JABIR MUMTAZ

主要任职:Ph.D. Associate Professor

其他任职:College of Mechanical and Electrical Engineering

个人简介

BIOGRAPHY
Dr. Jabir Mumtaz is an Associate Professor in College of Mechanical and Electrical Engineering at Wenzhou University, China. He has more than eight years of academic and industrial experience. His core competencies include operations research, production planning and scheduling, smart manufacturing, digital twin, industrial system optimization, and artificial intelligence optimization algorithms. Dr. Mumtaz's mission is to apply his expertise in industrial engineering and smart manufacturing systems to enhance their efficiency, productivity, and performance. He has published more than 45 research articles in high-impact journals and conferences, and has collaborated with various academic and industrial partners. He also teaches undergraduate and graduate courses in mechanical engineering, industrial engineering, and engineering management programs. He is open to research collaboration opportunities related to industry 4.0, supply chain management, production planning and scheduling, and optimization algorithms.


RESEARCH   PROJECT AND FUNDING


Title

Institute

Funding

Intelligent scheduling method for collaborative optimization of production and logistics in dynamic mixed flow workshop

Wenzhou Municipal Basic Scientific Research Projects (2024-27)

(No. G20240020)

Research and Academic Exchange on Intelligent Balance Optimization Methods for Assembly Lines.

International and Hong Kong, Macao and Taiwan high end talent exchange funding. China (2022-24)

(No. 2022A0505020007)

Hybrid flow shop intelligent   scheduling method driven by data-knowledge collaboration

Wenzhou Municipal Basic Scientific Research Projects (2023-26)

(No. G2023036)


PUBLICATIONS

 

INTERNATIONAL   BOOK CHAPTER

  1. Integration   of IoT and Blockchain for Smart and Secured Supply Chain Management: Case   Studies of China. In Utilizing Blockchain Technologies in Manufacturing and   Logistics Management (pp. 179-207). Yu, P., Liu, Z., Hanes, E., & Mumtaz, J.   (2022). IGI Global.

INTERNATIONAL JOURNAL PUBLICATIONS                                                    

  1. An   evolution strategies-based reinforcement learning algorithm for   multi-objective dynamic parallel machine scheduling problems.  Chen, Y., Zhang, *J., Mumtaz, J., Huang, S., & Zhou, S. (2025). Swarm and Evolutionary Computation

  2. An   efficient Q-learning integrated multi-objective hyper-heuristic approach for   hybrid flow shop scheduling problems with lot streaming.  Yarong C., Du, J., *Mumtaz J., Jingyan W., & Rauf M.   (2025). Expert Systems with Applications. 

  3. An   efficient Q-learning-based multi-objective intelligent hybrid genetic algorithm   for mixed-model assembly line efficiency. Rauf M., Mumtaz, J., Adeel R., Minhas K.A., and Usman, M. (2025). Symmetry-Basel. 

  4. Multi-objective   artificial bee colony algorithm for energy-efficient scheduling of unrelated   parallel batch processing machines with flexible preventive maintenance. Yarong C., Xu L., Rauf M., and   *Mumtaz J.   (2025). International Journal of Industrial   Engineering Computations

  5. Batch   processing machine scheduling problems using a self-adaptive approach based   on dynamic programming.  Chen, Y., Zhao, X., *Mumtaz, J., Guangyuan, C., & Wang,   C. (2024). Computers & Operations   Research.

  6. Multi-objective   human-robot collaborative disassembly line balancing problem considering   components remanufacture demand and hazard characteristics.  Lixia Z., Chen, Y. & *Mumtaz J. (2024).    Computers & Industrial   Engineering

  7. Dynamic   scheduling of hybrid flow shop problem with uncertain process time and   flexible maintenance using NeuroEvolution of Augmenting Topologies. Chen, Y., Zhang, J., Rauf, M.,   Mumtaz,   J.,   & Huang, S. (2024). IET Collaborative   Intelligent Manufacturing

  8. Two-stage   Double Deep Q-network Algorithm Considering External Non-Dominant Set for   Multi-objective Dynamic Flexible Job Shop Scheduling Problems.  Yue, L., Peng, K.,   Ding, L., Mumtaz, J., & Zou, T. (2024). Swarm   and Evolutionary Computation

  9. Solving   Line Balancing and AGV Scheduling Problems for Intelligent Decisions using a   Genetic-Artificial Bee Colony Algorithm. Mumtaz, J., Minhas, K. A., Rauf, M., Lei, Y., & Chen,   Y. (2024). Computers & Industrial   Engineering

  10. Simulation‐based optimization for order   release of printed circuit board workshop with process switching constraints.   Yue, L., Xu, Q., Wang,   H., Rauf, M., & Mumtaz, J. (2024). IET   Collaborative Intelligent Manufacturing

  11. An improved spider monkey optimization   algorithm for multi-objective planning and scheduling problems of PCB   assembly line. Yarong C., Jingyan Z., *Mumtaz   J., Shengwei Z., Lixia Z. (2023). Expert   Systems with Applications. 

  12. An efficient production planning approach-based   demand driven MRP under resource constraints. Guang Y.X., Zailin G., Lei Y., &   *Mumtaz J. (2023). International Journal of Industrial Engineering   Computations

  13. Joint optimization of production and   maintenance scheduling for unrelated parallel machine using hybrid discrete   spider monkey optimization algorithm. Chen, Y., Zhong, L., Shena, C., Mumtaz, J.,   & Chou, F. (2023). International Journal of Industrial Engineering   Computations

  14. Optimization of Bead Geometry during   Tungsten Inert Gas Welding Using Grey Relational and Finite Element Analysis.   Hanif, M., Shah, I., & *Mumtaz, J. (2023). Materials. 

  15. Energy-efficient scheduling of a two-stage   flexible printed circuit board flow shop using a hybrid Pareto spider monkey optimization   algorithm. Yue,   L., Wang, H., Mumtaz, J., Rauf, M., & Li, Z. (2023). Journal   of Industrial Information Integration

  16. An in-depth evaluation of surface   characteristics and key machining responses in WEDM of aerospace alloy under   varying electric discharge environments. Usman, M., Ishfaq, K., & Mumtaz, J.   (2022). International Journal of Advanced Manufacturing Technology. 

  17. Order Releasing and Scheduling for   Multi-Item MTO Industry: Efficient Heuristic Based on Drum Buffer Rope. Yue,   L., Xu, G., Mumtaz, J., Zou, T. (2022). Applied Sciences. 

  18. Abrasive water jet machining for a   high-quality green composite: the soft computing strategy for modeling and   optimization. Jagadish, G., Manjunath, P., Mumtaz,   J. and Li, Z. (2022). Journal of the   Brazilian Society of Mechanical Sciences and Engineering. 

  19. Modeling and Optimization for   Multi-Objective Nonidentical Parallel Machining Line Scheduling with a   Jumping Process Operation Constraint. Xu, G., Guan, Z., Chen, Y., Mumtaz, J., and Liang, J.   (2021). Symmetry-Basel. 

  20. Dynamic Mixed Model Lot sizing and   Scheduling for Flexible Machining Lines Using a Constructive Heuristic. Yue,   L., Chen, Y., Mumtaz, J., and Ullah, S. (2021). Processes.

  21. Surface modification for osseointegration of  Ti6Al4V ELI using powder mixed sinking EDM. M P. Mughal, M. U. Farooq, Mumtaz, J., M. Mia, M. Shareef (2021). Journal of the Mechanical Behavior of Biomedical Materials.

  22. A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines. Rauf,  M., Guan, Z., Mumtaz, J., Shehab, E., Jahanzaib, M., & Hanif, M. (2020). Robotics and Computer-Integrated Manufacturing.

  23. Integrated Planning and Scheduling of Multiple Manufacturing Projects Under Resource Constraints Using Raccoon Family Optimization Algorithm. Rauf, M., Guan, Z., Yue,      L., Mumtaz, J., & Ullah, S. (2020). IEEE Access.

  24. Multi-Level Planning and Scheduling for Parallel PCB Assembly Lines Using Hybrid Spider Monkey Optimization Approach. Mumtaz, J., Guan, Z., Yue, L., Wang, Z., Ullah, S., and Rauf, M. (2019). IEEE Access.

  25. Hybrid Spider Monkey Optimization Algorithm for Multi-Level Planning and Scheduling Problems of Assembly Lines. Mumtaz, J., Zailin Guan, Lei Yue, Zhang Li and He Cong (2019). International Journal of Production Research

  26. Multi-objective optimization for MQL assisted milling process based on Hybrid RSM & Multi-Objective Genetic   Algorithm. Mumtaz, J., Imran, M., Yue, Lei., Kaynat, Afzal (2019). Advances in Mechanical Engineering.

  27. Hybrid Particle Swarm Algorithm for Products’ Scheduling Problem in Cellular Manufacturing System. Khalid, Qazi        Salman, Muhammad Arshad, Mumtaz, J., and Sunghwan Kim. 11, no. 6 (2019): 729. Symmetry-Basel.

  28. Investigation of Electric Discharge Machining Parameters to Minimize Surface Roughness. M. Sarosh, M. Jahanzaib, Mumtaz, J. and S. Sarfraz (2016). Pakistan Journal of Science, 2016.

 

INTERNATIONAL  CONFERENCE PUBLICATIONS

  1. FlexSim-Simulated PCB Assembly Line Optimization Using Deep Q-Network.   Du, J., *Mumtaz, J., Zhao, W., *Mumtaz, J., & Huang, J. (2024). Engineering Proceedings.

  2. Improved Evolutionary Strategy Reinforcement Learning for   Multi-Objective Dynamic Scheduling of Hybrid Flow Shop Problem.  Zhang, J., Chen, Y., & *Mumtaz, J.   (2024). Engineering Proceedings.

  3. Using SABC Algorithm for Scheduling Unrelated Parallel Batch Processing   Machines Considering Deterioration Effects and Variable Maintenance.  Ji, Z., *Mumtaz, J., & Ke, K. (2024). Engineering Proceedings.

  4. Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of   Flexible Job Shop Problem.  Huang, J.,   Chen, Y., *Mumtaz, J., & Zhong, L. (2024). Engineering   Proceedings.

  5. Improved Spider Monkey Optimization Algorithm for Hybrid Flow Shop   Scheduling Problem with Lot Streaming. Du, J., *Mumtaz, J.,   & Zhong, J. (2023). Engineering Proceedings.

  6. A Flexible Job Shop Scheduling Method Based on   Multi-Fidelity Optimization. Zhong, L., Chen, Y., & *Mumtaz, J. (2023). Engineering Proceedings.

  7. Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of   Hybrid Flow Shop Problem. Zhang, J., Chen, Y., *Mumtaz, J., &   Zhou, S. (2023). Engineering Proceedings.

  8. Unrelated Parallel Batch Machine Scheduling Using a Modified ABC   Algorithm. Ke, K., Chen, Y., Mumtaz,   J., & Huang, S. (2023). Engineering   Proceedings.

  9. A Study of Mixed-Flow Human–Machine Collaborative Disassembly Line   Balancing Problem Based on Improved Artificial Fish Swarm Algorithm. Wang,   G., Chen, Y., Mumtaz,   J., & Zhu, L. (2023). Engineering   Proceedings.

  10. Multi-Level Rolling Horizon Planning and Scheduling Integrated with   Material Constraints using DBR Approach: A Heuristic for Smart   Manufacturing.  Liu, M., Mumtaz, J., & Li, G. (2022, November). 5th World   Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) IEEE.

  11. A Multi-Objective Scheduling Optimization Method for PCB Assembly Lines   Based on the Improved Spider Monkey Algorithm. Zhong, J., Chen, Y. and Mumtaz, J., 2022. Engineering Proceedings.

  12. An Integrated Model for Assembly Line Balancing and AGV Scheduling in   the wake of industry 4.0. Kaynat Afzal Minhas, Mumtaz, J. and Muhammad Umar Farooq. 7th International Conference on Business   Management (2020), Lahore, Pakistan.

  13. Application of an improved Spider Monkey Optimization algorithm for   component assignment problem in PCB assembly. Zhengya Wang, Mumtaz, J., Li Zhang. 11th CIRP Conference on Industrial   Product-Service Systems, 2019.

  14. Digital twin-based smart manufacturing system for project-based   organizations: a conceptual framework. R. Mudassar, G. Zailin, Mumtaz, J., Y. Lei and W. Hao. The 49th International Conference on Computer   & Industrial Engineering (CIE-49), Beijing, China (2019).

  15. A Conceptual Framework of Smart Manufacturing for PCB Industries. Mumtaz, J., G. Zailin, R. Mudassar, Y. Lei, H. Cong & W.   Hao. Computers & Industrial Engineering (CIE 48), New Zealand   (2018).

  16. Makespan Minimization for Flow Shop Scheduling Problems Using Modified   Operators in Genetic Algorithm. Mumtaz, J., Guan   Zailin, Jahanzaib Mirza, Mudassar Rauf, Shoaib Sarfraz, Essam Shehab. International   Conference on Manufacturing Research (ICMR), Sweden (2018).

  17. Multi-Criteria Inventory Classification Based on Multi-Criteria   Decision-Making (MCDM) Technique. Mudassar Rauf, Zailin Guan, Shoaib Sarfraz,   Mumtaz,   J., International Conference on Manufacturing   Research (ICMR), Sweden (2018).


教育经历

[1] 2009.8-2013.9
University of Engineering and Technology Lahore | 机械制造及其自动化 | 本科(学士) | 工学学士学位 | BS (Industrial & Manufacturing Engineering)
[2] 2014.8-2016.4
University of Engineering and Technology Taxila | 工学 | 研究生(硕士)毕业 | 工程硕士专业学位 | MS (Industrial Engineering)
[3] 2016.9-2019.7
华中科技大学 | Mechanical Engineering | 博士研究生毕业

工作经历

[1]  The University of Lahore 
[2]  Comet Sports 

社会兼职

[1] Google Scholar:
https://scholar.google.com/citations?user=7y93dWEAAAAJ&hl=en
[2] LinkedIn:
https://www.linkedin.com/in/dr-jabir-mumtaz-80376325/

团队成员

团队名称:智能制造和机器人技术研究所

团队介绍:

一、研究所师资力量

  智能制造与机器人技术研究所现有教师34人,其中正高职称7人、副高职称8人,具有博士学位28人,具有硕士生导师资格15人。团队成员情况如下:

  (1)所长:周宏明

  (2)副所长:陈希章、张祥雷

  (3)成员:周富得、谢尔盖、Subramanian.Jayalakshmi、陈亚绒、黄沈权、付培红、綦法群、陈一镖、俞平、黄克、王成湖、邱辉、李沛、Arivnd Singh、孙玉冰、张友志、冯铭、李偲偲、游威振、胡玮骏、陈芝向、朱立夏、刘德、于鲁川、周飘、彭康、Jabir Mumtaz、余胜东(外校人员)、孔向东(外校人员)

  (4)硕士生导师:周宏明、陈希章、张祥雷、陈亚绒、黄沈权、綦法群、陈一镖、俞平、黄克、冯铭、李偲偲、张友志、余胜东(校外)

  (5)研究所联系人:冯铭

  二、研究方向和特色

  研究所依托科技部激光加工机器人国家级国际科技合作基地、浙江省激光加工机器人重点实验室和温州大学激光与光电智能制造研究院等平台,长期从事智能制造、工业工程、3D打印、精密焊接、超精密加工、智能装备研发等方面的理论与技术研究。近年来,研究所主要承担国家自然科学基金项目、国家重点研发项目课题、浙江省重大科技专项以及温州市重大科技专项等项目,与当地企业联系紧密,承担多项横向委托技术并在相关企事业单位实现技术成果转化,取得了显著的经济效益和社会效益。主要研究方向如下:

  (1)3D打印:主要研究3D打印装备、过程控制与模拟计算、关键技术、高性能新材料制备等;智能传感机器人系统集成:主要涉及机器人触觉、视觉等关键技术研究,多传感器信息融合方法,机器人安全交互与控制技术等方面的研究。材料加工过程智能化:面向企业应用针对高端材料的冷、热加工先进工艺,开展制造过程模拟计算、工艺优化、性能提升和失效分析等研发工作。

  (2)超精密加工:主要涉及精细陶瓷、先进半导体以及镍/钛合金等难加工材料的多场辅助超精密加工;光学球面/非球面器件、航空叶轮等复杂曲面以及表面微结构的超精密加工;超精密加工过程分子动力学仿真分析。

  (3)智能制造与装备研发:在线检测、数据融合、智能决策、智能产品、智能装备、智能产线、智能车间、智能工厂。

  (4)工业工程研究方向:生产系统建模、优化和控制、云制造技术;物流系统规划设计与资源优化配置、制造系统调度;质量控制、系统可靠性建模及维护决策。

  (5)机器人触觉传感与识别:主要涉及柔性高密度触觉传感阵列结构设计、材料增敏、微纳加工工艺及多维力解耦方法研究;柔性高密度触觉传感阵列在机器人上的集成化研究;基于触觉传感阵列的机器人接触状态、接触对象物理特征识别方法研究。