Flexible Job Shop Research Articles

Multi-objective Dynamic Flexible Job Shop Scheduling with Biased Objectives via Multitask Genetic Programming

Multi-objective Dynamic Flexible Job Shop Scheduling with Biased Objectives via Multitask Genetic Programming

Evaluate Flexible Job Scheduling Problem Using Elitist Ant

One significant approach that is frequently utilized in many applications, particularly in industry, is scheduling .An extension of job scheduling, the Flexible Job Shop Scheduling Problem (FJSP), has a number of real-world uses .This subject has received a lot of attention due to its significance and complexity .This research discusses the importance of scheduling in real-life operations, particularly in production scheduling. It presents a model using Elitist Ant (EAS) to compute the maximum value (makspane) of the flexible job shop scheduling problem(FJSP). The model programmed by Python ver. (3.) with two instance from Brandimarte dataset to optimize logistics costs and meet customer demand. The study deduce that there are two relations by using two new formula. Firstly, the relation between number of elitist ant and total ants . Also, the evaporate value is effected by the first relation directly.

Flexible Job Shop Scheduling Problem Considering Upper Bounds for the Amount of Interruptions Between Operations and Machines Maintenance Activities

Flexible Job Shop Scheduling Problem Considering Upper Bounds for the Amount of Interruptions Between Operations and Machines Maintenance Activities

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

Simulation analysis and development of priority dispatching rules for a partial flexible job shop

Simulation analysis and development of priority dispatching rules for a partial flexible job shop

Genetic Programming for Dynamic Flexible Job Shop Scheduling: Evolution With Single Individuals and Ensembles

Genetic Programming for Dynamic Flexible Job Shop Scheduling: Evolution With Single Individuals and Ensembles

Learning to Dispatch for Flexible Job Shop Scheduling Based on Deep Reinforcement Learning via Graph Gated Channel Transformation

Learning to Dispatch for Flexible Job Shop Scheduling Based on Deep Reinforcement Learning via Graph Gated Channel Transformation

A Flexible Job Shop Scheduling Problem Considering On-Site Machining Fixtures: A Case Study From Customized Manufacturing Enterprise

A Flexible Job Shop Scheduling Problem Considering On-Site Machining Fixtures: A Case Study From Customized Manufacturing Enterprise

A multi-objective fuzzy flexible job shop scheduling problem considering the maximization of processing quality

This paper analyzes practical production characteristics, including customer's stringent quality requirements and uncertain processing time in aircraft shaft parts manufacturing. Considering the above characteristics, we propose a multi-objective fuzzy aircraft shaft parts production scheduling problem considering the maximization of production quality. We define this problem as a multi-objective fuzzy flexible job shop scheduling problem (MO-fFJSP) with fuzzy processing time. To address this problem, we developed an improved multi-objective spider monkey optimization (IMOSMO) algorithm. IMOSMO integrates strategies such as genetic operators, variable neighborhood search and Pareto optimization theory on the framework of the conventional Spider Monkey Optimization (SMO) framework and discretize the continuous SMO algorithm to solve MO-fFJSP. To enhance the efficiency of the algorithm, we further adjust the sequence of the local leader learning phase and the global leader learning phase within the proposed IMOSMO framework. We conduct a comparative analysis between the performance of IMOSMO and NSGA-Ⅱ using 28 cases of varying scales. The computational results demonstrate the superiority of our algorithm over NSGA-Ⅱ in terms of both solution diversity and quality. Moreover, the performance of the proposed algorithm upgrades as the problem scale increases.

Corrections to “An IoT-Enabled Real-Time Dynamic Scheduler for Flexible Job Shop Scheduling (FJSS) in an Industry 4.0-Based Manufacturing Execution System (MES 4.0)”

Corrections to “An IoT-Enabled Real-Time Dynamic Scheduler for Flexible Job Shop Scheduling (FJSS) in an Industry 4.0-Based Manufacturing Execution System (MES 4.0)”

A cooperative evolutionary algorithm with simulated annealing for integrated scheduling of distributed flexible job shops and distribution

Production and distribution are two essential parts in supply chains. An integration of production and distribution has received amount of attention from both academia and industry. This article investigates an integrated scheduling problem of distributed flexible job shops and distribution. First, a mixed integer programming model is developed to minimize maximum completion time. Second, a cooperative evolutionary algorithm with simulated annealing methods is proposed in consideration of the problem's characteristics. A three-link integer string is designed to represent three parts, i.e., factory assignment, machine assignment and operation sequence, in a solution. Three populations are constructed to search the above three parts via genetic operations and simulated annealing methods, respectively. A cooperation strategy is designed to generate a schedule. Then, a heuristic rule is devised to determine the delivery scheme for distribution. Finally, experiments are carried out via extending a set of benchmark instances. The proposed algorithm is compared to four state-of-the-art algorithms and an exact solver CPLEX. The results and discussions verify that the developed model and method have strong competitiveness for solving the considered problems.

Knowledge-Based Lot-Splitting Optimization Method for Flexible Job Shops Considering Energy Consumption

Knowledge-Based Lot-Splitting Optimization Method for Flexible Job Shops Considering Energy Consumption

Practical approach to flexible job shop scheduling with tool switching constraints using quantum annealing

Practical approach to flexible job shop scheduling with tool switching constraints using quantum annealing

Co-Evolution With Deep Reinforcement Learning for Energy-Aware Distributed Heterogeneous Flexible Job Shop Scheduling

Co-Evolution With Deep Reinforcement Learning for Energy-Aware Distributed Heterogeneous Flexible Job Shop Scheduling

An IoT-Enabled Real-Time Dynamic Scheduler for Flexible Job Shop Scheduling (FJSS) in an Industry 4.0-Based Manufacturing Execution System (MES 4.0)

An IoT-Enabled Real-Time Dynamic Scheduler for Flexible Job Shop Scheduling (FJSS) in an Industry 4.0-Based Manufacturing Execution System (MES 4.0)

Flexible Job Shop Scheduling Method for Optimizing Mold Resource Setup Time

Flexible Job Shop Scheduling Method for Optimizing Mold Resource Setup Time

Solving a Flexible Job-Shop Scheduling Problem considering Changes in Resources Capacity

Solving a Flexible Job-Shop Scheduling Problem considering Changes in Resources Capacity

混合改进麻雀算法解决柔性车间调度问题

混合改进麻雀算法解决柔性车间调度问题

Optimizing Automated Manufacturing Processes Using a Hybrid BRKGA Algorithm: A Case Study on Flexible Job-Shop Scheduling

In the modern era, industrial automation ofers significant competitive advantages. Replacing human labor with robots allows processes to be carried out much faster and more efficiently, with lower waste rates and the possibility of scheduling production 24/7. However, this also presents new challenges and opportunities in optimizing these automated processes. This paper proposes a hybrid algorithm BRKGA (Biased Random Key Genetic Algorithm) to optimize fexible job shop scheduling (FJSSP) problems in automated manufacturing systems. The algorithm is applied to a real-world case study in the Cinvestav Intelligent Manufacturing Laboratory, seeking to optimize the production of a four-piece product. The results demonstrate that the proposed algorithm outperforms previous results in terms of both, solution quality and computational efficiency.

A Feedback Learning-Based Memetic Algorithm for Energy-Aware Distributed Flexible Job-Shop Scheduling With Transportation Constraints

A Feedback Learning-Based Memetic Algorithm for Energy-Aware Distributed Flexible Job-Shop Scheduling With Transportation Constraints