A study of the flexible job shop scheduling problem with parallel machines and reentrant process

This paper develops a scheduling algorithm for the job shop scheduling problem with parallel machines and reentrant process. This algorithm includes two major modules: the machine selection module (MSM) and the operation scheduling module (OSM). An order has several jobs and each job has several operations in a hierarchical structure. The MSM helps an operation to select one of the parallel machines to process it. The OSM is then used to schedule the sequences and the timing of all operations assigned to each machine. A real-life weapons production factory is used as a case study to evaluate the performance of the proposed algorithm. Due to the high penalty of delays in military orders, the on-time delivery rate is the most important performance measure and then makespan is the next most important measure. Well-known performance measures in the scheduling literature, such as maximum lateness and average tardiness, are also evaluated. The simulation results demonstrate that the MSM and OSM using the combination of earliest due date (EDD), the operations’ lowest level code (LLC) of the bill of materials (BOM), and the longest processing time (LPT) outperforms the other scheduling methods.     

A Prototype Genetic Algorithm-Enhanced Multi-Objective Scheduler for Manufacturing Systems

This paper describes the development of a prototype genetic algorithm-enhanced multi-objective scheduler for manufacturing systems. A framework of the prototype scheduler is proposed which accepts data input from a database or file and outputs a near-optimal schedule. A scheduling toolbox with scheduling models for job shop, flow shop and cellular manufacturing, forms part of the prototype scheduler, and the schedule builder transforms the near-optimal solution into a valid shop floor schedule. The prototype system was validated for various cases, with and without constraints and multiple objective functions, (makespan and tardiness), enforced simultaneously with constraints. In the first case, the schedule generated was comparable to those obtained by other researchers. The prototype system was also tested for its ability to handle dynamic scheduling, e.g. a rush order. The results showed that all the job orders could be rescheduled within the original makespan, even though the order of one of the jobs was doubled.     

Production planning of a flexible manufacturing system in a material requirements planning environment

Early flexible manufacturing system (FMS) production planning models exhibited a variety of planning objectives; typically, these objectives were independent of the overall production environment. More recently, some researchers have proposed hierarchical production planning and scheduling models for FMS. In this article, we examine production planning of FMS in a material requirements planning (MRP) environment. We propose a hierarchical structure that integrates FMS production planning into a closed-loop MRP system. This structure gives rise to the FMS/MRP rough-cut capacity planning (FMRCP) problem, the FMS/MRP grouping and loading (FMGL) problem, and the FMS/MRP detailed scheduling problem.We examine the FMRCP and FMGL problems in detail and present mathematical programming models for each of these problems. In particular, the FMRCP problem is modeled as a generalized assignment problem (GAP), and a GAP-based heuristic procedure is defined for the problem. We define a two-phase heuristic for the FMGL problem and present computational experience with both heuristics. The FMRCP heuristic is shown to solve problems that exhibit a dependent-demand relation within the FMS and with FMS capacity utilization as high as 99 percent. The FMGL heuristic requires very little CPU time and obtains solutions to the test problems that are on average within 1.5 percent of a theoretical lower bound.This FMS/MRP production planning framework, together with the resulting models, constitutes an important step in the integration of FMS technology with MRP production planning. The hierarchical planning mechanism directly provides for system-level MRP planning priorities to induce appropriate production planning and control objectives on the FMS while simultaneously allowing for necessary feedback from the FMS. Moreover, by demonstrating the tractability of the FMRCP and FMGL problems, this research establishes the necessary groundwork upon which to explore systemwide issues pertaining to the coordination of the hierarchical structure.     

An integrated scheme for process planning and scheduling in FMS

Process planning and scheduling are the two important manufacturing functions involved in any shop floor activities. As functional integration is essential to realize the benefits of computer integrated manufacturing, a scheme for integration of process planning and scheduling has been introduced in this paper. The proposed scheme can be implemented in a company with existing process planning and scheduling departments when multiple process plans remain available on the shop floor. It consists of two main modules, introduced in detail in this paper. They are process plan selection module and scheduling module.     

Integrating Flexible Process Plans with Scheduling in Flexible Manufacturing Systems

This paper highlights the importance of integration between process planning and scheduling in flexible manufacturing systems (FMS). An effective integration increases the potential for enhanced system performance and enhanced decision making A framework that integrates flexible process plans with off-line (predictive) scheduling in FMS is presented. The flexibility in process planning, including process flexibility, sequence flexibility, and alternative machine tools, is discussed. The proposed framework consists of four integrated stages with the objective of reducing the completion time. The integrated stages include: 1. Machine tool selection. 2. Process plan selection. 3. Scheduling. 4. Re-scheduling modules. In addition, the paper proposes a new approach, namely the Dissimilarity Maximisation Method (DMM), for selecting the appropriate process plans for a part mix where parts have alternative process plans. The recursive structure of the framework provides a different approach, namely overlapping schedules, which considers a longer scheduling period as comprising several short scheduling periods. Knowing that neither the process plans nor the planned (predicted) schedules are truly followed on the shop floor, the related literature and the corresponding approaches are compared in order to envisage new approaches for closing the gap between process planning and scheduling.     

基于多Agent和动态逻辑制造单元的车间控制系统研究

根据敏捷化生产的要求，分析车间控制系统的多Agent层次结构，提出动态逻辑制造单元的概念，用于组织车间制造系统资源，实现在动态多变的环境中车间制造系统快速重组，动态重构的能力，研究在动态逻辑制造单元的组织形式下，面向订单的生产调度的实现方法，并给出了车间多Agent系统的软件体系结构。     

Integrated scheduling of flexible manufacturing system using evolutionary algorithms

Effective sequencing and scheduling of the material handling system (MHS) have an impact on the productivity of the flexible manufacturing system (FMS). The MHS cannot be neglected while scheduling the production tasks. It is necessary to take into account the interaction between machines and MHS. This paper highlights the importance of integration between production schedule and MHS schedule in FMS. The Giffler and Thompson algorithm with different priority dispatching rules is developed to minimize the makespan in the FMS production schedule. Its output is used for MHS scheduling where the distance traveled and the number of backtrackings of the automated-guided vehicles are minimized using an evolutionary algorithms such as an ant colony optimization algorithm and particle swarm optimization (PSO) algorithm. The proposed evolutionary algorithms are validated with benchmark problems. The results available for the existing algorithms are compared with results obtained by the proposed evolutionary algorithms. The analysis reveals that PSO algorithm provides better solution with reasonable computational time.     

Effectiveness of flexible routing control

Flexibility in part process representation and in highly adaptive routing algorithms are two major sources for improvement in the control of flexible manufacturing systems (FMSs). This article reports the investigation of the impact of these two kinds of flexibilities on the performance of the system. We argue that, when feasible, the choices of operations and sequencing of the part process plans should be deferred until detailed knowledge about the real-time factory state is available.To test our ideas, a flexible routing control simulation system (FRCS) was constructed and a programming language for modeling FMS part process plans, control strategies, and environments of the FMS was designed and implemented. In addition, a scheme for implementing flexible process routing called data flow dispatching rule (DFDR) was derived.The simulation results indicate that flexible processing can reduce mean flow time while increasing system throughput and machine utilization. We observed that this form of flexibility makes automatic load balancing of the machines possible. On the other hand, it also makes the control and scheduling process more complicated and calls for new control algorithms.     

具有柔性加工路径的作业车间批量调度优化研究

古典作业车间调度问题已经被研究了几十年并证明为 NP- hard问题。柔性作业车间调度是古典作业车间调度问题的扩展 ,它允许工序可以由一个机床集合中的多台机床完成加工 ,调度的目的是将工序分配给各机床 ,并对各机床上的工序进行排序以使完成所有工序的时间最小化。本文采用遗传算法进行柔性作业车间调度研究 ,针对柔性作业车间问题提出了一种新颖直观的基因编码方法以适用于批量调度 ,并分析了几种批量调度方案 ,最后给出了这些调度的仿真结果 ,证明单件最佳调度不适合扩展成批量最佳调度     

模拟退火粒子群优化算法在工艺规划决策中的应用

为了实现面向精益生产的制造系统中的工艺与调度并行设计,在工艺规划决策中,建立了面向精益生产的工艺规划与调度集成模型.在考虑设备负载平衡以及完成加工任务时间最短的条件下,设计了一种离散模拟退火粒子群优化(Particle Swarm Optimization based on Simulated Annealing,SAPSO)算法,并行设计及优化零件的工艺方案和调度方案,并最终获得其最优工艺方案及与之相对应的优化调度方案.通过对10台设备10种零件的示例仿真验证了算法的有效性.     

Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency

The dynamic rescheduling problem in flexible manufacturing systems (FMS) has historically emphasized the schedule efficiency. However, energy consumption is a basic need for different purposes in manufacturing systems around the world. This paper proposes an innovative approach to study the dynamic scheduling problem in FMS, taking the objectives of minimum or maximum energy consumption into account. A new goal programming mathematical model, which considers the energy consumption and the schedule efficiency simultaneously, is presented for solving this problem. A rescheduling method based on the genetic algorithm is introduced to address the dynamic rescheduling problem in FMS. A period policy is selected to deal with the dynamic feature of the problem. Numerical experiments have been designed to test and evaluate the performance of the proposed model. The experimental results show that the minimum energy consumption model can save the energy consumption and enhance the schedule efficiency.     

Integration of process planning and scheduling in a job shop environment

Today’s manufacturing systems are striving for an integrated manufacturing environment. To achieve truly computer-integrated manufacturing systems (CIMS), the integration of process planning and production scheduling is essential. This paper proposes a framework for integration of process planning with production scheduling in a job shop environment for axisymmetric components. Based on the design specifications of incoming parts, feasible process plans are generated taking into account the real time shop floor status and availability of machine tools. The scheduling strategy prioritizes the machine tools based on cost considerations.     

Simulation for maintenance of an FMS: an integrated system of maintenance and decision-making

Flexible manufacturing systems (FMSs) are designed to produce a variety of different part types with high machine utilisation, so the maintenance technique is necessary and very important in an FMS. This paper discusses the maintenance problem in an FMS and its simulation, analyses the maintainability of a real FMS shop, and presents the architecture of an integrated system of maintenance and decision-making/scheduling for manufacturing shop control. The modelling and simulation of the maintenance activities in the shop are shown. The simulation program is written in SLAM II and the special subroutines are realised in Fortran. The interest is directed towards the use of the method of simulation for maintenance and decision-making in FMSs.     

基于仿真的车间作业计划系统研究

基于仿真的车间作业计划系统可以增加系统的透明度,提高计划执行率.针对多品种小批量的生产模式,在eM-Plant软件上提出一种基于离散事件的仿真方案,开发了相应的系统模型和仿真算法,运行仿真系统,得到调度结果,验证所编制的车间计划的可行性.采用上述仿真系统就某军工车间一计划周期的生产任务进行仿真,根据不同优先级的仿真算法,得到合理的车间作业计划.实例证明本系统提高了车间作业计划编制有效性.     

FMS调度与控制的统一建模与集成设计

分析了FMS调度与控制统一建模与集成设计的必要性，从FMS运行过程的IDEF0功能建模入手，建立了基于模块化彩色赋时Petri网（CTPN）的FMS调度与控制的统一模型。该模型不仅可描述FMS的调度问题，而且能表达FMS的运行控制逻辑与仿真，从而为FMS控制系统的集成设计奠定了基础。为简化FMS控制系统的设计，提出了虚拟工作站的概念与方法，将FMS控制器的部分控制功能下放到低层的工作站控制层。基于上述CTPN统一模型，开发了FMS调度与控制的集成设计原理和方法。     

Scheduling of Flexible-Sequenced Process Plans in a Mould Manufacturing Shop

Process planning and scheduling used to be two very separate processes. However, owing to the recognition of the intricate relationship between them, recent work has focused on integrating the two processes. The use of flexible process plans in scheduling allows more flexibility in production and thus gives substantial cost savings. It also increases the solution space of the optimisation problem and makes it more critical to have an effective optimisation algorithm than for traditional scheduling problems. This paper describes a process-planning and scheduling system that makes use of the branch and bound approach to optimise priority weighted earliness of jobs scheduled in a mould manufacturing shop. Instead of consideing a flexible manufacturing system, this paper focuses on the demands of less integrated factories, which are especially typical of mould manufacturing shops. The layout of the system, the methodology of the algorithm and effectiveness of performance measures for real industrial use are discussed in the paper.     

A dispatching rule-based hybrid genetic algorithm focusing on non-delay schedules for the job shop scheduling problem

Job shop scheduling is an important decision process in contemporary manufacturing systems. In this paper, we aim at the job shop scheduling problem in which the total weighted tardiness must be minimized. This objective function is relevant for the make-to-order production mode with an emphasis on customer satisfaction. In order to save the computational time, we focus on the set of non-delay schedules and use a genetic algorithm to optimize the set of dispatching rules used for schedule construction. Another advantage of this strategy is that it can be readily applied in a dynamic scheduling environment which must be investigated with simulation. Considering that the rules selected for scheduling previous operations have a direct impact on the optimal rules for scheduling subsequent operations, Bayesian networks are utilized to model the distribution of high-quality solutions in the population and to produce the new generation of individuals. In addition, some selected individuals are further improved by a special local search module based on systematic perturbations to the operation processing times. The superiority of the proposed approach is especially remarkable when the size of the scheduling problem is large.     

分段式车间作业调度算法

车间作业调度问题是制造系统运筹技术、管理技术与优化技术发展的核心。本文对离散作业型 (Job Shop)车间中的作业调度问题做了探讨 ,并根据离散作业调度的阶段性提出了基于作业状态空间的逐段式车间作业调度算法。通过对一个实际车间作业调度仿真比较 ,此算法运算速度比最短加工时间 (SPT)和最少工作量剩余 (L WR)算法快 ,其调度结果具有可执行性     

Mathematical modelling of multi-objective job shop scheduling with dependent setups and re-entrant operations

For the last thirty years, in job shop scheduling, the setup times of operations have been either ignored or combined with their corresponding processing times, independent setups, to simplify the work of model construction. However, in practice, the setup times of an operation are usually considered be sequence-dependent. Moreover, the performance of a scheduling system is not evaluated to satisfy a single objective, but to obtain a trade-off solution regarding multiple objectives. Therefore, this paper investigates job shop scheduling problems with re-entrant operations where the setup times, which vary according to the preceding operation which is processed on the same machine, and which can be separated from their corresponding processing times, cannot be omitted. Three practical performance measures – minimum total job flow time, minimum total job tardiness and minimum machine idle time – are considered. An integer programming model is first developed to optimise each single objective and an acceptable trade-off schedule, which makes use of a multiple-decision-making technique, the global criterion method, is obtained by evaluating three objectives simultaneously .     

Scheduling hybrid flow shop with sequence-dependent setup times and machines with random breakdowns

Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems which have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real life industries, a machine can be unavailable for many reasons, such as unanticipated breakdowns, i.e., stochastic unavailability, or due to a scheduled preventive maintenance where the periods of unavailability are known in advance, i.e., deterministic unavailability. This paper deals with the hybrid flow shop scheduling problems in which there are sequence-dependent setup times, commonly known as the SDST, and machines which suffer stochastic breakdown to optimize objectives based on expected makespan. This type of production system is found in industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacture. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. The genetic algorithm can be used to tackle complex problems and produce a reasonable manufacturing schedule within an acceptable time. This paper describes how we can incorporate simulation into genetic algorithm approach to the scheduling of a SDST hybrid flow shop with machines that suffer stochastic breakdown. An overview of the hybrid flow shops and scheduling under stochastic unavailability of machines are presented. Subsequently, the details of incorporated simulation into genetic algorithm approach are described and implemented. Consequently, the results obtained are analyzed with Taguchi experimental design.