Development and evaluation of feature-focused dynamic routing policy

A flexible manufacturing system (FMS) is a highly automated, complex system. Simulation is a well-proven method to design or analyze an FMS. Deployment of a radio frequency identification (RFID) system in FMS produces large volumes of RFID data streams, which provide valuable information to improve the operation of FMS. Different frameworks are presented in this paper regarding the use of RFID data streams in an FMS simulation. Simulations are performed with AutoMod software. Related technical details are also presented. The paper’s structure is complied as the following steps: step 1, the introduction of AMHS, FMS and AutoMod; step 2, FMS simulation using AutoMod software; step 3, simulation frameworks driven by RFID data streams; step 4, conclusion.

This paper describes the ways of using program tools for design and simulation of flexible manufacturing systems (FMS) for the purpose of teaching the undergraduate course Flexible Manufacturing Systems. The concept of the course is based on the synergism of theoretical knowledge and practical hands-on experience gained by studying numerous problem cases, using common tools for description of FMS (Petri nets, max-plus algebra and matrix modeling) and by working with more specialized program tools for FMS design and simulation. An emphasis is given to the description o the program tool FlexMan that enables elegant setting of an FMS configuration, supports automatic generation of a matrix-based FMS description and consequently, enables effective off-site design and simulation of the FMS by using virtual reality models and Internet-related technologies.

Flexible Manufacturing Systems (FMS) provide capabilities to accommodate flexibility requirements in production. Such system due to the levels of flexibility are complex to reconfigure optimally. Simulation is one of the tools that provide a platform for supporting the FMS reconfiguration without breaking optimal production set-up. For the simulation to be successful it is important to appropriately design the simulated environment as well as provide quality data inputs. As FMS are usually driven by production line controllers (PLC), the behaviour of elements in the system can be difficult to be fully understood. The quality of data fed in to the simulation affects the quality of outputs which in consequence translates to the trust that the simulation is reliable source of analysis. This paper presents the approach of data collection from the shop floor aiding FMS simulation in scenarios where it is difficult to understand the system behaviour. Also, case study of a manual handling robot in the FMS is presented to demonstrate obtaining quality data from the production shop floor.

The paper deals with the problem of the development of 3D graphic representation and simulation of flexible manufacturing systems. A 3D graphic model of a plant can improve the work performed in different control processes during the system life cycle, such as commissioning, monitoring and maintenance. These aspects become more relevant dealing with complex systems. Furthermore, for flexible manufacturing systems, mechanisms to manage the system modularity in the development of the 3D graphic representations are needed. A key factor which is bringing visibility to 3D techniques is the technical improvement of personal computer allowing to have relatively cheap, high graphical performances. Consequently, during last few years, several 3D visualization tools appeared on the marked. Within the paper, two existing simulation tools, 3D create and day dream daemon, are analyzed and compared. Both, have successively been used for the development of a simulation environment for the validation of control functions of a real manufacturing system.

Software developers often meet the problem of creating new components of an application that someone probably has already produced. Without having effective reuse tools usually it is more natural to create new components from scratch than to seek for useful elements in other programmes and/or systems.In the field of simulation of Flexible Manufacturing Systems (FMS) this issue often occurs, when different systems with some similar features have to be managed. FMSs are highly automated production systems with minimal human control. FMS usually consist Flexible Manufacturing Cells (FMCs). An FMC is considered as a group of one or more machine tools, robots, adjacent mechanical elements, tool and fixture supporting workstations and an internal transport system.The components of different FMSs and FMCs are the same type of machine tools, transfer equipment, etc. In the relevant aspects they usually differ from each other only in their amounts and working parameters. This fact itself breeds the idea of reuse of FMS and FMC simulation model elements.Simulation can be used for analysing the behaviour of an existing or would-be system. It is useful for calculating utilisation statistics, finding bottlenecks, pointing out scheduling errors, and even for creating manufacturing schedules. Simulation is a very effective technique for dynamic analysis, but it is short of optimising ability. A satisfactory solution can be obtained through modelling and simulating alternative approaches with iteration.There are many concepts, object classes, and lines of source code created, that could be reused in the new applications. Usage of reuse methodology and practice will reduce our efforts in developing new systems.In this paper the reuse of the components of simulation models in a hybrid simulation and scheduling system for new simulation applications will be introduced. This simulation-scheduler system has been implemented as a combination of a traditional simulation language (SIMAN/Cinema) and an intelligent expert environment (G2). The design methodology used for model development is based on the object-oriented Rational Rose CASE tool and on a use-case model.

SummaryIncreasing pressures from a variety of directives and standards have caused manufacturing enterprises to consider and implement energy assessment and energy performance goals—to improve both their economic and their environmental performance. Flexible manufacturing systems (FMSs) pose a challenge for energy management because of their complexity and the difficulty of data collection. FMSs allow manufacturers to adjust to customers’ requirements and meet changing demands, which makes it possible for manufacturers to respond quickly to the market. To address this challenge, a practical energy model for machining systems based on colored timed Petri nets (CTPNs) is proposed. In this article, the energy consumption of FMS are first analyzed. Then, an energy model of FMSs based on CTPNs is proposed in which the uncertainty of task assignment and volatility of operation time are treated by Petri net (PN) functions so that data on the consumption of FMSs can be collected automatically. Finally, through the case study of an FMS with two jobs, the proposed energy model is simulated and the simulation results with colored PN tools are shown to be significant in practice.

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.

Flexible manufacturing systems (FMS) are characterized by their highly dynamic environment, manifested in the frequent occurrence of disturbances that necessitate the continuous update of the production schedule. In practice, these disturbances are handled manually which results in low productivity and fails to utilize the high potential of automation possessed by the flexible resources on the shop floor of FMS. In this paper, we introduce an agent-based method for automating disturbance handling for FMS. The proposed method is designed to handle the different types of disturbances as machine breakdown and rush orders. A case study of a real FMS in Egypt is described and used to test the feasibility of the proposed method. Test results show a very good potential of the proposed method in automating disturbance handling while optimizing two measures, namely makespan and machine utilization and maintaining good reactivity.

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.

Modeling and Simulation of Flexible Manufacturing Systems Controlled by Petri Nets

Tools for Design and Optimal Control of Production Line and Flexible Manufacturing Systems

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.

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.

The modeling of flexible manufacturing systems (FMS) under uncertainty and evaluation of a rule base for online scheduling is considered. To represent uncertain events in FMS, such as failure of machine tools, repair time and processing time, apply continuous-time and discrete-time stochastic Petri nets with hierarchical structure to constructing the FMS model. To obtain an efficient schedule of FMS with on-line real-time basis, the authors construct a rule base and evaluate the performance of the rule base using the FMS simulation system. >

Presents the master production scheduling (MPS) problem of a flexible manufacturing system (FMS). Earliness/tardiness production scheduling and planning (ETPSP) is one of the solutions used to integrate MRP and JIT effectively. Previous researches on ETPSP have only applied to problems of single/parallel machines with earliness and tardiness penalties on a common due date and capacity. Proposes a revised ETPSP for the purpose of developing an MPS that can fit into the FMS environment where a multiple machine capacity on multi‐processes and batch sizes is included. Outlines the application of an enhanced ETPSP method using a genetic algorithm (GA) solution to solve a multi‐product FMS production problem. Shows that the use of the improved ETPSP model can represent a real life FMS environment and that a solution can be effectively and efficiently obtained using the GA approach.