Scheduling and discrete event control of flexible manufacturing systems based on Petri nets

A flexible manufacturing system (FMS) is a computerized production system that can simultaneously manufacture multiple types of products using various resources such as robots and multi-purpose machines. The central problems associated with design of flexible manufacturing systems are related to process planning, scheduling, coordination control, and monitoring. Many methods exist for scheduling and control of flexible manufacturing systems, although very few methods have addressed the complexity of whole FMS operations. This thesis presents a Petri net based method for deadlock-free scheduling and discrete event control of flexible manufacturing systems. A significant advantage of Petri net based methods is their powerful modeling capability. Petri nets can explicitly and concisely model the concurrent and asynchronous activities, multi-layer resource sharing, routing flexibility, limited buffers and precedence constraints in FMSs. Petri nets can also provide an explicit way for considering deadlock situations in FMSs, and thus facilitate significantly the design of a deadlock-free scheduling and control system. The contributions of this work are multifold. First, it develops a methodology for discrete event controller synthesis for flexible manufacturing systems in a timed Petri net framework. The resulting Petri nets have the desired qualitative properties of liveness, boundedness (safeness), and reversibility, which imply freedom from deadlock, no capacity overflow, and cyclic behavior, respectively. This precludes the costly mathematical analysis for these properties and reduces on-line computation overhead to avoid deadlocks. The performance and sensitivity of resulting Petri nets, thus corresponding control systems, are evaluated. Second, it introduces a hybrid heuristic search algorithm based on Petri nets for deadlock-free scheduling of flexible manufacturing systems. The issues such as deadlock, routing flexibility, multiple lot size, limited buffer size and material handling (loading/unloading) are explored. Third, it proposes a way to employ fuzzy dispatching rules in a Petri net framework for multi-criterion scheduling. Finally, it shows the effectiveness of the developed methods through several manufacturing system examples compared with benchmark dispatching rules, integer programming and Lagrangian relaxation approaches

Determination of Routing and Sequencing in a Flexible Manufacturing System Based on Fuzzy Logic

AbstractThis paper is concerned with scheduling in Flexible Manufacturing Systems (FMS) using a Fuzzy Logic (FL) approach. Four fuzzy input variables; machine allocated processing time, machine priority, machine available time and transportationpriority are defined. The job priority is the fuzzy output variable, showing the priority status of a job to be selected for next operation on a machine. The model will first assign operation of parts to machines under the given production plan and then determine the input sequence of the assigned operations for each machine based on a multi-criteria scheduling scheme. A complete fuzzy scheduling algorithm is developed to solve the operation allocation and operation scheduling problems in FMS environments aiming to approach the objectives of minimizing mean flowtime, maximizing machine utilization and balancing machine usage. The test results demonstrate the superiority of the fuzzy logic approach in most performance measures.

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Hybrid multiobjective genetic algorithm for integrated dynamic scheduling and routing of jobs and automated guided vehicles in flexible manufacturing systems

The dynamic continues trend of adoption and improvement inventive automated technologies is one of the main competing strategies of many manufacturing industries. Effective integrated operations management of Automated Guided Vehicle (AGV) system in Flexible Manufacturing System (FMS) environment results in the overall system performance. Routing AGVs was proved to be NP-Complete and scheduling of jobs was also proved to be NP hard problems. The running time of any deterministic algorithms solving these types of problems increases very rapidly with the size of the problem, which can be many years with any computational resources available presently. Solving AGVs conflict free routing, dispatching and simultaneous scheduling of the jobs and AGVs in FMS in an integrated manner is identified as the only means of safeguarding the feasibility of the solution to each sub-problem. Genetic algorithm has recorded of huge success in solving NP-Complete optimization problems with similar nature to this problem. The objectives of this research are to develop an algorithm for integrated scheduling and conflict-free routing of jobs and AGVs in FMS environment using a hybrid genetic algorithm, ensure the algorithm validity and improvement on the performance of the developed algorithm. The algorithm generates an integrated scheduling and detail paths route while optimizing makespan, AGV travel time, mean flow time and penalty cost due to jobs tardiness and delay as a result of conflict avoidance. The integrated algorithms use two genetic representations for the individual solution entire sub-chromosomes. The first three sub-chromosomes use random keys to represent jobs sequencing, operations allocation on machines and AGV dispatching, while the remaining sub-chromosomes are representing particular routing paths to be used by each dispatched AGV. The multiobjective fitness function use adaptive weight approach to assign weights to each objective for every generation based on objective improvement performance. Fuzzy expert system is used to control genetic operators using the overall population performance history. The algorithm used weight mapping crossover (WMX) and Insertion Mutation (IM) as genetic operators for sub-chromosomes represented with priority-based representation. Parameterized uniform crossover (PUX) and migration are used as genetic operators for sub-chromosomes represented using random-key based encoding. Computational experiments were conducted on the developed algorithm coded in Matlab to test the effectiveness of the algorithm. First scenario uses static consideration, the second scenario uses dynamic consideration with machine failure recovery. Sensitivity analysis and convergence analysis was also conducted. The results show the effectiveness of the proposed algorithm in generating the integrated scheduling, AGVs dispatching and conflict-free routing. The comparison of the result of the developed integrated algorithm using two benchmark FMS scheduling algorithms datasets is conducted. The comparison shows the improvement of 1.1% and 16% in makespan of the first and the second benchmark production dataset respectively. The major novelty of the algorithm is an integrated approach to the individual sub-problems which ensures the legality, and feasibility of all solutions generated for various sub-problems which in the literature are considered separately

An Integrated Approach for the Analysis of Manufacturing System States

With advancement in the manufacturing technology and rise in the purchasing ability, demand for newer products is increasing continuously. This is forcing manufacturing companies to persistently look for new techniques to improve the productivity of a manufacturing system and ensure optimum utilization of all the elements of a manufacturing system, including facility layout. Traditional research had viewed facility layout, material handling and productivity improvement as separate activities.  Researchers depending on their area of specialization focused on either the production aspects of a company, the material handling aspects or facility layout. However, to ensure productivity, this study proposes a new theory to analyze the current state of the system with an integrated approach of production system and material handling system. In this study, the current state of the system is classified into three different states and a methodology is proposed to identify the current state of the system. This new theory can be used by manufacturers to identify appropriate strategies for improving productivity.  The identification of the state of the system is necessary for effective improvement of the system

A multi-objective flexible manufacturing system design optimization using a hybrid response surface methodology

The present study proposes a hybrid framework combining multiple methods to determine the optimal values of design variables in a flexible manufacturing system (FMS). The framework uses a multi-objective response surface methodology (RSM) to achieve optimum performance. The performance of an FMS is characterized using various weighted measures using the best-worst method (BWM). Subsequently, an RSM approximates the functional relationship between the FMS performance and design variables. The central composite design (CCD) is used for this aim, and a polynomial regression model is fitted among the factors. Eventually, a bi-objective model, including the fitted and cost functions, is formulated and solved. As a result, the optimal percentage for deploying the FMS equipment and machines to achieve optimal performance with the lowest deployment cost is determined. The proposed framework can serve as a guideline for manufacturing organizations to lead strategic decisions regarding the design problems of FMSs. It significantly increases productivity for the manufacturing system, reduces redundant labor and material handling costs, and facilitates productio

The investigation of the effect of scheduling rules on FMS performance

The application of Flexible Manufacturing Systems (FMSs) has an effect in competitiveness, not only of individual companies but of those countries whose manufactured exports play a significant part in their economy (Hartley, 1984). However, the increasing use of FM Ss to effectively provide customers with diversified products has created a significant set of operational challenges for managers (Mahmoodi et al., 1999). In more recent years therefore, there has been a concentration of effort on FMS scheduling without which the benefits of an FMS cannot be realized. The objective of the reported research is to investigate and extend the contribution which can be made to the FMS scheduling problem through the implementation of computer-based experiments that consider real-time situations. [Continues.