Dynamic scheduling of flexible manufacturing system using heuristic approach

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.

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.

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.

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

A Novel Optimal Online Scheduling Scheme for Flexible Manufacturing Systems

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. >

A distributed approach to job scheduling in a Flexible Manufacturing System

This paper presents a knowledge-based scheduling approach based on the problem-solving techniques developed in artificial intelligence. The approach is based on three key techniques. The first is the pattern-directed inference technique to capture the dynamic nature of the scheduling environment. The second is the non-linear planning technique to coordinate manufacturing processes and resource assignments. The third technique is the A∗ search algorithm to expedite the searching procedure. It models the scheduling process by state-space transitions; the job routing is obtained through selecting a sequence of scheduling operators guided by heuristics. Keeping track of the manufacturing system by a symbolic world model, this approach is adaptive to such environmental changes as new job arrivals and machine breakdowns, suitable for making real-time scheduling decisions.

The objective of this study is to develop a neural network based decision support system for selection of appropriate dispatching rules for a real-time manufacturing system, in order to obtain the desired performance measures given by a user, at different scheduling periods. A simulation experiment is integrated with a neural network to obtain the multi-objective scheduler, where simulation is used to provide the training data. The proposed methodology is illustrated on a flexible manufacturing system (FMS) which consists of several number of machines and jobs, loading/unloading stations and automated guided vehicles (AGVs) to transport jobs from one location to another.

A scheme for the scheduling of flexible manufacturing systems (FMSs) have been developed. It integrates neural networks, parallel Monte-Carlo simulation, genetic algorithms, and machine learning. Modular neural networks are used to generate a small set of attractive plans and schedules from a larger list of such plans and schedules. Parallel Monte-Carlo simulation predicts the impact of each on the future evolution of the manufacturing system. Genetic algorithms are utilized to combine attractive alternatives into a single best decision. Induction mechanisms are used for learning and simplify the decision process for future performance. The development of a modular neural network architecture for candidate rule selection for a FMS cell is investigated. A scheduling example illustrates the scheme capabilities including speed, adaptability, and computational efficiency. >

Process planning and scheduling are important manufacturing planning activities which deal with resource utilization and time span of manufacturing operations. The process plans and the schedules generated in the planning phase shall be modified in the execution phase due to the disturbances in the manufacturing systems. This paper deals with a multi-agent architecture of an integrated and dynamic system for process planning and scheduling for multi jobs. A negotiation protocol is discussed, in this paper, to generate the process plans and the schedules of the manufacturing resources and the individual jobs, dynamically and incrementally, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks discussed in the previous paper, and the suitable process plans and schedules are searched and generated to cope with both the dynamic status and the disturbances of the manufacturing systems. We initiatively combine the heuristic search algorithms of the process plan networks with the negotiation protocols, in order to generate suitable process plans and schedules in the dynamic manufacturing environment. A simulation software has been developed to carry out case studies, aimed at verifying the performance of the proposed multi-agent architecture.

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.

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.

This paper addresses the integrated flexible job shop and operators scheduling problem, introducing shift-based constraints on operators. We investigate how the advanced modeling and solution techniques, specifically, Mixed-Integer Programming (MIP) and Constraint Programming (CP) perform on this intricate scheduling problem. We test the effectiveness of both the MIP and CP models on an illustrative example as well as on a set of larger instances and draw conclusions, which indicate the need to integrate these two models into some approximation scheme to tackle large-scale instances for this complex problem.