American Institute of Mathematical Sciences

Advanced
October  2009, 5(4): 749-765. doi: 10.3934/jimo.2009.5.749

The hot-rolling batch scheduling method based on the prize collecting vehicle routing problem

Tao Zhang 1, , W. Art Chaovalitwongse 2, , Yue-Jie Zhang 3, and  P. M. Pardalos 4,

1. 

School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China
2. 

Department of Industrial and Systems Engineering, Rutgers University, NJ 08854, United States
3. 

School of Computer Science, Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
4. 

Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, United States

Received  August 2008 Revised  July 2009 Published  August 2009

This paper studies a hot-rolling batch scheduling problem, which is a challenging problem commonly arising in the iron-steel industry. This problem deals with forming steel strips into rolling units and determining the rolling sequences while minimizing changes in characteristics (e.g., width, thickness and rigidity) of all neighbor steel strips and maximizing the machine utilization. Based on technical rules used in iron-steel production practice, we formulate this problem as a prize collecting vehicle routing problem, which is a hard combinatorial multi-objective optimization problem. We develop a new heuristic approach to solve this problem by enhancing the framework of particle swarm optimization (PSO). The key features of our approach are the utilization of number mapping function, which allows the PSO algorithm to deal with discrete problems, and the employment of tabu search at the end of every PSO iteration. We investigate and measure the performance of the proposed approach using three real life datasets obtained from a well-known iron-steel production company in China. The results suggest that our approach is very efficient and effective in providing high-quality and practical solutions, and our approach outperforms traditional PSO and tabu search algorithms based on these datasets.
Keywords: prize collecting vehicle routing problem, mixed integer programming; iron-steel industry., Hot-rolling batch scheduling, particle swarm optimization.
Mathematics Subject Classification: Primary: 90B30, 90C59; Secondary: 90B3.
Citation: Tao Zhang, W. Art Chaovalitwongse, Yue-Jie Zhang, P. M. Pardalos. The hot-rolling batch scheduling method based on the prize collecting vehicle routing problem. Journal of Industrial and Management Optimization, 2009, 5 (4) : 749-765. doi: 10.3934/jimo.2009.5.749
[1]

Wan Nor Ashikin Wan Ahmad Fatthi, Adibah Shuib, Rosma Mohd Dom. A mixed integer programming model for solving real-time truck-to-door assignment and scheduling problem at cross docking warehouse. Journal of Industrial and Management Optimization, 2016, 12 (2) : 431-447. doi: 10.3934/jimo.2016.12.431
[2]

Jiayu Shen, Yuanguo Zhu. An uncertain programming model for single machine scheduling problem with batch delivery. Journal of Industrial and Management Optimization, 2019, 15 (2) : 577-593. doi: 10.3934/jimo.2018058
[3]

Tao Zhang, Yue-Jie Zhang, Qipeng P. Zheng, P. M. Pardalos. A hybrid particle swarm optimization and tabu search algorithm for order planning problems of steel factories based on the Make-To-Stock and Make-To-Order management architecture. Journal of Industrial and Management Optimization, 2011, 7 (1) : 31-51. doi: 10.3934/jimo.2011.7.31
[4]

Louis Caccetta, Syarifah Z. Nordin. Mixed integer programming model for scheduling in unrelated parallel processor system with priority consideration. Numerical Algebra, Control and Optimization, 2014, 4 (2) : 115-132. doi: 10.3934/naco.2014.4.115
[5]

Elham Mardaneh, Ryan Loxton, Qun Lin, Phil Schmidli. A mixed-integer linear programming model for optimal vessel scheduling in offshore oil and gas operations. Journal of Industrial and Management Optimization, 2017, 13 (4) : 1601-1623. doi: 10.3934/jimo.2017009
[6]

Yasmine Cherfaoui, Mustapha Moulaï. Biobjective optimization over the efficient set of multiobjective integer programming problem. Journal of Industrial and Management Optimization, 2021, 17 (1) : 117-131. doi: 10.3934/jimo.2019102
[7]

Mingyong Lai, Xiaojiao Tong. A metaheuristic method for vehicle routing problem based on improved ant colony optimization and Tabu search. Journal of Industrial and Management Optimization, 2012, 8 (2) : 469-484. doi: 10.3934/jimo.2012.8.469
[8]

Mingyong Lai, Hongming Yang, Songping Yang, Junhua Zhao, Yan Xu. Cyber-physical logistics system-based vehicle routing optimization. Journal of Industrial and Management Optimization, 2014, 10 (3) : 701-715. doi: 10.3934/jimo.2014.10.701
[9]

Jian Sun, Haiyun Sheng, Yuefang Sun, Donglei Du, Xiaoyan Zhang. Approximation algorithm with constant ratio for stochastic prize-collecting Steiner tree problem. Journal of Industrial and Management Optimization, 2021  doi: 10.3934/jimo.2021116
[10]

Miao Yu. A solution of TSP based on the ant colony algorithm improved by particle swarm optimization. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 979-987. doi: 10.3934/dcdss.2019066
[11]

Junyuan Lin, Timothy A. Lucas. A particle swarm optimization model of emergency airplane evacuations with emotion. Networks and Heterogeneous Media, 2015, 10 (3) : 631-646. doi: 10.3934/nhm.2015.10.631
[12]

Yaw Chang, Lin Chen. Solve the vehicle routing problem with time windows via a genetic algorithm. Conference Publications, 2007, 2007 (Special) : 240-249. doi: 10.3934/proc.2007.2007.240
[13]

Qifeng Cheng, Xue Han, Tingting Zhao, V S Sarma Yadavalli. Improved particle swarm optimization and neighborhood field optimization by introducing the re-sampling step of particle filter. Journal of Industrial and Management Optimization, 2019, 15 (1) : 177-198. doi: 10.3934/jimo.2018038
[14]

Ye Tian, Cheng Lu. Nonconvex quadratic reformulations and solvable conditions for mixed integer quadratic programming problems. Journal of Industrial and Management Optimization, 2011, 7 (4) : 1027-1039. doi: 10.3934/jimo.2011.7.1027
[15]

Jiao-Yan Li, Xiao Hu, Zhong Wan. An integrated bi-objective optimization model and improved genetic algorithm for vehicle routing problems with temporal and spatial constraints. Journal of Industrial and Management Optimization, 2020, 16 (3) : 1203-1220. doi: 10.3934/jimo.2018200
[16]

Huai-Che Hong, Bertrand M. T. Lin. A note on network repair crew scheduling and routing for emergency relief distribution problem. Journal of Industrial and Management Optimization, 2019, 15 (4) : 1729-1731. doi: 10.3934/jimo.2018119
[17]

Min Zhang, Gang Li. Multi-objective optimization algorithm based on improved particle swarm in cloud computing environment. Discrete and Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1413-1426. doi: 10.3934/dcdss.2019097
[18]

Ning Lu, Ying Liu. Application of support vector machine model in wind power prediction based on particle swarm optimization. Discrete and Continuous Dynamical Systems - S, 2015, 8 (6) : 1267-1276. doi: 10.3934/dcdss.2015.8.1267
[19]

Mohamed A. Tawhid, Kevin B. Dsouza. Hybrid binary dragonfly enhanced particle swarm optimization algorithm for solving feature selection problems. Mathematical Foundations of Computing, 2018, 1 (2) : 181-200. doi: 10.3934/mfc.2018009
[20]

Xia Zhao, Jianping Dou. Bi-objective integrated supply chain design with transportation choices: A multi-objective particle swarm optimization. Journal of Industrial and Management Optimization, 2019, 15 (3) : 1263-1288. doi: 10.3934/jimo.2018095

2021 Impact Factor: 1.411

Tools

Metrics

  • PDF downloads (99)
  • HTML views (0)
  • Cited by (18)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy