Mathematical modeling and a discrete artificial bee colony algorithm for the welding shop scheduling problem

AbstractThe welding process which is one of the most important assembly processes is widespread in the modern manufacturing industry, including aerospace, automotive and engineering machinery. The welding shop scheduling greatly impacts the efficiency of whole production system. However, few studies on the welding shop scheduling problem (WSSP) were reported. In this paper, a mathematical model and an improved discrete artificial bee colony algorithm (DABC) are proposed for the WSSP. Firstly, it is defined where multi-machine can process one job at the same time in the WSSP. Secondly, the mathematical models of WSSP have been constructed. Thirdly, an effective DABC is proposed to solve the WSSP, considering job permutation and machine allocation simultaneously. To improve the performance of proposed DABC algorithm, the effective operators have been designed. Three instances with different scales are used to evaluate the effectiveness of proposed algorithm. The comparisons with other two algorithms including genetic algorithm and grey wolf optimizer are also provided. Experimental results show that the proposed model and algorithm achieve good performance. Finally, the proposed model and DABC algorithm are applied in a real-world girder welding shop from a crane company in China. The results show that proposed model and algorithm reduces 55.17% production time comparing with the traditional algorithm and the scheduled machine allocation provides more reasonable arrangements for workers and machine loads.

[1]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[2]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[3]  Quan-Ke Pan,et al.  A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime , 2013, Comput. Oper. Res..

[4]  Mehmet Fatih Tasgetiren,et al.  Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion , 2016, Knowl. Based Syst..

[5]  K. Velazquez,et al.  Statistical Analysis for Quality Welding Process: An Aerospace Industry Case Study , 2014 .

[6]  Quan-Ke Pan,et al.  A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion , 2013 .

[7]  Chao Lu,et al.  A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry , 2017, Eng. Appl. Artif. Intell..

[8]  Chao Lu,et al.  An Effective Multiobjective Algorithm for Energy-Efficient Scheduling in a Real-Life Welding Shop , 2018, IEEE Transactions on Industrial Informatics.

[9]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[10]  Jie Hu,et al.  Research of new strategies for improving CBR system , 2012, Artificial Intelligence Review.

[11]  Mehmet Fatih Tasgetiren,et al.  A discrete artificial bee colony algorithm for the permutation flow shop scheduling problem with total flowtime criterion , 2010, IEEE Congress on Evolutionary Computation.

[12]  Shijin Wang,et al.  A branch-and-bound algorithm for two-stage no-wait hybrid flow-shop scheduling , 2015 .

[13]  José M. Valério de Carvalho,et al.  A branch-and-price algorithm for scheduling parallel machines with sequence dependent setup times , 2007, Eur. J. Oper. Res..

[14]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[15]  Ziad Kobti,et al.  A memetic algorithm for job shop scheduling using a critical-path-based local search heuristic , 2012, Memetic Comput..

[16]  Xiangtao Li,et al.  Multiobjective Discrete Artificial Bee Colony Algorithm for Multiobjective Permutation Flow Shop Scheduling Problem With Sequence Dependent Setup Times , 2017, IEEE Transactions on Engineering Management.

[17]  Quan-Ke Pan,et al.  An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time , 2016, Expert Syst. Appl..

[18]  Rong Su,et al.  Meta-Heuristics for Bi-Objective Urban Traffic Light Scheduling Problems , 2019, IEEE Transactions on Intelligent Transportation Systems.

[19]  Mohamed Barkaoui A co-evolutionary approach using information about future requests for dynamic vehicle routing problem with soft time windows , 2018, Memetic Comput..

[20]  Tao Zhang,et al.  Evolutionary Many-Objective Optimization: A Comparative Study of the State-of-the-Art , 2018, IEEE Access.

[21]  Peter J. Fleming,et al.  Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization , 2013, IEEE Transactions on Evolutionary Computation.

[22]  Liang Gao,et al.  An Effective Hybrid Genetic Algorithm and Variable Neighborhood Search for Integrated Process Planning and Scheduling in a Packaging Machine Workshop , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[23]  Thomas Stützle,et al.  A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem , 2007, Eur. J. Oper. Res..

[24]  J. Kamburowski,et al.  On the NEH heuristic for minimizing the makespan in permutation flow shops , 2007 .

[25]  Liang Gao,et al.  A hybrid backtracking search algorithm for permutation flow-shop scheduling problem minimizing makespan and energy consumption , 2015, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[26]  Hong Liu,et al.  Multi-objective artificial bee algorithm based on decomposition by PBI method , 2016, Applied Intelligence.

[27]  Liang Gao,et al.  An Improved Artificial Bee Colony algorithm for real-world hybrid flowshop rescheduling in Steelmaking-refining-Continuous Casting process , 2018, Comput. Ind. Eng..

[28]  Jianyong Sun,et al.  A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems , 2018, Knowl. Based Syst..

[29]  Tao Zhang,et al.  Localized Weighted Sum Method for Many-Objective Optimization , 2018, IEEE Transactions on Evolutionary Computation.

[30]  Yuyan Han,et al.  Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions , 2018 .