Holonic job shop scheduling using a multiagent system

Manufacturing job shop scheduling is a notoriously difficult problem that lends itself to various approaches - from optimal algorithms to suboptimal heuristics. We combined popular heuristic job shop-scheduling approaches with emerging AI techniques to create a dynamic and responsive scheduler. We fashioned our job shop scheduler's architecture around recent holonic manufacturing systems architectures and implemented our system using multiagent systems. Our scheduling approach is based on evolutionary algorithms but differs from common approaches by evolving the scheduler rather than the schedule. A holonic, multiagent systems approach to manufacturing job shop scheduling evolves the schedule creation rules rather than the schedule itself. The authors test their approach using a benchmark agent-based scheduling problem and compare performance results with other heuristic-scheduling approaches.     

Multi-objective real-time dispatching for integrated delivery in a Fab using GA based simulation optimization

In a wafer fabrication Fab, the “integrated delivery”, which integrates the automated material handling system (AMHS) with processing tools to automate the material flow, is difficult to implement due to the system complexity and uncertainty. The previous dispatching studies in semiconductor manufacturing have mainly focused on the tool dispatching. Few studies have been done for analyzing combinatorial dispatching rules including lot dispatching, batch dispatching and automated guided vehicle (AGV) dispatching. To handle this problem, a GA (genetic algorithm) based simulation optimization methodology, which consists of the on-line scheduler and the off-line scheduler, is presented in this paper. The on-line scheduler is used to monitor and implement optimal combinatorial dispatching rules to the semiconductor wafer fabrication system. The off-line scheduler is employed to search for optimal combinatorial dispatching rules. In this study, the response surface methodology is adopted to optimize the GA parameters. Finally, an experimental bay of wafer fabrication Fab is constructed and numerical experiments show that the proposed approach can significantly improve the performance of the “integrated delivery system” compared with the traditional single dispatching rule approach.     

Scheduling unit length jobs on parallel machines with lookahead information

This paper studies two closely related online-list scheduling problems of a set of n jobs with unit processing times on a set of m multipurpose machines. It is assumed that there are k different job types, where each job type can be processed on a unique subset of machines. In the classical definition of online-list scheduling, the scheduler has all the information about the next job to be scheduled in the list while there is uncertainty about all the other jobs in the list not yet scheduled. We extend this classical definition to include lookahead abilities, i.e., at each decision point, in addition to the information about the next job in the list, the scheduler has all the information about the next h jobs beyond the current one in the list. We show that for the problem of minimizing the makespan there exists an optimal (1-competitive) algorithm for the online problem when there are two job types. That is, the online algorithm gives the same minimal makespan as the optimal offline algorithm for any instance of the problem. Furthermore, we show that for more than two job types no such online algorithm exists. We also develop several dynamic programming algorithms to solve a stochastic version of the problem, where the probability distribution of the job types is known and the objective is to minimize the expected makespan.     

Data mining based job dispatching using hybrid simulation-optimization approach for shop scheduling problem

A data mining based approach to discover previously unknown priority dispatching rules for job shop scheduling problem is presented. This approach is based on seeking the knowledge that is assumed to be embedded in the efficient solutions provided by the optimization module built using tabu search. The objective is to discover the scheduling concepts using data mining and hence to obtain a set of rules capable of approximating the efficient solutions for a job shop scheduling problem (JSSP). A data mining based scheduling framework is presented and implemented for a job shop problem with maximum lateness as the scheduling objective.     

Real-time scheduling for reentrant hybrid flow shops: A decision tree based mechanism and its application to a TFT-LCD line

A reentrant hybrid flow shop, typically found in the electronics industry, is an extended system of the ordinary flow shop in such a way that there exist one or more parallel machines at each serial stage and each job has the reentrant product flow, i.e., a job may visit a stage several times. Among the operational issues in reentrant hybrid flow shops, we focus on the scheduling problem that determines the allocation of jobs to the machines at each stage as well as the sequence of the jobs assigned to each machine. Unlike the theoretical approach on reentrant hybrid flow shop scheduling, we suggest a real-time scheduling mechanism with a decision tree when selecting appropriate dispatching rules. The decision tree, one of the commonly used data mining techniques, is adopted to eliminate the computational burden required to carry out simulation runs to select dispatching rules. To illustrate the mechanism suggested in this study, a case study was performed on a thin film transistor-liquid crystal display (TFT-LCD) manufacturing line and the results are reported for various system performance measures.     

Learning-based multi-pass adaptive scheduling for a dynamic manufacturing cell environment

Because the essential attributes are uncertain in a dynamic manufacturing cell environment, to select a near-optimal subset of manufacturing attributes to enhance the generalization ability of knowledge bases remains a critical, unresolved issue for classical artificial neural network-based (ANN-based) multi-pass adaptive scheduling (MPAS). To resolve this problem, this study develops a hybrid genetic /artificial neural network (GA/ANN) approach for ANN-based MPAS systems. The hybrid GA/ANN approach is used to evolve an optimal subset of system attributes from a large set of candidate manufacturing system attributes and, simultaneously, to determine configuration and learning parameters of the ANN according to various performance measures. In the GA/ANN-based MPAS approach, for a given feature subset and the corresponding topology and learning parameters of an ANN decoded by a GA, an ANN was applied to evaluate the fitness in the GA process and to generate the MPAS knowledge base used for adaptive scheduling control mechanisms. The results demonstrate that the proposed GA/ANN-based MPAS approach has, according to various performance criteria, a better system performance over a long period of time than those obtained with classical machine learning-based MPAS approaches and the heuristic individual dispatching rules.     

Multiprocessor scheduling and rescheduling with use of cellular automata and artificial immune system support

The paper presents cellular automata (CA)-based multiprocessor scheduling system, in which an extraction of knowledge about scheduling process occurs and this knowledge is used while solving new instances of the scheduling problem. There are three modes of the scheduler: learning, normal operating, and reusing. In the learning mode, a genetic algorithm is used to discover CA rules suitable for solving instances of a scheduling problem. In the normal operating mode, discovered rules are able to find automatically, without a calculation of a cost function, an optimal or suboptimal solution of the scheduling problem for any initial allocation of program tasks in a multiprocessor system. In the third mode, previously discovered rules are reused with support of an artificial immune system (AIS) to solve new instances of the problem. We present a number of experimental results showing the performance of the CA-based scheduler.     

Hybrid genetic algorithms with dispatching rules for unrelated parallel machine scheduling with setup time and production availability

This article considers the unrelated parallel machine scheduling problem with sequence- and machine-dependent setup times and machine-dependent processing times. Furthermore, the machine has a production availability constraint to each job. The objective of this problem is to determine the allocation policy of jobs and the scheduling policy of machines to minimize the total completion time. To solve the problem, a mathematical model for the optimal solution is derived, and hybrid genetic algorithms with three dispatching rules are proposed for large-sized problems. To assess the performance of the algorithms, computational experiments are conducted and evaluated using several randomly generated examples.     

一种MapReduce实时调度算法设计及实现

MapReduce是云计算中重要的批数据处理框架,多任务共享MapReduce机群并满足任务实时性要求是调度算法急需解决的问题。提出两阶段实时调度算法,将调度划分为任务间调度和任务内调度。对于任务间调度,使用抽样法和经验值法确定子任务执行时间,利用该参数建立资源分配模型,动态确定任务优先级进行调度;对于子任务使用延迟调度策略进行调度,保证计算的本地性。实验结果显示,两阶段实时调度算法相比公平调度算法和FIFO算法,在保证吞吐量的同时能够满足任务实时性要求。     

工件可拒绝的有限等待置换流水车间调度算法

有限等待限定了工件在相邻机器间的等待时间上下限,普遍存在于中间产品性质不稳定且存在运输作业的车间环境中.工件可拒绝的有限等待置换流水车间调度是对工件拒绝和工件调度的联合决策,要求确定拒绝工件集合并给出被接受工件的调度方案.针对这一联合决策问题,以最小化总拒绝成本与总拖期成本之和为目标,并为最大完工时间(Makespan)设置上限约束,结合问题特征提出一种协同进化遗传算法.该算法将染色体编码分解为工件拒绝和工件序列两个子集,基于调度规则生成初始种群,引入协同进化策略依次进化子集种群,并提出基于记忆的动态概率参数设计方法以确定遗传算子的执行概率,设计解码规则以保证解的可行性并优化总成本.最后,通过数据实验验证了所提出算法及相关策略的可行性和有效性,并分析了问题参数对算法性能的影响.     

A genetic algorithm application using fuzzy processing times in non-identical parallel machine scheduling problem

There are many scheduling problems which are NP-hard in the literature. Several heuristics and dispatching rules are proposed to solve such hard combinatorial optimization problems. Genetic algorithms (GA) have shown great advantages in solving the combinatorial optimization problems in view of its characteristic that has high efficiency and that is fit for practical application [1]. Two different scale numerical examples demonstrate the genetic algorithm proposed is efficient and fit for larger scale identical parallel machine scheduling problem for minimizing the makespan. But, even though it is a common problem in the industry, only a small number of studies deal with non-identical parallel machines. In this article, a kind of genetic algorithm based on machine code for minimizing the processing times in non-identical machine scheduling problem is presented. Also triangular fuzzy processing times are used in order to adapt the GA to non-identical parallel machine scheduling problem in the paper. Fuzzy systems are excellent tools for representing heuristic, commonsense rules. That is why we try to use fuzzy systems in this study.     

Integration of priority dispatching and due-date assignment in a job shop

A simulation study to investigate the effect on missed due-dates and job flow-time is discussed by combining the job dispatching and due-date assignment decisions in job shop scheduling. A ‘semi-local’ due-date-oriented dispatching rule is designed which is able to monitor the progress of jobs closely. The performance of the dispatching rule is enhanced by a rational due-date assignment procedure which takes account of both job content and shop status information in determining due-dates. The simulation results show that the combined scheduling procedure performs better than some common simple dispatching rules which are used with the total-work-content (TWK) due-date assignment method.     

IS: An intelligent scheduler for batch manufacturing systems

This paper describes the design and development of IS (for Intelligent Scheduler), a true multiple criteria knowledge-based scheduler which can be used for operational level scheduling of batch manufacturing systems, sometimes called job shops. IS incorporates a heuristic algorithm coupled with two knowledge bases, one for job scheduling and the other for selecting a suitable schedule based on the user provided criterion or criteria. With fourteen dispatching rules, it can generate both static and dynamic schedules. IS is a far more realistic and sophisticated model, accounting for many important factors, such as multiple machines, multiple fixtures, multiple tools, alternate processing routes, machine setup time, machine processing time, due date, job arrival time, initial shop loading, hot jobs, and considering either one criterion or multiple criteria simultaneously. In addition, IS coded in C and has all the features of a modern professional quality interactive program. It has moving bar and pull down menus and an on-line help function, a friendly human-computer interface, and an intuitive and easy to understand representation of the schedules.     

Dynamic scheduling of manufacturing job shops using genetic algorithms

Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date.A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.     

Data Intensive and Network Aware (DIANA) Grid Scheduling

In Grids scheduling decisions are often made on the basis of jobs being either data or computation intensive: in data intensive situations jobs may be pushed to the data and in computation intensive situations data may be pulled to the jobs. This kind of scheduling, in which there is no consideration of network characteristics, can lead to performance degradation in a Grid environment and may result in large processing queues and job execution delays due to site overloads. In this paper we describe a Data Intensive and Network Aware (DIANA) meta-scheduling approach, which takes into account data, processing power and network characteristics when making scheduling decisions across multiple sites. Through a practical implementation on a Grid testbed, we demonstrate that queue and execution times of data-intensive jobs can be significantly improved when we introduce our proposed DIANA scheduler. The basic scheduling decisions are dictated by a weighting factor for each potential target location which is a calculated function of network characteristics, processing cycles and data location and size. The job scheduler provides a global ranking of the computing resources and then selects an optimal one on the basis of this overall access and execution cost. The DIANA approach considers the Grid as a combination of active network elements and takes network characteristics as a first class criterion in the scheduling decision matrix along with computations and data. The scheduler can then make informed decisions by taking into account the changing state of the network, locality and size of the data and the pool of available processing cycles.     

Learning-based scheduling of flexible manufacturing systems using case-based reasoning

A common method of dynamically scheduling jobs in Flexible Manufacturing Systems (FMSs) is to employ dispatching rules. However, the problem associated with this method is that the performance of the rules depends on the state of the system, but there is no rule that is superior to all the others for all the possible states the system might be in. It would therefore be highly desirable to employ the most suitable rule for each particular situation. To achieve this, this paper presents a scheduling approach that uses Case-Based Reasoning (CBR), which analyzes the system's previous performance and acquires "scheduling knowledge," which determines the most suitable dispatching rule at each particular moment in time. Simulation results indicate that the proposed approach produces significant performance improvements over existing dispatching rules.     

Multi-criteria and satisfaction oriented scheduling for hybrid distributed computing infrastructures

Assembling and simultaneously using different types of distributed computing infrastructures (DCI) like Grids and Clouds is an increasingly common situation. Because infrastructures are characterized by different attributes such as price, performance, trust, and greenness, the task scheduling problem becomes more complex and challenging. In this paper we present the design for a fault-tolerant and trust-aware scheduler, which allows to execute Bag-of-Tasks applications on elastic and hybrid DCI, following user-defined scheduling strategies. Our approach, named Promethee scheduler, combines a pull-based scheduler with multi-criteria Promethee decision making algorithm. Because multi-criteria scheduling leads to the multiplication of the possible scheduling strategies, we propose SOFT, a methodology that allows to find the optimal scheduling strategies given a set of application requirements. The validation of this method is performed with a simulator that fully implements the Promethee scheduler and recreates an hybrid DCI environment including Internet Desktop Grid, Cloud and Best Effort Grid based on real failure traces. A set of experiments shows that the Promethee scheduler is able to maximize user satisfaction expressed accordingly to three distinct criteria: price, expected completion time and trust, while maximizing the infrastructure useful employment from the resources owner point of view. Finally, we present an optimization which bounds the computation time of the Promethee algorithm, making realistic the possible integration of the scheduler to a wide range of resource management software.     

A Neurogenetic approach for the resource-constrained project scheduling problem

A variety of metaheuristic approaches have emerged in recent years for solving the resource-constrained project scheduling problem (RCPSP), a well-known NP-hard problem in scheduling. In this paper, we propose a Neurogenetic approach which is a hybrid of genetic algorithms (GA) and neural-network (NN) approaches. In this hybrid approach the search process relies on GA iterations for global search and on NN iterations for local search. The GA and NN search iterations are interleaved in a manner that allows NN to pick the best solution thus far from the GA pool and perform an intensification search in the solution's local neighborhood. Similarly, good solutions obtained by NN search are included in the GA population for further search using the GA iterations. Although both GA and NN approaches, independently give good solutions, we found that the hybrid approach gives better solutions than either approach independently for the same number of shared iterations. We demonstrate the effectiveness of this approach empirically on the standard benchmark problems of size J30, J60, J90 and J120 from PSPLIB.     

Provably Efficient Online Nonclairvoyant Adaptive Scheduling

Multiprocessor scheduling in a shared multiprogramming environment can be structured in two levels, where a kernel-level job scheduler allots processors to jobs and a user-level thread scheduler maps the ready threads of a job onto the allotted processors. We present two provably-efficient two-level scheduling schemes called G-RAD and S-RAD respectively. Both schemes use the same job scheduler RAD for the processor allotments that ensures fair allocation under all levels of workload. In G-RAD, RAD is combined with a greedy thread scheduler suitable for centralized scheduling; in S-RAD, RAD is combined with a work-stealing thread scheduler more suitable for distributed settings. Both G-RAD and S-RAD are non-clairvoyant. Moreover, they provide effective control over the scheduling overhead and ensure efficient utilization of processors. We also analyze the competitiveness of both G-RAD and S-RAD with respect to an optimal clairvoyant scheduler. In terms of makespan, both schemes can achieve O(1)-competitiveness for any set of jobs with arbitrary release time. In terms of mean response time, both schemes are O(1)-competitive for arbitrary batched jobs. To the best of our knowledge, G-RAD and S-RAD are the first non-clairvoyant scheduling algorithms that guarantee provable efficiency, fairness and minimal overhead.     

一种柔性车间快速启发式调度算法

柔性作业车间调度问题比传统的Job-shop问题更复杂也更符合实际生产实际.为了快速有效地求解这类问题,设计出一种基于综合分派规则的快速启发式调度算法.基于综合分派规则的调度算法,以一批工件总完工时间最短为目标,在调度过程中通过动态调整工件的加工优先级并为每道工序分配最适合的机器进行加工,可迅速求得满意的较优解.与其他方法进行对比实验结果证实了算法的有效性,在实际调度系统的应用中也证明了算法的实用性.