A Hybrid Approach to Flexible Manufacturing Systems Scheduling Using Neural and Symbolic Processing

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

In this paper, existing flexible manufacturing system (FMS) scheduling procedures are classified and reviewed. First, a classification scheme is provided that is based on key factors such as the FMS type, the mode of system operation, the nature of the demands placed on the system, the scheduling environment, and the responsiveness of the system to disturbances. The choice of appropriate scheduling criteria is discussed. Differences between conventional job shops and FMSs, and the eiVects of these differences on potential scheduling procedures are provided. The classification, comparisons, and FMS scheduling reviews aim to be useful to vendors, users, and researchers. We point out specific areas which appear to have been overlooked. Future research directions are also indicated.

In this study, a new and simple alternate routing heuristics, which would be superior to the conventional routing strategies in terms of various system performance measures and easier to apply practically, was presented. Because of the high investment costs of FMSs, it is also definitely worth choosing the best operating policy and system configuration by analyzing the system model, and adapting to changes over time. In future research, the heuristic rule base will be extended to include other supplementary constraints such as dynamic tool allocation and sequence dependent setup times. For practical implementation of the proposed decision support system, additional research in the area of human interfaces could be useful to develop more user friendly system which automatically constructs simulation model of the system from the knowledge base of a production system.

The authors discuss the modeling of flexible manufacturing systems (FMSs) under uncertainty and evaluate a rule base for online scheduling. To represent uncertain events in an FMS, such as failure of machine tools, repair time, and processing time, they develop continuous-time and discrete-time stochastic Petri nets with hierarchical structures for constructing the FMS model. For obtaining an efficient schedule for the FMS with an online real-time basis, they construct a rule base and evaluate its performance using the FMS simulation system proposed. >

Control of flexible manufacturing systems (FMS) requires the complex interaction of two components: (1) computers to perform automated control and routing activities, and (2) humans to supervise the automation, to monitor system flows and outputs, and to intervene to diagnose and either correct or compensate for unanticipated events. Current academic FMS scheduling research has focused mainly on the first component in the control loop, development of scheduling algorithms for optimization and control. Here, the second component is included in both the definition of the FMS control problem and the corresponding control approach. A more realistic definition of the FMS control environment is presented using a supervisory control framework. Within this context, the concept of aiding a human operator who supervises the predominantly automated FMS operations is developed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A global MILP model for FMS scheduling

In this paper, we propose an integrated approach of inductive learning and competitive neural networks for developing multi-objective flexible manufacturing system (FMS) schedulers. Simulation and competitive neural networks are applied sequentially to extract a set of classified training data which is used to create a compact set of scheduling rules through inductive learning. The FMS scheduler can assist the operator to make decisions in real time, while satisfying multiple objectives desired by the operator. A simulation-based experiment is performed to evaluate the performance of the resulting scheduler.

This paper deals with the problem of production scheduling of a loop type of Flexible Manufacturing Systems (FMS). This system consists of the multipurpose machines which are arranged around the loop conveyor. Generally, the property of this scheduling problem of the FMS is that each machine is able to perform many different types of operations. The problem considered here is the two-stage one:first to select an appropriate machine route and second to determine a better loading sequence for that route. A two-stage algorithm based on a heuristic approach is proposed. A simple example is exhibited to demonstrate the validity of the proposed algorithm.

This paper describes a study which explores human decision-making abilities in scheduling and dispatching of a flexible manufacturing system (FMS) An experiment is described, using an FMS, in which subjects make scheduling and dispatching decisions using a real-time interactive computer-simulation based system. The experimental results demonstrate that human decision-making is superior to general dispatching rules. An explanation of these results and an analysis of subjects' behaviour is presented in the light of information obtained from verbal protocol data

The problem of decreasing production costs through an appropriate management of available resources is fundamental in the field of industrial production. The performance of a flexible manufacturing system (FMS), not properly supported by an efficient resource management strategy, may be drastically limited, & the advantages derived from its flexibility in terms of production costs may suffer a sharp reduction. Furthermore, an FMS is composed of a large number of components, thus making the identification of a correct strategy for the management more difficult. In this paper a heuristic based genetic algorithm is proposed for generating optimized production plans in flexible manufacturing systems. The ability of the system to generate alternative plans following part-flow changes & unforeseen situations is particularly stressed (dynamic scheduling). The Key-point objective is the reduction of machine idle time obtained by an optimized evolutionary strategy needed to reach the optimal schedule in complex manufacturing systems.

A method of flexible scheduling for flexible manufacturing systems

Minimization of maximum production time (Makespan) in Flexible Manufacturing Systems (FMS) scheduling problems is an important objective and may result in good system performances. To realize a makespan close to the global minimum production time is a suitable goal for scheduling But answering the question how close this goal can be approached is a very complicated problem. In this paper bottleneck scheduling and Lot Streaming (LS) are used for a Job shop Scheduling Problem (JSP). The new contribution of the paper is applying a Brute Force Method (BFM) to LS. The effect of setup times is considered and numerical example is given.

Dynamic workload balancing to part input sequencing for integrated scheduling decision-making of flexible manufacturing systems

Genetic algorithms for match-up rescheduling of the flexible manufacturing systems

This paper is concerned with scheduling in flexible manufacturing systems (FMSs) using a fuzzy logic (FL) approach. Four fuzzy input variables: machine allocated processing time, machine priority, due date priority and setup time priority are defined. The job priority is the output fuzzy variable, showing the priority status of a job to be selected for next operation on a machine. The model will first select the machines and then assign operations based on a multi-criteria scheduling scheme. The performance of the approach is compared against established methods reported in the literature. The performance measures considered average machine utilisation, meeting due dates, setup times, work in process and mean flow times. The test results demonstrate the superiority of the fuzzy logic approach in most performance measures.