Problem In Flexible Manufacturing System Research Articles

Genetic Algorithm for Solving Mobile Robot Scheduling Problem in Flexible Manufacturing Environment

The scheduling genuinely a complex process, aimed at optimizing operational activities in pursuit of one or more objectives by leveraging production data which may include previous schedules. The scheduling problem in Flexible Manufacturing System (FMS) is commonly categorized as Nondeterministic Polynomial (NP)-hard combinatorial optimization problems and it remains as an endure problem to industrial practitioners and researchers. As part of real production scheduling, once one task is finished processing on a machine, transportation equipment such as mobile robot transports the completed task to the next machine. The problem of scheduling mobile robot in FMS pertains to the task allocation process for the robots, considering the transportation costs and the time spent to complete all operations. In recent years, Genetic Algorithm (GA) has been a remarkably effective search algorithm for solving a wide range of scheduling problems in a manner that achieves near-optimal solutions. This paper presents the metaheuristic techniques, specifically genetic algorithm, to address the NP-hard scheduling problem of two identical mobile robots in Job-Shop FMS environment. The algorithm is developed with the aim of finding feasible solutions to the integrated problem by minimizing the amount of time it takes to finish all tasks, commonly referred to as makespan. The performance of GA is evaluated with some numerical experiments which is executed via Matlab software. The scheduling results shows that the developed GA able to obtained the near-optimal solution of minimal makespan and converge within a short period of time.

Identifying the Saturated Line Based on the Number of Idle Places: Achieving Precise Maximal Permissiveness without Deadlocks Using Control Transitions or Control Places

In the flexible manufacturing system deadlock prevention domain, researchers’ almost final target is to seek the maximally permissive controllers for solving the deadlock problems of flexible manufacturing systems. However, it seems a challenging work. Whatever you adopt, what kinds of methods, policies, and strategies, it seems complicated to obtain optimal controllers for deadlock prevention even if they claim their algorithms are optimal until the deadlock recovery is developed. Therefore, many experts, including us, have decided to design all kinds of actual maximally permissive recovery policies based on control transitions. It is a pity that these policies usually solve some particular flexible manufacturing systems’ deadlock problems. The controllers failed to recover the deadlock situation of flexible manufacturing systems once the idle or resource places were changed. In other words, these policies could never know or identify the real number of maximally permissive controllers in the same structure with different idle places. The precise combination of the number of idle places is called the saturated line, and it divides the reachability states into two region (saturated region and unsaturated region). Accordingly, this paper proposes a novel concept that achieves the precise maximum permissiveness and fits all kinds of flexible manufacturing systems. The experimental results show how our policy does in practice.

A multi-agent system simulation based approach for collision avoidance in integrated Job-Shop Scheduling Problem with transportation tasks

At the operational level of Flexible Manufacturing Systems (FMS), several research works have developed solutions not fully realizable considering the operational constraints, which can be difficult to model. One of the well-known problems in FMS is the Job-Shop Scheduling Problem (JSSP) with transportation tasks. Indeed, this problem is addressed by many researchers, but most of their studies do not consider operational constraints such as collision avoidance between transporters. Moreover, optimized scheduling may contain deadlock situations due to often neglected geometric constraints. Therefore, in this paper, we propose a simulation approach based on a multi-agent system to test the optimized results known in the literature in more realistic conditions, where collision avoidance between transporters is considered. We propose as well a sim-optimization approach to explore suitable solutions considering collision avoidance constraints. In addition, an algorithm is proposed to solve deadlocks. The obtained results highlight the impact of collision avoidance on the performance of the literature solutions while being more realistic. Furthermore, these results demonstrate the efficiency of the proposed algorithms, since all identified collisions are avoided and all deadlocks are also solved. The proposed approach is expected to be more attractive to the industries as it ensures a small gap between the theoretical and actual results.

Incomparable Single Controller for Solving Deadlock Problems of Flexible Manufacturing Systems

This paper focuses on studying and proposes deadlocks are an obstacle in the flexible manufacturing system (FMS) of Petri nets (PNs) since their occurrence will influence the partial or whole model to stop working. Due to its structural characteristic, the Petri net is an essential model tool to overcome the deadlocks problem. The place invariant (PI) associated with the control algorithm to obtain the maximally permissive by deadlocks forbidding are popular recently; however, the algorithms have reached the maximal permissiveness, but their results are so complex. Our paper applies a single controller and single token to eliminate all the deadlocks in the FMS. of PNs We combine the PI and simplified dead markings to form our single controller deadlock prevention policy (SCDPP) algorithm.

A Perspective of Flexible Manufacturing System in Manufacturing Sectors

The rapid changes in customer demand force manufacturers to develop new technologies for flexibility enhancement in the manufacturing system. Different kinds of literature are available to explain the issues in the Flexible manufacturing system (FMS) and different traditional techniques employed for solving such issues. However, several new techniques have emerged in the past 25 years and no literature reviews exist to differentiate such modern approaches from traditional ones. The core purpose of this review is to study and investigate different levels of techniques involved in the effective implementation of FMS. This paper serves with some reasonable investigation of flexible issues faced in manufacturing industries such as scheduling, machine loading, material handling, and decision-making stage. For each manufacturing issue, the emerged manufacturing techniques are described in this review. This will also help future researchers interested in doing their research work in the area of FMS. It provides information to develop the best model for solving issues of the observed FMS problem. This paper contains the details of issues related to flexibility in manufacturing sectors with its resolving techniques that are reviewed and symbolized between the years 2000 and 2021.

Joint optimisation for dynamic flexible job-shop scheduling problem with transportation time and resource constraints

Dynamic flexible job-shop scheduling is traditionally a challenge in real-world manufacturing systems, especially considering the constraints of transportation resources and transportation time. To address the dynamic optimisation problem in flexible manufacturing systems, this paper proposes a novel proactive-reactive methodology to adapt to the dynamic changes in working environments and addresses the joint scheduling problem for machine tools and transportation resources. The joint optimisation model is first formulated as a mixed-integer programming model considering production efficiency and transportation constraints. The flowchart of the dynamic scheduling system is then designed for dynamic decision-making, and a novel particle swarm optimisation algorithm integrated with genetic operators is developed to respond to dynamic events and generate the reschedule plan in time. Finally, several numerical experiments and case studies in reality are applied to verify the efficiency of the developed methodology. Common dispatching rules and heuristic methods are also applied to test and evaluate the efficiency of the developed algorithm. Computational results demonstrate that the developed methods and decision models are efficient for dynamic job-shop scheduling problems in flexible manufacturing systems, which can acquire rather a good effect in practical production.

Dynamically scheduling and maintaining a flexible server

AbstractDeciding how to jointly schedule jobs and perform preventive maintenance is a fundamental problem in flexible manufacturing systems, particularly those arising in semiconductor manufacturing. At the same time, past work in this area shows that, even when there is only one station and one type of job, identifying policies that minimize the amount of work‐in‐process (WIP) is a difficult problem. In this paper, we study a single‐station version of this problem with an arbitrary number of job classes, with the objective of minimizing average maintenance costs plus the weighted average amount of WIP. We identify conditions under which it suffices to schedule jobs according to both a server‐state‐dependent version of the cμ‐rule, and a static cμ‐rule where the average service rates are used. One of these conditions states that the ratio between the service rates should remain constant as the server deteriorates. When this assumption does not hold, scheduling with the cμ‐rule can in fact lead to an unstable system; we illustrate this using a simple example. On the other hand, we also present numerical evidence that cμ‐based scheduling performs well compared to other scheduling rules, and relative to a policy based on solving a Markov decision process.

Stochastic Programming Models for Dynamic Facility Layout Problem in Flexible Manufacturing Systems

An appropriate facility layout is required to reduce total manufacturing cost, especially in uncertain environments. The design of a desirable facility layout is essential when the rearrangement of the facilities is expensive. Using Routing Flexibility (RF) as a principle of the Flexible Manufacturing System (FMS) can lead to the fulfillment of this need. This paper propounds two new mathematical models for the Dynamic Facility Layout Problem (DFLP) with stochastic approaches. The RF is considered when the independent parts demands follow Exponential and Normal distributions in which their parameters randomly alter from period to period. The primary nonlinear models are first linearized by the proposed innovative technique. Then, the performance of the proposed models and the linearization technique is assessed by solving two test problems. Next, the RF effect on the manufacturing system is analyzed. The obtained results verify the validity and applicability of the proposed models. It is also shown that the suggested linearization technique is an efficient technique with 99% accuracy, even if convexity conditions are not met.

One Computational Innovation Transition-Based Recovery Policy for Flexible Manufacturing Systems Using Petri nets

In the third and fourth industrial revolutions, smart or artificial intelligence flexible manufacturing systems (FMS) seem to be the key machine equipment for capacity of factory production. However, deadlocks could hence appear due to resources competition between robots. Therefore, how to prevent deadlocks of FMS occurring is a very important and hot issue. Based on Petri nets (PN) theory, in existing literature almost all research adopts control places as their deadlock prevention mean. However, under this strategy the real optimal reachable markings are not achieved even if they claimed that their control policy is maximally permissive. Accordingly, in this paper, the author propose one novel transition-based control policy to solve the deadlock problem of FMS. The proposed control policy could also be viewed as deadlock recovery since it can recover all initial deadlock and quasi-deadlock markings. Furthermore, control transitions can be calculated and obtained once the proposed three-dimension matrix, called generating and comparing aiding matrix (GCAM) in this paper, is built. Finally, an iteration method is used until all deadlock markings become live ones. Experimental results reveal that our control policy seems still the best one among all existing methods in the literature regardless of whether these methods belong to places or transitions based.

Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network

To benefit from the accurate simulation and high-throughput data contributed by advanced digital twin technologies in modern smart plants, the deep reinforcement learning (DRL) method is an appropriate choice to generate a self-optimizing scheduling policy. This study employs the deep Q-network (DQN), which is a successful DRL method, to solve the dynamic scheduling problem of flexible manufacturing systems (FMSs) involving shared resources, route flexibility, and stochastic arrivals of raw products. To model the system in consideration of both manufacturing efficiency and deadlock avoidance, we use a class of Petri nets combining timed-place Petri nets and a system of simple sequential processes with resources (S3PR), which is named as the timed S3PR. The dynamic scheduling problem of the timed S3PR is defined as a Markov decision process (MDP) that can be solved by the DQN. For constructing deep neural networks to approximate the DQN action-value function that maps the timed S3PR states to scheduling rewards, we innovatively employ a graph convolutional network (GCN) as the timed S3PR state approximator by proposing a novel graph convolution layer called a Petri-net convolution (PNC) layer. The PNC layer uses the input and output matrices of the timed S3PR to compute the propagation of features from places to transitions and from transitions to places, thereby reducing the number of parameters to be trained and ensuring robust convergence of the learning process. Experimental results verify that the proposed DQN with a PNC network can provide better solutions for dynamic scheduling problems in terms of manufacturing performance, computational efficiency, and adaptability compared with heuristic methods and a DQN with basic multilayer perceptrons.

Minimization of Machine Idle Time and Penalty Cost in Flexible Manufacturing System Scheduling

This paper address the application of Jaya algorithm to solve Multi objective scheduling problem in Flexible Manufacturing System(FMS) to Minimize the Combined Objective Function(COF) Value. The effectiveness of this algorithm is tested on the problem of 43 jobs processed on 16 machines taken from literature. The MATLAB code is written to find best sequence and Combined Objective Function value by implementing Jaya Algorithm. Results obtained by Jaya Algorithm are compared with different algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Shortest Processing Time (SPT), Cuckoo Search (CS) and Modified Cuckoos Search (MCS) for the problem considered. It is observed from the results that COF value for the sequence obtained by Jaya Algorithm is better than other algorithms. It is concluded that the Jaya algorithm is best suitable for solving the Scheduling problem considered in Flexible Manufacturing System.

One Novel and Optimal Deadlock Recovery Policy for Flexible Manufacturing Systems Using Iterative Control Transitions Strategy

This paper focuses on solving deadlock problems of flexible manufacturing systems (FMS) based on Petri nets theory. Precisely, one novel control transition technology is developed to solve FMS deadlock problem. This new proposed technology can not only identify the maximal saturated tokens of idle places in Petri net model (PNM) but also further reserve all original reachable markings whatever they are legal or illegal ones. In other words, once the saturated number of tokens in idle places is identified, the maximal markings of system reachability graph can then be checked. Two classical S3PR (the Systems of Simple Sequential Processes with Resources) examples are used to illustrate the proposed technology. Experimental results indicate that the proposed algorithm of control transition technology seems to be the best one among all existing algorithms.

Deadlock analysis and control using Petri net decomposition techniques

Petri nets are an effective tool for modeling and analyzing deadlock problems of flexible manufacturing systems (FMSs). Based on Petri nets, this paper proposes a decomposition method for deadlock control of a class of Petri net modeling FMSs. It first decomposes a Petri net into two (or more) subnets via shared transitions and designs a liveness-enforcing Petri net supervisor for each subnet. Then the root of causing deadlocks is analyzed when merging the controlled subnets. Finally, control places are adjoined to the merged net to obtain a liveness-enforcing Petri net supervisor. The proposed method only needs to calculate the reachability graphs of the subnets, which are in general much smaller than that of the whole original net system. This method in general reduces significantly the computational overhead of designing a liveness-enforcing Petri net supervisor. A subnet can be further decomposed in a hierarchical way, which can further mitigate the computational burden. Typical examples are used to demonstrate the proposed methods.

Approach to Integrated Scheduling Problems Considering Optimal Number of Automated Guided Vehicles and Conflict-Free Routing in Flexible Manufacturing Systems

Usually, machine and automated guided vehicle (AGV) scheduling are studied simultaneously. However, previous studies often used a fixed number of AGVs or did not consider routing problems and transportation time. This paper focuses on the machine and AGV scheduling problem in a flexible manufacturing system by simultaneously considering the optimal number of AGVs, the shortest transportation time, a path planning problem, and a conflict-free routing problem (CFRP). To study these problems simultaneously, we propose a genetic algorithm combined with the Dijkstra algorithm that is based on a time window. The tri-string chromosome coding method is designed to ensure that the solutions are feasible after the genetic operator has been applied. Global, local, and random searches are adopted in reasonable proportions to improve the quality and diversity of the initial population. The Dijkstra algorithm based on the time window is embedded into the genetic algorithm to search for the shortest route, detect collisions for multiple vehicles simultaneously, and finally, solve the shortest CFRP. The objective is to minimize the makespan while considering the influence of the number of AGVs. Increasing the number of AGVs has a significant impact on the makespan in the initial stage. However, the makespan tends to stabilize as the number of AGVs reaches some threshold. To balance the relationship between the minimum makespan and the optimal number of AGVs, we set the minimum decrease rate to 5% when determining the minimum makespan and confirming the corresponding number of AGVs to be the optimal value. In this paper, to verify the effectiveness of our approach, we propose two sets of computational experiments. The first set of results shows that the proposed algorithm is as efficient and effective at solving the scheduling problem as the benchmark approaches. The second set of computational experiments indicates that the proposed approach is applicable for solving integrated scheduling problems in flexible manufacturing systems.

An Efficient Deadlock Recovery Policy for Flexible Manufacturing Systems Modeled With Petri Nets

This paper focuses on solving deadlock problems in flexible manufacturing systems modeled with Petri nets by adding a set of recovery transitions. Different from the traditional deadlock control policies that add control places to a net model, this paper employs transitions to recover all the deadlock markings to be legal ones. A live net system can be obtained with all reachable markings. First, according to reachability graph analysis, a set of recovery transitions is obtained for each deadlock marking to be recovered. Second, we present a vector intersection approach to compute a recovery transition to recover multiple deadlock markings. Then, an iterative method is developed to find a set of recovery transitions to recover all deadlock markings. At each iteration step, a recovery transition is found to recover more than one deadlock markings. This iterative method cannot obtain the minimal number of recovery transitions in theory, but it can usually lead to a minimal one supported by extensive experimental studies. Finally, several widely used examples are provided to demonstrate the presented approach. The experimental results show that the reported deadlock recovery technique is effective and efficient.

Data-driven scheduling optimization under uncertainty using Renyi entropy and skewness criterion

Abstract In order to deal with the resource cost uncertainties, this paper introduces a Renyi mean-entropy-skewness (RMES) information criterion for the scheduling optimization problems in flexible manufacturing systems (FMSs). Motivated by potential limitations in the existing measures, this third-order information criterion is carefully integrated to be more general and more robust in representing the schedule dispersion under uncertainties. The RMES information criterion is estimated using data-driven techniques so that it does not rely on the assumptions of exact probability distributions which are usually unknown in practice. Modeled with Petri net (PN) and system state reachable graph (RG), the RG-based dynamic programming (DP) algorithm and an approximate dynamic programming (ADP) algorithm are presented to solve the proposed model. The effectiveness of the introduced information criterion is verified by both technical proofs and extensive simulation studies of systems with a wide range of scales and data types. A real stamping industrial case study is also conducted as a justification of the model’s practical applicability.

Maximally permissive deadlock prevention policies for flexible manufacturing systems using control transition

Nowadays, many kinds of flexible manufacturing systems are used to process many complex manufacturing works due to their machine flexibility and routing flexibility. However, such competition (i.e. robots and machines) for shared resources by concurrent job processes can lead to the problem of a system deadlock. In existing researches, almost experts adopted place-based as controllers to solve the deadlock problems of flexible manufacturing systems whatever the concept of siphons or the reachability graph method are used. Among them, only the reachability graph ones can obtain maximally permissive live states. In this article, the authors try to propose one novel transition-based deadlock prevention concept to solve flexible manufacturing system’s deadlock problem. In addition, two algorithms are developed to support above concept. The experimental results indicate that the proposed policy not only can obtain maximally permissive controllers but also recover all original deadlock markings.

SCO-Concat: a Solution to a Planning Problem in Flexible Manufacturing Systems using Supervisory Control Theory and Optimization Techniques

This work presents a modified version of the SCO (Supervisory Control and Optimization) methodology, proposed in Pena et al. (Inf Sci 329:491–502, 2016) to deal with planning problems in flexible manufacturing systems. Although having proved to be an alternative to deal with this class of problems, the SCO methodology is limited by the fact that it can only be applied to deal with small batches of products. Previous works show that when considering manufacturing systems of a moderate degree of complexity, this approach is only efficient to generate solutions for batches containing very few products, as for larger batches, the necessary computational time to process a solution is very high. It is obvious that, for the problems in the real world, this dimension of production is very small, which, at first, makes the application of SCO methodology quite limited. Therefore, this work proposes a complementary approach to SCO, here called SCO-Concat, developed to carry out the planning in larger batches of production. The proposed methodology was tested in a plant of moderate size, and the results obtained show that planning for batches as large as desired can be achieved in an efficient manner by SCO-Concat at a very reduced computational cost.

Material flow optimisation of production planning and scheduling problem in flexible manufacturing system by real coded genetic algorithm (RCGA)

In a loading problem of flexible manufacturing system (FMS), part type selection and operations allocation are two critical problems. The total completion time of a product for the selected process plan in the system can be minimum for the loading problem. But, in a real time scheduling system, this process plan may not be optimum because of consideration of waiting time of product and machine. So, the total completion time and thereby the material flow of the selected process plan in the FMS may be high. Due to this problem an integrated approach of part type selection and an operation allocation problem i.e. production planning problem and scheduling problem is considered to optimise material flow in FMS. Loading and scheduling problems are NP-hard in nature. So, to solve complex problems like this, real coded genetic algorithm (RCGA) is used which overcomes some limitations of genetic algorithm. It is observed that, the results of optimisation using RCGA outperforms those obtained by earlier researchers.

Simulation-based optimization approach for simultaneous scheduling of vehicles and machines with processing time uncertainty in FMS

Many stochastic factors, such as vehicle congestion, deadlock or conflict, or stochastic processing time have significant effects on performance in scheduling problem in flexible manufacturing system (FMS). This paper proposed a simulation-based optimization, L-GAOCBA, to address the simultaneous scheduling of vehicles and machines in FMS. The simulation model is constructed to evaluate the performance of scheduling decision, and includes stochastic elements, such as vehicle congestion, deadlock, and uncertain processing time. Genetic algorithm (GA) combined with local search, L-GA, plays important role in exploring the good design alternative based on simulation output. Optimal computing budget allocation (OCBA) embedded with L-GA is used to allocate the number of replications for reducing simulation replications. The design of experiments is used to analyze and set the parameters of L-GA and OCBA. This study shows that L-GAOCBA is superior for enhancing solution quality and search efficiency.