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A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand

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Abstract

This paper addresses a robust multi-objective multi-period aggregate production planning (APP) problem based on different scenarios under uncertain seasonal demand. The main goals are to minimize the total cost including in-house production, outsourcing, workforce, holding, shortage and employment/unemployment costs, and maximize the customers’ satisfaction level. To deal with demand uncertainty, robust optimization approach is applied to the proposed mixed integer linear programming model. A goal programming method is then implemented to cope with the multi-objectiveness and validate the suggested robust model. Since APP problems are classified as NP-hard, two solution methods of non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization algorithm (MOIWO) are designed to solve the problem. Moreover, Taguchi design method is implemented to increase the efficiency of the algorithms by adjusting the algorithms’ parameters optimally. Finally, several numerical test problems are generated in different sizes to evaluate the performance of the algorithms. The results obtained from different comparison criteria demonstrate the high quality of the proposed solution methods in terms of speed and accuracy in finding optimal solutions.

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References

  1. Ahmadi E, Zandieh M, Farrokh M, Emami SM (2016) A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms. Comput Oper Res 73:56–66

    Article  MathSciNet  MATH  Google Scholar 

  2. Babaee Tirkolaee E, Alinaghian M, Bakhshi Sasi M, Seyyed Esfahani MM (2016) Solving a robust capacitated arc routing problem using a hybrid simulated annealing algorithm: a waste collection application. J Ind Eng Manag Stud 3(1):61–76

    Google Scholar 

  3. Balasubbareddy M, Sivanagaraju S, Suresh CV (2015) Multi-objective optimization in the presence of practical constraints using non-dominated sorting hybrid cuckoo search algorithm. Eng Sci Technol Int J 18(4):603–615

    Article  Google Scholar 

  4. Baykasoglu, A. (2001). MOAPPS 1.0: Aggregate production planning using the multiple-objective tabu search. International Journal of Production Research, 39(16):3685–3702

  5. Behnamian J, Ghomi SF (2011) Hybrid flowshop scheduling with machine and resource-dependent processing times. Appl Math Model 35(3):1107–1123

    Article  MathSciNet  MATH  Google Scholar 

  6. Ben-Tal A, Nemirovski A (1998) Robust convex optimization. Math Oper Res 23(4):769–805

    Article  MathSciNet  MATH  Google Scholar 

  7. Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53

    Article  MathSciNet  MATH  Google Scholar 

  8. Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev 53(3):464–501

    Article  MathSciNet  MATH  Google Scholar 

  9. Charnes A, Cooper WW (1977) Goal programming and multiple objective optimizations: part 1. Eur J Oper Res 1(1):39–54

    Article  MATH  Google Scholar 

  10. Chen YK, Liao HC (2003) An investigation on selection of simplified aggregate production planning strategies using MADM approaches. Int J Prod Res 41(14):3359–3374

    Article  MATH  Google Scholar 

  11. Cheraghalipour A, Paydar MM, Hajiaghaei-Keshteli M (2018) A bi-objective optimization for citrus closed-loop supply chain using Pareto-based algorithms. Appl Soft Comput 69:33–59

    Article  Google Scholar 

  12. Deb K, Agrawal S, Pratap A, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithms: NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

  13. Denisov VI, Timofeeva AY, Khailenko EA, Buzmakova OI (2014) Robust estimation of nonlinear structural models. J Appl Ind Math 8(1):28–39

    Article  MathSciNet  MATH  Google Scholar 

  14. Entezaminia A, Heidari M, Rahmani D (2017) Robust aggregate production planning in a green supply chain under uncertainty considering reverse logistics: a case study. Int J Adv Manuf Technol 90:1507–1528

    Article  Google Scholar 

  15. Filatovas E, Kurasova O, Sindhya K (2015) Synchronous R-NSGA-II: an extended preference-based evolutionary algorithm for multi-objective optimization. Informatica 26(1):33–50

    Article  Google Scholar 

  16. Giannoccaro, I., Pontrandolfo, P. (2001). Models for supply chain management: a taxonomy. Proceedings of the Production and Operations Management. Conference POMS mastery in the new millennium, Orlando, FL, USA

  17. Goli, A., and Davoodi, S.M.R. (2018). Coordination policy for production and delivery scheduling in the closed loop supply chain. Production Engineering, 1-11

  18. Goodman DA (1974) A goal programming approach to aggregate planning of production and work force. Manag Sci 20(12):1569–1575

    Article  MATH  Google Scholar 

  19. Holt CC, Modigliani F, Simon HA (1955) A Linear decision rule for production and employment scheduling. Manag Sci 2(1):1–30

    Article  Google Scholar 

  20. Holt CC, Modigliani F, Muth JF, Simon HA (1961) Planning Production Inventories and Workforce. Am Econ Rev 51(4):697–699

    Google Scholar 

  21. Ismail HA, Packianather MS, Grosvenor RI (2017) Multi-objective invasive weed optimization of the LQR controller. Int J Autom Comput 14(3):321–339

    Article  Google Scholar 

  22. Jamalnia, A. (2017). Evaluating the performance of aggregate production planning strategies under uncertainty (Doctoral dissertation, Manchester Business School)

  23. Kanyalkar AP, Adil GK (2010) A robust optimisation model for aggregate and detailed planning of a multi-site procurement-production-distribution system. Int J Prod Res 48(3):635–656

    Article  MATH  Google Scholar 

  24. Kleindorfer P, Kunreuther H (1978) Stochastic horizons for the aggregate planning problem. Manag Sci 24(5):485–497

    Article  MathSciNet  MATH  Google Scholar 

  25. Kogan K, Portougal V (2006) Multi-period aggregate production planning in a news-vendor framework. J Oper Res Soc 57:423–433

    Article  MATH  Google Scholar 

  26. Kundu D, Suresh K, Ghosh S, Das S, Panigrahi BK, Das S (2011) Multi-objective optimization with artificial weed colonies 18(12):2441–2454

    Google Scholar 

  27. Leung SCH, Chan SSW (2009) A goal programming model for aggregate production planning with resource utilization constraint. Elsevier Comput Ind Eng 56:1053–1064

    Article  Google Scholar 

  28. Leung SCH, Wu Y (2004) A robust optimization model for stochastic aggregate production planning. Prod Plan Control Manag Oper 15(5):502–514

    Article  Google Scholar 

  29. Leung SCH, Wu Y, Lai KK (2006) A stochastic programming approach for multi-site aggregate production planning. J Oper Res Soc 57(2):123–132

    Article  MATH  Google Scholar 

  30. Leung SCH, Wu Y, Lai K (2003) Multi-site aggregate production planning with multiple objectives: a goal programming approach. Prod Plan Control Manag Oper 14(5):425–436

    Article  Google Scholar 

  31. Liange W (2013) A simultaneous decision model for production marketing and finance. Manag Sci 19(2):161–172

    Google Scholar 

  32. Lieckens, K. and Vandaele, N. (2014). A decision support system for the stochastic aggregate planning problem: March 28 2014

  33. Lockett AG, Muhlemann AP (1978) A stochastic programming model for aggregate production planning. Eur J Oper Res 2(5):350–356

    Article  MATH  Google Scholar 

  34. Makui A, Heydari M, Aazami A, Dehghani E (2016) Accelerating Benders decomposition approach for robust aggregate production planning of products with a very limited expiration date. Comput Ind Eng 100(2016):34–51

    Article  Google Scholar 

  35. Masoud ASM, Hwang CL (1980) An aggregate production planning model and application of three multiple objective decision methods. Int J Prod Res 18:741–752

    Article  Google Scholar 

  36. Mirzapour Al-e-hashem SMJ, Malekly H, Aryanezhad MB (2011) A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. Int J Prod Econ 134(1):28–42

    Article  Google Scholar 

  37. Mirzapour Al-e-hashem, S.M.J., Aryanezhad, M.B. and Sadjadi, S.J. (2012). An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment.The International Journal of Advanced Manufacturing Technology, 58(5):765-782

  38. Mirzapour Al-e-hashem SMJ, Baboli A, Sazvar Z (2013) A stochastic aggregate production planning model in a green supply chain: considering flexible lead times, nonlinear purchase and shortage cost functions. Eur J Oper Res 230(1):26–41

    Article  MathSciNet  MATH  Google Scholar 

  39. Modarres M, Izadpanahi E (2016) Aggregate production planning by focusing on energy saving: a robust optimization approach. J Clean Prod 133(2016):1074–1085

    Article  Google Scholar 

  40. Mula J, Poler R, García-Sabater JP, Lario FC (2006) Models for production planning under uncertainty: a review. Int J Prod Econ 103:271–285

    Article  Google Scholar 

  41. Mulvey JM, Vanderbei RJ, Zenios SA (1995) Robust optimization of large-scale systems. Oper Res 43(2):264–281

    Article  MathSciNet  MATH  Google Scholar 

  42. Mazulla JM (1978) Production switching heuristics for the aggregate planning problem. Manag Sci 24(12):1242–1251

    Article  Google Scholar 

  43. Nam SJ, Logendran R (1992) Aggregate production planning: a survey of models and methodologies. Eur J Oper Res 61:255–272

    Article  Google Scholar 

  44. Niknamfar AH, Akhavan Niaki ST, Pasandideh SHR (2015) Robust optimization approach for an aggregate production–distribution planning in a three-level supply chain. Int J Adv Manuf Technol 76(1):623–634

    Article  Google Scholar 

  45. Ning Y, Liu J, Yan L (2013) Uncertain aggregate production planning. Soft Comput 17(4):617–624

    Article  Google Scholar 

  46. Nowak M (2013) An interactive procedure for aggregate production planning. Croatian Oper Res Rev 4(1):247–257

    MathSciNet  MATH  Google Scholar 

  47. Pradenas L, Penailillo F, Ferland J (2004) Aggregate production planning problem. A new algorithm. Elsevier Electron Notes Discrete Math 18:193–199

    Article  MathSciNet  MATH  Google Scholar 

  48. Rahmati SHA, Hajipour V, Niaki STA (2013) A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem. Appl Soft Comput 13(4):1728–1740

    Article  Google Scholar 

  49. Rakes TR, Franz LS, Wynne AJ (1984) Aggregate production planning using chance-constrained goal programming. Int J Prod Res 22(4):673–684

    Article  Google Scholar 

  50. Ramezanian R, Rahmani D, Barzinpour F (2012) An aggregate production planning model for two phase production systems: solving with genetic algorithm and tabu search. Expert Syst Appl 39:1256–1263

    Article  Google Scholar 

  51. Sangaiah AK, Thangavelu AK, Gao XZ, Anbazhagan N, Durai MS (2015) An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm. Appl Soft Comput 30:628–635

    Article  Google Scholar 

  52. Singh N, Singh SB (2017) A novel hybrid GWO-SCA approach for optimization problems. Eng Sci Technol Int J 20(6):1586–1601

    Article  Google Scholar 

  53. Soyster AL (1973) Convex programming with set-inclusive constraints and applications to inexact linear programming. Oper Res 21(5):1154–1157

    Article  MathSciNet  MATH  Google Scholar 

  54. Srinivasan S, Ramakrishnan S (2013) A social intelligent system for multi-objective optimization of classification rules using cultural algorithms. Computing 95(4):327–350

    Article  MathSciNet  MATH  Google Scholar 

  55. Srinivass N, Deb K (1994) Multi-objective optimization using non-dominated sorting in genetic algorithm. Evol Comput 2:221–248

    Article  Google Scholar 

  56. Taguchi, G. (1986). Introduction to quality engineering: designing quality into products and processes (No. 658.562 T3)

  57. Taguchi G, Chowdhury S, Wu Y (2005) Taguchi’s quality engineering handbook, vol 1736. Wiley, Hoboken

    MATH  Google Scholar 

  58. Tirkolaee EB, Goli A, Bakhsi M, Mahdavi I (2017) A robust multi-trip vehicle routing problem of perishable products with intermediate depots and time windows. Numer Algebra Control Optim 7(4):417–433

    Article  MathSciNet  MATH  Google Scholar 

  59. Tirkolaee EB, Mahdavi I, Esfahani MMS (2018) A robust periodic capacitated arc routing problem for urban waste collection considering drivers and crew’s working time. Waste Manag 76:138–146

    Article  Google Scholar 

  60. Techawiboonwong A, Yenradee P (2010) Aggregate production planning with workforce transferring plan for multiple product types. Prod Plan Control Manag Oper 14(5):447–458

    Article  Google Scholar 

  61. Thompson SD, Wantanabe DT, Davis WJ (1993) A comparative study of aggregate production planning strategies under conditions of uncertainty and cyclic product demands. Int J Prod Res 31(8):1957–1979

    Article  Google Scholar 

  62. Vörös J (1999) On the risk-based aggregate planning for seasonal products. Int J Prod Econ 59(1–3):195–201

    Article  Google Scholar 

  63. Zitzler E, Deb K, Thiele L (2000) Comparison of multiobjective evolutionary algorithms: empirical results. Evol Comput 8(2):173–195

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial Engineering, Yazd University, Yazd, Iran

    Alireza Goli

  2. Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

    Erfan Babaee Tirkolaee

  3. Young Researchers and Elite Club, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

    Erfan Babaee Tirkolaee

  4. Department of Systems and Information Engineering, University of Virginia, Charlottesville, 22904, USA

    Behnam Malmir

  5. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Gui-Bin Bian

  6. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India

    Arun Kumar Sangaiah

Authors
  1. Alireza Goli
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  2. Erfan Babaee Tirkolaee
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  3. Behnam Malmir
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  4. Gui-Bin Bian
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  5. Arun Kumar Sangaiah
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Correspondence to Arun Kumar Sangaiah.

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Goli, A., Tirkolaee, E.B., Malmir, B. et al. A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand. Computing 101, 499–529 (2019). https://doi.org/10.1007/s00607-018-00692-2

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  • DOI: https://doi.org/10.1007/s00607-018-00692-2

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