Solving the Flexible Job Shop Scheduling Problem Using a Discrete Improved Grey Wolf Optimization Algorithm

Kong, Xianglong; Yao, Yunhang; Yang, Wei; Yang, Zhile; Su, Jinzhe

doi:10.3390/machines10111100

Cited by 13 publications

(20 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In swarm intelligence algorithms, the global search process is not random, but moves according to the position of the best individuals in the population. The sparrow search algorithm [ 32 ], gray wolf optimization algorithm [ 33 , 34 ], and Harris hawks optimizer [ 35 ] have been enjoying great interest. In this paper, the ACO algorithm was selected due to its popularity, easy implementation, self-learning ability, and simple framework.…”

Section: Integration Methodsmentioning

confidence: 99%

Integration of Discrete Simulation, Prediction, and Optimization Methods for a Production Line Digital Twin Design

Krenczyk

Paprocka

2023

Materials

View full text Add to dashboard Cite

The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods.

show abstract

Section: Integration Methodsmentioning

confidence: 99%

Integration of Discrete Simulation, Prediction, and Optimization Methods for a Production Line Digital Twin Design

Krenczyk

Paprocka

2023

Materials

View full text Add to dashboard Cite

show abstract

“…Ma et al 26 proposed a brain storm optimization method with particular multi-objective search mechanisms (MOBSO) to address the home health care scheduling and routing problem (HHCSRP). In addition to the above algorithms, meta-heuristic algorithms artificial bee colony (ABC), 27 grey wolf algorithm (GWO), 28 differential evolution algorithm (DE), 29 tabu search (TS) 30 have also been developed to solve different complex combinatorial optimization problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal deployment of the online monitoring equipment at the edges of substations considering spatial constraint

Xu,

Tong,

et al. 2024

Advances in Mechanical Engineering

View full text Add to dashboard Cite

To realize the optimal deployment of online monitoring equipment at the edges of substations under the cloud-edge collaboration framework, an optimal deployment model of edges considering spatial constraints is proposed. In the model, the constraints including edge deployment point, line of sight, as well as device pose, etc. are taken into account. To achieve the one-to-many collection of the deployed equipment, a mathematical model is constructed with the objectives of minimizing the shooting distance and the number of edge equipments. And an archive based multi-objective simulated annealing algorithm based on improved trending Markov chain (IAMOSA) is proposed to solve the problem. This algorithm utilizes greedy clustering to initialize deployment points, and the improved disturbance step length with tendency is used to search the neighborhood space. Besides, polynomial fitting Pareto front is also used to select and guide the Markov chain and archive population. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified through an experiment of optimal deployment of the edge equipments in a certain substation.

show abstract

“…The grey wolf optimisation (GWO) algorithm is characterised by robust convergence, minimal parameter requirements, straightforward programming implementation, and a strong local search capability [22]. GWO has excellent results in solving combinatorial optimisation problems such as vehicle scheduling, path planning, and job shop scheduling problems [23][24][25]. Still, GWO algorithms have the problem of quickly falling into the local optimum problem [26].…”

Section: Introductionmentioning

confidence: 99%

Scheduling of Container Transportation Vehicles in Surface Coal Mines Based on the GA–GWO Hybrid Algorithm

Hu,

Xiong,

Sun

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The coal loading operation of the coal preparation plant of an open pit coal mine causes chaos in coal mine vehicle scheduling due to the unreasonable arrival times of outgoing and container transportation vehicles. To further reduce the length of time that vehicle transportation equipment waits for each other and to reduce the total cost of container transportation, the optimisation model of container transportation vehicle scheduling in an open pit coal mine is constructed to minimise the minimum sum of the shortest time of container reversal and the lowest cost of container transportation. To accurately measure the total cost of container backward transportation, waiting time and unit waiting time cost parameters are introduced, and the total cost of container transportation is measured using the transportation cost and the waiting time cost transformation method. An improved grey wolf algorithm is proposed to speed up the convergence of the algorithm and improve the quality of the solution. When employing the genetic algorithm (GA) and grey wolf optimisation algorithm (GWO) for optimising the scheduling of container transport vehicles in coal mines, it is noted that while the GA can achieve the global optimum, its convergence speed is relatively slow. Conversely, the GWO converges more quickly, but it tends to be trapped in local optima. To accelerate the convergence speed of the algorithm and improve the solution quality, a hybrid GA−GWO algorithm is proposed, which introduces three genetic operations of selection, crossover, and mutation of GA into the GWO algorithm to prevent the algorithm from falling into the local optimum due to the fall; at the same time, it introduces hunting and attacking operations into the elite retention strategy of GA, which improves the stability of the algorithm’s global convergence. Analysis indicates that, compared to SA, GWO, and GA, the hybrid algorithm enhances optimisation speed by 43.1%, 46.2%, and 43.7%, increases optimisation accuracy by 4.12%, 6.1%, and 3.2%, respectively, and reduces the total container reversal time by 35.46, 22, and 31 h. The total cost of container transportation is reduced by 2437 RMB, 3512 RMB, and 1334 RMB, respectively.

show abstract

Solving the Flexible Job Shop Scheduling Problem Using a Discrete Improved Grey Wolf Optimization Algorithm

Cited by 13 publications

References 63 publications

Integration of Discrete Simulation, Prediction, and Optimization Methods for a Production Line Digital Twin Design

Integration of Discrete Simulation, Prediction, and Optimization Methods for a Production Line Digital Twin Design

Optimal deployment of the online monitoring equipment at the edges of substations considering spatial constraint

Scheduling of Container Transportation Vehicles in Surface Coal Mines Based on the GA–GWO Hybrid Algorithm

Contact Info

Product

Resources

About