Scheduling Of Flexible Manufacturing Systems Research Articles

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

A Branch and Bound Algorithm for Scheduling of Flexible Manufacturing Systems

A Branch and Bound Algorithm for Scheduling of Flexible Manufacturing Systems

A new application of multi-criteria decision-making methods for the scheduling of flexible manufacturing systems: A case study

A new application of multi-criteria decision-making methods for the scheduling of flexible manufacturing systems: A case study

Genetic Algorithm with Local Search for the Multi-Target Scheduling in Flexible Manufacturing System

Traditional large-scale process manufacturing is gradually transformed into customized discrete manufacturing with the fierce global competition. Production planning has an important impact on improving manufacturing efficiency in the ever-changing from the view of engineering management. However, many nonprocessing-related factors in the flexible manufacturing system make it different between the formulation and implementation of the production plan. We established a multi-target optimization model based on the scheduling data of a discrete manufacturing company. In order to optimize the local effect of the scheduling model, we proposed an improved genetic algorithm with local search (GALS). The results of the experiments show that GALS is far superior to the current genetic algorithm scheduling in terms of the number and quality of scheduling solutions. Compared with the current scheduling strategy of the enterprise, the scheduling strategy given by GALS achieved an average improvement of 29.61% in minimizing completion time, achieved 44.8% in minimizing transportation time, and achieved 44.64% in machine load balancing.

Simultaneous part input sequencing and robot scheduling for mass customisation

In contemporary manufacturing environments, companies face various challenges in trying to meet customer demand in a timely manner. This paper investigates simultaneous part input sequencing and robot scheduling of flexible manufacturing systems for mass customisation. A mathematical model is formulated for this simultaneous and continuous time-based decision-making problem. A segment set-based approach is proposed to solve the problem. Numerical studies are performed by simulation and statistical analyses to evaluate the approach. The proposed approach is compared to different approaches and analysed under various conditions. Conclusions, managerial implications, and future research suggestions are provided.

Coexistent scheduling in the tandem flow path configuration of a flexible manufacturing system by using an advanced grey wolf optimizer

The use of material handling robots (MHRs) for efficient material handling operations in the flexible manufacturing systems (FMS) has gained wide popularity and acceptability across the automated production industries. The coexistent scheduling between jobs and MHRs improves the overall efficiency of the FMS significantly. In the present study, the coexistent scheduling between the MHRs and the jobs under production in the FMS is carried out by using an advanced grey wolf optimization (AGWO) algorithm. The proposed FMS layout is made up of the tandem flow path configurations for the movements of MHRs. The FMS constitutes six flexible manufacturing cells (FMCs) partitioned in six zones and served by six MHRs deployed in each partitioned zone for efficient material handling operations. To develop the coexistent schedule between MHRs and jobs, a combined objective function is formulated by combining the two diverging objectives and solved by using the AGWO algorithm. The combined objective function yield for coexistent production scheduling in FMS, operating with nineteen work centers (WC) and six MHRs to produce thirty-six types of jobs and sixty-six types of jobs in varying batch production quantities is also reported in the paper.

A Particle Swarm Optimization –Based Heuristic for Scheduling in FMS Review

This paper focus on the problem of simultaneous scheduling of machine and automated guided vehicle (AGV) in a flexible manufacturing system (FMS) so as to minimize the makespan The FMS scheduling problem has been tackled by various traditional optimization techniques. While these methods can give an optimal solution to small-scale problems, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple bjectives,i.e.,minimising the idle time of the machine andminimising the total penalty cost for not meeting the deadline concurrently. Two optimization algorithms ( genetic algorithm and particle swarm algorithm) are compared and conclusions are presented

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.

Optimal Scheduling of Flexible Manufacturing System Using Improved Lion-Based Hybrid Machine Learning Approach

Dispatching rules are generally useful for scheduling jobs in flexible manufacturing systems (FMSs). However, the appropriateness of these rules relies heavily on the condition of the system; thus, there is no single rule that always outperforms others. In this state of affairs, diverse machine-learning technology offers an effective approach for dynamic scheduling, as it allows managers to identify the most suitable rule at each moment. Nonetheless, various machine-learning algorithms may provide diverse recommendations. The main objective of this study is to implement FMS scheduling using intelligent hybrid learning algorithms with metaheuristic improvements. The developed model involves three phases: feature extraction, optimal weighted feature extraction, and prediction. After the benchmark datasets for the FMS are gathered, feature extraction is performed using t-distributed stochastic neighbor embedding, linear discriminant analysis, linear square regression, and higher-order statistical features. Further, an optimal weighted feature extraction method is developed to select the optimal features with less correlation using the improved lion algorithm (LA), which is called the modified nomadic-based LA (MN-LA). Finally, the optimally selected weighted features are subjected to a hybrid learning algorithm with the integration of a fuzzy classifier and a deep belief network (DBN). For improving the prediction model, the membership function of the fuzzy classifier is optimized using the proposed MN-LA. Moreover, the activation function and the number of hidden neurons in the DBN are optimized using the MN-LA. The main objective of the optimized hybrid classifier is to enhance prediction accuracy. The experimental results indicate the effectiveness of the proposed heuristic-based scheduling method for FMSs.

Robust scheduling of flexible manufacturing systems with unreliable operations and resources

This paper addresses the topic of robust scheduling for flexible manufacturing systems (FMS) with operation interruptions and unreliable resources. The proposed approach uses timed Petri nets as a model of the FMS in uncertain environments. This model includes controllable and uncontrollable transitions. The unexpected firings of the uncontrollable transitions represent operation and resource failures and the risk to deviate from the scheduled trajectories. This paper proposes an anytime graph search algorithm with a new objective function that combines performance and risk. In addition, the graph search incorporates a new filtering mechanism that learns from previous runs and two node expansion strategies. The performance of the proposed algorithm is compared with that of another already existing algorithm. A running example and a case study from a real company illustrate the efficiency of the proposed scheduling approach.

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.

A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems

Ongoing market requirements and real-time demands have led to intense competiveness in the manufacturing industry. Hence competitors are bound to employ newer means of manufacturing systems that can handle the ongoing market conditions in a flexible and efficient manner. To tackle these problems manufacturing control systems have evolved to the distributed manufacturing control system by exploiting their control architectures. These distributed control architectures provide an efficient mechanism that gives reactive and dynamically optimized system performance. This paper studies the impact of design and control factors on the performance of flexible manufacturing system. The system is evaluated on the basis of makespan, average machine utilization and the average waiting time of parts at the queue. Discrete-event based simulation models are developed to conduct simulation experiments. The results obtained were subjected to multi-response optimization as per Grey based Taguchi methodology. The effect of control architecture was statistically significant on the performance of flexible manufacturing system.

A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems

This paper considers the multiobjective scheduling of flexible manufacturing systems (FMSs). Due to high degrees of route flexibility and resource sharing, deadlocks often exhibit in FMSs. Manufacturing tasks cannot be finished if any deadlock appears. For solving such problem, this work develops a deadlock-free multiobjective evolutionary algorithm based on decomposition (DMOEA/D). It intends to minimize three objective functions, i.e., makespan, mean flow time, and mean tardiness time. The proposed algorithm can decompose a multiobjective scheduling problem into a certain number of scalar subproblems and solves all the subproblems in a single run. A type of a discrete differential evolution (DDE) algorithm is also developed for solving each subproblem. The mutation operator of the proposed DDE is based on the hamming distance of two randomly selected solutions, while the crossover operator is based on Generalization of Order Crossover. Experimental results demonstrate that the proposed DMOEA/D can significantly outperform a Pareto domination-based algorithm DNSGA-II for both 2-objective and 3-objective problems on the studied FMSs.

Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints

Manufacturing enterprises nowadays face huge environmental challenges because of energy consumption and associated environmental impacts. One of the effective strategies to reduce energy consumption is by employing intelligent scheduling techniques. Production scheduling can have significant impact on energy saving in manufacturing system from the operation management point of view. Resource flexibility and complex constraints in flexible manufacturing system make production scheduling a complicated nonlinear programming problem. To this end, a multi-objective optimization model with the objective of minimizing energy consumption and makespan is formulated for a flexible job shop scheduling problem with transportation constraints. Then, an enhanced genetic algorithm is developed to solve the problem. Finally, comprehensive experiments are carried out to evaluate the performance of the proposed model and algorithm. The experimental results revealed that the proposed model and algorithm can solve the problem effectively and efficiently. This may provide a basis for the decision makers to consider energy-efficient scheduling in flexible manufacturing system.

Modified Dynamic Programming Algorithm for Optimization of Total Energy Consumption 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.

A Novel Optimal Online Scheduling Scheme for Flexible Manufacturing Systems

Abstract With increasing demand of flexible production facilities, the scheduling of flexible manufacturing system has become a key challenge of modern manufacturing We present an approach to solve this problem for a class of flexible manufacturing systems by means of Model Predictive Control (MPC). In Wenzelburger and Allgower (2019), a modeling framework was proposed that automatically generates a model of the manufacturing system, which we use as basis for our approach. We introduce an MPC scheme that computes the optimal schedule for the manufacturing problem with respect to a given cost function. The presented scheme is guaranteed to find a solution in which all orders present in the manufacturing system will be fulfilled and therefore the overall production problem is solved. The MPC scheme is shown to be flexible enough to cope with orders of unknown type arriving during runtime, which is a drastic change of the production problem that cannot be handled seamlessly with classical approaches. The guarantees, which are given by the Model Predictive Controller for the original production problem, are preserved despite those changes and the completion of all orders is still guaranteed.

Learning-based scheduling of flexible manufacturing systems using ensemble methods

Abstract Dispatching rules are commonly applied to schedule jobs in Flexible Manufacturing Systems (FMSs). However, the suitability of these rules relies heavily on the state of the system; hence, there is no single rule that always outperforms the others. In this scenario, machine learning techniques, such as support vector machines (SVMs), inductive learning-based decision trees (DTs), backpropagation neural networks (BPNs), and case based-reasoning (CBR), offer a powerful approach for dynamic scheduling, as they help managers identify the most appropriate rule in each moment. Nonetheless, different machine learning algorithms may provide different recommendations. In this research, we take the analysis one step further by employing ensemble methods, which are designed to select the most reliable recommendations over time. Specifically, we compare the behaviour of the bagging, boosting, and stacking methods. Building on the aforementioned machine learning algorithms, our results reveal that ensemble methods enhance the dynamic performance of the FMS. Through a simulation study, we show that this new approach results in an improvement of key performance metrics (namely, mean tardiness and mean flow time) over existing dispatching rules and the individual use of each machine learning algorithm.

Schedule generation schemes for flexible manufacturing systems with additional resources

The paper presents the problem of scheduling in flexible manufacturing systems with additional resources consideration. In the developed model, the basic resources e.g. and additional resources - staff, tools, etc. - are assigned to resource groups. The allocation of operations to particular resources is carried out through these groups - by indicating the number of resources needed from a given group. At the scheduling stage, depending on the expected values of beginning and ending of the operation, the most advantageous resource for allocation is selected. Forward and backward scheduling strategies and serial and parallel schedule generation schemes are discussed in this context.

Joint production and transportation scheduling in flexible manufacturing systems

This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.

Role of batch size in scheduling optimization of flexible manufacturing system using genetic algorithm

Flexible manufacturing system (FMS) readily addresses the dynamic needs of the customers in terms of variety and quality. At present, there is a need to produce a wide range of quality products in limited time span. On-time delivery of customers’ orders is critical in make-to-order (MTO) manufacturing systems. The completion time of the orders depends on several factors including arrival rate, variability, and batch size, to name a few. Among those, batch size is a significant construct for effective scheduling of an FMS, as it directly affects completion time. On the other hand, constant batch size makes MTO less responsive to customers’ demands. In this paper, an FMS scheduling problem with n jobs and m machines is studied to minimize lateness in meeting due dates, with focus on the impact of batch size. The effect of batch size on completion time of the orders is investigated under following strategies: (1) constant batch size, (2) minimum part set, and (3) optimal batch size. A mathematical model is developed to optimize batch size considering completion time, lateness penalties and setup times. Scheduling of an FMS is not only a combinatorial optimization problem but also NP-hard problem. Suitable solutions of such problems through exact methods are difficult. Hence, a meta-heuristic Genetic algorithm is used to optimize scheduling of the FMS.