Smart Production by Integrating Product-Mix Planning and Revenue Management for Semiconductor Manufacturing

  • Marzieh Khakifirooz
  • Jei-Zheng Wu
  • Mahdi FathiEmail author
Part of the Springer Optimization and Its Applications book series (SOIA, volume 152)


Semiconductor manufacturing is a capital-intensive industry, in which matching the demand and capacity is the most important and challenging decision due to the long lead time for capacity expansion and shortening product life cycles of various demands. Most of the previous works focused on capacity investment strategy or product-mix planning based on single evaluation criteria such as total cost or total profit. However, a different combination of product-mix will contribute to a different combination of key financial indicators such as revenue, profit, gross margin. This study aims to model the multi-objective product-mix planning and revenue management for the manufacturing systems with unrelated parallel machines. Indeed, the present problem is a multi-objective nonlinear integer programming problem. Thus, this study developed a multi-objective genetic algorithm for revenue management (MORMGA) with an efficient algorithm to generate the initial solutions and a Pareto ranking selection mechanism using elitist strategy to find the effective Pareto frontier. A number of standard multi-objective metrics including distance metrics, spacing metrics, maximum spread metrics, rate metrics, and coverage metrics are employed to compare the performance of the proposed MORMGA with mathematical models and experts’ experiences. The proposed model can help a company to formulate a competitive strategy to achieve the first-priority objective without sacrificing other benefits. A case study in real settings was conducted in a leading semiconductor company in Taiwan for validation. The results showed that MORMGA outperformed the efficient multi-objective genetic algorithm, i.e., NSGA-II, as well as expert knowledge of the case corporation in both revenue and gross margin. An evaluation scheme was demonstrated by comparing the effectiveness of manufacturing flexibility from the multi-objective perspective.


Multiple objectives Genetic algorithm Pareto ranking Semiconductor manufacturing Revenue management Manufacturing flexibility 



This study is supported by the Ministry of Science and Technology, Taiwan (MOST106-2218-E-007-024; MOST104-2410-H-031-033-MY3; NSC-100-2410-H-031-011-MY2; MOST107-2634-F-007-002; MOST107-2634-F-007-009).


  1. 1.
    Bard, J.F., Jia, S., Chacon, R., Stuber, J.: Integrating optimisation and simulation approaches for daily scheduling of assembly and test operations. Int. J. Prod. Res. 53(9), 2617–2632 (2015)CrossRefGoogle Scholar
  2. 2.
    Beach, R., Muhlemann, A.P., Price, D.H., Paterson, A., Sharp, J.A.: A review of manufacturing flexibility. Eur. J. Oper. Res. 122(1), 41–57 (2000)zbMATHCrossRefGoogle Scholar
  3. 3.
    Bean, J.C.: Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput. 6(2), 154–160 (1994)zbMATHCrossRefGoogle Scholar
  4. 4.
    Bengtsson, J., Olhager, J.: The impact of the product mix on the value of flexibility. Omega 30(4), 265–273 (2002)CrossRefGoogle Scholar
  5. 5.
    Bitran, G., Caldentey, R.: An overview of pricing models for revenue management. Manuf. Serv. Oper. Manag. 5(3), 203–229 (2003)CrossRefGoogle Scholar
  6. 6.
    Burgelman, R.A.: Fading memories: a process theory of strategic business exit in dynamic environments. Adm. Sci. Q. 39, 24–56 (1994)CrossRefGoogle Scholar
  7. 7.
    Cakanyıldırım, M., Roundy, R.O., Wood, S.C.: Optimal machine capacity expansions with nested limitations under stochastic demand. Nav. Res. Logist. (NRL) 51(2), 217–241 (2004)Google Scholar
  8. 8.
    Chien, C.F., Chen, Y.J., Peng, J.T.: Manufacturing intelligence for semiconductor demand forecast based on technology diffusion and product life cycle. Int. J. Prod. Econ. 128(2), 496–509 (2010)CrossRefGoogle Scholar
  9. 9.
    Chien, C.F., Hsu, C.Y.: Data mining for optimizing IC feature designs to enhance overall wafer effectiveness. IEEE Trans. Semicond. Manuf. 27(1), 71–82 (2014)CrossRefGoogle Scholar
  10. 10.
    Chien, C.F., Huynh, N.T.: An integrated approach for ic design r&d portfolio decision and project scheduling and a case study. IEEE Trans. Semicond. Manuf. 31(1), 76–86 (2018)CrossRefGoogle Scholar
  11. 11.
    Chien, C.F., Kuo, R.T.: Beyond make-or-buy: cross-company short-term capacity backup in semiconductor industry ecosystem. Flex. Serv. Manuf. J. 25(3), 310–342 (2013)CrossRefGoogle Scholar
  12. 12.
    Chien, C.F., Wu, J.Z., Weng, Y.D.: Modeling order assignment for semiconductor assembly hierarchical outsourcing and developing the decision support system. Flex. Serv. Manuf. J. 22(1–2), 109–139 (2010)CrossRefGoogle Scholar
  13. 13.
    Chien, C.F., Wu, J.Z., Wu, C.C.: A two-stage stochastic programming approach for new tape-out allocation decisions for demand fulfillment planning in semiconductor manufacturing. Flex. Serv. Manuf. J. 25(3), 286–309 (2013)CrossRefGoogle Scholar
  14. 14.
    Chien, C.F., Wu, J.Z., Zheng, J.N.: Multi-objective semiconductor product capacity planning system and method thereof (2017). US Patent 9,563,857Google Scholar
  15. 15.
    Chien, C.F., Zheng, J.N.: Mini–max regret strategy for robust capacity expansion decisions in semiconductor manufacturing. J. Intell. Manuf. 23(6), 2151–2159 (2012)CrossRefGoogle Scholar
  16. 16.
    Chinchuluun, A., Pardalos, P.M.: A survey of recent developments in multiobjective optimization. Ann. Oper. Res. 154(1), 29–50 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  18. 18.
    D’Souza, D.E., Williams, F.P.: Toward a taxonomy of manufacturing flexibility dimensions. J. Oper. Manag. 18(5), 577–593 (2000)CrossRefGoogle Scholar
  19. 19.
    Ehrgott, M., Gandibleux, X.: A survey and annotated bibliography of multiobjective combinatorial optimization. OR-Spektrum 22(4), 425–460 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Ewen, H., Mönch, L., Ehm, H., Ponsignon, T., Fowler, J.W., Forstner, L.: A testbed for simulating semiconductor supply chains. IEEE Trans. Semicond. Manuf. 30(3), 293–305 (2017)CrossRefGoogle Scholar
  21. 21.
    Flamm, K.: Measuring Moore’s law: evidence from price, cost, and quality indexes. Technical report. National Bureau of Economic Research (2018)Google Scholar
  22. 22.
    Gong, Z., Hu, S.: An economic evaluation model of product mix flexibility. Omega 36(5), 852–864 (2008)CrossRefGoogle Scholar
  23. 23.
    Jamrus, T., Chien, C.F., Gen, M., Sethanan, K.: Multistage production distribution under uncertain demands with integrated discrete particle swarm optimization and extended priority-based hybrid genetic algorithm. Fuzzy Optim. Decis. Mak. 14(3), 265–287 (2015)MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Khakifirooz, M., Chien, C.F., Chen, Y.J.: Bayesian inference for mining semiconductor manufacturing big data for yield enhancement and smart production to empower industry 4.0. Appl. Soft Comput. 68, 990–999 (2018)Google Scholar
  25. 25.
    Kusiak, A.: Put innovation science at the heart of discovery: the success rate of discoveries would be improved if we could find out how to innovate. Nature 530(7590), 255–256 (2016)CrossRefGoogle Scholar
  26. 26.
    Kusiak, A.: Smart manufacturing must embrace big data. Nature 544(7648), 23–25 (2017)CrossRefGoogle Scholar
  27. 27.
    Kusiak, A.: Smart manufacturing. Int. J. Prod. Res. 56(1–2), 508–517 (2018)CrossRefGoogle Scholar
  28. 28.
    Leachman, R.C., Ding, S., Chien, C.F.: Economic efficiency analysis of wafer fabrication. IEEE Trans. Autom. Sci. Eng. 4(4), 501–512 (2007)CrossRefGoogle Scholar
  29. 29.
    Lee, A.H., Kang, H.Y., Wang, W.P.: Analysis of priority mix planning for the fabrication of semiconductors under uncertainty. Int. J. Adv. Manuf. Technol. 28(3–4), 351–361 (2006)CrossRefGoogle Scholar
  30. 30.
    Lee, P.T.W., Wu, J.Z., Hu, K.C., Flynn, M.: Applying analytic network process (ANP) to rank critical success factors of waterfront redevelopment. Int. J. Shipping Transp. Logist. 5(4–5), 390–411 (2013)CrossRefGoogle Scholar
  31. 31.
    Lee, P.T.W., Wu, J.Z., Suthiwartnarueput, K., Hu, K.C., Rodjanapradied, R.: A comparative study of key critical factors of waterfront port development: case studies of the Incheon and Bangkok ports. Growth Change 47(3), 393–405 (2016)CrossRefGoogle Scholar
  32. 32.
    Li, B.B., Wang, L., Liu, B.: An effective PSO-based hybrid algorithm for multiobjective permutation flow shop scheduling. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 38(4), 818–831 (2008)CrossRefGoogle Scholar
  33. 33.
    Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscipl. Optim. 26(6), 369–395 (2004)MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Martello, S.: Knapsack Problems: Algorithms and Computer Implementations. Wiley-Interscience Series in Discrete Mathematics and Optimization. Wiley, Chichester/New York (1990)Google Scholar
  35. 35.
    Michalewicz, Z.: Genetic Algorithms +  Data Structures =  Evolution Programs. Springer Science & Business Media (2013)Google Scholar
  36. 36.
    Molina, J., Santana, L.V., Hernández-Díaz, A.G., Coello, C.A.C., Caballero, R.: g-dominance: reference point based dominance for multiobjective metaheuristics. Eur. J. Oper. Res. 197(2), 685–692 (2009)Google Scholar
  37. 37.
    Moore, G.E.: Cramming more components onto integrated circuits. Electronics 38(8), 114–117 (1965)Google Scholar
  38. 38.
    Netessine, S.: Dynamic pricing of inventory/capacity with infrequent price changes. Eur. J. Oper. Res. 174(1), 553–580 (2006)zbMATHCrossRefGoogle Scholar
  39. 39.
    Rastogi, A.P., Fowler, J.W., Carlyle, W.M., Araz, O.M., Maltz, A., Büke, B.: Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations. Int. J. Prod. Econ. 134(2), 322–332 (2011)CrossRefGoogle Scholar
  40. 40.
    Rezvan, S., Dauzere-Peres, S., Yugma, C., Sarraj, R.: Managing capacity production with time constraints in semiconductor manufacturing. In: ROADEF-15ème congrès annuel de la Société française de recherche opérationnelle et d’aide à la décision (2014)Google Scholar
  41. 41.
    Reeves, C.R.: A genetic algorithm for flowshop sequencing. Comput. Oper. Res. 22(1), 5–13 (1995)zbMATHCrossRefGoogle Scholar
  42. 42.
    Seitz, A., Grunow, M.: Increasing accuracy and robustness of order promises. Int. J. Prod. Res. 55(3), 656–670 (2017)CrossRefGoogle Scholar
  43. 43.
    Talluri, K.T., Van Ryzin, G.J., Karaesmen, I.Z., Vulcano, G.J.: Revenue management: models and methods. In: Simulation Conference, 2008. WSC 2008. Winter, pp. 145–156. IEEE (2008)Google Scholar
  44. 44.
    Tan, K.C., Goh, C.K., Yang, Y., Lee, T.H.: Evolving better population distribution and exploration in evolutionary multi-objective optimization. Eur. J. Oper. Res. 171(2), 463–495 (2006)zbMATHCrossRefGoogle Scholar
  45. 45.
    Tu, Y.M., Lu, C.W., Chang, S.H.: Model to evaluate production performance of twin-fab under capacity support. In: Advanced Materials Research, vol. 694, pp. 3453–3457. Trans Tech Publ, Durnten-Zurich (2013)Google Scholar
  46. 46.
    Ulungu, E., Teghem, J., Ost, C.: Efficiency of interactive multi-objective simulated annealing through a case study. J. Oper. Res. Soc. 49(10), 1044–1050 (1998)zbMATHCrossRefGoogle Scholar
  47. 47.
    Mönch, L., Uzsoy, R., Fowler, J.W.: A survey of semiconductor supply chain models part III: master planning, production planning, and demand fulfilment. Int. J. Prod. Res. 56(13), 4565–4584 (2018)CrossRefGoogle Scholar
  48. 48.
    Van Veldhuizen, D.A., Lamont, G.B.: On measuring multiobjective evolutionary algorithm performance. In: Evolutionary Computation, 2000. Proceedings of the 2000 Congress on, vol. 1, pp. 204–211. IEEE (2000)Google Scholar
  49. 49.
    Wang, L.C., Chu, P.C., Lin, S.Y.: Impact of capacity fluctuation on throughput performance for semiconductor wafer fabrication. Robot. Comput. Integr. Manuf. 55, 208–216 (2018)CrossRefGoogle Scholar
  50. 50.
    Wu, J.Z.: Inventory write-down prediction for semiconductor manufacturing considering inventory age, accounting principle, and product structure with real settings. Comput. Ind. Eng. 65(1), 128–136 (2013)CrossRefGoogle Scholar
  51. 51.
    Wu, J.Z., Chien, C.F.: Modeling strategic semiconductor assembly outsourcing decisions based on empirical settings. OR Spectr. 30(3), 401–430 (2008)MathSciNetCrossRefGoogle Scholar
  52. 52.
    Wu, J.Z., Chien, C.F., Gen, M.: Coordinating strategic outsourcing decisions for semiconductor assembly using a bi-objective genetic algorithm. Int. J. Prod. Res. 50(1), 235–260 (2012)CrossRefGoogle Scholar
  53. 53.
    Wu, J.Z., Hao, X.C., Chien, C.F., Gen, M.: A novel bi-vector encoding genetic algorithm for the simultaneous multiple resources scheduling problem. J. Intell. Manuf. 23(6), 2255–2270 (2012)CrossRefGoogle Scholar
  54. 54.
    Wu, J.Z., Hsu, C.Y.: Critical success factors for improving decision quality on collaborative design in the IC supply chain. J. Qual. 16(2), 95–108 (2009)Google Scholar
  55. 55.
    Wu, J.Z., Hua, Y.H.: Key risk factors of financial holding companies in Taiwan: an integrated approach of DEMATEL-based ANP and risk management balanced scorecard. NTU Manag. Rev. 28(2), 1–38 (2018)MathSciNetGoogle Scholar
  56. 56.
    Wu, J.Z., Santoso, C.H., Roan, J.: Key factors for truly sustainable supply chain management: an investigation of the coal industry in Indonesia. Int. J. Logist. Manag. 28(4), 1196–1217 (2017)CrossRefGoogle Scholar
  57. 57.
    Wu, J.Z., Tiao, P.J.: A validation scheme for intelligent and effective multiple criteria decision-making. Appl. Soft Comput. 68, 866–872 (2018)CrossRefGoogle Scholar
  58. 58.
    Yahya, B.N., Wu, J.Z., Bae, H.R.: Generation of business process reference model considering multiple objectives. Ind. Eng. Manag. Syst. 11(3), 233–240 (2012)Google Scholar
  59. 59.
    Yan, B., Yan, C., Long, F., Tan, X.C.: Multi-objective optimization of electronic product goods location assignment in stereoscopic warehouse based on adaptive genetic algorithm. J. Intell. Manuf. 29(6), 1273–1285 (2018)CrossRefGoogle Scholar
  60. 60.
    Zhao, L., Huchzermeier, A.: Supply Chain Finance: Integrating Operations and Finance in Global Supply Chains. Springer, Cham (2018)zbMATHCrossRefGoogle Scholar
  61. 61.
    Zhao, S., Haskell, W.B., Cardin, M.A.: Decision rule based method for flexible multi-facility capacity expansion problem. IISE Trans. (Just-Accepted) (2018)Google Scholar
  62. 62.
    Zhou, L., Chen, Z., Chen, S.: An effective detailed operation scheduling in MES based on hybrid genetic algorithm. J. Intell. Manuf. 29(1), 135–153 (2018)CrossRefGoogle Scholar
  63. 63.
    Zhuang, Z.Y., Chang, S.C.: Deciding product mix based on time-driven activity-based costing by mixed integer programming. J. Intell. Manuf. 28(4), 959–974 (2017)CrossRefGoogle Scholar
  64. 64.
    Ziarnetzky, T., Mönch, L.: Simulation-based optimization for integrated production planning and capacity expansion decisions. In: Winter Simulation Conference (WSC), 2016, pp. 2992–3003. IEEE (2016)Google Scholar
  65. 65.
    Zio, E., Bazzo, R.: A clustering procedure for reducing the number of representative solutions in the Pareto front of multiobjective optimization problems. Eur. J. Oper. Res. 210(3), 624–634 (2011)CrossRefGoogle Scholar
  66. 66.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marzieh Khakifirooz
    • 1
  • Jei-Zheng Wu
    • 2
  • Mahdi Fathi
    • 3
    Email author
  1. 1.School of Engineering and ScienceTecnológico de MonterreyMonterreyMexico
  2. 2.Soochow UniversityTaipeiTaiwan
  3. 3.Department of Industrial and Systems EngineeringMississippi State UniversityStarkvilleUSA

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