genetic Algorithm (MLSGA) model to improve the production efficiency by integrating a bin packing genetic algorithm model in an Ion Plating Cell (IPC), such that the entire system performance can be improved significantly. The proposed production scheduling system will take into account the quality of product and service, inventory holding cost, and machine utilization in Ion Plating. Genetic Algorithm is being chosen since it is one of the best heuristics algorithms on solving optimization problems. In the case studies, industrial data of a precious metal finishing company has been used to simulate the proposed models, and the computational results have been compared with the industrial data. The results of developed models demonstrated that less resource could be required by applying the proposed models in solving production scheduling problem in the IPC.     

Predictive control of a building hybrid heating system for energy cost reduction

Today, the buildings’ energy consumption is considerable amount of whole. Therefore, optimizing energy in buildings leads to a noticeable decrease in total energy consumption of the world. Energy-efficient buildings have developed by carrying out great research effort. The control procedures serve as a privileged method to help new buildings to comply with the most optimal system as an energy consumer and thus meet ‘nearly zero-energy’.The purpose of this paper is to present a method of controlling the building temperature and simultaneously reducing the cost of providing the hybrid heating systems with sufficient energy. Investigating a room in Tehran city on a day as an example, methods of (a) Model Predictive Control (MPC) with economic optimization (MPC consecutively with On-Off), (b) MPC without economic optimization, (c) Proportional-Integral-Derivative (PID) controller optimized by Genetic Algorithm (GA) in presence of gas thermal source, (d) PID controller optimized with GA in presence of electric thermal source and (e) PID controller optimized with multi-objective GA in the presence of two gas and electric thermal sources have been designed and implemented in this research. Furthermore, the effect of each of these methods on cost reduction and temperature regulation of inside of the room has been studied. Eventually it has been specified that using MPC method with economical optimization has the highest influence on cost reduction and keeps the temperature of inside of the room in the predefined range. This method achieved cost saving of 50% compared to the MPC and GA. But the main targets of this study are both of regulating inside temperature and cost optimization. According to the main targets of this study, using MPC methods without economical optimization and multi-objective genetic algorithm would be more effective.     

A modified genetic algorithm approach for scheduling of perfect maintenance in distributed production scheduling

Distributed Scheduling (DS) problems have attracted attention by researchers in recent years. DS problems in multi-factory production are much more complicated than classical scheduling problems because they involve not only the scheduling problems in a single factory, but also the problems in the higher level, which is: how to allocate the jobs to suitable factories. It mainly focuses on solving two issues simultaneously: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production schedules in each factory. Its objective is to maximize system efficiency by finding an optimal plan for a better collaboration among various processes. However, in many papers, machine maintenance has usually been ignored during the production scheduling. In reality, every machine requires maintenance, which will directly influence the machine's availability, and consequently the planned production schedule. The objective of this paper is to propose a modified genetic algorithm approach to deal with those DS models with maintenance consideration, aiming to minimize the makespan of the jobs. Its optimization performance has been compared with other existing approaches to demonstrate its reliability. This paper also tests the influence of the relationship between the maintenance repairing time and the machine age to the performance of scheduling of maintenance during DS in the studied models.     

A hybrid optimization approach to conformance testing of finite automata

Unique Input–Output sequences (UIOs) are quite commonly used in conformance testing. Unfortunately finding UIOs of minimal length is an NP hard problem. This study presents a hybrid approach to generate UIOs automatically on a basis of the finite state machine (FSM) specification. The proposed hybrid approach harnesses the benefits of hill climbing (Greedy search) and heuristic algorithm. Hill climbing, which exploits domain knowledge, is capable of quickly generating good result however it may get stuck in local minimum. To overcome the problem we used a set of parameters called the seed, which allows the algorithm to generate different results for a different seed. The hill climbing generates solutions implied by the seed while the Genetic Algorithm is used as the seed generator. We compared the hybrid approach with Genetic Algorithm, Simulated Annealing, Greedy Algorithm, and Random Search. The experimental evaluation shows that the proposed hybrid approach outperforms other methods. More specifically, we showed that Genetic Algorithm and Simulated Annealing exhibit similar performance while both of them outperform Greedy Algorithm. Finally, we generalize the proposed hybrid approach to seed-driven hybrid architectures and elaborate on how it can be adopted to a broad range of optimization problems.     

A three-dimensional computerized isometric strength measurement system

The three-dimensional Computerized Isometric Strength Measurement System (CISMS) reliably and accurately measures isometric pull and push strengths in work spaces of paraplegic populations while anticipating comparative studies with other populations. The main elements of the system were: an extendable arm, a vertical supporting track, a rotating platform, a force transducer, stability sensors and a computerized data collection interface. The CISMS with minor modification was successfully used to measure isometric push-up and pull-down strengths of paraplegics and isometric push, pull, push-up and pull-down strength in work spaces for seated and standing able-bodied populations. The instrument has satisfied criteria of versatility, safety and comfort, ease of operation, and durability. Results are accurate within 2N for aligned forces. Costing approximately $1,500 (US) including computer, the system is affordable and accurate for aligned isometric strength measurements.     

基于网络编码的P2P流媒体推拉结合数据调度方法customR2

相比传统的基于Pull模式的P2P流媒体数据调度方法,基于随机网络编码的随机Push方法R2具有提高视频服务质量、降低服务器负载和增强对节点动态的适应能力等优势,但是容易产生大量的冗余编码数据块,消耗一定的网络带宽的问题。因此提出了一个基于网络编码的P2P流媒体推拉结合数据调度方法customR2。当下游节点某个数据段所需的编码数据块将要接收完成时,通知它的种子节点停止向它Push该数据段编码数据块,转为向它的种子节点定制所需的编码数据块,从而有效减少了冗余编码数据块的产生。通过仿真实验,从播放延迟、流速率、网络规模等方面对customR2和R2进行了性能对比,实验结果表明customR2相比R2具有较小的编码数据块冗余度和较高的节点播放质量。     

A decision support system for personnel scheduling in a manufacturing environment

A Decision Support System (DSS) is developed to support managers in the task of scheduling labor in the area of manufacturing. The DSS is designed to generate labor requirements by worker category and work center based on master production schedules. It is a PC-based, menu-driven program that generates a capacity plan based on data supplied by the user of the system.     

发布/订阅系统中的缓存副本一致性研究

针对发布/订阅系统中缓存副本一致性维护问题,首先,对原有基于谣言的一致性维护算法进行改进,提出一种基于轨迹标签的谣言一致性维护算法。该算法通过在更新消息报文中添加节点轨迹信息,避免向已更新节点发送冗余更新消息。其次,为提高系统消息传播的可靠性,设计一种基于轨迹标签的层次化反馈恢复机制,结合发布/订阅系统推拉传输模式,减少反馈消息数目,避免反馈爆炸。模拟实验结果证明,改进的一致性维护算法降低了一致性维护消息开销和时间开销,提高了系统的可靠性和可扩展性。     

A hybrid stock trading system for intelligent technical analysis-based equivolume charting

This paper presents the use of an intelligent hybrid stock trading system that integrates neural networks, fuzzy logic, and genetic algorithms techniques to increase the efficiency of stock trading when using a volume adjusted moving average (VAMA), a technical indicator developed from equivolume charting. For this research, a neuro–fuzzy-based genetic algorithm (NF-GA) system utilizing a VAMA membership function is introduced. The results show that the intelligent hybrid system takes advantage of the synergy among these different techniques to intelligently generate more optimal trading decisions for the VAMA, allowing investors to make better stock trading decisions.     

A continuous optimization framework for hybrid system identification

We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid system identification. In both cases, the identification problem is recast as a continuous optimization program involving only the real parameters of the model as variables, thus avoiding the use of discrete optimization. This program can be solved efficiently by using standard optimization methods even for very large data sets. In addition, the proposed framework easily incorporates robustness to different kinds of outliers through the choice of the loss function.     

Batch production control in a computer integrated manufacturing environment

Since unit level control systems have already been widely implemented in the chemical industry, it has become abundantly clear that the next levels of application lie in supervisory control and scheduling. This is particularly true for the chemical processing industry, operating batchwise with multi-product and multi-purpose characteristics. A ‘flexible schedule’ (FLESCHE) concept providing the link between operations scheduling and the recipe-driven batch operatioon with its basic instrumentation and control system is being developed and implemented in a computer integrated manufacturing environment. The FLESCHE system incorporates an integrated decision-making framework which includes all the relevant decision functions in a multi-level hierarchy: optimal plant-wide resources coordination, demand management, plant-wide operations scheduling and plant retrofitting on the one hand, as well as master recipe adaptation, batch rerouting and schedule adaptation in response to short-time, unplanned operational disturbances, on the other. A summary of the main characteristics, methodology and present applications of the system is provided. Actual limitations and future extensions are also discussed.