A pattern-directed approach to flexible manufacturing: A framework for intelligent scheduling, learning, and control

The planning, scheduling, and control of manufacturing systems can all be viewed as problem-solving activities. In flexible manufacturing systems (FMSs), the computer program carrying out these problem-solving activities must additionally be able to handle the shorter lead time, the flexibility of job routing, the multiprocessing environment, the dynamic changing states, and the versatility of machines. This article presents an artificial intelligence (AI) method to perform manufacturing problem solving. Since the method is driven by manufacturing scenarios represented by symbolic patterns, it is referred to as pattern-directed. The method is based on three AI techniques. The first is the pattern-directed inference technique to capture the dynamic nature of FMSs. The second is the nonlinear planning technique to construct schedules and assign resources. The third is the inductive learning method to generate the pattern-directed heuristics. This article focuses on solving the FMS scheduling problem.In addition, this article reports the computation results to evaluate the utility of various heuristic functions, to identify important design parameters, and to analyze the resulting computational performance in using the pattern-directed approach for manufacturing problem-solving tasks such as scheduling.     

A Heuristic Search Approach Using Approximate Solutions to Petri Net State Equations for Scheduling Flexible Manufacturing Systems

This paper proposes a new heuristic search approach based on an analytic theory of the Petri net state equations for scheduling flexible manufacturing systems (FMSs) with the goal of minimizing makespan. The proposed method models an FMS using a timed Petri net and exploits approximate solutions of the net's state equation to predict the total cost (makespan) from the initial state through the current state to the goal. That is, the heuristic function considers global information provided by the state equation. This makes the method possible to obtain solutions better than those obtained using prior works (Lee and DiCesare, 1994a, 1994b) that consider only the current status or limited global information. In addition, to reduce memory requirement and thus to increase the efficiency of handling larger systems, the proposed scheduling algorithm contains a procedure to reduce the searched state space.     

The State of the Art in Simulation Study on FMS Scheduling: A Comprehensive Survey

Scheduling of flexible manufacturing systems (FMSs) has been one of the most attractive areas for both researchers and practitioners. A considerable body of literature has accumulated in this area since the late 1970s when the first batch of papers was published. A number of approaches have been adopted to schedule FMSs, including simulation techniques and analytical methods. Numerous articles can be found on each of these approaches. This paper reviews scheduling studies of FMSs which employ simulation techniques as an analysis tool, since simulation is the most widely used tool for modelling FMSs. Scheduling methodologies are categorised into simulation of general scheduling studies, multi-criteria scheduling approaches, and artificial intelligence (AI) approaches in FMSs. Comments on the publications, and suggestions for further research and development are given.     

Lot splitting scheduling procedure for makespan reduction and machine capacity increase in a hybrid flow shop with batch production

The general definition of the hybrid flow shop (HFS) environment is a set of S?≥?2 production stages where at least one of these stages includes more than one machine, which can process one job at a time. A job can be defined as several operations to be performed by none, one, or more machines at each stage. Usually, these jobs are completed in some sequence between the different production stages, and in the case of setup activities, products are grouped in batches with buffers of work in progress between different production stages. Today, flexible production systems permit in some instances to relax job precedence constrains with alternative process cycles and to group together different batches of similar products in order to reduce setup activity incidence. On the other hand, the availability of multiple parallel machines in a single production stage makes it possible to split the lot size between different resources. This paper aims to solve the HFS scheduling problem in a flexible multistage batch production system, offering a heuristic procedure, to minimize the production makespan and increase the productive capacity utilization using a batch aggregation/splitting strategy while introducing the “workload leveling function” concept. The results are compared with other important scheduling rules widely accepted in the industry and made part of an industrial application. The company used as a test sample is an Italian rotor shaft manufacturer. The final result is illustrated to validate the proposed heuristics.     

Multiple-Objective Scheduling for the Hierarchical Control of Flexible Manufacturing Systems

This paper presents a hierarchical approach to scheduling flexible manufacturing systems (FMSs) that pursues multiple performance objectives and considers the process flexibility of incorporating alternative process plans and resources for the required operations. The scheduling problem is solved at two levels: the shop level and the manufacturing system level. The shop level controller employs a combined priority index developed in this research to rank shop production orders in meeting multiple scheduling objectives. To overcome dimensional complexity and keep a low level of work-in-process inventory, the shop controller first selects up to three production orders with the highest ranking as candidates and generates all possible release sequences for them, with or without multitasking. These sequences are conveyed to the manufacturing system controller, who then performs detailed scheduling of the machines in the FMS using a fixed priority heuristic for routing parts of multiple types while considering alternative process plans and resources for the operations. The FMS controller provides feedback to the shop controller with a set of suggested detailed schedules and projected order completion times. On receiving these results, the shop controller further evaluates each candidate schedule using a multiple-objective function and selects the best schedule for execution. This allows multiple performance objectives of an FMS to be achieved by the integrated hierarchical scheduling approach.     

基于蚁群算法的模具制造动态调度研究

为解决模具制造动态调度问题,建立了动态调度系统。该系统利用蚁群算法和优先分配启发式算法相结合的调度算法,解决具有工件约束的模具零件的调度问题。该算法首先由蚁群算法确定模具零件各工序所用加工机床,然后利用优先分配启发式算法确定在同一台机床上加工的各零件的先后顺序。考虑动态调度的实时性,提出了局部更新和全局更新相结合的、基于滑动窗口机制的动态调度方法。对于发生频率高但对调度计划执行影响不大的扰动事件采用局部更新策略,反之则采用全局更新策略,在保证获得近优解的同时提高了动态调度的效率。     

A hybrid genetic algorithm to minimize makespan for the single batch machine dynamic scheduling problem

This paper considers a single batch machine dynamic scheduling problem, which is readily found in the burn-in operation of semiconductor manufacturing. The batch machine can process several jobs as a batch simultaneously, within the capacity limit of the machine, and the processing time is represented by the longest processing time among all jobs in a batch. For a single batch machine problem with arbitrary job release time, we proposed an improved algorithm (merge-split procedure) to refine the solution obtained by the LPT-BFF heuristic, and two versions of a hybrid genetic algorithm (GA) are introduced in this paper. Each version of the hybrid GA diversifies job sequences using the GA operators in stage 1, forms batches in stage 2, and finally sequence the batches in stage 3. The difference is that merge-split procedures are involved in the second version of the hybrid GA. Computational experiments showed that the hybrid GA would obtain satisfactory average solution quality and the merge-split procedures would be good at reinforcing the solution consistency of the hybrid GA.     

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.     

Near optimal manufacturing flow controller design

Flow control of flexible manufacturing systems (FMSs) addresses an important real-time scheduling requirement of modern manufacturing facilities, which are prone to failures and other controllable or stochastic discrete events affecting production capacity, such as change of setup and maintenance scheduling. Flow controllers are useful both in the coordination of interconnected flexible manufacturing cells through distributed scheduling policies and in the hierarchical decomposition of the planning and scheduling problem of complex manufacturing systems. Optimal flow-control policies are hedging-point policies characterized by a generally intractable system of stochastic partial differential equations. This article proposes a near optimal controller whose design is computationally feasible for realistic-size systems. The design exploits a decomposition of the multiple-part-type problem to many analytically tractable one-part-type problems. The decomposition is achieved by replacing the polyhedra production capacity sets with inscribed hypercubes. Stationary marginal densities of state variables are computed iteratively for successive trial controller designs until the best inscribed hypercubes and the associated optimal hedging points are determined. Computational results are presented for an illustrative example of a failureprone FMS.     

一种基于Petri网的顺序共享资源AMS＊s

Ｐｅｔｒｉ网控制器自动设计及控制程序自动生成方法的研究是自动制造系统ＡＭＳｓ和柔性制造系统ＦＭＳｓ领域研究的热点问题之一〔２～６〕。本文以ＡＭＳｓ计划和调度产生的生产序列ＰＳ和资源需求序列ＲＲＳ为基础，为顺序共资源ＡＭＳｓ设计了一种规格说明语言，以自动生成ＡＭＳｓＰｅｔｒｉ网模型的矩阵形式，并给出了转换步骤。由于引入了资源状态反馈，故生成的Ｐｅｔｒｉ网模型是无死锁的。最后，用一个实例说明了设计过程。     

Dynamic control of flexible manufacturing systems

Flexible manufacturing systems (FMSs) are a relatively new technological and organisational approach to helping companies respond to real-time marketing conditions for their production. Under a proposal of the National Bureau of Standards the FMSs are subdivided into virtual manufacturing cells in a dynamic manner, on the basis of group technology.A method of dynamic optimisation for the design of manufacturing processes, capacity balancing and checking, and also production scheduling or rescheduling in virtual manufacturing cells is described. It can be used during real-time production control in FMSs.     

A distributed knowledge-based approach to flexible automation: The contract net framework

This article applied distributed artificial intelligence to the real-time planning and control of flexible manufacturing systems (FMS) consisting of asynchronous manufacturing cells. A knowledge-based approach is used to determine the course of action, resource sharing, and processor assignments. Within each cell there is an embedded automatic planning system that executes dynamic scheduling and supervises manufacturing operations. Because of the decentralized control, real-time task assignments are carried out by a negotiation process among cell hosts. The negotiation process is modeled by augmented Petri nets —the combination of production rules and Petri nets—and is excuted by a distributed, rule-based algorithm.     

Daily planning and scheduling system for the EDS process in a semiconductor manufacturing facility

Operations management in a complex manufacturing environment is practiced hierarchically: production planning and scheduling in sequence since the integrated approach is not efficient and practical. Production targets and allocation to the equipment are determined in the planning procedure by considering the only the critical factors, and the detailed scheduling is determined with more information needed to accomplish the production targets provided in the planning step. There is always, therefore, a gap between the planning and scheduling procedure, and some control parameters or factors are used to control those gaps. In this paper an approach connecting two procedures is suggested, which can be applied in the EDS (electrical die sorting) or the probe process in the semiconductor manufacturing. The EDS process requires very flexible operation management since the manufacturing processing time is relatively short and the supply of upstream fabrication and the demand of downstream assembly have to be met simultaneously. Daily planning and scheduling procedure are modeled to minimize the tester change-over within the daily target. Because of the relatively long setup change-over time, daily planning and scheduling have to be performed so that the time needed for the device and probe card change is minimized as possible. Mathematical programming is suggested for the problem, and the modified model is developed which can be solved in a practical computational time on a daily base. The scheduling heuristic with the planning data obtained from the suggested model, is designed, and their performance is evaluated through the computational experiment.     

Experimental investigation of an FMS due-date scheduling problem: Evaluation of machine and AGV scheduling rules

Although extensive research has been conducted to solve design and operational problems of automated manufacturing systems, many of the problems still remain unsolved. This article investigates the scheduling problems of flexible manufacturing systems (FMSs). Specifically, the relative performances of machine and automated guided vehicle (AGV) scheduling rules are analyzed against various due-date criteria. First, the relevant literature is briefly reviewed, and then the rules are tested under different experimental conditions by using a simulation model of an FMS. The sensitivity to AGV workload, buffer capacity, and processing-time distribution is also investigated to assess the robustness of the scheduling rules.     

A heuristic algorithm for the loading problem in flexible manufacturing systems

The paper considers the loading problem in flexible manufacturing systems (FMSs). This problem involves the assignment to the machine tools of all operations and associated cutting tools required for part types that have been selected to be produced simultaneously. The loading problem is first formulated as a linear mixed 0–1 program with the objective to minimize the greatest workload assigned to each machine. A heuristic procedure is presented in which an assignment of operations to machine tools is obtained by solving a parameterized generalized assignment problem with an objective function that approximates the use of tool slots required by the operations assigned to the machines. The algorithm is coded in FORTRAN and tested on an IBM-compatible personal computer. Computational results are presented for different test problems to demonstrate the efficiency and effectiveness of the suggested procedure.     

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.     

A modular simulator for design,planning, and control of flexible manufacturing systems

Flexible manufacturing systems are designed to produce a variety of different part types with high machine utilisation, short lead times and little work-in-progress inventory. Simulation is an efficient tool to verify design concepts, to select machinery, to evaluate alternative configurations and to test system control strategies of an FMS. This paper discusses a general-purpose, user-oriented discrete simulator (the Modular FMS Simulator) which can be used for design as well as for operation and scheduling of FMSs. The package can be implemented in different hierarchical steps of FMS production planning. It is also a useful tool in validating the results of analytical models or heuristic procedures developed for FMS problems. This package contains features for the system hardware and the control hierarchy. The model can study multiple part families, various station types, different number of work-in-process buffers and carts and almost any system layout. It is also possible to analyse the performance of the system. The package contains a set of decision rules from which the user can make his choice.     

Integrated model for production planning and scheduling in a supply chain using benchmarked genetic algorithm

Production planning and scheduling is one of the core functions in manufacturing systems. Furthermore, this task is drawing even more attention in supply chain environments as problems become harder and more complicated. Most of the traditional approaches to production planning and scheduling have adopted a multi-phased, hierarchical and decompositional approach. This traditional approach does not guarantee a feasible production schedule. And even when capacity constraints are satisfied, it may generate an expensive schedule. In order to overcome the limitations of the traditional approach, several previous studies tried to integrate the production planning and scheduling problems. However, these studies also have some limitations, due to their intrinsic characteristics and the method for incorporating the hierarchical product structure into the scheduling model. In this paper we present a new integrated model for production planning and scheduling for multi-item and multi-level production. Unlike previous lot sizing approaches, detailed scheduling constraints and practical planning criteria are incorporated into our model. We present a mathematical formulation, propose a heuristic solution procedure, and demonstrate the performance of our model by comparing the experimental results with those of a traditional approach and optimal solution.     

Object-oriented graphical modeling of FMSs

Presented in the article is a method for constructing a graphical model of an FMS by using a new modeling tool called JR-net (Job Resource relation-net). JR-net is an object-oriented graphical tool for modeling automated manufacturing systems (AMSs), such as FMSs, FASs, and AS/RSs. As with the object-oriented modeling paradigm of Rumbaugh et al. (1991), the JR-net modeling framework supports the three stages of models: static layout model (object model); job flow model (functional model); and supervisory control model (dynamic model). In this article, the existing JR-net structure (Park 1992, Han et al., 1995) is extended further to make it a graphical tool for FMS modeling. Using the extended JR-net, a step-by-step procedure for constructing a graphical model of FMSs is presented. Also addressed are issues of classifying FMSs in terms of their generic functions and of utilizing the JR-net model of FMSs.     

Mathematical model and parallel genetic algorithm for hybrid flexible flowshop lot streaming problem

Lot streaming is the technique of splitting a given job into sublots to allow the overlapping of successive operations in multi-stage manufacturing systems thereby reducing production makespan. Several research articles appeared in literature to solve this problem and most of these studies are limited to pure flowshop environments where there is only a single machine in each stage. On the other hand, because of the applicability of hybrid flowshops in different manufacturing settings, the scheduling of these types of shops is also extensively studied by several authors. However, the issue of lot streaming in hybrid flowshop environment is not well studied. In this paper, we aim to contribute in bridging the gap between the research efforts in flowshop lot streaming and hybrid flowshop scheduling. We propose a mathematical model and a genetic algorithm for the lot streaming problem of several jobs in multi-stage flowshops where at each stage there are unrelated parallel machines. The jobs may skip some of the stages, and therefore, the considered system is a complex generalized flowshop. The proposed genetic algorithm is executed on both sequential and parallel computing platforms. Numerical examples showed that the parallel implementation greatly improved the computational performance of the developed heuristic.