Deadlock control of autonomous vehicle storage and retrieval systems via coloured timed Petri nets and digraph tools

This paper addresses the deadlock control problem for the autonomous vehicle storage and retrieval system (AVS/RS). For an AVS/RS with several autonomous vehicles running in bi-direction, deadlock control is one of the key issues in the implementation of AVS/RS. The cycle-deadlock is the main type of deadlock in AVS/RS. In the paper, the dynamic model of AVS/RS is investigated by using coloured timed Petri nets (CTPN). Based on the CTPN model, a route digraph is built to detect cycle-deadlock in AVS/RS with digraph tools; the necessary and sufficient conditions of deadlock-free are established. Moreover deadlock-free control policies are proposed, the critical state in deadlock free is also identified and FCFS policy is applied to solve it. Finally, a case study is given to validate the policies.     

Delayed reconfigurable manufacturing system

Reconfigurable manufacturing systems (RMS) is a new manufacturing paradigm aiming at providing exactly functionality and capacity needed and exactly when needed. Reconfiguration is the main method to achieve this goal. But, the reconfiguration is an interruption to production activities causing production loss and system ramp-up problem and the ‘exact functionality’ may increase the reconfiguration efforts and aggravate the production loss and the ramp-up time. Therefore, a special RMS – delayed reconfigurable manufacturing system (D-RMS) is proposed to promote the practicality of RMS. Starting from the RMS built around part family with the characteristic of delayed differentiation, whose reconfiguration activities mainly occur in the latter stages of manufacturing system and the former stages have the potential to maintain partial production activities to reduce production loss during reconfiguration. Inspired from this, the basic structure of RMS is divided into two subsystems, subsystem 1 is capable of maintain partial production with a certain more functionality than needed, subsystem 2 reconfigure to provide exactly functionality and capacity of a specific part exactly when needed. And then, the benefits of D-RMS are analysed from inventory and ramp-up time aspects. Finally, a case study is presented to show the implementation process of D-RMS and validates the practicability of D-RMS.     

Real-time deadlock-free scheduling for semiconductor track systems based on colored timed Petri nets

This paper addresses the problem of real-time deadlock-free scheduling for a semiconductor track system. The system is required to process wafers continuously, cassette by cassette. The process is not necessarily a repeated one. In addition, the system is deadlock-prone and its modules are failure-prone. Thus, real-time scheduling approaches are required to achieve high-performance. The problem can be solved in a hierarchical way. A deadlock avoidance policy is developed for the system as a lower-layer controller. With the support of the deadlock avoidance policy, heuristic rules are proposed to schedule the system in real-time. An effective modeling tool, colored–timed resource-oriented Petri net, is presented. It is shown that with this model we can schedule a system to achieve satisfactory results in real-time. This method is tolerant to module failures.     

Value creation through design for scalability of reconfigurable manufacturing systems

Rapid and cost-effective scalability of the throughput of manufacturing systems is an invaluable feature for the management of manufacturing enterprises. System design for scalability allows the enterprise to build a manufacturing system to supply the current demand, and upgrade its throughput in the future, in a cost-effective manner, to meet possible higher market demand in a timely manner. To possess this capability, the manufacturing system must be designed at the outset for future expansions in its throughput to enable growths in supply exactly when needed by the market. A mathematical method that maximises the system throughput after reconfiguration is proposed, and an industrial case is presented to validate the method. The paper offers a set of principles for system design for scalability to guide designers of modern manufacturing systems.     

Configuration design of scalable reconfigurable manufacturing systems for part family

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products’ demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.     

Supply chain modelling and managing,using timed coloured Petri nets: a case study

This paper aims at developing a new methodology for designing and managing a supply chain (SC) and, at the same time, for evaluating the performance of every stakeholder involved in a production chain. The methodology proposed has been applied to a footwear supply chain and is based on coloured Petri nets (CPNs). The supply chain analysed in this paper is a complex production system consisting of a network of manufacturers and service suppliers related to logistics systems that provide transportation and storage. The model developed uses coloured, timed Petri nets to represent a supply chain and it is such that resources are the Petri Net (PN) places, the tokens are jobs, orders and/or products, while the colours represent job attributes. These colours are used to encode different data types and values that are attached to tokens. A “coloured token” represents a specific production order or a certain amount of a particular material supplied. Thus, it can be processed in different ways and it can be easily localised within the CPN model. The use of coloured Petri nets allows companies to create a compact representation of states, actions and events of the modelled system. The particular structure of this network allows the designers the easy realisation of a simulator using an “object-oriented”, dedicated programming, which is a useful tool for developing what-if analyses.     

Petri nets and genetic algorithms for complex manufacturing systems scheduling

In this paper we propose the GAPN (genetic algorithms and Petri nets) approach, which combines the modelling power of Petri nets with the optimisation capability of genetic algorithms (GAs) for manufacturing systems scheduling. This approach uses both Petri nets to formulate the scheduling problem and GAs for scheduling. Its primary advantage is its ability to model a wide variety of manufacturing systems with no modifications either in the net structure or in the chromosomal representation. In this paper we tested the performance on both classical scheduling problems and on a real life setting of a manufacturer of car seat covers. In particular, such a manufacturing system involves features such as complex project-like routings, assembly operations, and workstations with unrelated parallel machines. The implementation of the algorithm at the company is also discussed. Experiments show the validity of the proposed approach.     

Deadlock-free genetic scheduling for flexible manufacturing systems using Petri nets and deadlock controllers

In this paper, a new deadlock-free scheduling method based on genetic algorithm and Petri net models of flexible manufacturing systems is proposed. The optimisation criterion is to minimise the makespan. In the proposed genetic scheduling algorithm, a candidate schedule is represented by a chromosome that consists of two sections: route selection and operation sequence. With the support of a deadlock controller, a repairing algorithm is proposed to check the feasibility of each chromosome and fix infeasible chromosomes to feasible ones. A feasible chromosome can be easily decoded to a deadlock-free schedule, which is a sequence of transitions without deadlocks. Different kinds of crossover and mutation operations are performed on two sections of the chromosome, respectively, to improve the performance of the presented algorithm. Computational results show that the proposed algorithm can get better schedules. Furthermore, the proposed scheduling method provides a new approach to evaluate the performance of different deadlock controllers.     

Evaluation of the logistic model of the reconfigurable manufacturing system based on generalised stochastic Petri nets

To reveal the influence on system performance by the logistic model of reconfigurable manufacturing system (RMS), the generalised stochastic Petri nets (GSPN) modular modelling approach is presented in this paper. It is based upon the characteristics of a bottleneck service. According to this approach, the bottleneck service in the production process is found first. By corresponding different resources in the service to different modules of the GSPN, the module is reconfigured. The analysis of the model using the Markov chain is hereby presented, as is the average utilisation factor of RMS. Following this, the production capacity of different products and the average productivity of reconfigurable manufacturing cells (RMCs) are discussed.     

Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing

This paper deals with the design and loading of Cellular Reconfigurable Manufacturing Systems in the presence of alternative routing and multiple time periods. These systems consist of multiple reconfigurable machining cells, each of which has Reconfigurable Machine Tools and Computer Numerical Control (CNC) machines. Each reconfigurable machine has a library of feasible auxiliary machine modules for achieving particular operational capabilities, while each CNC machine has an automatic tool changer and a tool magazine of a limited capacity. The proposed approach consists of two phases: the machine cell design phase which involves the grouping of machines into machine cells, and the cell loading phase that determines the routing mix and the tool and module allocation. In this paper, the cell design problem is modelled as an Integer Linear Programming formulation, considering the multiple process plans of each part type as if they were separate part types. Once the manufacturing cells are formed, a Mixed Integer Linear Programming model is developed for the cell loading problem, considering multi-period demands for the part types, and minimising transportation and holding costs while keeping the machine and cell utilisations in each period, and the system utilisation across periods, approximately balanced. An illustrative problem and experimental results are presented.     

Modelling and simulation of manufacturing systems with first-order hybrid Petri nets

First-order hybrid Petri nets are models that consist of continuous places holding fluid, discrete places containing a non-negative integer number of tokens, and transitions, either discrete or continuous. In the first part of the paper, we provide a framework to describe the overall hybrid net behaviour that combines both time-driven and event-driven dynamics. The resulting model is a linear discrete-time, time-varying state variable model that can be directly used by an efficient simulation tool. In the second part of the paper, we focus on manufacturing systems. Manufacturing systems are discrete-event dynamic systems whose number of reachable states is typically very large, hence approximating fluid models have often been used in this context. We describe the net models of the elementary components of a flexible manufacturing system (machines and buffers) and we show in a final example how these modules can be put together in a bottom-up fashion.     

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.     

Nested coloured timed Petri nets for production configuration of product families

Production configuration is as an effective technique to deal with product variety while maintaining production stability and efficiency. It involves a diverse set of process elements (e.g., machines, operations), a high variety of component parts and assemblies and many constraints arising from product and process variety. Production configuration entails the selection and subsequent arrangement of process elements into complete production processes and the final evaluation of configured multiple alternatives. To better understand production configuration and its implementation, we study the underlying logic for configuring production processes using a dynamic modelling and visualisation approach. This is accomplished by developing a new formalism of nested coloured timed Petri nets (PNs). In view of the inherent modelling difficulties, in the formalism three types of nets–process nets, assembly nets and manufacturing nets–together with a nested net system are defined. Using an industrial example of vibration motors, we show how the proposed formalism can be applied to specify production processes at different levels of abstraction to achieve production configuration.     

Data envelopment analysis with multiple modes of functioning. Application to reconfigurable manufacturing systems

In principle, data envelopment analysis (DEA) does not consider the possibility, which can occur in practice, of a production system being able to operate in different modes of functioning. In this paper, a new DEA modelling approach is proposed in which the different modes of functioning are taken into account and included in the analysis. The observed input consumption and output production in each mode of functioning is used to derive a mode-specific technology. The overall DEA technology aggregates these mode-specific technologies according to their respective time allocations. The proposed model computes a target operating point for each mode of functioning so that the operation of the overall system is efficient. The proposed approach is applied to assess the technical, cost and allocative efficiency of a reconfigurable manufacturing system. The inputs considered are modules/tools usage, labour and energy consumption. The outputs are the number of units produced of each part type. The production possibility set is determined by previous observations of the system functioning, from which the best practices can be identified. Technical, cost and allocative efficiency scores can be computed. The proposed approach not only generates input cost savings but also lead time reductions.     

Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL)

The dynamic nature of today’s manufacturing industry, which is caused by the intense global competition and constant technological advancements, requires systems that are highly adaptive and responsive to demand fluctuations. Reconfigurable manufacturing systems (RMS) enable such responsiveness through their main characteristics. This paper addresses the problem of RMS configuration design, where the demand of a single product varies throughout its production life cycle, and the system configuration must change accordingly to satisfy the required demand with minimum cost. A two-phased method is developed to handle the primary system configuration design and the necessary system reconfigurations according to demand rate changes. This method takes advantage of Reconfigurable Machine Tools in RMS. In fact, by adding/removing modules to/from a specific modular reconfigurable machine, its production capability could be increased, with lower cost. A novel mixed integer linear programming formulation is presented in the second phase of the method to optimise the process of selecting the best possible transformation for the existing machine configurations. Two different cases are designed and solved by implementing the established method. The results of these cases in terms of capital cost, capacity expansion cost, unused capacity and number of transformations, are compared with two hypothetical scenarios. Analyses of the obtained results indicate the efficiency of the proposed approach and offer a promising outlook for further research.     

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

To achieve a significant improvement in the overall performance of a flexible manufacturing system, the scheduling process must consider the interdependencies that exist between the machining and transport systems. However, most works have addressed the scheduling problem as two independent decision making problems, assuming sufficient capacity in the transport system. In this paper, we study the simultaneous scheduling (SS) problem of machines and automated guided vehicles using a timed coloured Petri net (TCPN) approach under two performance objectives; makespan and exit time of the last job. The modelling approach allows the evaluation of all the feasible vehicle assignments as opposed to the traditional dispatching rules and demonstrates the benefits of vehicle-controlled assignments over machine-controlled for certain production scenarios. In contrast with the hierarchical decomposition technique of existing approaches, TCPN is capable of describing the dynamics and evaluating the performance of the SS problem in a single model. Based on TCPN modelling, SS is performed using a hybrid heuristic search algorithm to find optimal or near-optimal schedules by searching through the reachability graph of the TCPN with heuristic functions. Large-sized instances are solved in relatively short computation times, which were a priori unsolvable with conventional search algorithms. The algorithm’s performance is evaluated on a benchmark of 82 test problems. Experimental results indicate that the proposed algorithm performs better than the conventional ones and compares favourably with other approaches.     

An improved time line search algorithm for manufacturing decision-making

The coloured Petri net formalism has been recently used to analyse and optimise manufacturing systems making use of the state space (SS) analysis. This approach has great potential for scheduling and production planning purposes when it is properly implemented. In this article, an improved version of the algorithm known as the time line search for optimising the makespan of manufacturing models is presented. The algorithm has been developed for the use in a compact SS of coloured Petri net models in order to analyse the highest possible number of manufacturing configurations for the improvement of the makespan of a production system. The proposed algorithm can be used for the developing of decision support tools in manufacturing or operational decision-making.     

Scheduling manufacturing systems with blocking: a Petri net approach

This paper introduces a Petri net-based approach for scheduling manufacturing systems with blocking. The modelling of the job routings and the resource and blocking constraints is carried out with the Petri net formalism due to their capability of representing dynamic, concurrent discrete-event dynamic systems. In addition Petri nets can detect deadlocks typically found in systems with blocking constraints. The scheduling task is performed with an algorithm that combines the classical A* search with an aggressive node-pruning strategy. Tests were conducted on a variety of manufacturing systems that included classical job shop, flexible job shop and flexible manufacturing scheduling problems. The optimisation criterion was makespan. The experiments show that the algorithm performed well in all types of problems both in terms of solution quality and computing times.     

Energy-oriented bi-objective optimisation for a multi-module reconfigurable manufacturing system

This paper investigates a multi-module reconfigurable manufacturing system for multi-product manufacturing. The system consists of a rotary table and multiple machining modules (turrets and spindles). The production plan of the system is divided into the system design phase and the manufacturing phase, where the installation cost and the energy consumption cost correspond to the two phases, respectively. A mixed-integer programming model for a more general problem is presented. The objectives are to minimise the total cost and minimise the cycle time simultaneously. To solve the optimisation problem, the ε-constraint method is adopted to obtain the Pareto front for small size problems. Since the ε-constraint method is time consuming when problem size increases, we develop a multi-objective simulated annealing algorithm for practical size problems. To demonstrate the efficiency of the proposed algorithm, we compare it with a classic non-dominated sorting genetic algorithm. Experimental results demonstrate the efficiency of the multi-objective simulated annealing algorithm in terms of solution quality and computation time.