Deadlock-free scheduling for flexible manufacturing systems using Petri nets and heuristic search

Lei, Hang; Xing, Keyi; Han, Lei; Xiong, Fuli; Ge, Zhaoqiang

doi:10.1016/j.cie.2014.04.002

Cited by 53 publications

(15 citation statements)

References 26 publications

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“…In Castiglione et al [21] an analytic modeling technique based on the use of Markovian Agents and Mean Field Analysis has been proposed that allows the effective modeling of concurrent Big Data applications. In heuristic search algorithm methods, PNs model the system and a search algorithm based on a heuristic function is used to expand a portion of the reachability graph, containing the most promising nodes that leads to an optimal scheduling solution [3,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. The heuristic function must be admissible in order to obtain the optimal solution.…”

Section: Pns and Cpns Combined With Ai Techniques For Manufacturing Smentioning

confidence: 99%

“…The heuristic function must be admissible in order to obtain the optimal solution. In Lee and DiCesare [3] a heuristic search algorithm based on the A* algorithm, a branch and bound type algorithm, was introduced to limit the search in the PN reachability graph in order to find the optimal solution for FMS scheduling problems. Modified and improved versions of the A* based heuristic search method have been presented in the literature [25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Pns and Cpns Combined With Ai Techniques For Manufacturing Smentioning

confidence: 99%

“…Manufacturing scheduling is the optimization process of assigning a limited number of resources within a temporal horizon to a set of manufacturing processes of a plan subject to a set of constraints [1]. Computation time to obtain a global optimum increases exponentially with problem size, as such scheduling is generally considered NP-hard (non-polynomial hard) [1][2][3], whereas integrated process planning, scheduling and control, and dynamic response to emergence increase the complexity of manufacturing scheduling. New generation real-time control systems allow optimal performance under different conditions for flexible, autonomous, and adaptive manufacturing systems, generally called intelligent manufacturing systems, such as multi-agent systems (MAS) and Holonic Manufacturing Systems (HMS) [2].…”

Section: Introductionmentioning

confidence: 99%

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Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Drakaki

Tzionas

2017

Applied Sciences

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Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs) and reinforcement learning (RL). CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties.

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Section: Pns and Cpns Combined With Ai Techniques For Manufacturing Smentioning

confidence: 99%

Section: Pns and Cpns Combined With Ai Techniques For Manufacturing Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Drakaki

Tzionas

2017

Applied Sciences

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“…CPNs with an additive normally distributed noise) have been investigated to relaxe the condition (2) [13]. In addition, a partial relaxation of SPNs leads to Hybrid Markovian Continuous Petri Nets and can be used to relax condition (1) [14], [19], [21]. But in the former works the continuous models are non-deterministic and the steady state is no longer easy to work out.…”

Section: Problem Statementmentioning

confidence: 99%

Fluidification of Stochastic Petri Net by Non Linear Timed Continuous Petri Net

Akchioui¹

2017

AJESA

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Reliability analysis is often based on stochastic discrete event models like Markov models or stochastic Petri nets.For complex dynamical systems with numerous components, analytical expressions of the steady state are tedious to work out because of the combinatory explosion with discrete models. Moreover, the convergence of stochastic estimators is slow. For these reasons, fluidification can be investigated to estimate the asymptotic behaviour of stochastic processes with timed continuous Petri nets. The contributions of this paper are to sum up some properties of the asymptotic mean marking and average throughputs of stochastic and timed continuous Petri nets, then to point out the limits of the fluidification in the context of the stochastic steady state approximation. To overcome these limitations, the new semantic and the condition for convergence is proposed: fluid Petri nets with Non Linear Timed Continuous Petri Net (NL-CPN).

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“…Modern flexible manufacturing systems are generally defined as integrated computer systems involving technological machines (machine tools), means of transportation, and tools enabling the effective implementation of low-and medium-volume production [1,2]. They are characterized by a high level of integration between technological processes, transport and planning.…”

Section: Introductionmentioning

confidence: 99%

The Optimal Sequence of Production Orders, Taking into Account the Cost of Delays

Dylewski¹,

Jardzioch²,

Krebs³

2016

Management and Production Engineering Review

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Accepted: 17 May 2016 In flexible manufacturing systems the most important element in determining the proper course of technological processes, transport and storage is the control and planning subsystem. The key planning task is to determine the optimal sequence of production orders. This paper proposes a new method of determining the optimal sequence of production orders in view of the sum of the costs related to the delayed execution of orders. It takes into account the different unit costs of delays of individual orders and the amount of allowable delays of orders involving no delay costs. The optimum sequence of orders, in the single-machine problem, in view of the sum of the costs of delays may be significantly different from the optimal order, taking into account the sum of delay times.

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Deadlock-free scheduling for flexible manufacturing systems using Petri nets and heuristic search

Cited by 53 publications

References 26 publications

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning

Fluidification of Stochastic Petri Net by Non Linear Timed Continuous Petri Net

The Optimal Sequence of Production Orders, Taking into Account the Cost of Delays

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