Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint

Resource failures may happen in automated manufacturing systems (AMSs) because of different reasons in the real world, making most existing deadlock control policies unapplicable. This paper develops methods for the robust deadlock control of AMSs with unreliable resources based on Petri nets. The considered AMSs are modeled with generalized systems of simple sequential processes with resources (GS3PR). First, a method based on reachability graph partition technique is provided to analyze the robust legal markings and the forbidden ones in an unreliable GS3PR (U-GS3PR), in which resource failures and recovery procedures are modeled with recovery subnets. Then, the control problem for such a system is converted into a problem for controlling the forbidden states in a U-GS3PR and control places can be designed by solving the maximal number of forbidden markings problems. Since the robust legal reachability spaces computed may be nonconvex and such a system cannot be optimally controlled by the conjunctions of linear constraints, we propose an interval-inhibitor-arc-based robust deadlock control policy for a system with nonconvex legal reachability spaces by solving the maximal number of ${t_{q}}$ -critical marking/transition separation instances problems (MNTMPs( ${t_{q}}$ )). Finally, examples are presented to demonstrate the proposed methods.

Robust Deadlock Control for Automated Manufacturing Systems With Unreliable Resources Based on Petri Net Reachability Graphs

Under the background of cyber-physical systems and Industry 4.0, intelligent manufacturing has become an orientation and produced a revolutionary change. Compared with the traditional manufacturing environments, the intelligent manufacturing has the characteristics as highly correlated, deep integration, dynamic integration, and huge volume of data. Accordingly, it still faces various challenges. In this paper, we summarize and analyze the current research status in both domestic and aboard, including industrial big data collection, modeling of the intelligent product lines based on ontology, the predictive diagnosis based on industrial big data, group learning of product line equipment and the product line reconfiguration of intelligent manufacturing. Based on the research status and the problems, we propose the research strategies, including acquisition schemes of industrial big data under the environment of intelligent, ontology modeling and deduction method based intelligent product lines, predictive diagnostic methods on production lines based on deep neural network, deep learning among devices based on cloud supplements and 3-D self-organized reconfiguration mechanism based on the supplements of cloud. In our view, this paper will accelerate the implementation of smart factory.

Industrial Big Data Analysis in Smart Factory: Current Status and Research Strategies

So far, the majority of deadlock control policies for automated manufacturing systems (AMSs) are based on the assumption that no resource fails; while for AMSs with unreliable resources, the main concerns are deadlock avoidance problems. This paper focuses on the robust deadlock prevention problem for AMSs with a type of unreliable resources, and assumes that at most one of unreliable resources fails at a time. Petri net is introduced to model the considered AMS. Deadlock can be characterized in terms of maximal perfect resource transition-circuits (MPRT-circuits). To develop robust Petri net deadlock controllers with small structures for the system, a new concept of strong transition covers is presented, which is a special kind of transition covers. By designing a control place with a proper control variable to each MPRT-circuit in the strong transition cover, a 1-robust Petri net controller is obtained, whereas the control variables can be determined by an integer linear programming. Such a 1-robust controller ensures that the system can process all types of parts infinitely even if one of unreliable resources fails. Since the number of MPRT-circuits in a strong transition cover is much less than that of all MPRT-circuits, our Petri net controller is of small structural size. Each AMS with a type of unreliable resources has at least a transition cover. An algorithm is presented for checking the strongness of transition covers, and transforming weak transition covers into strong ones. Finally, some examples are given to illustrate the effectiveness of the proposed method.

Transition Cover-Based Robust Petri Net Controllers for Automated Manufacturing Systems With a Type of Unreliable Resources

Zero waste strategy for green supply chain management with minimization of energy consumption

Robust deadlock control for automated manufacturing systems with an unreliable resource

Total energy consumption optimization via genetic algorithm in flexible manufacturing systems

Based on the Petri net (PN) models of the flexible manufacturing systems (FMSs), this paper focuses on solving the scheduling problem of minimizing the total energy consumption of FMSs In the view of different energy consumption rates of resources under different working statuses, two energy consumption functions are considered The dynamic programming (DP) models of the scheduling problems based on PNs are established, where a reachable marking of a PN model, start processing time vector, route vector, and the transition sequence leading to the reachable marking from the initial one, is regarded as a state, and the Bellman equation is based on transition firing For small-size scheduling problems, their optimal solutions can be obtained by the presented DP algorithm However, it is difficult to solve larger-scale ones since the number of explored states increases exponentially with the problem size, which makes DP computationally infeasible To obtain an optimal or suboptimal schedule in an acceptable time, modified DP (MDP) algorithm is proposed, in which fewer states are explored In MDP, two ways are introduced through which only the most promising states are explored One is keeping only one transition sequence for each marking through an evaluation function Another is selecting the most promising states in each stage for further exploration through a heuristic function To guarantee that the generated states are safe, a deadlock controller is applied in the recursion procedure of MDP Experimental results on manufacturing systems and comparisons with existing works are provided to show the effectiveness of MDP Note to Practitioners —This paper is motivated by the need of optimizing the energy consumption of manufacturing systems, considering the increasing energy cost and environmental concerns Existing studies on energy optimization rarely focus on flexible manufacturing systems (FMSs) which exhibit a high degree of resource sharing and route flexibility and can be highly adaptable to various production plans and goals Scheduling becomes more challenging when facing deadlock-prone FMSs This paper provides a method to solve this complex scheduling problem efficiently In this paper, dynamic programming (DP) is formulated for it, and the optimal energy consumption schedules are found Considering the computational burden of implementing DP in the scheduling of large-size FMSs, a novel scheduling method named modified DP (MDP) is proposed Experimental tests on an FMS and a stamping system suggest that MDP can successfully find feasible solutions Besides, it can be applied to other FMS scheduling problems and industrial cases, once their processing time of operations and energy consumption of resources per unit time are known

Yunchao Wu

Papers

Robust deadlock control for automated manufacturing systems with an unreliable resource

Transition Cover-Based Robust Petri Net Controllers for Automated Manufacturing Systems With a Type of Unreliable Resources

Total energy consumption optimization via genetic algorithm in flexible manufacturing systems

Modified Dynamic Programming Algorithm for Optimization of Total Energy Consumption in Flexible Manufacturing Systems

Two-stage design method of robust deadlock control for automated manufacturing systems with a type of unreliable resources