Necessary Optimality Conditions for Scheduling Problems

Deadlock/livelock problems can cause catastrophic results in flexible manufacturing systems (FMS) by forcing them to stop production processes at an unpredictable stage of production. Therefore, they must be handled properly in order to obtain flawless production in FMS. Petri net (PN) based deadlock/livelock studies for FMS have been widely carried out to obtain live PN models. When a PN model is live, this ensures that the modelled FMS is deadlock-free. Firstly, an uncontrolled (plant) PN model is constructed for the FMS suffering from deadlocks/livelocks. Then reachability graph (RG) analysis of this PN model is carried out to provide all possible reachable states of the modelled system. An RG of a PN model of an FMS suffering from deadlock/livelock problems can be split into the live zone (LZ) and the deadlock zone (DZ). The former is the set of system states considered to be legal (good), while the latter is the set of system states regarded to be illegal (bad). The LZ represents the optimal (maximally permissive) system behavior that must be provided by an optimal control policy. Generally, in PN based liveness studies, a liveness enforcing supervisor (LES) consisting of a set of control places (CP) is computed. Then the controlled (closed-loop) PN model is obtained by merging the plant PN model and the LES. If the number of reachable live states of the controlled PN model is the same as the ones that exist in the LZ, then the controlled model is optimal. Otherwise, it is suboptimal. The greater the number of live reachable states of the RG, the more products to be produced within the FMS. Therefore, the number of reachable live states of the controlled PN model is considered as a quality measure. It defines the behavioral permissiveness of the controlled model. In the literature, to verify the behavioral permissiveness of the PN models, the PN tool called INA (Integrated Net Analyzer) has been widely used. Recently, by means of INA, a method was proposed to compute the number of states within the LZ and the DZ of a given PN model suffering from deadlocks/livelocks. Unfortunately, INA cannot compute RGs of large PNs having a few millions of states. Therefore, in this paper by means of TINA (TIme petri Net Analyzer), an alternative faster and much more efficient method is proposed for the computation of the number of states within the LZ and the DZ for PN models with large RGs. In addition, in order to enable the development of new PN based deadlock prevention methods, further computations are also proposed. These include the computation of the set of states in the DZ, LZ and first met bad markings (FBMs) for a given PN model suffering from deadlocks/livelocks. The applicability and the effectiveness of the proposed methods are demonstrated by considering several well-known examples of FMS from the relevant literature.INDEX TERMS Flexible manufacturing systems (FMS), deadlock, deadlock prevention, Petri Nets (PN), liveness enforcing supervisor (LES), PN computation tools.

Fixtures are used to locate, hold and support workpieces in manufacturing operations such as machining, inspection, and assembly. Designing and fabricating fixtures can take up to 10-20% of the total cost of a manufacturing system. Fixture planning is a complex activity restricted by the extreme diversity of workpiece, product batch size, product geometry, part accessibility, and working force. Computer-Aided Fixture Planning (CAFP) has been used to improve the fixture design for over 20 years. CAFP contributes to the reduction of lead-time and human interaction in fixture planning. It helps verification of the fixture quality and integration of fixture design with CAD/CAM systems. This paper summarizes constraints of fixture planning and four phases of CAFP. Various approaches to CAFP are surveyed. Application systems of CAFP including some recent patents are reviewed. The paper concludes with the research trend of CAFP. The global competition and customer demand change lead to greater product variety, smaller product batch size, shorter production time and higher product quality. Industrial organizations have to pursue flexibility and automation in manufacturing systems to remain competitive and profitable. Flexible Manufacturing System (FMS) developed in the past decades is to meet such challenge. FMS usually consists of one or more CNC machines which are capable of machining a variety of workpieces. With the development of Computer- Aided Design and Computer-Aided Manufacturing (CAD/ CAM) technology, product design and machining program- ming have been greatly computerized. However, one of the difficulties in FMS is the fixture design for locating and securing a workpiece during manufacturing operations. Fixture planning is time-consuming as it is customized for workpieces. Computer-aided fixture planning (CAFP) aims to reduce the lead-time and human interaction in fixture planning. It can verify fixture quality to integrate fixture design with CAD/CAM systems by automating the reasoning process. The integration of CAFP with CAD/CAM has greatly reduced the production lead-time and cost for an overall optimal solution in product design and manufacturing (1). Fixtures are used to locate, hold and support workpieces in manufacturing operations such as machining, inspection, and assembly. Fixture design and manufacturing is costly due to the tight tolerance and restricted machining opera- tions. Designing and fabricating fixtures can take up to 10- 20% of the total cost of a manufacturing system. Dedicated fixtures are mostly applied in mass production to gain high efficiency. For smaller batch size, the fixture flexibility becomes more important than efficiency due to the workpiece variety and machining requirements. Flexible and

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.

Summary form only given as follows. As manufacturers face a more competitive environment, they are forced to satisfy needs for productivity and flexibility simultaneously. To satisfy these needs and remain competitive, many manufacturers are upgrading their manufacturing technology by introducing flexible manufacturing systems (FMS). The evidence, however, suggests that the payoffs from investing in FMS will not be adequate unless manufacturers also ensure their human resources lever the new technology. The programmable capabilities of FMS are limited to handling quickly and efficiently the more routine aspects of variable manufacturing processes. For FMS to achieve economies of scope and fulfil their promise, FMS need to be staffed by individuals who can lever FMS. These are individuals who can work with FMS, and who can go beyond the limitations of FMS by handling quickly and efficiently the less routine aspects of these manufacturing processes. To lever FMS, human resources need to be managed differently than in traditional factories. They need to be organized into self-managing teams; they need to be classified into few job categories; they need to be selected because of their FMS matching characteristics; they need to be trained; and they need to be rewarded for their varied skills and cooperation. Moreover, they need to be empowered by managers who substitute the coaching approach for the command and control approach, and who seek closer union-management relationships.

Optimizing flexible routing in flexible manufacturing systems is one of the aspects that increase the efficiency of flexible manufacturing systems especially in dynamic environment systems. This article presents a multistage approach to solve flexible routing problem in flexible manufacturing systems. Multistage approach includes three stages; the first stage is a production simulation system to find the fitness of the flexible manufacturing systems corresponding to different products’ routes’ groups. The second stage proposes an artificial neural network approach to predict the products’ routes’ group in flexible manufacturing systems. The last stage combines genetic algorithms and artificial neural network to optimize proper routes for all product types in flexible manufacturing systems. Multistage approach proposed in this study aims to reduce the computational time required to obtain and optimize the flexible routes in flexible manufacturing systems. The results of this study show that the artificial neural network can be used efficiently to predict the flexible routes in flexible manufacturing systems and it reduces the computational time for routes’ optimization required with production simulation system. This characteristic improves the flexibility of flexible manufacturing systems since it can be adapted frequently against any change in production ratios.

Simulation and graphical animation of advanced manufacturing systems

Manufacturing systems are increasingly becoming automated and complex in nature. Highly reliable and flexible manufacturing systems (FMSs) are the necessity of manufacturing industries to fulfill the increasing customized demands. Worldwide, FMSs are used in industries to attain high productivity in production environments with rapidly and continuously changing manufactured goods structures and demands. Reliability prediction plays a very significant role in system design in the manufacturing industry, and two crucial issues in the prediction of system reliability are failures of equipment and system configuration. This novel work presents a stochastic model to analyze the performance of an FMS through its reliability characteristics, in the concern of its equipment. To improve the reliability of FMS, determine the sensitivity of the reliability measures of FMS. FMS consists of many components such as machine tools like CNC, automatic handling and material storage, controller and robot for serving load. The designed system is studied by using the Markov process, supplementary variable technique, Laplace transformation, coverage factor and Gumbel–Hougaard family copula to obtain various reliability measures. For some realistic approach, particular cases and graphical illustrations are also obtained.

Flexible manufacturing systems (FMS) consists of a set of numerically controlled machines, material handling systems (MHS), information system and service centres all working towards a common production schedule goal being the achievement of economic product manufacture in short period of time[1]. An FMS is highly versatile production system capable of performing many different operations given the proper tooling, fixtures and materials. Another feature of this system is the short switching time between different operations assigned to its individual workstations. An FMS to function effectively must have an on-line control system capable of reacting quickly to any problem occurring in the system by providing decisions to deal with the problem in a short period of time. The FMS control system thus needs to receive accurate and reliable information from the shop floor. The operational control of an FMS in general involves accessing a large set of static and dynamic data, and requires complex control algorithms. This paper presents a control system developed for real-time operational control of an FMS. The system is organised hierarchically, each level responsible for making decisions according to the operational needs and the time criticality of the particular level. The overall aim of the control system is to optimise FMS performance while meeting the production requirements.

The main goal of the paper is to propose a methodology to model and analyze the performance of a flexible manufacturing system (FMS), based on the use of Petri net stochastic models. The advantage of using a probabilistic model is that it features the capability to represent the behavior of complex deterministic manufacturing systems, avoiding a detailed description of the operations they involve. The approach the authors propose is carefully described in the paper, highlighting the choice of the probabilistic transitions to model the behavior of each activity performed in the manufacturing system. The model of the FMS is then used to evaluate its performance through simulation. The paper refers to a case-study concerning a flexible manufacturing factory for the production of semiconductor devices which represents a highly critical environment.

Sheet metal machining is a special area in manufacturing industry. The flexible manufacturing system (FMS) has been used in the fabrication of sheet metal products in order to achieve high flexibility and high production efficiency. The configure and parameters are key factors for the performance optimization of the FMS. In this paper the discrete event system simulation is used to evaluate the configure and parameters, and to optimize the performance of the FMS. The composition and characteristics of sheet metal FMS are introduced briefly, the evaluation criteria for FMS' performance and the maximal service capability that the piler can provide to the machining cells are given out. Some simulation schemes are constructed to evaluate whether the configure and parameters are reasonable, and whether the piler has sufficient serviceability. Some instructive conclusions have been reached from the simulation: (1) The parameters of the piler have great effect on the FMS's performance. (2) As the production process of sheet metal are comparatively simple and fixed, the scheduling rules have minor influence on the FMS's performance. (3) On given parameters, a piler can provide sufficient service for four punch-shear machining cells.

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.

Rapid prototyping of complex systems and their control strategies is an important approach in system modeling, especially in early design stages. Naturally, these systems can be seen as a network of concurrently running communicating objects. A practical application field are flexible manufacturing systems (FMS) used in computer integrated manufacturing (CIM). An FMS consists of concurrently working machines which are connected by a computerized material handling system. This leads to well-known problems like scheduling and resource allocation where the usefulness of the fuzzy logic approach has been shown. Concurrent objects as a modeling paradigm enable the designer to describe the system's structure and behavior naturally on a high abstraction level. In this paper we argue that the control strategies based on complex hierarchical fuzzy rule bases fit nicely in the concurrent object based modeling view and, consequently, in a consistent design approach. Moreover, we introduce a suitable programming language (DFKI Oz), extended by a fuzzy library, which supports the described modeling approach. Validation of our approach is described by modeling a complex FMS in the field of car production.

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies.

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.

Flexible manufacturing systems (FMS): Current diffusion and main advantages