Chapter in Encyclopedia:-

Mitsuo Gen and L. Lin: “Genetic Algorithms”, in Benjamin Wah ed.: Wiley
Encyclopedia of Computer Science and Engineering, pp.1367-1381, John Wiley &Sons, Hoboken, N.J., 2009.

Chapters in Handbooks:
1. Mitsuo Gen and L. Lin: Chapter C.29: “Evolutionary Techniques for Automation”, in
Shimon Y. Nof Ed.: Springer Handbook of Automation, pp.487-502, Springer, London,
2009.
2. Mitsuo Gen & L. Lin: ”Genetic Algorithm”, in Benjamin Wah Ed.: Wiley Encyclopedia of
Computer Science and Engineering, pp.1367-1381, John Wiley & Sons, Hoboken, 2008.
3. Mitsuo Gen: Chapter 38: “Genetic Algorithms and their Applications”, in Hoang Pham
Ed.: Springer Handbook of Engineering Statistics, pp.749-773, Springer-Verlag, New
York, 2006.
4. R. Cheng and Mitsuo Gen: “Production planning and scheduling”, in J. Wang and A. Kusiak
Eds: Handbook of Computational Intelligence in Design and Manufacturing, CRC Press
LLC, 2001
5. 玄光男: 第42 章信頼性工学，日本ファジィ学会編：ファジィとソフトコンピュ-ティン
グ・ハンドブック，共立出版，2000.9;(Mitsuo Gen: Chapter 42 “Reliability Engineering”, in SOFT Ed.: “Handbook of Fuzzy
and Soft Computing”, Kyoritsu Publisher, Tokyo; in Japanese)
6. 玄光男: 第19 章進化アルゴリズム，日本ファジィ学会編：ファジィとソフトコンピュ-ティング・ハンドブック，共立出版，2000.9;(Mitsuo Gen: Chapter 19 “Evolutionary Algorithms”, in SOFT Ed.: “Handbook of Fuzzy
and Soft Computing”, Kyoritsu Publisher, Tokyo; in Japanese)
7. 玄光男: 第8 章最適化手法, pp.506-513, 生産管理の事典, 朝倉書店, 1999.
(Mitsuo Gen: Chapter 8: “Optimization Methods”, in Ohno et al Eds.: “Handbook of
Production Control”, Asakura Publisher, Tokyo, 1999; in Japanese)
8. Mitsuo Gen: G19.4 “Inventory Control”, in E. Ruspini, P. Bonissone and W. Pedrycz Eds.:“Handbook of Fuzzy Computation”, Oxford Univ. Press, New York, 1999.
9. 玄光男: Section 1.6.6 遺伝的アルゴリズムによるシステム信頼性の最適設計，信頼性ハンドブック, 日科技連, 1997; (Mitsuo Gen: Section 1.6.6 “Optimal Design for System
Reliability by Genetic Algorithm”, in Reliability Eng. Assoc. of Japan Ed.: “Handbook of
Reliability”, 1997; in Japanese)

Chapters/Papers in Books:
1.

Mitsuo Gen and K. Ida: “Advances in multiobjective hybrid genetic algorithms forintelligent manufacturing and logistics systems”, in Yoshida et al Eds.: Active Media
Technology, Lecture Notes in Computer Science 8210, pp. 379-389, 2013.

2. Y. Kikuchi, K. Ida and Mitsuo Gen: “GA for JSP with delivery time”, in Yoshida et al
Eds.: Active Media Technology, Lecture Notes in Computer Science 8210, pp. 369378, 2013.
3. Mitsuo Gen and M. Yoo: Chapter 13: “Real Time Task Scheduling using Hybrid Genetic
Algorithms”, in A-E Hassanien and, A. Abraham Eds.: Computational Intelligence inMultimedia Processing: Recent Advances, in Studies in Computational Intelligence,
pp.319-350, Springer, 2010.
4. Mitsuo Gen and H. Zhang: “Effective designing chromosome for optimizing advancedplanning and scheduling”, in C. H. Dagli et al. Eds.: Intelligent Engineering Systems
through Artificial Neural Networks, vol.16, ASME Press, pp.61-66, 2006.
5. L. Lin and Mitsuo Gen: “An effective analysis of multiobjective EAs for bicriteria
communication spanning tree problem”, in C.H. Dagli et al. Eds.: Intelligent
Engineering Systems through Artificial Neural Networks, vol.16, ASME Press, pp.5560, 2006.
6. G. Zhou, H. Min, and Mitsuo Gen: “The allocation of customers to capacitated primaryand secondary warehouses: Genetic algorithm approach”, pp.149-162, in K. D.
Lawrence Ed. Mathematical Programming, Elsevier Pub., Amsterdam, 2004.
7. Mitsuo Gen and A. Syarif: Part 3: “Multi-stage supply chain network by hybrid
genetic algorithms with fuzzy logic controller”, pp.181-196, in Jose Luis Verdegay
Ed.: Fuzzy Sets based Heuristics for Optimization, Springer Verlag, New York,
2003.
8.

Y. Yun and Mitsuo Gen: Part 3: “Adaptive Hybrid Genetic Algorithm with Fuzzy
Logic Controller”, pp.251-263, in Jose Luis Verdegay Ed.: Fuzzy Sets based
Heuristics for Optimization, Springer Verlag, New York, 2003.

9. Mitsuo Gen and J. R. Kim: Chapter 8: “GA-based Optimization of Reliability Design”,
pp.191-218, in P. Bentley Ed.: Evolutionary Design by Computers, Morgan Kaufman,
San Francisco, 1999.
10. Y. Tsujimura and Mitsuo Gen: Chapter 35: “Evolutionary Recognition of Featuresfrom CAD Data”, in X. Yao, J.H. Kim and T. Furuhashi Eds.: Simulated Evolution andLearning, Springer-Verlag, 1999.
11. Mitsuo Gen, Y. Li and K. Ida: “Solving multi-objective transportation problem byspanning tree-based genetic algorithm”, pp.95-108, in I. Parmee Ed.: Adaptive
Computing in Design and Manufacture, Springer-Verlag, London, 1998.

12. Mitsuo Gen and J. Kim: “GA-Based Optimal Network Design: A State-of-the-Art
Survey”, in C.H. Dagli, et al Eds.: Intelligent Engineering System, vol.8, pp.247-252,
1998.
13. R. Cheng and Mitsuo Gen: “Fuzzy vehicle routing and scheduling problem using genetic
algorithms”, in Herrera, F. and J. Verdegay Eds: Genetic Algorithms and Soft
Computing, pp.683-709, Physica-Verlag, 1996.

Research Papers published in International Journals: 2002-20182018:==================================================================
1. Mitsuo Gen, L. Lin, Y.S. Yun and H. Inoue, 2018: Recent advances in hybrid prioritybased genetic algorithms for logistics and SCM network design, Computers &Industrial Engineering, vol.115, pp.394-412. doi.org/10.1016/j.cie.2018.08.025.
2. L. Lin and Mitsuo Gen, 2018: Hybrid Evolutionary Optimization with Learning for
Production Scheduling: State-of-the-Art Survey on Algorithms and Applications,Int. J. of Production Research, vol. 56, no.1-2, 193–223;
doi.org/10.1080/00207543.2018.1437288.
3. T. Jamrus, C-F Chien, Mitsuo Gen, and K. Sethanan, 2018: Hybrid particle
swarm optimization combined with genetic operators for flexible job-shop
scheduling under uncertain processing time for semiconductor manufacturing,IEEE Trans. on Semiconductor Manuf., vol.31, no.1, pp.32-41; doi.org/
10.1109/TSM.2017.2758380.
4. L.Z. Zhao, C-F Chien & Mitsuo Gen, 2018: A bi-objective genetic algorithm forintelligent rehabilitation scheduling considering therapy precedence constraints, J.
of Intelligent Manufacturing, 29: 973–988; DOI: 10.1007/s10845-015-1149-y.
5. L. Sun, L. Lin, H. Li, Mitsuo Gen, 2018: Hybrid cooperative co-evolution
algorithm for uncertain vehicle scheduling, IEEE Access, 11p;
doi.org/10.1109/ACCESS.2018.2797268.

2017:==================================================================
6. Mitsuo Gen, W.Q. Zhang, L. Lin, and Y.S. Yun, 2017: Recent Advances in Hybrid
Evolutionary Algorithms for Multiobjective Manufacturing Scheduling, Computers& Industrial Engineering, vol.112, pp.616-633. doi.org/10.1016/j.cie.2016.12.045.
7. X.C. Hao, Mitsuo Gen, L. Lin and G.A. Suer, 2017: Effective multiobjective EDA for
bi-criteria stochastic job-shop scheduling problem, J. of Intelligent
Manufacturing, vol.28, pp,833–845; DOI: 10.1007/s10845-014-1026-0.

8. C. Chamnanlor, K. Sethanan, Mitsuo Gen and C-F Chien, 2017: Embedding ant
system in genetic algorithm for re-entrant hybrid flow shop scheduling problems
with time window constraints, Journal of Intelligent Manufacturing, 28: 1915–1931;
DOI: 10.1007/s10845-015-1078-9.
9. K. Hu, X.F Zhang, Mitsuo Gen and J.B. Jo, 2017: A new model for single machinescheduling with uncertain processing time, J. of Intelligent Manufacturing, 28:
717–725; DOI: 10.1007/s10845-015-1033-9.
10. JQ Guo, XY Wang, SY Fan, and Mitsuo Gen, 2017: Dynamic joint construction andoptimal strategy of multi-objective multi-period multi-stage governmententerprise reverse logistics network: A case study of lead battery in Shanghai,Computers & Industrial Engineering, vol.106, pp.351-360.
11. W.Q. Zhang, W.T. Xu, G. Liu and Mitsuo Gen, 2017: An effective hybrid evolutionary
algorithm for stochastic multiobjective assembly line balancing problem, J. ofIntelligent Manufacturing, 28: 783-790, DOI: 10.1007/s10845-015-1037-5.

2016:==================================================================
12. Mitsuo Gen, X.C. Hao and W.Q. Zhang, 2016: Advances in Hybrid Metaheuristics
for Stochastic Manufacturing Scheduling: Part I Models and Methods, Advances inIntelligent Systems and Computing, vol.502, pp.1063-1077.
13. Mitsuo Gen, W.Q. Zhang and X.C. Hao, 2016: Advances in Hybrid Metaheuristics
for Stochastic Manufacturing Scheduling: Part II Case Studies, Advances inIntelligent Systems and Computing, vol.502, pp.1079-1094.
14. S.W. Cho, Y.H. Lee, D.W. Cho and Mitsuo Gen, 2016: Logistics network optimizationconsidering balanced allocation and vehicle routing, Maritime Economics &Logistics, vol.18, no.1, pp.41-60; DOI: 10.1057/mel.2015.17.

2015:==================================================================
15. Mitsuo Gen, L. Lin and W.Q. Zhang, 2015: Multiobjective Hybrid Genetic
Algorithms for Manufacturing Scheduling: Part I Models and Algorithms, Advancesin Intelligent Systems and Computing, vol.362, pp.3-25.
16. Mitsuo Gen, W.Q. Zhang and L. Lin, 2015: Multiobjective Hybrid Genetic
Algorithms for Manufacturing Scheduling: Part II Case Studies of HDD and TFTLCD, Advances in Intelligent Systems and Computing, vol.362, pp.27-54.
17. H-K Wang, C-F Chien and Mitsuo Gen, 2015: An Algorithm of Multi-Subpopulation
Parameters with Hybrid Estimation of Distribution for Semiconductor Schedulingwith Constrained Waiting Time, IEEE Transactions on Semiconductor
Manufacturing, vol.28, no.3, pp.353-366.

18. T. Jamrus, C-F Chien, Mitsuo Gen and K. Sethanan, 2015: Multistage production
distribution under uncertainty demands by discrete PSO approaches and extended
priority based-HGA, Fuzzy Optimization and Decision Making, vol.14, pp.265–287;
DOI 10.1007/s10700-014-9200-6.
19. J-N Zheng, C-F Chien and Mitsuo Gen, 2015: Multi-objective multi-populationbiased random-key genetic algorithm for the 3-D container loading problem,
Computers & Industrial Engineering, 85: 80-87; DOI: 10.1016/j.cie.2014.07.012.
20. C. Sangsawang, K. Sethanan, T. Fujimoto and Mitsuo Gen, 2015: Metaheuristics
optimization approaches for two-stage reentrant flexible flow shop with blocking,Expert Systems with Applications, vol.42, pp.2395–2410. DOI:
10.1016/j.eswa.2014.10.043.
21. J.E. Lee, K.Y. Chung, K.D. Lee, Mitsuo Gen, 2015: A multi-objective hybrid genetic
algorithm to minimize the total cost and delivery tardiness in a reverse logistics,
Multimedia Tools and Applications, vol.74, no.20, pp.9067-9085.

2014:==================================================================
22. Mitsuo Gen and Lin Lin, 2014: Multiobjective Evolutionary Algorithm for
Manufacturing Scheduling Problems: State-of-the-Art Survey, J. of Intelligent
Manufacturing, vol.25, no.5, pp.849-866.
23. W.Q. Zhang, Mitsuo Gen and J.B. Jo, 2014: Hybrid sampling strategy-based
multiobjective evolutionary algorithm for process planning and schedulingproblem, J. of Intelligent Manufacturing, vol.25, no.5, pp.881-897.
24. C-W Chou, C-F Chien and Mitsuo Gen, 2014: A Multiobjective Hybrid Genetic
Algorithm for TFT-LCD Module Assembly Scheduling, IEEE Trans. on Automation
Science & Engineering, vol.11, no.3, .pp.692-705.
25. X-C Hao, J-Z Wu, C-F Chien and Mitsuo Gen, 2014: The Cooperative Estimation ofDistribution Algorithm: A Novel Approach for Semiconductor Final Test
Scheduling Problems, J. of Intelligent Manufacturing, vol.25, no.5, pp.867-879.
26. C. Chamnanlor, K. Sethanan, C-F Chien and Mitsuo Gen, 2014: Re-entrant flow shop
scheduling problem with time windows using hybrid genetic algorithm based on
autotuning strategy, International Journal of Production Research, vol.52, no.9,
pp.2612-1629.
27. C-J Liang, M. Chen, Mitsuo Gen and J.B. Jo, 2014: A multi-objective genetic
algorithm for yard crane scheduling problem with multiple work lines, J. ofIntelligent Manufacturing, vol.25, no. 5, pp.1013-1024.

28. D.W. Cho, Y.H. Lee, T.Y. Lee and Mitsuo Gen, 2014: An Adaptive Genetic
Algorithm for the Time Dependent Inventory Routing Problem, J. of Intelligent
Manufacturing, vol.25, no.5, pp.1025-1042.
29. R. Boutaba, K-Y Chung and Mitsuo Gen, 2014: Recent trends in interactive
multimedia computing for industry, Cluster Computing, DOI 10.1007/s10586-0140349-0.

2013:==================================================================
30. Mitsuo Gen and K. Ida, 2013: Advances in Multiobjective Hybrid Genetic
Algorithms for Intelligent Manufacturing and Logistics Systems, Lecture Notes in
Computer Science 8210, Springer, pp. 381–391.
31. Seren O. Tasan and Mitsuo Gen, 2013: An integrated selection and scheduling for
disjunctive network problems, Computers & Industrial Engineering, vol.65, no.1, pp.
65–76.
32. W. Neungnatcha, K. Sethanan, Mitsuo Gen and S. Theerakulpisut, 2013: Adaptive
genetic algorithm for solving sugarcane loading stations with multi-facility
services problem, Computers and Electronics in Agriculture, vol.98, pp.85-99.
33. C. Chamnanlor, K. Sethanan, C-F Chien & Mitsuo Gen, 2013: Hybrid Genetic
Algorithms for Solving Reentrant Flow-Shop Scheduling with Time Windows,
Industrial Engineering & Management Systems, vol. 12, no. 4, pp.306-316.
34. J-N Zheng, C-F Chien and Mitsuo Gen, 2013: Multi-objective multi-populationbiased random-key genetic algorithm for the 3-D container loading problem,
Computers & Industrial Engineering, 8pp, DOI 10.1016/j.cie.2014.07.012.
35. X.C. Hao, L. Lin, Mitsuo Gen and K. Ohno, 2013: Effective estimation of
distribution algorithm for stochastic job shop scheduling problem, Procedia
Computer Science, vol.20, pp.102 – 107.
36. Y.S. Yun, Mitsuo Gen and R.K. Hwang, 2013. Adaptive genetic algorithm to multistage reverse logistics network design for product resale, Information: An
International Interdisciplinary Journal, 15(12), 6117-6138.
37. W.Q. Zhang, W.T. Xu and Mitsuo Gen, 2013: Multi-objective evolutionary algorithmwith strong convergence of multi-area for assembly line balancing problem withworker capability, Procedia Computer Science, vol.20, pp.83 – 89.

2012:===============================================================

38. Mitsuo Gen and L. Lin, 2012: Multiobjective Genetic Algorithm for Scheduling
Problems in Manufacturing Systems, Industrial Engineering & Management
Systems, vol.11, no.4, pp.310-330.
39. J.E. Lee, Mitsuo Gen, K-G Rhee and H-H Lee, 2012: Building of Reusable Reverse
Logistics Model and its Optimization Considering the Decision of Backorder or
Next Arrival of Goods, Electronics and Communications in Japan, vol.95, no.5, pp.
42–55.
40. L. Lin, X-C Hao, Mitsuo Gen and J-B Jo, 2012: Network modeling and evolutionary
optimization for scheduling in manufacturing, Journal of Intelligent
Manufacturing, vol.23, pp.2237–2253.
41. Serdar O. Tasan and Mitsuo Gen, 2012: A genetic algorithm based approach tovehicle routing problem with simultaneous pick-up and deliveries, Computers &Industrial Engineering, vol.62, no.3, pp.755-761.
42. C.J. Liang, H. Hwang and Mitsuo Gen, 2012: A berth allocation planning problem
with direct transshipment consideration, Journal of Intelligent Manufacturing,vol.23, pp.2207–2214.
43. J-Z Wu, C-F Chien and Mitsuo Gen, 2012: Coordinating strategic outsourcing
decisions for semiconductor assembly using a bi-objective genetic algorithm,
International Journal of Production Research, vol. 50, no. 1, pp. 235–260.
44. J-Z Wu, X-C Hao, C-F Chien and Mitsuo Gen, 2012: A novel bi-vector encoding
genetic algorithm for the simultaneous multiple resources scheduling problem,
Journal of Intelligent Manufacturing, vol. 23, pp.2255–2270.
45. H. Inoue and Mitsuo Gen, 2012: A Multistage Logistics System Design Problem
with Inventory Considering Demand by Hybrid Genetic Algorithm, Electronics andCommunications in Japan, vol.95, no.5, pp. 56–65.
46. L. Lin, Mitsuo Gen, Y. Liang & K. Ohno, 2012: A Hybrid EA for Reactive Flexible
Job-shop Scheduling, Procedia Computer Science, vol.12, pp.110 – 115.
47. Y.S. Yun, Mitsuo Gen and R.K. Hwang, 2012: Adaptive Genetic Algorithm to Multistage Reverse Logistics Network Design for Product Resale, Information, vol.15,
no. 12(C), pp.6117 – 6138.
48. W. Zhang, L. Lin, Mitsuo Gen & C-F Chien, 2012: Hybrid Sampling Strategy-based
Multiobjective Evolutionary Algorithm, Procedia Computer Science, vol.12, pp.96 –
101.

2011:
===============================================================

49. W.Q. Zhang, and Mitsuo Gen, 2011: An efficient multiobjective genetic algorithm
for mixed-model assembly line balancing problem considering demand ratio-based
cycle time, J. of Intelligent Manufacturing, vol. 22, no.3, pp.367–378.
50. F. Wen, Mitsuo Gen and X. Yu, 2011: A New Multiobjective Genetic Algorithm for
Route Selection, IEEJ Trans. on Electronic, Information & Systems, vol. 131，no.
3，pp. 619- 625.
51. X.C. Hao and Mitsuo Gen, 2011: Multi-objective Job Shop Rescheduling by Using
Evolutionary Algorithm, IEEJ Trans. on Electronic, Information & Systems, vol.
131，no. 5，pp.674 - 681.
52. J-E Lee, Mitsuo Gen, K-G Rhee and H-H Lee, 2011: Building of Reusable Reverse
Logistics Model and its Optimization Considering the Decision of Backorder or
Next Arrival of Goods, IEEJ Trans. on Electronic, Information & Systems, vol.
131，no.5, pp.1009 -1019 (in Japanese).
53. K. Murakami, S. O. Tasan, Mitsuo Gen and T. Oyabu, 2011: “A Case Study of Human
Resource Allocation for Effective Hotel Management”, Industrial Engineering &Management Systems, vol. 10, no.1, pp.55-65.

2010:===============================================================
54. Mitsuo Gen, H. Kawakami, Y. Tsujimura, H. Handa, L. Lin and A. Okamoto, 2010:“Evolutionary technologies: Fundamentals and Applications to Information,
Communication Systems and Manufacturing & Logistics Systems, IEEJ Trans. on
Electronics and Information Systems, vol. 130, no.5, pp. .731-736 (in Japanese).
55. W.Q. Zhang and Mitsuo Gen, 2010: “Process planning and scheduling in distributed
manufacturing system using multiobjective genetic algorithm”, IEEJ Trans. on
Electric and Electronic Eng., vol. 5, no.1, pp.62-72.
56. Y. Yang, , K.H. Kim and Mitsuo Gen, 2010: Quay Crane Scheduling considering Yard
Cranes Workload by Multiobjective Genetic Algorithm, J. of The Japan Society of
Logistics Systems, vol. 10, no. 1, pp.117-123.
57. H. Inoue and Mitsuo Gen, 2010: Study on Multi-stage Logistics System Design
Problem with Inventory Considering Demand Change by Hybrid Genetic
Algorithm，IEEJ Trans. on Electronics, Information & Systems, vol. 130, no.4, pp.
704-711 (in Japanese).
58. S. Ataka, B. Kim and Mitsuo Gen, 2010: "Optimal design of two-stage logistics
network considered inventory by Boltzmann random key-based GA, IEEJ Trans. on
Electrical and Electronics Eng. vol. 5, no.2, pp.195-202.

59. Y. Sun, Z. Mao, Mitsuo Gen, G. Zheng, and R. Cheng, 2010: Advertising budget
optimization based on statistical learning and evolutionary computation, Inter. J.
of Innovative Computing, Information and Control, vol. 6, no. 8, pp.3705-3714.

2009:===============================================================
60. Mitsuo Gen, W. Zhang & L. Lin, 2009: Survey of evolutionary algorithms inadvanced planning and scheduling, Journal of the Korean Institute of Industrial
Engineering, vol.35, no.1, pp.15-39.
61. Mitsuo Gen, L. Lin & H. Zhang, 2009: “Evolutionary techniques for optimization
problems in integrated manufacturing system: State-of-the-art-survey”,
Computers & Industrial Engineering, vol.56, no.3, pp.779-808.
62. Mitsuo Gen & O. Katai, 2009: “Evolutionary Computation Technology and its
Application”, IEEJ Trans. on Electrical and Electronics Eng., vol.4, no.1, pp.34-35 (inJapanese).
63. Mitsuo Gen, Jie Gao & L. Lin, 2009: “Multistage-Based Genetic Algorithm for
Flexible Job-Shop Scheduling Problem”, Intelligent and Evolutionary Systems, SCI
187, Springer, pp. 183–196.
64. H. Zhang and Mitsuo Gen, 2009: “A parallel hybrid ant colony optimization
approach for job-shop scheduling problem”, International J. of Manufacturing
Technology and Management, vol.16, no.1/2, pp.22-41.
65. F. Altiparmak, Mitsuo Gen, Lin Lin, I. Karaoglan, 2009: A steady-state genetic
algorithm for multiproduct supply chain network design, Computers & Industrial
Engineering, vol.56, no.2, pp.521-537.
66. L. Lin and Mitsuo Gen, 2009: “Auto-tuning strategy for evolutionary algorithms:
balancing between exploration and exploitation”, Soft Computing, vol.13, no.2, pp.
157-168.
67. L. Lin, Mitsuo Gen and X. Wang, 2009: “Integrated Multistage Logistics Network
Design by Using Hybrid Evolutionary Algorithm”, Computers & Industrial
Engineering, vol. 56, no. 3, pp.854-873.
68. J. Gao, Mitsuo Gen and L. Sun, 2009: “Modeling and scheduling preventative
maintenance in semiconductor manufacturing industry with MAs”, International J.
of Manufacturing Technology and Management, vol.16, no.1/2, pp.101-126.
69. L. Lin, Y.S. Seo, Mitsuo Gen, R. Cheng, 2009: “Unusual human behavior recognition
using evolutionary technique”, Computers & Industrial Engineering, vol. 56, no.3,
pp.1137-1153.

70. A. Okamoto, Mitsuo Gen and M. Sugawara, 2009: “Integrated scheduling using
genetic algorithm with quasi-random sequences”, International J. of Manufacturing
Technology and Management, vol.16, no.1/2, pp.147-165.
71. J. E. Lee, Mitsuo Gen and K. Rhee, 2009: Network model and optimization ofreverse logistics by hybrid genetic algorithm, Computers & Industrial Engineering,vol.56, no.3, pp.951-964.
72. L. Lin and Mitsuo Gen, 2009: A random key-based genetic algorithm for AGV
dispatching in FMS, International J. of Manufacturing Technology andManagement, vol.16, no.1/2, pp.58-75.
73. J. Gao, L. Sun, L. Wang and Mitsuo Gen, 2009: “An efficient approach for type II
robotic assembly line balancing problems”, Computers & Industrial Engineering, vol.
56, no.3, pp.1065-1080.
74. F. Wen, Mitsuo Gen and X. Yu, 2009: “Multilayer traffic network optimized bymultiobjective genetic clustering algorithm”, IEICE Trans. on Fundamentals,
vol.E92-A, no. 8, pp.2107-2115.
75. J. Xu, Y. He and Mitsuo Gen, 2009: A class of random fuzzy programming and its
application to supply chain design, Computers & Industrial Engineering, vol. 56, no.
3, pp. 937-950.
76. F. Wen and Mitsuo Gen, 2009: A multistage method for multiobjective routeselection, IEICE Trans. on Fundamentals, vol.E92-A, no.10, pp. 2618-2625.
77. S. Ataka and Mitsuo Gen, 2009: Solution method for multi-product two-stagelogistics network with constraints on delivery route, Electronics andCommunications in Japan, vol. 92, no. 8, pp. 18-24.
78. I. Okada, L. Lin and Mitsuo Gen, 2009: Solving resource constrained multiple
project scheduling problems by random key-based genetic algorithm, Electronics
and Communications in Japan, vol. 92, no. 8, pp. 25-35.

2008:===============================================================
79. Mitsuo Gen, L. Lin and J. B. Jo, 2008: Logistics Network Models andOptimizations: Evolutionary Algorithm Approaches, Inter. J. of Logistics andTransport, vol. 2, no.1, pp. 91-133.
80. R.K. Hwang, Mitsuo Gen and H. Katayama, 2008: A comparison of multiprocessor
task scheduling algorithms with communication costs. Computers & Operations
Research, vol. 35, no. 3, pp. 976-993.

81. R.K. Hwang, H. Katayama and Mitsuo Gen, 2008: U-shaped assembly line balancing
problem with genetic algorithm, Inter. J. of Production Research, vol.46, no.16,
pp.4637-4649.
82. C.M. Lin and Mitsuo Gen, 2008: Multi-criteria human resource allocation forsolving multistage combinatorial optimization problems using multiobjective hybrid
genetic algorithm, Expert Systems with Applications, vol.34, no.4, pp.2480-2490.
83. J. Gao, L. Sun and Mitsuo Gen, 2008: A hybrid genetic and variable neighborhood
descent algorithm for flexible job shop scheduling problems, Computers &Operations Research, vol. 35, no. 9, pp.2892-2907.
84. W. Zhang, L. Lin and Mitsuo Gen, 2008: A Multiobjective Genetic Algorithm based
Approach to Assembly Line Balancing Problem with Worker Allocation, Journal ofSociety of Plant Engineers Japan, vol. 19, no. 4, pp.61-72.
85. W. Zhang, L. Lin and Mitsuo Gen, 2008: Using Multi-objective Genetic Algorithmwith Fuzzy Logic Controller for Assembly Line Balancing Problem with Worker
Allocation, International J. of Information Systems for Logistics and Management
vol. 3, no.2, pp. 79-88.
86. J. E. Lee, Mitsuo Gen and K. Rhee, 2008: A Multi-stage Reverse Logistics
Networks Problem by using Hybrid Priority-based Genetic Algorithm, IEEJ Trans.
on Electronics, Information & Systems, vol.128, no. 3, pp.450-455.
87. L. Lin and Mitsuo Gen, 2008: An effective evolutionary approach for bicriteria
shortest path routing problems, IEEJ Trans. on Electronics, Information &Systems, vol.128c, no.3, pp.416-423.
88. L. Lin, Mitsuo Gen and J. Gao, 2008: Optimization and improvement in robot-based
assembly line system by hybrid genetic algorithm, IEEJ Transactions on
Electronics, Information & Systems, vol.128c, no.3, pp.424-431.

2007:===============================================================
89. X. Wang, L. Lin, Mitsuo Gen and M. Shiota, 2007: Case study on optimal routing in
logistics network by priority-based genetic algorithm”, IEEJ Transactions on
Electronics, Information & Systems, vol.127, no.1, pp.10-16.
90. C.M. Lin and Mitsuo Gen, April 2007: Multiobjective Resource Allocation Problem
by Multistage Decision-based hybrid genetic algorithms, Applied Mathematics andComputation, vol. 187, pp.574-583.
91. C.M. Lin and Mitsuo Gen, 2007: An Effective Decision – based Genetic Algorithm
Approach to Multiobjective Portfolio Optimization Problem, Applied Mathematical
Sciences, vol.1, no.5, pp.201-210.

92. J.B. Jo, Y. Li and Mitsuo Gen, 2007: Nonlinear fixed charge transportation
problem by spanning tree-based genetic algorithm, Computers & Industrial
Engineering, vol.52, pp.290-298.
93. T. Yokota, S. Wada, T. Taguchi and Mitsuo Gen, 2007: Optimal weight design
problem of elastic structure by GA, Computers & Industrial Engineering, vol.52,
pp.299-305.
94. M.R. Yoo and Mitsuo Gen, 2007: Scheduling algorithm for real-time tasks using
multiobjective hybrid genetic algorithm in heterogeneous multiprocessors system,
Computers & Operations Research, vol.34, no.10, pp.3084-3098.
95. J. Gao, Mitsuo Gen, L. Sun and X. Zhao, 2007: A hybrid of genetic algorithm andbottleneck shifting for multiobjective flexible job shop scheduling problems,Computers & Industrial Engineering, vol.53, no.1, pp. 149-162.

2006:===============================================================
96. Mitsuo Gen, F. Altiparmak and L. Lin, 2006: A genetic algorithm for two-stagetransportation problem using priority-based encoding, OR Spectrum, vol.28,
pp.337-354.
97. Mitsuo Gen and Y. Yun, 2006: Soft computing approach for reliability optimization:
State-of-the-art survey, Reliability Engineering and System Safety, vol.91,
pp.1008-1026.
98. R.K. Hwang, Mitsuo Gen, and H. Katayama, 2006: A Performance Evaluation ofMultiprocessor Scheduling with Genetic Algorithm, Asia Pacific Management
Review. vol.11, no.2, pp.67-72.
99. F. Altiparmak, Mitsuo Gen, L. Lin and T. Paksoy, 2006: A genetic algorithm
approach for multi-objective optimization of supply chain networks, Computers &Industrial Engineering, vol. 51, pp.196-215.
100.
J. Gao, Mitsuo Gen and L. Sun, 2006: Scheduling jobs and maintenances inflexible job shop with a hybrid genetic algorithm, J. of Intelligent Manufacturing,vol.17, no.4, pp.493-508.
101.
K.W. Kim, C.U. Moon, Mitsuo Gen and M.H. Kim, 2006: APS with Multiobjective in Make-to-Order Process Using Hybrid Genetic Algorithm, Asia PacificManagement Review, vol.11, no.1, pp.447-456.
102.
H. Zhang, Mitsuo Gen and Y. Seo, 2006: An effective coding approach formultiobjective integrated resource selection and operation sequences problem, J.
of Intelligent Manufacturing, vol.17, no.4, pp.385-398.

103.
A. Okamoto, Mitsuo Gen and M. Sugawara, 2006: Integrated data structureand scheduling approach for manufacturing and transportation using hybrid genetic
algorithm, J. of Intelligent Manufacturing, vol.17, no.4, pp.411-422.
104.
L. Lin and Mitsuo Gen, 2006: Node-based genetic algorithm for communication
spanning tree problem”, IEICE Transactions on Communications, vol.E89-B, no.4,
pp.1091-1098.
105.
L. Lin, S.W. Shinn, Mitsuo Gen and H. Hwang, 2006: Network model andeffective evolutionary approach for AGV dispatching in manufacturing system”, J.
of Intelligent Manufacturing, vol.17, no.4, pp.465-477.
106.
A. Okamoto, Mitsuo Gen and M. Sugawara, 2006: Integrated Scheduling
Problem of Manufacturing and Transportation with Pickup and Delivery,
International J. of Logistics and SCM Systems, vol.1, pp.19–27.
107.
H. Hwang, K.A. Park and Mitsuo Gen, 2006: A Priority-based Genetic
Algorithm for a Variant of Orienteering Problem, International J. of Logistics and
SCM Systems, vol.1, pp.32-38.
108.
C. Moon, Y. Seo, Y. Yun and Mitsuo Gen, 2006: Adaptive genetic algorithm
for advanced planning in manufacturing supply chain, J. of Intelligent
Manufacturing, vol.17, no.4, pp.509-522.
109.
C. M. Lin, J. J. Huang, Mitsuo Gen and G. H. Tzeng, 2006: Recurrent Neural
Network for Dynamic Portfolio Selection”, Applied Mathematics and Computation,
vol.175, pp.1139-1146.
110.
K.W. Kim, Mitsuo Gen and, M.H. Kim, 2006: Adaptive genetic algorithms for
multi-resource constrained project scheduling problem with multiple modes.
International J. of Innovative Computing, Information and Control, vol.2, no.1,
pp.41-49.
111. J. Li, J. Xu and Mitsuo Gen, 2006: A class of multiobjective linear programming
model with fuzzy random coefficients, Mathematical and Computer Modeling,
vol.44, no.11-12, pp.1097-1113.

2005:===============================================================
112.
Mitsuo Gen, A. Kumar and J. R. Kim, 2005: Recent network design techniques
using evolutionary algorithms, International J. of Production Economics, vol.98,
no.2, pp.251-261.
113.
Mitsuo Gen and L. Lin, 2005: Multi-objective Hybrid Genetic Algorithm for
Bicriteria Network Design Problem, Complexity International, vol.11, pp.73-83.

114.
Mitsuo Gen and A. Syalif, 2005: Hybrid genetic algorithm for multi-time
period production/distribution planning, Computers & Industrial Engineering,
vol.48, no.4, pp.799-809.
115.
J. Gao and Mitsuo Gen, 2005: Preventive Maintenance Scheduling in
Semiconductor Manufacturing with Hybrid Genetic Algorithms, J. of the Societyof Plant Engineering Japan, vol.17, no.2, pp.31-40.
116.
H. Zhang and Mitsuo Gen, 2005: Multistage-based genetic algorithm forflexible job-shop scheduling problem, Complexity International, vol.11, pp.223-232.
117.
M. Watanabe, K. Ida and Mitsuo Gen, 2005: A genetic algorithm withmodified crossover operator and search area adaptation for the job-shop
scheduling problem, Computers & Industrial Engineering, vol.48, no.4, pp.743-752.
118.
K.W. Kim, Y. Yun, J. Yoon, Mitsuo Gen and G. Yamazaki, 2005: Hybrid genetic
algorithm with adaptive abilities for resource-constrained multiple projectscheduling, Computers in Industry, vol.56, no.2, pp.143-160.
119.
M.R. Yoo and Mitsuo Gen, 2005: Multimedia Tasks Scheduling Using Genetic
Algorithm, Asia Pacific Management Review, vol.10, no.6, pp.373-380.
120.
H. Min, G. Zhou, Mitsuo Gen and Z. Cao, 2005: A genetic algorithm approachto the balanced allocation of customers to multiple warehouses with varying
capacities, Inter. J. of Logistics: Research and Applications, vol. 8, no. 3, pp.181–
192.

2004:
===============================================================
121. Mitsuo Gen, L. Lin and R. Cheng, 2004: Bicriteria network optimization problem
using priority-based genetic algorithms, IEEJ Trans. on Electronic, Information &Systems, vol.124, no.10, pp.1972-1978.
122.
F. Altiparmak, Mitsuo Gen, B. Dengiz and A. E. Smith, 2004: A network-based
genetic algorithm for design of communication networks, J. of Society of Plant
Engineers Japan, vol.15, no.4, pp.184-190.
123.
F. Altiparmak, Mitsuo Gen, B. Dengiz and A. E. Smith, 2004: A genetic
algorithm with fuzzy logic controller for design of communication networks, IEEJ
Trans. on Electronic, Information & Systems, vol.124, no.10, pp.1979-1985.
124. K. W. Kim, G. Yamazaki, L. Lin and Mitsuo Gen, 2004: Network-based genetic
algorithm for scheduling in FMS environments, Artificial Life and Robotics, vol.8,
no.1, pp.67-76.

125. C. Moon, Y.H. Lee and Mitsuo Gen: Evolutionary Algorithm for Process Plan
Selection with Multiple Objectives, Industrial Engineering and Management
Systems, vol.3, no.2, pp.125-131, Dec. 2004.
126. C. Moon, J. Kim, and Mitsuo Gen, 2004: Advanced planning and scheduling based on
precedence and resource constraints for e-plant chains, International J. of
Production Research, vol.42, no.15, pp.2941-2954.
127.
M. Mukuda, YoungSu Yun and Mitsuo Gen, 2004: Reliability Optimization
Problems Using Adaptive Hybrid Genetic Algorithms, J. of Advanced Computational
Intelligence and Intelligent Informatics, vol.8, no.4, pp.437-441, 2004
128.
M. Mukuda, YoungSu Yun and Mitsuo Gen, 2004: Adaptive Genetic Local
Search Algorithms for Solving Reliability Optimization Problems, IEEJ Trans. on
Electronics, Information & Systems, vol.124, no.10, pp.1986-1990.
129.
J. Taniguchi, Xiaodong Wang, Mitsuo Gen and T. Yokota, 2004: Hybrid Genetic
Algorithm with Fuzzy Logic Controller for Obstacle Location-Allocation Problem,
IEEJ Trans. on Electronics, Information & Systems, vol.124, no.10, pp.2027-2033.
130.
J. Gao, B. Liu and Mitsuo Gen, 2004: A Hybrid Intelligent Algorithm for
Stochastic Multilevel Programming, IEEJ Trans. on Electronics, Information &Systems, vol.124, no.10, pp.1986-1990.

2003:===============================================================
131.
Mitsuo Gen and R. Cheng, 2003: Evolutionary network design: Hybrid genetic
algorithms approach, International J. of Computational Intelligence andApplications, vol.3, no.4, pp.357-380, Dec. 2003.
132. Mitsuo Gen, K. W. Kim, and G. Yamazaki, 2003: Project scheduling using hybrid
genetic algorithm with fuzzy logic controller in SCM environment, J. of Tsinghua
Science and Technology, vol.8, no.1.
133.
G. Zhou and Mitsuo Gen, 2003: A genetic algorithm approach on tree-liketelecommunication network design problem, J. of the Operational ResearchSociety, vol. 54, no. 3, pp.248-254.
134.
Y. Yun and Mitsuo Gen, 2003: Performance analysis of adapted genetic
algorithm with fuzzy logic and heuristics, Fuzzy Optimization and Decision Making,vol.2, pp.161-175.
135.
A. Syarif and Mitsuo Gen, 2003: Solving exclusionary side constrainedtransportation problem by using a hybrid spanning tree-based genetic algorithm, J.
of Intelligent Manufacturing, vol.14, no.3/4, pp.389-399.

136.
A. Syarif and Mitsuo Gen, 2003: Hybrid Genetic Algorithm for
Production/Distribution System in Supply Chain, Inter. Journal of SmartEngineering System Design, vol. 5:289-298, DOI: 10.1080=10255810390245609.
137.
Y. Yun, Mitsuo Gen and S. Seo, 2003: Various hybrid methods based on
genetic algorithm with fuzzy logic controller, J. of Intelligent Manufacturing,vol.14, no.3/4, pp.401-419.
138.
K. W. Kim, Mitsuo Gen and G. Yamazaki, 2003: Hybrid genetic algorithm withfuzzy logic for resource-constrained project scheduling, Applied Soft Computing,vol.2, no.3, pp.174-188.
139.
M. Sasaki and Mitsuo Gen, 2003: Fuzzy multiple objective optimal systemdesign by hybrid genetic algorithm, Applied Soft Computing, vol.2, no.3, pp.189196.
140.
G. Zhou, Hokay Min, and Mitsuo Gen, 2003: A genetic algorithm approach tothe bi-criteria allocation of customers to warehouses, International J. of
Production Economics, vol.86, pp.35-45.
141.
A. Syarif and Mitsuo Gen, 2003: Double spanning tree-based genetic
algorithm for two stage transportation problem, International J. of Knowledgebased Intelligent Engineering Systems, vol.7, no.4, pp.214-221.

2002:===============================================================
142.
C. Y. Lee, Mitsuo Gen, and Y. Tsujimura, 2002: Reliability optimization designusing hybrid NN-GA with fuzzy logic controller, IEICE Trans. on Fundamental,
vol.E85-A, no.2, pp.432-446.
143.
C.Y. Lee, Y. S. Yun, and Mitsuo Gen, 2002: Reliability optimization design forcomplex systems by hybrid GA with fuzzy logic controller and local search, IEICE
Trans. on Fundamental, vol.E85-A, no.4, pp.880-891.
144.
Y. S. Yun, and Mitsuo Gen, 2002: Advanced Scheduling Problem Using
Constraint Programming Techniques in SCM Environment, Computers & Industrial
Engineering, vol.43, nos.1-2, pp.213-219.
145.
A. Syarif, Y. S. Yun and Mitsuo Gen, 2002: Study on Multi-stage Logistics
Chain Network: A Spanning Tree-based Genetic Algorithm Approach, Computers &Industrial Engineering, vol.43, nos.1-2, pp.299-314.
146.
G. Zhou, Hokey Min and Mitsuo Gen, 2002: The Balanced Allocation ofCustomers to Multiple Distribution Centers in the Supply Chain Network: A
Genetic Algorithm Approach, Computers & Industrial Engineering, vol.43, nos.1-2,
pp.251-261.

147.
H. Hwang, S. Moon and Mitsuo Gen, 2002: An integrated model for thedesign of end-of-aisle order picking system and the determination of unit load
sizes of AGVs, Computers & Industrial Engineering, vol.42, pp.249-258.
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