Application of a continuous supervisory fuzzy control on a discrete scheduling of manufacturing systems

This paper considers the design and the practical implementation of a stable multiple objective real-time scheduling problem for a complex production system. In this paper, a complex production system is viewed as a kind of systems producing a variety of products (multiple-part-type) under constraints and multiple production objectives often conflicting. Previously, fuzzy control theory and fuzzy intervals arithmetic have been used to develop a distributed and supervised continuous-flow control architecture. In this framework, the objective of the distributed control structure is to balance the production process by adjusting the continuous production rates of the machines on the basis of the average local behavior. The supervisory control methodology aims at maintaining the overall performances within acceptable limits. In the new proposed approach, the problem of a stable real-time scheduling of jobs is considered at the shop-floor level. In this context, as the stability of the control structure is ensured, the actual dispatching times are determined from the continuous production rates through a discretization procedure. To deal with conflicts between jobs at a shared machine, a decision is made. It concerns the actual part to be processed and uses some criterions representing a measure of the job's priority. The simulation results show the validity of the proposed approach in terms of production cost, robustness and system stability.     

Approximation theorem of the fuzzy transform in fuzzy reasoning and its application to the scheduling problem

In this paper, the theoretical foundation of fuzzy reasoning is analyzed, and the idea that the fuzzy transform given in the fuzzy reasoning method should be continuous with respect to a certain fuzzy distance is proposed. Also, the fuzzy transforms given in the two fuzzy reasoning methods, the Mamdani method and the III method, are proved to be continuous. Based on the continuity of the fuzzy transform, the approximation theorem of the continuous fuzzy number transform is proven. Then, on the basis of the approximation theorem, a simple fuzzy number transform is constructed in order to implement the fuzzy reasoning based on multiple rules. At last, the fuzzy reasoning based on multiple rules implemented by a simple fuzzy number transform is applied to machine scheduling problems, and numerical computational results of different scale scheduling problems with the objective of minimizing the total number of tardy jobs show that it is more effective than usual heuristics based on rules, and in the computational time it has the obvious advantage over the reasoning by fuzzy rules directly.     

计算机控制中的模糊调度设计

研究了具有模糊截止期的多控制任务的实时调度问题,提出了奉献度的概念和最大奉献优先（LDF）的调度策略．为了减小因任务间频繁切换造成的系统开销。提出了基于抢占阈值的最大奉献优先（TLDF）调度策略．最后,通过仿真比较了LDF和TLDF两种调度策略,实现了具有模糊截止期的控制任务调度,在减少并均衡控制性能损失的同时提高系统计算资源的使用率．     

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 algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs)

We present a genetic algorithm (GA) based heuristic approach for job scheduling in virtual manufacturing cells (VMCs). In a VMC, machines are dedicated to a part as in a regular cell, but machines are not physically relocated in a contiguous area. Cell configurations are therefore temporary, and assignments are made to optimize the scheduling objective under changing demand conditions. We consider the case where there are multiple jobs with different processing routes. There are multiple machine types with several identical machines in each type and are located in different locations in the shop floor. Scheduling objective is weighted makespan and total traveling distance minimization. The scheduling decisions are the (i) assignment of jobs to the machines, and (ii) the job start time at each machine. To evaluate the effectiveness of the GA heuristic we compare it with a mixed integer programming (MIP) solution. This is done on a wide range of benchmark problem. Computational results show that GA is promising in finding good solutions in very shorter times and can be substituted in the place of MIP model.     

Scheduling Flexible Flow Shop in Labeling Companies to Minimize the Makespan

In the competitive global marketplace, production scheduling plays a vital role in planning in manufacturing. Scheduling deals directly with the time to output products quickly and with a low production cost. This research examines case study of a Radio-Frequency Identification labeling department at Avery Dennison. The main objective of the company is to have a method that allows for the sequencing and scheduling of a set of jobs so it can be completed on or before the customer’s due date to minimize the number of late orders. This study analyzes the flexible flow shop scheduling problem with a sequence dependent setup by modifying the processing time and setup time to minimize the makespan on multiple machines. Based on the defined mathematical model, this study includes an alternative approach and application of heuristic algorithm with the input being big data. Both optimization programs are used in this study and compared to determine which method can better solve the company’s problems. The proposed algorithm is able to improve machine utilization with large-scale problems.     

Two-stage hybrid batching flowshop scheduling with blocking and machine availability constraints using genetic algorithm

This research investigates a two-stage hybrid flowshop scheduling problem in a metal-working company. The first stage consists of multiple parallel machines and the second stage has only one machine. Four characteristics of the company have substantiated the complexity of the problem. First, all machines in stage one are able to process multiple jobs simultaneously but the jobs must be sequentially set up one after another. Second, the setup time of each job is separated from its processing time and depends upon its preceding job. Third, a blocking environment exists between two stages with no intermediate buffer storage. Finally, machines are not continuously available due to the preventive maintenance and machine breakdown. Two types of machine unavailability, namely, deterministic case and stochastic case, are identified in this problem. The former occurs on stage-two machine with the start time and the end time known in advance. The latter occurs on one of the parallel machine in stage one and a real-time rescheduling will be triggered. Minimizing the makespan is considered as the objective to develop the optimal scheduling algorithm. A genetic algorithm is used to obtain a near-optimal solution. The computational results with actual data are favorable and superior over the results from existing manual schedules.     

周期性和事件驱动的JobShop滚动调度策略

为了适应加工的连续性及环境的变化，借用了预测控制中的滚动优化思想提出了周期性和事件驱动的滚动调度策略。调度算法将遗传算法和分派规则相结合，以此来处理与操作序列有关的工件安装时 间和工件到期时间约束的复杂调度问题。     

A Neighbourhood-based Hybrid Method for Scheduling with Fuzzy Due-dates

In this paper, the single machine scheduling problem with fuzzy due-dates and controllable processing times is dealt with. The objective is to maximize the "grade of satisfaction" incurred, subject to a total processing cost constraint. The former is a function of the deviations of the jobs completion times from their most desirable due dates. The latter is considered to be a linear function of the jobs compression. The problem is solved via a heuristic approach, which encapsulates concepts from Tabu Search, Genetic Algorithms and Simulated Annealing. Both Complete Enumeration and Branch and Bound techniques are used to evaluate the efficiency of the proposed method.     

Scheduling two-stage hybrid flow shops with parallel batch, release time, and machine eligibility constraints

This paper investigates a difficult scheduling problem on a specialized two-stage hybrid flow shop with multiple processors that appears in semiconductor manufacturing industry, where the first and second stages process serial jobs and parallel batches, respectively. The objective is to seek job-machine, job-batch, and batch-machine assignments such that makespan is minimized, while considering parallel batch, release time, and machine eligibility constraints. We first propose a mixed integer programming (MIP) formulation for this problem, then gives a heuristic approach for solving larger problems. In order to handle real world large-scale scheduling problems, we propose an efficient dispatching rule called BFIFO that assigns jobs or batches to machines based on first-in-first-out principle, and then give several reoptimization techniques using MIP and local search heuristics involving interchange, translocation and transposition among assigned jobs. Computational experiments indicate our proposed re-optimization techniques are efficient. In particular, our approaches can produce good solutions for scheduling up to 160 jobs on 40 machines at both stages within 10?min.     

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.     

Implementation of a case-based production scheduling system in C language

This paper presents the application of Case-Based Reasoning to the production scheduling problem in a corrugated card-board factory, its main task is to find a promising sequence for jobs processing, a prototype of the scheduling system is implemented in C language. In this system, a schedule case is represented in the form of ordered tree and each job is represented in the format of attribute-value pairs. When jobs to be sequenced are given, a previous case which is the best matching with the problem is recalled from the case-base, the sequence of given jobs can be decided assigning the jobs to the nodes of the ordered tree of the recalled case. Several real world scheduling problems in the factory have been solved and the system behaved as correctly as the human experts did. It was showed that the Case-Based Reasoning approach is well suited to solve production scheduling problems.     

单机模糊加工时间下最迟开工时间调度问题

研究单机模糊加工时间下确定最迟开工时间的调度问题。目标是在满足每个工作都以大于等于各自指定的隶属度属于完工集合的约束下,建筑工作的最大最迟开工时间。通过模糊数学知识对模型进行分析,对于特殊情况给出了问题的最优解,对于一般情况给出了一个最优解的必要条件。     

An advanced fuzzy logic gain scheduling trajectory control for nonlinear systems

This paper investigates a novel fuzzy control approach developed for a class of nonlinear continuous systems. We combine an input–output feedback linearization (IOFL) method and a gain scheduling (GS) approach to obtain a tracking control structure. The latter is mainly based on the reversing trajectory method which allows us to estimate the asymptotic stability region around operating points. Needless to say that the limitation of this analytical approach lies in the challenge of determining the intermediate operating points in order to ensure a smooth transition from actual operating conditions to desired ones. This happens when there is no intersection between successive stability regions. The proposed fuzzy logic controller (FLC) is synthesized in order to determine the intermediate operating points which, in turn, will allows us to online implement the tracking control for nonlinear systems. Finally, the effectiveness of the fuzzy gain scheduling schema is demonstrated through its simulation to a temperature control problem in a CSTR.     

Scheduling Optimization Modelling: A Case Study of a Woven Label Manufacturing Company

Production scheduling involves all activities of building production schedules, including coordinating and assigning activities to each person, group of people, or machine and arranging work orders in each workplace. Production scheduling must solve all problems such as minimizing customer wait time, storage costs, and production time; and effectively using the enterprise’s human resources. This paper studies the application of flexible job shop modelling on scheduling a woven labelling process. The labelling process includes several steps which are handled in different work-stations. Each workstation is also comprised of several identical parallel machines. In this study, job splitting is allowed so that the power of work stations can be utilized better. The final objective is to minimize the total completion time of all jobs. The results show a significant improvement since the new planning may save more than 60% of lead time compared to the current schedule. The contribution of this research is to propose a flexible job shop model for scheduling a woven labelling process. The proposed approach can also be applied to support complex production scheduling processes under fuzzy environments in different industries. A practical case study demonstrates the effectiveness of the proposed model.     

用模拟退火法解任务的加工时间为模糊区间数的单机提前/拖期调度问题

研究了单机作业下任务的加工时间为模糊区间数的提前／拖期调度问题。     

Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach

In general, distributed scheduling problem focuses on simultaneously solving two issues: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production scheduling in each factory. The objective of this approach is to maximize the system efficiency by finding an optimal planning for a better collaboration among various processes. This makes distributed scheduling problems more complicated than classical production scheduling ones. With the addition of alternative production routing, the problems are even more complicated. Conventionally, machines are usually assumed to be available without interruption during the production scheduling. Maintenance is not considered. However, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it influences the production scheduling. In this connection, maintenance should be considered in distributed scheduling. The objective of this paper is to propose a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem.     

Digital twin-based job shop anomaly detection and dynamic scheduling

Scheduling scheme is one of the critical factors affecting the production efficiency. In the actual production, anomalies will lead to scheduling deviation and influence scheme execution, which makes the traditional job shop scheduling methods are not sufficient to meet the needs of real-time and accuracy. By introducing digital twin (DT), further convergence between physical and virtual space can be achieved, which enormously reinforces real-time performance of job shop scheduling. For flexible job shop, an anomaly detection and dynamic scheduling framework based on DT is proposed in this paper. Previously, a multi-level production process monitoring model is proposed to detect anomaly. Then, a real-time optimization strategy of scheduling scheme based on rolling window mechanism is explored to enforce dynamic scheduling optimization. Finally, the improved grey wolf optimization algorithm is introduced to solve the scheduling problem. Under this framework, it is possible to monitor the deviation between the actual processing state and the planned processing state in real time and effectively reduce the deviation. An equipment manufacturing job shop is taken as a case study to illustrate the effectiveness and advantages of the proposed framework.     

具有模糊多目标的网格任务调度算法

在异构的网格计算平台上,网格中有用户、资源管理员、组织管理者等实体,这些实体对网格的管理、使用、维护、安全性、可靠性等目标都提出了要求,并且这些目标有时是不可量化的。针对具有模糊多目标网格计算的任务调度问题,提出模糊多目标网格任务调度模型,使用模糊化等式对多目标进行模糊处理,给出求解该模型的模糊化定理,并对该定理进行证明。利用差分优化算法无需目标函数连续可微的特点,提出使用模糊差分优化算法完成模糊多目标的网格任务调度。实验结果表明,模糊差分优化算法较现有算法在执行时间上处于劣势,但在可靠性、安全性和丢失任务数三个指标上要优于现有算法。     

基于QPSO算法的模糊流水车间调度问题

实际生产过程中由于各种客观因素的影响,流水车间调度问题往往具有模糊不确定性。介绍了模糊流水车间调度问题,在此基础上提出了一种收敛速度快、全局性能好的量子微粒群算法来解决该问题。通过仿真实例对该算法进行了验证。结果表明,在求解模糊流水车间调度问题时,量子微粒群算法有很好的效果。