Reactive scheduling in a job shop where jobs arrive over time

The paper considers the dynamic job shop scheduling problem (DJSSP) with job release dates which arises widely in practical production systems. The principle characteristic of DJSSP considered in the paper is that the jobs arrive continuously in time and the attributes of the jobs, such as the release dates, routings and processing times are not known in advance, whereas in the classical job shop scheduling problem (CJSSP), it is assumed that all jobs to be processed are available at the beginning of the scheduling process. Reactive scheduling approach is one of the effective approaches for DJSSP. In the paper, a heuristic is proposed to implement the reactive scheduling of the jobs in the dynamic production environment. The proposed heuristic decomposes the original scheduling problem into a number of sub problems. Each sub problem, in fact, is a dynamic single machine scheduling problem with job release dates. The scheduling technique applied in theproposed heuristic is priority scheduling, which determines the next state of the system based on priority values of certain system elements. The system elements are prioritized with the help of scheduling rules (SRs). An approach based on gene expression programming (GEP) is also proposed in the paper to construct efficient SRs for DJSSP. The rules constructed by GEP are evaluated in the comparison of the rules constructed by GP and several prominent human made rules selected from literatures on extensive problem sets with respect to various measures of performance.     

Learning IF–THEN priority rules for dynamic job shops using genetic algorithms

Performing complex, informed priority rules might pose a challenge for traditional operator-driven systems. However, computer-integrated manufacturing systems may significantly benefit from the complex, informed rules such as state-dependent priority rules. A state-dependent priority rule can be defined as a list of IF–THEN priority rules that will be performed if certain system conditions are satisfied. Here, we propose a genetic algorithm based learning system for constructing interval-based, state-dependent priority rules for each interval of queue lengths in dynamic job shops. Our approach builds interval based state-dependent priority rules pairing the priority rules with the intervals of queue lengths, and determines priority rules and their corresponding length of intervals for a given objective. A genetic algorithm is developed for matching queue length intervals with appropriate priority rules during simulation. A system simulation evaluates the efficiencies of interval based state dependent priority rules. The experiments show that interval-based state dependent priority rules obtained by the proposed approach considerably outperform the priority rules including shortest processing time (SPT), minimum slack time (MST), earlier due date (EDD), modified due date (MDD), cost over time (COVERT), and critical ratio (CR) for total tardiness for most of the problems.     

Workload vs scheduling policies in a dual-resource constrained job shop

The purpose of this paper is to report on research conducted to examine the effectiveness of different scheduling policies in a dual-constrained job shop under various workload conditions. The standard assumption in most job shop scheduling research has been that a 90% utilization of the shop is achieved. However, since shop utilization levels vary widely, it was hypothesized that scheduling policies that are optimum under one load condition might not be as effective under other load conditions. The model for this simulation experiment represented a job shop constrained by both labor and machines. The shop contains four machine centers with random routing of jobs through the shop. Shop workload was defined at three levels: 70, 85 and 99% utilization. Four machine scheduling rules and three labor assignment rules were tested for each of the shop workload levels, with mean job flow time as. the performance criterion. The results of the 3 × 4 × 3 factorial experiment showed that the advantage of the SPT (shortest processing time) machine scheduling rule over other rules is diminished dramatically when shop utilization is reduced from 99 to 85% or below. This same observation holds for other rules considered. The LNQ (longest queue length) labor assignment rule outperformed other rules at the 99% utilization level, but yielded no significant difference in performance at the 85% and below workload levels.     

The sensitivity of jobshop due date lead time to changes in the processing time

This paper provides a procedure for calculating the sensitivity of the production lead time to the average job processing time for a single machine problem under a general priority rule using simulation. The aim of this work is to develop sensitivity routines, for queues with any non-preemptive priority rule, to calculate the sensitivity of the due date lead time to the average jobs processing time. Though GI/G/1 queuing model is given, the same methodology can be extended to GI/G/m queuing model. The new algorithm is valid for the SPT, LPT and FCFS priority rules.     

A neuro-genetic approach to design and planning of a manufacturing cell

In this paper, we propose a neuro-genetic decision support system coupled with simulation to design a job shop manufacturing system by achieving predetermined values of targeted performance measures such as flow time, number of tardy jobs, total tardiness and machine utilization at each work center. When a manufacturing system is designed, the management has to make decisions on the availability of resources or capacity, in our setting, the number of identical machines in each work station and the dispatching rule to be utilized in the shop floor to achieve performance values desired. Four different priority rules are used as Earliest due date (EDD), Shortest Processing Time (SPT), Critical ratio (CR) and First Come First Serve (FCFS). In reaching the final decision, design alternatives obtained from the proposed system are evaluated in terms of performance measures. An illustrative example is provided to explain the procedure.     

A concurrent rule scheduling algorithm for active rules

The use of rules in a distributed environment creates new challenges for the development of active rule execution models. In particular, since a single event can trigger multiple rules that execute over distributed sources of data, it is important to make use of concurrent rule execution whenever possible. This paper presents the details of the integration rule scheduling (IRS) algorithm. Integration rules are active database rules that are used for component integration in a distributed environment. The IRS algorithm identifies rule conflicts for multiple rules triggered by the same event through static, compile-time analysis of the read and write sets of each rule. A unique aspect of the algorithm is that the conflict analysis includes the effects of nested rule execution that occurs as a result of using an execution model with an immediate coupling mode. The algorithm therefore identifies conflicts that may occur as a result of the concurrent execution of different rule triggering sequences. The rules are then formed into a priority graph before execution, defining the order in which rules triggered by the same event should be processed. Rules with the same priority can be executed concurrently. The IRS algorithm guarantees confluence in the final state of the rule execution. The IRS algorithm is applicable for rule scheduling in both distributed and centralized rule execution environments.     

A multilevel weighted fuzzy reasoning algorithm for expert systems

The applications of fuzzy production rules (FPR) are rather limited if the relative degree of importance of each proposition in the antecedent contributing to the consequent (i.e., the weight) is ignored or assumed to be equal. Unfortunately, this is the case for many existing FPR and most existing fuzzy expert system development shells or environments offer no such functionality for users to incorporate different weights in the antecedent of FPR. This paper proposes to assign a weight parameter to each proposition in the antecedent of a FPR and a new fuzzy production rule evaluation method (FPREM) which generalizes the traditional method by taking the weight factors into consideration is devised. Furthermore, a multilevel weighted fuzzy reasoning algorithm (MLWFRA) incorporating this new FPREM, which is based on the reachability and adjacent place characteristics of a fuzzy Petri net, is developed. The MLWFRA has the advantages that i) it offers multilevel reasoning capability; ii) it allows multiple conclusions to be drawn if they exist; iii) it offers a new fuzzy production rule evaluation method; and iv) it is capable of detecting cycle rules     

An AIS-based hybrid algorithm with PDRs for multi-objective dynamic online job shop scheduling problem

The dynamic online job shop scheduling problem (JSSP) is formulated based on the classical combinatorial optimization problem – JSSP with the assumption that new jobs continuously arrive at the job shop in a stochastic manner with the existence of unpredictable disturbances during the scheduling process. This problem is hard to solve due to its inherent uncertainty and complexity. This paper models this class of problem as a multi-objective problem and solves it by hybridizing the artificial intelligence method of artificial immune systems (AIS) and priority dispatching rules (PDRs). The immune network theory of AIS is applied to establish the idiotypic network model for priority dispatching rules to dynamically control the dispatching rule selection process for each operation under the dynamic environment. Based on the defined job shop situations, the dispatching rules that perform best under specific environment conditions are selected as antibodies, which are the key elements to construct the idiotypic network. Experiments are designed to demonstrate the efficiency and competitiveness of this model.     

A linguistic approach to non-identical parallel processor scheduling with fuzzy processing times

This study presents an application of non-identical parallel processor scheduling under uncertain operation times. We have been motivated from a real case scheduling problem that contains some uncommon welding operations to be processed by workers in an automotive subcontract company. Here each operator may weld each job but in different processing times depending on learning effect because of operator’s ability and experience, and batch sizes. To determine the crisp operation times in such a fuzzy environment, a linguistic reasoning approach (with a 75-“If- Then” rules) considering the learning effect is proposed in the study. Since the fuzzy linguistic approach allows the representation of expert information more directly and adequately, it can be more possible to make realistic schedules under uncertainty. With the objective to balance the workload among all operators, the longest processing time heuristic algorithm is been used and measured average makespan. For evaluating the effectiveness of this approach, it is compared with the scheduling method that use the random operation times generated from a uniform distribution. Results showed that the proposed fuzzy linguistic scheduling approach has balanced the workload of operators with a standard deviation of 0.37 and improved the Cmax value as 16%. A general conclusion can be drawn the proposed approach is able to generate realistic schedules and especially useful to solve non-identical parallel processor scheduling problem under uncertainty. An important contribution of this study is that Mamdani inference method with learning effect is the first time used to obtain the crisp processing times of non-identical processors by the help of a rule base with expert knowledge.     

Fuzzy inference to risk assessment on nuclear engineering systems

This paper presents a nuclear case study, in which a fuzzy inference system (FIS) is used as alternative approach in risk analysis. The main objective of this study is to obtain an understanding of the aging process of an important nuclear power system and how it affects the overall plant safety. This approach uses the concept of a pure fuzzy logic system where the fuzzy rule base consists of a collection of fuzzy IF–THEN rules. The fuzzy inference engine uses these fuzzy IF–THEN rules to determine a mapping from fuzzy sets in the input universe of discourse to fuzzy sets in the output universe of discourse based on fuzzy logic principles. The risk priority number (RPN), a traditional analysis parameter, was calculated and compared to fuzzy risk priority number (FRPN) using scores from expert opinion to probabilities of occurrence, severity and not detection. A standard four-loop pressurized water reactor (PWR) containment cooling system (CCS) was used as example case. The results demonstrated the potential of the inference system for subsiding the failure modes and effects analysis (FMEA) in aging studies.     

补偿模糊神经网络在模糊规则训练中的应用

目前,自治式水下机器人（Autonomous Underwater Vehicle,AUV）、自动导引驾驶小汽车、轮船等领域应用模糊规则控制已经受到许多人的关注,模糊规则的制定与训练是其中之关键所在,该文将模朔规则控制应用在无人机自由编队飞行控制中。在训练模糊规则过程中,常规的BP神经网络法存在学习速度慢、无法结合号家知识以及容易陷入局部最小等缺点,为了克服上述不足,文中引人了补偿模糊神经网络,它足一个结合了补偿模糊逻辑和神经网络的混合系统,由面向控制和面向决策的神经元组成,其模糊运算采用动态的、全局优化运算,学习速度快、学习过程稳定。将其用于无人机自由编队飞行的模糊控制规则进行训练,结果表明用补偿模糊神经网络刘模糊规则的训练效果良好。     

Cluster analysis and neural network-based metamodeling of priority rules for dynamic sequencing

Most sequencing problems deal with deterministic environments where all information is known in advance. However, in real-world problems multiple sources of uncertainty need to be taken into consideration. To model such a situation, in this article, a dynamic sequencing problem with random arrivals, processing times and due-dates is considered. The examined system is a manufacturing line with multiple job classes and sequence-dependent setups. The performance of the system is measured under the metrics of mean WIP, mean cycle time, mean earliness, mean tardiness, mean absolute lateness, and mean percentage of tardy jobs. Twelve job dispatching rules for solving this problem are considered and evaluated via simulation experiments. A statistically rigorous analysis of the solution approaches is carried out with the use of unsupervised and supervised learning methods. The cluster analysis of the experimental results identified classes of priority rules based on their observed performance. The characteristics of each priority rule class are documented and areas in objective space not covered by existing rules are identified. The functional relationship between sequencing priority rules and performance metrics of the production system was approximated by artificial neural networks. Apart from gaining insight into the mechanics of the sequencing approaches the results of this article can be used (1) as a component for prediction systems of dispatching rule output, (2) as a guideline for building new dispatching heuristic with entirely different characteristics than existing ones, (3) to significantly decrease the length of what-if simulation studies.     

Scheduling rules for dynamic shops that manufacture multi-level jobs

The problem of scheduling in dynamic conventional jobshops has been extensively investigated over many years. However, the problem of scheduling in assembly jobshops (i.e. shops that manufacture multi-level jobs with components and subassemblies) has been relatively less investigated in spite of the fact that assembly jobshops are frequently encountered in real life. A survey of literature on dynamic assembly jobshop scheduling has revealed that the TWKR-RRP rule is the best one for minimizing the mean flowtime and staging delay, and the job due-date (JDD) rule is the best for minimizing the mean tardiness of jobs. However, the objectives of minimizing the maximum flowtime (and maximum staging delay) and standard deviation of flowtime (and standard deviation of staging delay) are as important as the minimization of mean flowtime and mean staging delay. Likewise, the objectives of minimizing the maximum tardiness and standard deviation of tardiness are also as important as the minimization of mean tardiness. The reason is that the maximum and standard deviation values of a performance measure indicate the worst-case performance of a dispatching rule. The present study seeks to develop efficient dispatching rules to minimize the maximum and standard deviation of flowtime and staging delay, and the maximum and the standard deviation of conditional tardiness of jobs. The dispatching rules are based on the computation of the earliest completion time of a job and consequently determining the latest finish time of operations on components/subassemblies of a job. An extensive simulation-based investigation of the performance evaluation of the existing dispatching rules and the proposed dispatching rules has been carried out by randomly generating jobs with different structures and different shop utilization levels. It has been found from the simulation study that the proposed rules are quite effective in minimizing the maximum and standard deviation of flowtime and staging delay, and the maximum conditional tardiness and standard deviation of conditional tardiness.     

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.     

Learning and tuning fuzzy logic controllers through reinforcements

A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.     

The construction of a fuzzy inference network by extension of the rule inference network

Fuzzy logic can bring about inappropriate inferences as a result of ignoring some information in the reasoning process. Neural networks are powerful tools for pattern processing, but are not appropriate for the logical reasoning needed to model human knowledge. The use of a neural logic network derived from a modified neural network, however, makes logical reasoning possible. In this paper, we construct a fuzzy inference network by extending the rule–inference network based on an existing neural logic network. The propagation rule used in the existing rule–inference network is modified and applied. In order to determine the belief value of a proposition pertaining to the execution part of the fuzzy rules in a fuzzy inference network, the nodes connected to the proposition to be inferenced should be searched for. The search costs are compared and evaluated through application of sequential and priority searches for all the connected nodes.     

Control of dynamic systems using fuzzy logic and neural networks

The use of artificial neural network is proposed for high-speed processing of rules in fuzzy logic controller (FLC). the logic element of an FLC is replaced by a single hidden layer feedforward network. the input and output fuzzy subsets are expressed it of numerical patterns. the network is trained using the back-propagation algori to establish fuzzy associations between the input and output fuzzy subsets. the inference mechanism of the network is compared with that of compositional law of inference. In the proposed implementation of FLC, all the rules are processed in paralle. This implementation has potential for high-speed processing of rules if the network is realized in hardware. the use of neural networks in fuzzy logic self-organizing is also ivestigated. © 1993 John Wiley & Sons, Inc.     

A two-stage heuristic for single machine capacitated lot-sizing and scheduling with sequence-dependent setup costs

This paper considers a single machine capacitated lot-sizing and scheduling problem. The problem is to determine the lot sizes and the sequence of lots while satisfying the demand requirements and the machine capacity in each period of a planning horizon. In particular, we consider sequence-dependent setup costs that depend on the type of the lot just completed and on the lot to be processed. The setup state preservation, i.e., the setup state at the end of a period is carried over to the next period, is also considered. The objective is to minimize the sum of setup and inventory holding costs over the planning horizon. Due to the complexity of the problem, we suggest a two-stage heuristic in which an initial solution is obtained and then it is improved using a backward and forward improvement method that incorporates various priority rules to select the items to be moved. Computational tests were done on randomly generated test instances and the results show that the two-stage heuristic outperforms the best existing algorithm significantly. Also, the heuristics with better priority rule combinations were used to solve case instances and much improvement is reported over the conventional method as well as the best existing algorithm.     

模糊反馈控制实时调度算法

为了解决模糊不确定任务集在不可预测环境下的动态抢占调度问题,应用模糊规则和模糊调度理论,提出一个基于模糊反馈控制的调度算法,并建立相应的调度架构.该架构由基本调度器和模糊反馈控制两部分组成.用模糊调度算法作为基本调度器的调度算法,将任务集按不同优先级等级进行划分,优先级等级高的任务优先调度,从而使得更多的重要任务得到调度;模糊控制器与任务流调节策略一起构成模糊反馈控制部分.仿真结果表明,模糊反     

Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell

A fuzzy logic based methodology for generating the sequence of part movements in a multi-product batch processing through a computerized machine cell is presented in this paper. A number of production objectives are taken into account. Two fuzzy based strategies: fuzzy-job and fuzzy-machine are proposed and their performance is compared to two well known dispatching rules such as SPT (Shortest Processing Time) and WEED (Weighted Earliest Due Date). The sequencing algorithm was implemented on a standard personnel computer and the scheduler was interfaced to a robot controller for implementing loading and unloading strategy within the cell. The proposed fuzzy-based methodologies especially fuzzy-job shows a superior performance compared to the traditional dispatching rules considered.