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Research Articles

A new knowledge-guided multi-objective optimisation for the multi-AGV dispatching problem in dynamic production environments

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Pages 6030-6051 | Received 31 Dec 2021, Accepted 28 Aug 2022, Published online: 23 Sep 2022

References

  • Ahmadi-Javid, Amir, and Amir Ardestani-Jaafari. 2021. “The Unequal Area Facility Layout Problem with Shortest Single-Loop AGV Path: How Material Handling Method Matters.” International Journal of Production Research 59 (8): 2352–2374.
  • Bae, Jungyun, and Woojin Chung. 2017. “A Heuristic for a Heterogeneous Automated Guided Vehicle Routing Problem.” International Journal of Precision Engineering and Manufacturing 18 (6): 795–801.
  • Bandaru, S., A. H. C. Ng, and K. Deb. 2017. “Data Mining Methods for Knowledge Discovery in Multi-Objective Optimization: Part B – New Developments and Applications.” Expert Systems with Applications 70: 119–138.
  • Barba-Gonzalez, Cristobal, Antonio J. Nebro, Jose Garcia-Nieto, Maria del Mar Roldan-Garcia, Ismael Navas-Delgado, and Jose F. Aldana-Montes. 2021. “Injecting Domain Knowledge in Multi-Objective Optimization Problems: A Semantic Approach.” Computer Standards & Interfaces 78: 103546.
  • Beume, Nicola, Boris Naujoks, and Michael Emmerich. 2007. “SMS-EMOA: Multiobjective Selection Based on Dominated Hypervolume.” European Journal of Operational Research 181 (3): 1653–1669.
  • Bilyk, A., and L. Monch. 2012. “A Variable Neighborhood Search Approach for Planning and Scheduling of Jobs on Unrelated Parallel Machines.” Journal of Intelligent Manufacturing 23 (5): 1621–1635.
  • Cheng, Ran, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. 2016. “A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization.” Ieee Transactions on Evolutionary Computation 20 (5): 773–791.
  • Deng, Wu, Xiaoxiao Zhang, Yongquan Zhou, Yi Liu, Xiangbing Zhou, Huiling Chen, and Huimin Zhao. 2022. “An Enhanced Fast non-Dominated Solution Sorting Genetic Algorithm for Multi-Objective Problems.” Information Sciences 585: 441–453.
  • Desaulniers, Guy, André Langevin, Diane Riopel, and Bryan Villeneuve. 2003. “Dispatching and Conflict-Free Routing of Automated Guided Vehicles: An Exact Approach.” International Journal of Flexible Manufacturing Systems 15 (4): 309–331.
  • Duan, Wenzhe, Zhengyang Li, Mengchen Ji, Yixin Yang, Shouyang Tang, Bo Liu, and Ieee. 2016. “A Hybrid Estimation of Distribution Algorithm for Distributed Permutation Flowshop Scheduling with Flowline Eligibility.” Paper presented at the IEEE congress on evolutionary computation (CEC) held as part of IEEE world congress on computational intelligence (IEEE WCCI), Vancouver, Canada, Jul 24–29.
  • Fang, Yilin, Hao Ming, Miqing Li, Quan Liu, and Pham Duc Truong. 2020. “Multi-objective Evolutionary Simulated Annealing Optimisation for Mixed-Model Multi-Robotic Disassembly Line Balancing with Interval Processing Time.” International Journal of Production Research 58 (3): 846–862.
  • Fazlollahtabar, H., and S. Hassanli. 2018. “Hybrid Cost and Time Path Planning for Multiple Autonomous Guided Vehicles.” Applied Intelligence 48 (2): 482–498.
  • Fazlollahtabar, Hamed, and Mohammad Saidi-Mehrabad. 2015. “Methodologies to Optimize Automated Guided Vehicle Scheduling and Routing Problems: A Review Study.” Journal of Intelligent & Robotic Systems 77 (3–4): 525–545.
  • Fazlollahtabar, Hamed, Mohammad Saidi-Mehrabad, and Jaydeep Balakrishnan. 2015. “Mathematical Optimization for Earliness/Tardiness Minimization in a Multiple Automated Guided Vehicle Manufacturing System via Integrated Heuristic Algorithms.” Robotics and Autonomous Systems 72: 131–138.
  • Guerreiro, Andreia P., Vasco Manquinho, and Jose Rui Figueira. 2021. “Exact Hypervolume Subset Selection Through Incremental Computations.” Computers & Operations Research 136: 105471.
  • Guo, Wei, Pingyu Jiang, and Maolin Yang. 2022. “Unequal Area Facility Layout Problem Solving: A Real Case Study on an air-Conditioner Production Shop Floor.” International Journal of Production Research 1–18.
  • Guo, Jun, Zhipeng Pu, Baigang Du, and Yibing Li. 2022. “Multi-objective Optimisation of Stochastic Hybrid Production Line Balancing Including Assembly and Disassembly Tasks.” International Journal of Production Research 60 (9): 2884–2900.
  • Heyong, Wang, and Hong Ming. 2019. “Supervised Hebb Rule Based Feature Selection for Text Classification.” Information Processing & Management 56 (1): 167–191.
  • Hu, Hongtao, Xurui Yang, Shichang Xiao, and Feiyang Wang. 2021. “Anti-conflict AGV Path Planning in Automated Container Terminals Based on Multi-Agent Reinforcement Learning.” International Journal of Production Research 1–16.
  • Hwang, Illhoe, and Young Jae Jang. 2020. “Q(Lambda) Learning-Based Dynamic Route Guidance Algorithm for Overhead Hoist Transport Systems in Semiconductor Fabs.” International Journal of Production Research 58 (4): 1199–1221.
  • Jazzbin. 2021. “Geatpy: The Genetic and Evolutionary Algorithm Toolbox with High Performance in Python.” http://www.geatpy.com/.
  • Kinderman, Albert J., and John F %J ACM Transactions on Mathematical Software Monahan. 1977. “Computer Generation of Random Variables Using the Ratio of Uniform Deviates.” ACM Transactions on Mathematical Software 3 (3): 257–260.
  • Krishnamurthy, Nirup N., Rajan Batta, and Mark H. Karwan. 1993. “Developing Conflict-Free Routes for Automated Guided Vehicles.” Operations Research 41 (6): 1077–1090.
  • Larrañaga, Pedro, and Jose A Lozano. 2001. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Vol. 2. Springer Science & Business Media.
  • Lee, Chen Wei, Wai Peng Wong, Joshua Ignatius, Amirah Rahman, and Ming-Lang Tseng. 2020. “Winner Determination Problem in Multiple Automated Guided Vehicle Considering Cost and Flexibility.” Computers & Industrial Engineering 142: 106337.
  • Li, Guomin, Xinyu Li, Liang Gao, and Bing Zeng. 2019. “Tasks Assigning and Sequencing of Multiple AGVs Based on an Improved Harmony Search Algorithm.” Journal of Ambient Intelligence and Humanized Computing 10 (11): 4533–4546.
  • Li, Maojia P., Prashant Sankaran, Michael E. Kuhl, Raymond Ptucha, Amlan Ganguly, Andres Kwasinski, and Ieee. 2019. “Task Selection by Autonomous Mobile Robots in a Warehouse Using Deep Reinforcement Learning.” Paper presented at the winter simulation conference (WSC), National Harbor, MD, Dec 8–11.
  • Li, Xijun, Mingxuan Yuan, Di Chen, Jianguo Yao, Jia Zeng, and Acm. 2018. “A Data-Driven Three-Layer Algorithm for Split Delivery Vehicle Routing Problem with 3D Container Loading Constraint.” Paper presented at the Kdd'18: proceedings of the 24th Acm sigkdd international conference on knowledge discovery & data mining, London, United Kingdom, August 19–23.
  • Malopolski, W. 2018. “A Sustainable and Conflict-Free Operation of AGVs in a Square Topology.” Computers & Industrial Engineering 126: 472–481.
  • Miyamoto, Toshiyuki, and Kensuke Inoue. 2016. “Local and Random Searches for Dispatch and Conflict-Free Routing Problem of Capacitated AGV Systems.” Computers & Industrial Engineering 91: 1–9.
  • Mousavi, Maryam, Hwa Jen Yap, Siti Nurmaya Musa, Farzad Tahriri, and Siti Zawiah Md Dawal. 2017. “Multi-objective AGV Scheduling in an FMS Using a Hybrid of Genetic Algorithm and Particle Swarm Optimization.” PLOS ONE 12 (3): e0169817.
  • Murakami, Keisuke. 2020. “Time-space Network Model and MILP Formulation of the Conflict-Free Routing Problem of a Capacitated AGV System.” Computers & Industrial Engineering 141: 106270.
  • Nishi, Tatsushi, Yuichiro Hiranaka, and Ignacio E. Grossmann. 2011. “A Bilevel Decomposition Algorithm for Simultaneous Production Scheduling and Conflict-Free Routing for Automated Guided Vehicles.” Computers & Operations Research 38 (5): 876–888.
  • Nishida, Kosei, and Tatsushi Nishi. 2022. “Dynamic Optimization of Conflict-Free Routing of Automated Guided Vehicles for Just-in-Time Delivery.” Ieee Transactions on Automation Science and Engineering 1–16.
  • Qin, Wei, Zilong Zhuang, Yaoming Zhou, and Yinbin Sun. 2021. “Dynamic Dispatching for Interbay Automated Material Handling with lot Targeting Using Improved Parallel Multiple-Objective Genetic Algorithm.” Computers & Operations Research 131: 105264.
  • Riazi, Sarmad, Thomas Diding, Petter Falkman, Kristofer Bengtsson, and Bengt Lennartson. 2019. “Scheduling and Routing of AGVs for Large-Scale Flexible Manufacturing Systems.” Paper presented at the 15th IEEE international conference on automation science and engineering (IEEE CASE), Vancouver, Canada, Aug 22–26.
  • Sathiya, V., M. Chinnadurai, S. Ramabalan, and Andrea Appolloni. 2021. “Mobile Robots and Evolutionary Optimization Algorithms for Green Supply Chain Management in a Used-car Resale Company.” Environment, Development and Sustainability 23 (6): 9110–9138.
  • Scheepers, Christiaan, Andries P. Engelbrecht, and Christopher W. Cleghorn. 2019. “Multi-guide Particle Swarm Optimization for Multi-Objective Optimization: Empirical and Stability Analysis.” Swarm Intelligence 13 (3–4): 245–276.
  • Singh, Nitish, Quang-Vinh Dang, Alp Akcay, Ivo Adan, and Tugce Martagan. 2022. “A Matheuristic for AGV Scheduling with Battery Constraints.” European Journal of Operational Research 298 (3): 855–873.
  • Sinriech, D., and J. Kotlarski. 2002. “A Dynamic Scheduling Algorithm for a Multiple-Load Multiple-Carrier System.” International Journal of Production Research 40 (5): 1065–1080.
  • Subbaiah, K. V., M. Nageswara Rao, and K. Narayana Rao. 2009. “Scheduling of AGVs and Machines in FMS with Makespan Criteria Using Sheep Flock Heredity Algorithm.” International Journal of the Physical Sciences 4 (3): 139–148.
  • Subramanian, Senthilkumar, Chandramohan Sankaralingam, Rajvikram Madurai Elavarasan, Raghavendra Rajan Vijayaraghavan, Kannadasan Raju, and Lucian Mihet-Popa. 2021. “An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III.” Sustainability 13 (1): 410.
  • Sun, Yu, and Haisheng Li. 2020. “An End-to-End Reinforcement Learning Method for Automated Guided Vehicle Path Planning.“ Paper presented at the international symposium on artificial intelligence and robotics 2020.
  • Tsolakis, Naoum, Dimitris Zissis, Spiros Papaefthimiou, and Nikolaos Korfiatis. 2022. “Towards AI Driven Environmental Sustainability: An Application of Automated Logistics in Container Port Terminals.” International Journal of Production Research 60 (14): 4508–4528.
  • Umar, U. A., M. K. A. Ariffin, N. Ismail, and S. H. Tang. 2015. “Hybrid Multiobjective Genetic Algorithms for Integrated Dynamic Scheduling and Routing of Jobs and Automated-Guided Vehicle (AGV) in Flexible Manufacturing Systems (FMS) Environment.” The International Journal of Advanced Manufacturing Technology 81 (9–12): 2123–2141.
  • Wang, Sheng-yao, Ling Wang, Min Liu, and Ye Xu. 2013. “An Effective Estimation of Distribution Algorithm for Solving the Distributed Permutation Flow-Shop Scheduling Problem.” International Journal of Production Economics 145 (1): 387–396.
  • Wang, Zehao, and Qingcheng Zeng. 2022. “A Branch-and-Bound Approach for AGV Dispatching and Routing Problems in Automated Container Terminals.” Computers & Industrial Engineering 166: 107968.
  • Xu, Wenxiang, Shunsheng Guo, Xixing Li, Chen Guo, Rui Wu, and Zhao Peng. 2019. “A Dynamic Scheduling Method for Logistics Tasks Oriented to Intelligent Manufacturing Workshop.” Mathematical Problems in Engineering 2019: 1–18.
  • Yao, Fengjia, Bugra Alkan, Bilal Ahmad, and Robert Harrison. 2020. “Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation.” Sensors 20 (21): 6333.
  • Yu, Guo, Yaochu Jin, and Markus Olhofer. 2021. “A Multi-Objective Evolutionary Algorithm for Finding Knee Regions Using Two Localized Dominance Relationships.” Ieee Transactions on Evolutionary Computation 25 (1): 145–158.
  • Zhang, B., Q. K. Pan, L. Gao, L. L. Meng, X. Y. Li, and K. K. Peng. 2020. “A Three-Stage Multiobjective Approach Based on Decomposition for an Energy-Efficient Hybrid Flow Shop Scheduling Problem.” IEEE Transactions on Systems, Man, and Cybernetics: Systems 50 (12):4984–4999.
  • Zhang, Zi-Qi, Bin Qian, Rong Hu, Huai-Ping Jin, and Ling Wang. 2021. “A Matrix-Cube-Based Estimation of Distribution Algorithm for the Distributed Assembly Permutation Flow-Shop Scheduling Problem.” Swarm and Evolutionary Computation 60: 100785.
  • Zhang, Xu-jin, Hong-yan Sang, Jun-qing Li, Yu-yan Han, and Peng Duan. 2022. “An Effective Multi-AGVs Dispatching Method Applied to Matrix Manufacturing Workshop.” Computers & Industrial Engineering 163: 107791.
  • Zhou, Xiuling, Ping Guo, C. L. Philip Chen, and Ieee. 2012. “An Algorithm for Calculating the Hypervolume Contribution of a Set.” Paper presented at the 2012 world automation congress (Wac), Yichang, China, Nov 20–22.
  • Zhou, Binghai, and Zhaoxu He. 2021. “A Novel Hybrid-Load AGV for JIT-Based Sustainable Material Handling Scheduling with Time Window in Mixed-Model Assembly Line.” International Journal of Production Research 1–22.
  • Zitzler, E., and L. Thiele. 1999. “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach.” Ieee Transactions on Evolutionary Computation 3 (4): 257–271.
  • Zou, Wen-Qiang, Quan-Ke Pan, Tao Meng, Liang Gao, and Yu-Long Wang. 2020. “An Effective Discrete Artificial bee Colony Algorithm for Multi-AGVs Dispatching Problem in a Matrix Manufacturing Workshop.” Expert Systems with Applications 161: 113675.

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