A review of applications of genetic algorithms in operations management

Engineering Applications of Artificial Intelligence

Volumn 76, Issue , 2018, Pages 1-12

  Lee, C K H ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Genetic algorithms; Operations management; Review

Indexed keywords

GASES; GENETIC ALGORITHMS; HEURISTIC ALGORITHMS; HEURISTIC METHODS; PRODUCT DESIGN; REVIEWS;

FUTURE RESEARCH DIRECTIONS; GENETIC ALGORITHM (GAS); HARD PROBLEMS; HEURISTIC APPROACH; HEURISTIC SEARCH METHODS; JOURNAL PAPER; OPERATIONS MANAGEMENT; OPERATIONS PLANNING;

PROBLEM SOLVING;

EID: 85052917573     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2018.08.011     Document Type: Article

References (143)

1. Abazari, A.M., Solimanpur, M., Sattari, H., Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm. Comput. Ind. Eng. 62:2 (2012), 469–478.
2. Abdelghany, A., Abdelghany, K., Azadian, F., Airline flight schedule planning under competition. Comput. Oper. Res. 87 (2017), 20–39.
3. Afrouzy, Z.A., Nasseri, S.H., Mahdavi, I., A genetic algorithm for supply chain configuration with new product development. Comput. Ind. Eng. 101 (2016), 440–454.
4. Aghaie, M., Norouzi, A., Zolfaghari, A., Minuchehr, A., Mohamadi, Z., Tumari, R., Advanced progressive real coded genetic algorithm for nuclear system availability optimization through preventive maintenance scheduling. Ann. Nucl. Energy 60 (2013), 64–72.
5. Aguilar-Rivera, R., Valenzuela-Rendón, M., Rodríguez-Ortiz, J.J., Genetic algorithms and Darwinian approaches in financial applications: a survey. Expert Syst. Appl. 42:21 (2015), 7684–7697.
6. Aiello, G., Scalia, G. La, Enea, M., A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding. Expert Syst. Appl. 39:12 (2012), 10352–10358.
7. Al-Hinai, N., ElMekkawy, T.Y., Robust and stable flexible job shop scheduling with random machine breakdowns using a hybrid genetic algorithm. Int. J. Prod. Econ. 132:2 (2011), 279–291.
8. Aliev, R.A., Fazlollahi, B., Guirimov, B.G., Aliev, R.R., Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management. Inform. Sci. 177:20 (2007), 4241–4255.
9. Altiparmak, F., Gen, M., Lin, L., Karaoglan, I., A steady-state genetic algorithm for multi-product supply chain network design. Comput. Ind. Eng. 56:2 (2009), 521–537.
10. Amirtaheri, O., Zandieh, M., Dorri, B., Motameni, A.R., A bi-level programming approach for production-distribution supply chain problem. Comput. Ind. Eng. 110 (2017), 527–537.
11. Arakaki, R.K., Usberti, F.L., Hybrid genetic algorithm for the open capacitated arc routing problem. Comput. Oper. Res. 90 (2018), 221–231.
12. Aravendan, M., Panneerselvam, R., Literature review on network design problems in closed loop and reverse supply chains. Intell. Inf. Manage. 6:3 (2014), 104–117.
13. Aytug, H., Khouja, M., Vergara, F.E., Use of genetic algorithms to solve production and operations management problems: a review. Int. J. Prod. Res. 41:17 (2003), 3955–4009.
14. Azadeh, A., Elahi, S., Farahani, M.H., Nasirian, B., A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment. Comput. Ind. Eng. 104 (2017), 124–133.
15. Balin, S., Parallel machine scheduling with fuzzy processing times using a robust genetic algorithm and simulation. Inform. Sci. 181:17 (2011), 3551–3569.
16. Bhunia, A.K., Kundu, S., Sannigrahi, T., Goyal, S.K., An application of tournament genetic algorithm in a marketing oriented economic production lot-size model for deteriorating items. Int. J. Prod. Econ. 119:1 (2009), 112–121.
17. Çelebi, D., Inventory control in a centralized distribution network using genetic algorithms: a case study. Comput. Ind. Eng. 87 (2015), 532–539.
18. Chang, Y.H., Adopting co-evolution and constraint-satisfaction concept on genetic algorithms to solve supply chain network design problems. Expert Syst. Appl. 37:10 (2010), 6919–6930.
19. Chaudhry, S.S., Luo, W., Application of genetic algorithms in production and operations management: a review. Int. J. Prod. Res. 43:19 (2005), 4083–4101.
20. Chen, J.C., Chen, Y.-Y., Liang, Y., Application of a genetic algorithm in solving the capacity allocation problem with machine dedication in the photolithography area. J. Manuf. Syst. 41 (2016), 165–177.
21. Chen, T., Li, J., Jin, P., Cai, G., Reusable rocket engine preventive maintenance scheduling using genetic algorithm. Reliab. Eng. Syst. Saf. 114 (2013), 52–60.
22. Chen, R., Liang, C.-Y., Hong, W.-C., Gu, D.-X., Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl. Soft Comput. 26 (2015), 434–443.
23. Cheng, R., Gen, M., Tsujimura, Y., A tutorial survey of job-shop scheduling problems using genetic algorithms: I. Representation. Comput. Ind. Eng. 30:4 (1996), 983–997.
24. Cheong, D., Kim, Y.M., Byun, H.W., Oh, K.J., Kim, T.Y., Using genetic algorithm to support clustering-based portfolio optimization by investor information. Appl. Soft Comput. 61 (2017), 593–602.
25. Cheshmehgaz, H.R., Haron, H., Kazemipour, F., Desa, M.I., Accumulated risk of body postures in assembly line balancing problem and modelling through a multi-criteria fuzzy-genetic algorithm. Comput. Ind. Eng. 63:2 (2012), 503–512.
26. Chi, H.-M., Ersoy, O.K., Moskowitz, H., Ward, J., Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms. European J. Oper. Res. 180:1 (2007), 174–193.
27. Coello, C.A.C., An updated survey of GA-based Multiobjective optimization techniques. ACM Comput. Surv. 32:2 (2000), 109–143.
28. Compare, M., Martini, F., Zio, E., Genetic algorithms for condition-based maintenance optimization under uncertainty. European J. Oper. Res. 244:2 (2015), 611–623.
29. Costa, A., Celano, G., Fichera, S., Trovato, T., A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms. Comput. Ind. Eng. 59:4 (2010), 986–999.
30. Dai, Z., Zheng, X., Design of close-loop supply chain network under uncertainty using hybrid genetic algorithm: A fuzzy and chance-constrained programming model. Comput. Ind. Eng. 88 (2015), 444–457.
31. Datta, D., Amaral, A.R.S., Figueira, J.R., Single row facility layout problem using a permutation-based genetic algorithm. European J. Oper. Res. 213:2 (2011), 388–394.
32. De Giovanni, L., Pezzella, F., An improved genetic algorithm for the distributed and flexible job-shop scheduling problem. European J. Oper. Res. 200:2 (2010), 395–408.
33. Diabat, A., Deskoores, R., A hybrid genetic algorithm based heuristic for an integrated supply chain problem. J. Manuf. Syst. 38 (2016), 172–180.
34. Drenovak, M., Ranković V., Ivanović M., Urošević B., Jelic, R., Market risk management in a post-Basel II regulatory environment. European J. Oper. Res. 257:3 (2017), 1030–1044.
35. Engin, O., Çelik, A., Kaya, İ., A fuzzy approach to define sample size for attributes control chart in multistage processes: An application in engine valve manufacturing process. Appl. Soft Comput. 8:4 (2008), 1654–1663.
36. Engin, O., Ceran, G., Yilmaz, M.K., An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems. Appl. Soft Comput. 11:3 (2011), 3056–3065.
37. Engin, O., Gözen, S., Parallel machine scheduling problems with fuzzy processing time and fuzzy duedate: An application in an engine valve manufacturing process. J. Mult.-Valued Logic Soft Comput. 15:2 (2009), 107–123.
38. Farahani, R.Z., Elahipanah, M., A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. Int. J. Prod. Econ. 111:2 (2008), 229–243.
39. Gonçalves, J.F., Mendes, J.J.M., Resende, M.G.C., A genetic algorithm for the resource constrained multi-project scheduling problem. European J. Oper. Res. 189:3 (2008), 1171–1190.
40. Govindan, K., Fattahi, M., Keyvanshokooh, E., Supply chain network design under uncertainty: A comprehensive review and future research directions. European J. Oper. Res. 263:1 (2017), 108–141.
41. Govindan, K., Soleimani, H., Kannan, D., Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. European J. Oper. Res. 240:3 (2015), 603–626.
42. Guido, R., Conforti, D., A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem. Comput. Oper. Res., 2017, 10.1016/j.cor.2016.11.009.
43. He, F.-B., Chang, J., Combined forecasting of regional logistics demand optimized by genetic algorithm. Grey Syst. Theory Appl. 4:2 (2014), 221–231.
44. Hiassat, A., Diabat, A., Rahwan, I., A genetic algorithm approach for location-inventory-routing problem with perishable products. J. Manuf. Syst. 42 (2017), 93–103.
45. Ho, W., Xu, X., Dey, P., Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. European J. Oper. Res. 202:1 (2010), 16–24.
46. Hong, W.-C., Dong, Y., Chen, L.-Y., Wei, S.-Y., SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Appl. Soft Comput. 11:2 (2011), 1881–1890.
47. Hu, Z.-H., Multi-objective genetic algorithm for berth allocation problem considering daytime preference. Comput. Ind. Eng. 89 (2015), 2–14.
48. Hu, Z., Ma, H., Gao, W., Lv, C., Yao, L., Modelling diffusion for multi-generational product planning strategies using bi-level optimization. Knowl.-Based Syst. 123 (2017), 254–266.
49. Huang, J., Süer, G.A., A dispatching rule-based genetic algorithm for multi-objective job shop scheduling using fuzzy satisfaction levels. Comput. Ind. Eng. 86 (2015), 29–42.
50. Ishikawa, S., Kubota, R., Horio, K., Effective hierarchical optimization by a hierarchical multi-space competitive genetic algorithm for the flexible job-shop scheduling problem. Expert Syst. Appl. 42:24 (2015), 9434–9440.
51. Jayaraman, V., Srivastava, R., Expert systems in production and operations management: current applications and future prospects. Int. J. Oper. Prod. Manage. 16:12 (1996), 27–44.
52. Jiang, S., Chin, K.-S., Wang, L., Qu, G., Tsui, K.L., Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Expert Syst. Appl. 82 (2017), 216–230.
53. Kar, A.K., Bio inspired computing –a review of algorithms and scope of applications. Expert Syst. Appl. 59 (2016), 20–32.
54. Kaya, İ., Engin, O., A new approach to define sample size at attributes control chart in multistage processes: An application in engine piston manufacturing process. J. Mater Process. Technol. 183:1 (2007), 38–48.
55. Kia, R., Khaksar-Haghani, F., Javadian, N., Tavakkoli-Moghaddam, R., Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm. J. Manuf. Syst. 33:1 (2014), 218–232.
56. Kılıç M., Ulusoy, G., Şerifoğlu, F.S., A bi-objective genetic algorithm approach to risk mitigation in project scheduling. Int. J. Projection Econ. 112:1 (2008), 202–216.
57. Kobbacy, K.A.H., Vadera, S., A survey of AI in operations management from 2005 to 2009. Int. J. Manuf. Technol. Manage. 22:6 (2011), 706–733.
58. Köksal, G., Batmaz, İ., Testik, M.C., A review of data mining applications for quality improvement in manufacturing industry. Expert Syst. Appl. 38:10 (2011), 13448–13467.
59. Lau, H.C.W., Ho, G.T.S., Zhao, Y., Hon, W.T., Optimizing patrol force deployment using a genetic algorithm. Expert Syst. Appl. 37:10 (2010), 8148–8154.
60. Lee, L.H., Lee, C.U., Tan, Y.P., A multi-objective genetic algorithm for robust flight scheduling using simulation. European J. Oper. Res. 177:3 (2007), 1948–1968.
61. Li, Y., Chen, J., Cai, X., Heuristic genetic algorithm for capacitated production planning problems with batch processing and remanufacturing. Int. J. Prod. Econ. 105:2 (2007), 301–317.
62. Li, X., Gao, L., An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. Int. J. Prod. Econ. 174 (2016), 93–110.
63. Li, X., Gao, L., Shao, X., An active learning genetic algorithm for integrated process planning and scheduling. Expert Syst. Appl. 39:8 (2012), 6683–6691.
64. Li, X., Zhang, C., Gao, L., Li, W., Shao, X., An agent-based approach for integrated process planning and scheduling. Expert Syst. Appl. 37:2 (2010), 1256–1264.
65. Lin, K.-P., Chang, P.-T., Hung, K.-C., Pai, P.-F., A simulation of vendor managed inventory dynamics using fuzzy arithmetic operations with genetic algorithms. Expert Syst. Appl. 37:3 (2010), 2571–2579.
66. Lin, C.-M., Gen, M., Multi-criteria human resource allocation for solving multistage combinatorial optimization problems using multiobjective hybrid genetic algorithm. Expert Syst. Appl. 34:4 (2008), 2480–2490.
67. Liu, Y., Dong, H., Lohse, N., Petrovic, S., A multi-objective genetic algorithm for optimization of energy consumption and shop floor production performance. Int. J. Prod. Econ. 179 (2016), 259–272.
68. Lova, A., Tormos, P., Cervantes, M., Barber, F., An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes. Int. J. Prod. Econ. 117:2 (2009), 302–316.
69. Lu, Z., Cui, W., Han, X., Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty. Comput. Ind. Eng. 80 (2015), 236–244.
70. Maiti, M.K., A fuzzy genetic algorithm with varying population size to solve an inventory model with credit-linked promotional demand in an imprecise planning horizon. European J. Oper. Res. 213:1 (2011), 96–106.
71. Mazinani, M., Abedzadeh, M., Mohebali, N., Dynamic facility layout problem based on flexible bay structure and solving by genetic algorithm. Int. J. Adv. Manuf. Technol. 65:5–8 (2013), 929–943.
72. Mertens, P., Kanet, J.J., Expert systems in production management: An assessment. J. Oper. Manage. 6:3–4 (1986), 393–404.
73. Moin, N.H., Salhi, S., Aziz, N.A.B., An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem. Int. J. Prod. Econ. 133:1 (2011), 334–343.
74. Mok, P.Y., Kwong, C.K., Wong, W.K., Optimization of fault-tolerant fabric-cutting schedules using genetic algorithms and fuzzy set theory. European J. Oper. Res. 177:3 (2007), 1876–1893.
75. Musharavati, F., Hamouda, A.S.M., Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines. Expert Syst. Appl. 38:9 (2011), 10770–10779.
76. Nachiappan, S.P., Jawahar, N., A genetic algorithm for optimal operating parameters of VMI system in a two-echelon supply chain. European J. Oper. Res. 187:3 (2007), 1433–1452.
77. Naso, D., Surico, M.m., Turchiano, B., Kaymak, U., Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete. European J. Oper. Res. 177:3 (2007), 2069–2099.
78. Núñez Letamendia, L., Fitting the control parameters of a genetic algorithm: An application to technical trading systems design. European J. Oper. Res. 179:3 (2007), 847–868.
79. Ongkunaruk, P., Wahab, M.I.M., Chen, Y., A genetic algorithm for a joint replenishment problem with resource and shipment constraints and defective items. Int. J. Prod. Econ. 175 (2016), 142–152.
80. Palencia, A.E.R., Delgadillo, G.E.M., A computer application for a bus body assembly line using Genetic Algorithms. Int. J. Prod. Econ. 140:1 (2012), 431–438.
81. Palomo-Romero, J.M., Salas-Morera, L., García-Hernández, L., An island model genetic algorithm for unequal area facility layout problems. Expert Syst. Appl. 68 (2017), 151–162.
82. Pang, K.-W., A genetic algorithm based heuristic for two machine no-wait flowshop scheduling problems with class setup times that minimizes maximum lateness. Int. J. Prod. Econ. 141:1 (2013), 127–136.
83. Park, Y.-B., Yoo, J.-S., Park, H.-S., A genetic algorithm for the vendor-managed inventory routing problem with lost sales. Expert Syst. Appl. 53 (2016), 149–159.
84. Partovi, F.Y., Burton, J., Banerjee, A., Application of analytical hierarchy process in operations management. Int. J. Oper. Prod. Manage. 10:3 (1990), 5–19.
85. Pasandideh, S.H.R., Niaki, S.T.A., Nia, A.R., A genetic algorithm for vendor managed inventory control system of multi-product multi-constraint economic order quantity model. Expert Syst. Appl. 38:3 (2011), 2708–2716.
86. Pasandideh, S.H.R., Niaki, S.Y.A., Tokhmehchi, N., A parameter-tuned genetic algorithm to optimize two-echelon continuous review inventory systems. Expert Syst. Appl. 38:9 (2011), 11708–11714.
87. Perkgoz, C., Azaron, A., Katagiri, H., Kato, K., Sakawa, M., A multi-objective lead time control problem in multi-stage assembly systems using genetic algorithms. European J. Oper. Res. 180:1 (2007), 292–308.
88. Pfeifer, J., Barker, K., Ramirez-Marquez, J.E., Morshedlou, N., Quantifying the risk of project delays with a genetic algorithm. Int. J. Prod. Econ. 170:Part A (2015), 34–44.
89. Piasson, D., Bíscaro, A.A., Leão Mantovani, J.R.S., A new approach for reliability-centered maintenance programs in electric power distribution systems based on a multiobjective genetic algorithm. Electr. Power Syst. Res. 137 (2016), 41–50.
90. Pinto, G., Ainbinder, I., Rabinowitz, G., A genetic algorithm-based approach for solving the resource-sharing and scheduling problem. Comput. Ind. Eng. 57:3 (2009), 1131–1143.
91. Pishvaee, M.S., Rabbani, M., Torabi, S.A., A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl. Math. Model. 35:2 (2011), 637–649.
92. Prakash, A., Chan, F.T.S., Deshmukh, S.G., FMS scheduling with knowledge based genetic algorithm approach. Expert Syst. Appl. 38:4 (2011), 3161–3171.
93. Proudlove, N.C., Vaderá S., Kobbacy, K.A.H., Intelligent management systems in operations: a review. J. Oper. Res. Soc. 49:7 (1998), 682–699.
94. Puente, J., Gómez, A., Fernández, I., Priore, P., Medical doctor rostering problem in a hospital emergency department by means of genetic algorithms. Comput. Ind. Eng. 56:4 (2009), 1232–1242.
95. Ramezanian, R., Rahmani, D., Barzinpour, F., An aggregate production planning model for two phase production systems: Solving with genetic algorithm and tabu search. Expert Syst. Appl. 39:1 (2012), 1256–1263.
96. Rao, H.R., Lingaraj, B.P., Expert systems in production and operations management: Classification and prospects. Interfaces 18:6 (1988), 80–91.
97. Razali, N.M., An efficient genetic algorithm for large scale vehicle routing problem subject to precedence constraints. Procedia - Soc. Behav. Sci. 195 (2015), 1922–1931.
98. Rezaei, J., Davoodi, M., Multi-objective models for lot-sizing with supplier selection. Int. J. Prod. Econ. 130:1 (2011), 77–86.
99. Sadrzadeh, A., A genetic algorithm with the heuristic procedure to solve the multi-line layout problem. Comput. Ind. Eng. 62:4 (2012), 1055–1064.
100. Sakhuja, S., Jain, V., Kumar, S., Chandra, C., Ghidayal, S.K., Genetic algorithm based fuzzy time series tourism demand forecast model. Ind. Manage. Data Syst. 116:3 (2016), 483–507.
101. Samuel, G.G., Rajan, C.C.A., Hybrid: Particle swarm optimization-genetic algorithm and particle swarm optimization-shuffled frog leaping algorithm for long-term generator maintenance scheduling. Electr. Power Energy Syst. 65 (2015), 432–442.
102. Schroeder, R.G., Operations Management: Decision Making in the Operations Function. forth ed, 1993, McGraw Hill, New York.
103. Sermpinis, G., Stasinakis, C., Theofilatos, K., Karathanasopoulos, A., Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms–support vector regression forecast combinations. European J. Oper. Res. 247:3 (2015), 831–846.
104. Shao, X., Li, X., Gao, L., Zhang, C., Integration of process planning and scheduling –a modified genetic algorithm-based approach. Comput. Oper. Res. 36:6 (2009), 2082–2096.
105. Sharma, D.K., Jana, R.K., A hybrid genetic algorithm model for transhipment management decisions. Int. J. Prod. Econ. 122:2 (2009), 703–713.
106. Shi, Y., Boudouh, T., Grunder, O., A hybrid genetic algorithm for a home health care routing problem with time window and fuzzy demand. Expert Syst. Appl. 72 (2017), 160–176.
107. Shi, J., Liu, Z., Tang, L., Xiong, J., Multi-objective optimization for a closed-loop network design problem using an improved genetic algorithm. Appl. Math. Model. 45 (2017), 14–30.
108. Slack, N., Brandon-Jones, A., Johnston, R., Operations Management. seventh ed, 2013, Pearson Education Limited, United Kingdom.
109. Soleimani, H., Govindan, K., Saghafi, H., Jafari, H., Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Comput. Ind. Eng. 109 (2017), 191–203.
110. Soleimani, H., Kannan, G., A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Appl. Math. Model. 39:14 (2015), 3990–4012.
111. Sourirajan, K., Ozsen, L., Uzsoy, R., A genetic algorithm for a single product network design model with lead time and safety stock considerations. European J. Oper. Res. 197:2 (2009), 599–608.
112. Subramanian, N., Ramanathan, R., A review of applications of Analytic Hierarchy Process in operations management. Int. J. Prod. Econ. 138:2 (2012), 215–241.
113. Sukkerd, W., Wuttipornpun, T., Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations. Comput. Ind. Eng. 97 (2016), 157–169.
114. Syntetos, A.A., Babai, Z., Boylan, J.E., Kolassa, S., Nikolopoulos, K., Supply chain forecasting: Theory, practice, their gap and the future. European J. Oper. Res. 252:1 (2016), 1–26.
115. Taleizadeh, A.A., Niaki, S.T.A., Aryanezhad, M.-B., Shafii, N., A hybrid method of fuzzy simulation and genetic algorithm to optimize constrained inventory control systems with stochastic replenishments and fuzzy demand. Inform. Sci. 220 (2013), 425–441.
116. Tiacci, L., Coupling a genetic algorithm approach and a discrete event simulator to design mixed-model un-paced assembly lines with parallel workstations and stochastic task times. Int. J. Prod. Econ. 159 (2015), 319–333.
117. Touat, M., Bouzidi-Hassini, S., Benbouzid-Sitayeb, F., Benhamou, B., A hybridization of genetic algorithms and fuzzy logic for the single-machine scheduling with flexible maintenance problem under human resource constraints. Appl. Soft Comput. 59 (2017), 556–573.
118. Tseng, L.-Y., Lin, Y.-T., A genetic local search algorithm for minimizing total flowtime in the permutation flowshop scheduling problem. Int. J. Prod. Econ. 127:1 (2010), 121–128.
119. Tseng, L.-Y., Lin, Y.-T., A hybrid genetic algorithm for no-wait flowshop scheduling problem. Int. J. Prod. Econ. 128:1 (2010), 144–152.
120. Vitayasak, S., Pongcharoen, P., Hicks, C., A tool for solving stochastic dynamic facility layout problems with stochastic demand using either a Genetic Algorithm or modified Backtracking Search Algorithm. Int. J. Prod. Econ., 2016, 10.1016/j.ijpe.2016.03.019.
121. Wang, S.M., Chen, J.C., Wang, K.-J., Resource portfolio planning of make-to-stock products using a constraint programming-based genetic algorithm. Omega 35:2 (2007), 237–246.
122. Wang, S., Liu, M., Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive maintenance planning. J. Manuf. Syst. 37:Part 1 (2015), 182–192.
123. Wang, K.-J., Nguyen, P.H., Capacity planning with technology replacement by stochastic dynamic programming. European J. Oper. Res. 260:2 (2017), 739–750.
124. Wang, L.-C., Shih, H.-L., An advanced overlapping production planning model in manufacturing supply chain. J. Manuf. Technol. Manage. 22:7 (2011), 870–890.
125. Wang, K.-J., Wang, S.-M., Simultaneous resource portfolio planning under demand and technology uncertainty in the semiconductor testing industry. Robot. Comput.-Integr. Manuf. 29:5 (2013), 278–287.
126. Wang, K.-J., Wang, S.-M., Chen, J.-C., A resource portfolio planning model using sampling-based stochastic programming and genetic algorithm. European J. Oper. Res. 184:1 (2008), 327–340.
127. Wang, K.-J., Wang, S.-M., Yang, S.-J., A resource portfolio model for equipment investment and allocation of semiconductor testing industry. European J. Oper. Res. 179:2 (2007), 390–403.
128. Wong, B.K., Lai, V.S., A survey of the application of fuzzy set theory in production and operations management: 1998-2009. Int. J. Prod. Econ. 129:1 (2011), 157–168.
129. Wu, X., Chu, C.-H., Wang, Y., Yan, W., A genetic algorithm for cellular manufacturing design and layout. European J. Oper. Res. 181:1 (2007), 156–167.
130. Wu, X., Chu, C.-H., Wang, Y., Yue, D., Genetic algorithms for integrating cell formation with machine layout and scheduling. Comput. Ind. Eng. 52:2 (2007), 277–289.
131. Xia, H., Li, X., Gao, L., A hybrid genetic algorithm with variable neighbourhood search for dynamic integrated process planning and scheduling. Comput. Ind. Eng. 102 (2016), 99–112.
132. Yang, L., Jones, B.F., Yang, S.-H., A fuzzy multi-objective programming for optimization of fire station locations through genetic algorithms. European J. Oper. Res. 181:2 (2007), 903–915.
133. Yang, H.-a., Sun, Q.-f., Saygin, C., Job shop scheduling based on earliness and tardiness penalties with due dates and deadlines: an enhanced genetic algorithm. Int. J. Adv. Manuf. Technol. 61:5–8 (2012), 657–666.
134. Yassine, A.A., Mostafa, O., Browning, T.R., Scheduling multiple, resource-constrained, iterative, product development projects with genetic algorithms. Comput. Ind. Eng. 107 (2017), 39–56.
135. Yu, F., Xu, X., A short-term load forecasting model of natural gas based on optimized genetic algorithm and improve BR neural network. Appl. Energy 134 (2014), 102–113.
136. Yuce, B., Fruggiero, F., Packianather, M.S., Pham, D.T., Mastrocinque, E., Lambiase, A., Fera, M., Hybrid Genetic Bees Algorithm applied to single machine scheduling with earliness and tardiness penalties. Comput. Ind. Eng., 2017, 10.1016/j.cie.2017.07.018.
137. Zegordi, S.H., Abadi, I.N.K., Nia, M.A.B., A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain. Comput. Ind. Eng. 58:3 (2010), 373–381.
138. Zhang, R.-q., Research on capacity planning under stochastic production and uncertain demand. Syst. Eng. Theory Pract. 27:1 (2007), 51–59.
139. Zhang, R., Chang, P.-C., Wu, C., A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: Investigations motivated by vehicle production. Int. J. Prod. Econ. 145:1 (2013), 38–52.
140. Zhang, R., Ong, S.K., Nee, A.Y.C., A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling. Appl. Soft Comput. 37 (2015), 521–532.
141. Zhang, Z., Tang, R., Peng, T., Tao, L., Jia, S., A method for minimizing the energy consumption of machining system: integration of process planning and scheduling. J. Cleaner Prod. 137 (2016), 1647–1662.
142. Zhang, L., Wong, T.N., An object-coding genetic algorithm for integrated process planning and scheduling. European J. Oper. Res. 244:2 (2015), 434–444.
143. Zhou, G., Min, H., Gen, M., The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach. Comput. Ind. Eng. 43:1–2 (2002), 251–261.