Publications

[1]

K. Toda, T. Nishi, and Z. Liu, “Local explanation method for ordering policy in perishable inventory management problem using LLM and LIME,” in IFIP advances in information and communication technology, vol. 765, in IFIP advances in information and communication technology, vol. 765., Cham: Springer Nature Switzerland, 2026, pp. 289–302. doi: 10.1007/978-3-032-03534-9\_20.

[2]

M. S. Akter, T. Nishi, and Z. Liu, “A multi-period multi-objective model for sustainable supply chain optimization using MILP framework,” in IFIP advances in information and communication technology, vol. 765, in IFIP advances in information and communication technology, vol. 765., Cham: Springer Nature Switzerland, 2026, pp. 303–319. doi: 10.1007/978-3-032-03534-9\_21.

[3]

T. Kawabe, T. Nishi, Z. Liu, and T. Fujiwara, “Surrogate-assisted motion planning and layout design of robotic cellular manufacturing systems,” Engineering Applications of Artificial Intelligence, vol. 150, 2025, doi: 10.1016/j.engappai.2025.110530.

[4]

Y. Nomura, Z. Liu, and T. Nishi, “Deep reinforcement learning for dynamic pricing and ordering policies in perishable inventory management,” Applied Sciences, vol. 15, no. 5, 2025, doi: 10.3390/app15052421.

[5]

T. Taniguchi, T. Nishi, Z. Liu, and T. Fujiwara, “Simultaneous optimization of placement planning and motion planning for a single robotic arm using genetic algorithm,” in 2024 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2024, pp. 708–712. doi: 10.1109/ieem62345.2024.10857105.

[6]

H. Matsuda, T. Nishi, Z. Liu, and T. Fujiwara, “A transfer learning for estimation of operation time for 6-axis robot arms,” in 2024 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2024, pp. 1154–1158. doi: 10.1109/ieem62345.2024.10857125.

[7]

S. Korekane, T. Nishi, K. Tierney, and Z. Liu, “Neural network assisted branch and bound algorithm for dynamic berth allocation problems,” European Journal of Operational Research, vol. 319, no. 2, pp. 531–542, 2024, doi: 10.1016/j.ejor.2024.06.040.

[8]

藤原始史, 西竜志, and 劉子昂, “粒子群最適化による産業用スカラ型ロボットの省エネルギー動作計画の検討,” システム制御情報学会論文誌, vol. 37, no. 11, pp. 283–290, Nov. 2024, doi: 10.5687/iscie.37.283.

[9]

T. Kawabe, T. Nishi, Z. Liu, and T. Fujiwara, “Task planning for robot manipulator using natural language task input with large language models,” in 2024 IEEE 20th international conference on automation science and engineering (CASE), IEEE, 2024, pp. 3484–3489. doi: 10.1109/case59546.2024.10711671.

[10]

福島昂之介, 西竜志, 劉子昂, and 藤原始史, “Deep q-Networkとグラフ探索を組み合わせた複数台移動ロボットの経路計画法,” システム制御情報学会論文誌, vol. 37, no. 8, pp. 207–215, 2024, doi: 10.5687/iscie.37.207.

[11]

M. Kaimujjaman, T. Nishi, T. Fujiwara, and Z. Liu, “Designing a mobile manipulator and motion planning for autonomous navigation with a* and Q-learning algorithms,” in 2024 international symposium on flexible automation, American Society of Mechanical Engineers, 2024. doi: 10.1115/isfa2024-141112.

[12]

Z. Liu and T. Nishi, “Surrogate-assisted evolutionary computation for distributed simulation-based inventory optimization in serial supply chains,” in 2024 IEEE congress on evolutionary computation (CEC), IEEE, 2024, pp. 1–7. doi: 10.1109/cec60901.2024.10612189.

[13]

Z. Liu and T. Nishi, “Surrogate-assisted evolutionary optimization for perishable inventory management in multi-echelon distribution systems,” Expert Systems With Applications, vol. 238, 2024, doi: 10.1016/j.eswa.2023.122179.

[14]

Z. Liu and T. Nishi, “Data-driven evolutionary computation for service constrained inventory optimization in multi-echelon supply chains,” Complex & Intelligent Systems, vol. 10, no. 1, pp. 825–846, 2024, doi: 10.1007/s40747-023-01179-0.

[15]

N. Mikyu, T. Nishi, Z. Liu, and T. Fujiwara, “Three-dimensional bin packing problems with the operating time of a robot manipulator,” in IFIP advances in information and communication technology, in IFIP advances in information and communication technology., Cham: Springer Nature Switzerland, 2024, pp. 44–60. doi: 10.1007/978-3-031-65894-5\_4.

[16]

H. Kurakado, T. Nishi, and Z. Liu, “Data-driven scheduling of cellular manufacturing systems using process mining with petri nets,” in IFIP advances in information and communication technology, in IFIP advances in information and communication technology., Cham: Springer Nature Switzerland, 2024, pp. 17–28. doi: 10.1007/978-3-031-65894-5\_2.

[17]

T. Nishi, N. Debuchi, and Z. Liu, “Distributed optimization algorithm for multi-agent optimization problems using consensus control,” Journal of Advanced Mechanical Design Systems and Manufacturing, vol. 18, no. 5, 2024, doi: 10.1299/jamdsm.2024jamdsm0073.

[18]

T. Nishi, H. Kurakado, and Z. Liu, “Resource constrained project scheduling formulation for optimization of product input sequence and workforce scheduling for multi-stage multi-product cellular production lines,” Journal of Advanced Mechanical Design Systems and Manufacturing, vol. 18, no. 5, 2024, doi: 10.1299/jamdsm.2024jamdsm0062.

[19]

Y. Nomura, Z. Liu, and T. Nishi, “Deep reinforcement learning for perishable inventory optimization problem,” in 2023 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2023, pp. 0370–0374. doi: 10.1109/ieem58616.2023.10406759.

[20]

Z. Liu, S. Ito, T. Kawabe, T. Nishi, and T. Fujiwara, “Multi-objective optimization for three-dimensional packing problem using the sequence-triple representation with robot motion planning,” in 2023 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2023, pp. 1–5. doi: 10.1109/ieem58616.2023.10406772.

[21]

M. Shiraga, T. Nishi, Z. Liu, and T. Fujiwara, “Motion planning of industrial robot by data-driven optimization using petri nets,” in 2023 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2023, pp. 1199–1203. doi: 10.1109/ieem58616.2023.10406354.

[22]

Y. Nishihata, Z. Liu, and T. Nishi, “Evolutionary-game-theory-based epidemiological model for prediction of infections with application to demand forecasting in pharmaceutical inventory management problems,” Applied Sciences, vol. 13, no. 20, 2023, doi: 10.3390/app132011308.

[23]

J. Nakao, T. Nishi, and Z. Liu, “Robust optimization for bilevel production planning problems under customer’s uncertainties,” in 2023 IEEE international conference on systems, man, and cybernetics (SMC), IEEE, 2023, pp. 3546–3551. doi: 10.1109/smc53992.2023.10394540.

[24]

M. M. Alam, T. Nishi, Z. Liu, and T. Fujiwara, “A novel sampling-based optimal motion planning algorithm for energy-efficient robotic pick and place,” Energies, vol. 16, no. 19, 2023, doi: 10.3390/en16196910.

[25]

Z. Liu and T. Nishi, “Inventory control with lateral transshipment using proximal policy optimization,” in 2023 5th international conference on data-driven optimization of complex systems (DOCS), IEEE, 2023, pp. 1–6. doi: 10.1109/docs60977.2023.10294547.

[26]

K. Fukushima, T. Nishi, and Z. Liu, “A combined deep Q-network and graph search for three dimensional route planning problems for multiple mobile robots,” in 2023 IEEE 19th international conference on automation science and engineering (CASE), IEEE, 2023, pp. 1–6. doi: 10.1109/case56687.2023.10260638.

[27]

K. Hara, T. Nishi, Z. Liu, and T. Fujiwara, “Collision-free motion planning for multiple robot arms by combining deep Q-network and graph search algorithm,” in 2023 IEEE 19th international conference on automation science and engineering (CASE), IEEE, 2023, pp. 1–6. doi: 10.1109/case56687.2023.10260329.

[28]

T. Kawabe, T. Nishi, L. Ziang, and T. Fujiwara, “Symbolic sequence optimization approach for task and motion planning of robot manipulators,” in 2023 IEEE 19th international conference on automation science and engineering (CASE), IEEE, 2023. doi: 10.1109/case56687.2023.10260452.

[29]

Z. Liu, T. Kawabe, T. Nishi, S. Ito, and T. Fujiwara, “Surrogate-assisted multi-objective optimization for simultaneous three-dimensional packing and motion planning problems using the sequence-triple representation,” Applied Artificial Intelligence, vol. 38, no. 1, 2024, doi: 10.1080/08839514.2024.2398895.

[30]

T. Kawabe, T. Nishi, and Z. Liu, “Flexible route planning for multiple mobile robots by combining q–learning and graph search algorithm,” Applied Sciences, vol. 13, no. 3, 2023, doi: 10.3390/app13031879.

[31]

Z. Liu, R. Shirakashi, R. Kamiebisu, T. Nishi, and M. Matsuda, “Simulation-based optimization using virtual supply chain structured by the configuration platform,” IFAC-PapersOnLine, vol. 56, no. 2, pp. 7840–7845, 2023, doi: 10.1016/j.ifacol.2023.10.1145.

[32]

T. Kawabe, Z. Liu, T. Nishi, M. M. Alam, and T. Fujiwara, “Optimal motion planning and layout design in robotic cellular manufacturing systems,” in 2022 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2022, pp. 1541–1545. doi: 10.1109/ieem55944.2022.9989566.

[33]

Y. Nishihata, Z. Liu, and T. Nishi, “Epidemiological model of COVID-19 based on evolutionary game theory: Considering the viral mutations,” in 2022 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2022, pp. 686–690. doi: 10.1109/ieem55944.2022.9989989.

[34]

Y. Oyama, T. Nishi, Z. Liu, M. M. Alam, and T. Fujiwara, “Decision support system for selecting robot systems for pick-and-place operation of robot manipulator,” in 2022 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2022, pp. 0530–0534. doi: 10.1109/ieem55944.2022.9989780.

[35]

T. Bando, T. Nishi, M. M. Alam, Z. Liu, and T. Fujiwara, “Automatic generation of optimization model using process mining and petri nets for optimal motion planning of 6-DOF manipulators,” in 2022 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, 2022, pp. 11767–11772. doi: 10.1109/iros47612.2022.9982201.

[36]

H. Togo, K. Asanuma, T. Nishi, and Z. Liu, “Machine learning and inverse optimization for estimation of weighting factors in multi-objective production scheduling problems,” Applied Sciences, vol. 12, no. 19, 2022, doi: 10.3390/app12199472.

[37]

K. Nonoyama, Z. Liu, T. Fujiwara, M. M. Alam, and T. Nishi, “Energy-efficient robot configuration and motion planning using genetic algorithm and particle swarm optimization,” Energies, vol. 15, no. 6, 2022, doi: 10.3390/en15062074.

[38]

Z. Liu and T. Nishi, “Strategy dynamics particle swarm optimizer,” Information Sciences, vol. 582, pp. 665–703, 2022, doi: 10.1016/j.ins.2021.10.028.

[39]

Z. Liu and T. Nishi, “Adaptive heterogeneous particle swarm optimization with comprehensive learning strategy,” Journal of Advanced Mechanical Design Systems and Manufacturing, vol. 16, no. 4, 2022, doi: 10.1299/jamdsm.2022jamdsm0035.

[40]

R. Kamiebisu, T. Saso, J. Nakao, Z. Liu, T. Nishi, and M. Matsuda, “Use cases of the platform for structuring a smart supply chain in discrete manufacturing,” Procedia CIRP, vol. 107, pp. 687–692, 2022, doi: 10.1016/j.procir.2022.05.046.

[41]

N. Debuchi, T. Nishi, and Z. Liu, “Distributed optimization for supply chain planning for multiple companies using subgradient method and consensus control,” in IFIP advances in information and communication technology, in IFIP advances in information and communication technology., Cham: Springer Nature Switzerland, 2022, pp. 216–223. doi: 10.1007/978-3-031-16411-8\_27.

[42]

M. Matsuda, T. Nishi, R. Kamiebisu, M. Hasegawa, R. Alizadeh, and Z. Liu, “Use of virtual supply chain constructed by cyber-physical systems concept,” Procedia CIRP, vol. 104, pp. 351–356, 2021, doi: 10.1016/j.procir.2021.11.059.

[43]

Z. Liu and T. Nishi, “Multipopulation ensemble particle swarm optimizer for engineering design problems,” Mathematical Problems in Engineering, vol. 2020, no. 1, 2020, doi: 10.1155/2020/1450985.

[44]

T. Nishi, M. Matsuda, M. Hasegawa, R. Alizadeh, Z. Liu, and T. Terunuma, “Automatic construction of virtual supply chain as multi-agent system using enterprise E-catalogues,” International Journal of Automation Technology, vol. 14, no. 5, pp. 713–722, 2020, doi: 10.20965/ijat.2020.p0713.

[45]

Z. Liu and T. Nishi, “Analyzing just-in-time purchasing strategy in supply chains using an evolutionary game approach,” Journal of Advanced Mechanical Design Systems and Manufacturing, vol. 14, no. 5, 2020, doi: 10.1299/jamdsm.2020jamdsm0070.

[46]

Z. Liu and T. Nishi, “An evolutionary game model in closed-loop supply chain,” in 2019 IEEE international conference on industrial engineering and engineering management (IEEM), IEEE, 2019, pp. 896–900. doi: 10.1109/ieem44572.2019.8978741.

[47]

Z. Liu and T. Nishi, “Government regulations on closed-loop supply chain with evolutionarily stable strategy,” Sustainability, vol. 11, no. 18, 2019, doi: 10.3390/su11185030.