Abstract
Effective manpower allocation is among the most vital and complicated decisions for most companies on account of imprecise nature of input information of the problem. This paper presents a novel combination of the chance-constrained programming and the global criterion model for manpower allocation problem that is called chance-constrained global criterion. The proposed model is a deterministic equivalent for the multi-objective stochastic problem of manpower allocation. To illustrate the model, a tri-objective stochastic manpower allocation case problem for determining optimal number of manpower in a job-shop manufacturing system is formulated and solved, and then the competitive advantages of the model are discussed. To have a better judgment on the validity and performance efficiency of the model, 20 different problems are generated and solved. The results show that increasing the size of problem do not have much effect on the number of iterations required for finding the optimal solution, and this model decreases complicacy in modeling the problem.
Similar content being viewed by others
References
Huang DK, Chiu HN, Yeh RH, Chang JH (2009) A fuzzy multi-criteria decision making approach for solving a bi-objective personnel assignment problem. Comput Ind Eng 56:1–10
Caballero R, Galache T, Gómez T, Molina J, Torrico A (2004) Budgetary allocations and efficiency in the human resources policy of a university following multiple criteria. Econ Educ Rev 23:67–74
Kwak NK, Lee C (1997) A linear goal programming model for human resource allocation in a health-care organization. J Med Syst 21:129–140
Summers EL (1972) The audit staff assignment problem: a linear programming analysis. Account Rev 47:443–453
Killough LN, Souders TL (1973) A goal programming model for public accounting firms. Account Rev XLVIII(2):268–279
Bailey AD, Boe WJ, Schnack T (1974) The audit staff assignment problem: a comment. Account Rev LIX(3):572–574
Welling P (1977) A goal programming model for human resource accounting in a CPA firm. Account Organ Soc 2:307–316
Balachandran KR, Steuer RE (1982) An interactive model for the CPA firm audit staff planning problem with multiple objectives. Account Rev LVII(1):125–140
Andersson J (2000) A survey of multiobjective optimization in engineering design. Optimization (LiTH-IKP-R-1097), 34.Citeseer. Retrieved from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.8.5638&rep=rep1&type=pdf
Kwak W, Shi Y, Jung K (2003) Human resource allocation in a CPA firm: a fuzzy set approach. Rev Quant Finan Acc 20:277–290
Gardner JC, Huefner RJ, Lotfi V (1990) A multi period audit staff planning model using multiple objectives: development and evaluation. Decis Sci 21:154–170
Chebat J-C, Filiatrault P, Katz A, Tal SM (1994) Strategic auditing of human and financial resource allocation in marketing an empirical study using data envelopment analysis. J Bus Res 31:197–208
Pan Q-K, Suganthan PN, Chua TJ, Cai TX (2010) Solving manpower scheduling problem in manufacturing using mixed-integer programming with a two-stage heuristic algorithm. Int J Adv Manuf Technol 46:1229–1237
Liang GS, Wang MJ (1992) Personnel placement in a fuzzy environment. Comput Oper Res 19:107–121
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Toroslu IH (2003) Personnel assignment problem with hierarchical ordering constraints. Comput Ind Eng 45:493–510
Liang GS, Wang MJ (1994) Personnel selection using fuzzy MCDM algorithm. Eur J Oper Res 78:22–33
Yaakob SB, Kawata S (1999) Workers’ placement in an industrial environment. Fuzzy Set Syst 106:289–297
Karsak EE (2000) A fuzzy multiple objective programming approach for personnel selection. IEEE International Conference on Systems, Man, and Cybernetics, vol 3, pp 2007–2012
Abdelaziz FB, Aouni B, Fayedh RE (2007) Multi-objective stochastic programming for portfolio selection. Eur J Oper Res 177:1811–1823
Sen S (2001) In: Gass S, Harris C (eds) Stochastic programming: computational issues and challenges, encyclopedia of OR/MS. Kluwer Academic, Dordrecht, pp 784–789
Murphy FH, Sen S, Soyster AL (1982) Electric utility capacity expansion planning with uncertain load forecasts. AIIE Trans 14:52–59
Carino DR, Kent T, Meyers DH, Stacy C, Sylvanus M, Turner AL, Watanabe K, Ziemba WT (1994) The Russell–Yasuda Kasai model: an asset/liability model for a Japanese insurance company using multistage stochastic programming. Interfaces 24(1):29–49
Sen S, Doverspike RD, Cosares S (1994) Network planning with random demand. Telecommun Syst 3:11–30
Ip WH, Fung R, Keung KW (1999) An investigation of stochastic analysis of flexible manufacturing systems simulation. Int J Adv Manuf Technol 15:244–250
Fisher ML, Hammond J, Obermeyer W, Raman A (1997) Configuring a supply chain to reduce the cost of demand uncertainty. Prod Oper Manag 6:211–225
Lim SJ, Jeong SJ, Kim KS, Park MW (2006) Hybrid approach to distribution planning reflecting a stochastic supply chain. Int J Adv Manuf Technol 28:618–625
Sheikh Sajadieh M, AkbariJokar MR (2009) An integrated vendor–buyer cooperative model under stochastic supply lead-time. Int J Adv Manuf Technol 41:1043–1050
Mirzapour Al-e-Hashem SMJ, Aryanezhad MB, Sadjadi SJ (2011) An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment. Int J Adv Manuf Technol. doi:10.1007/s00170-011-3396-1
Kall P, Wallace SW (1994) Stochastic programming. Wiley, England
Birge JR, Louveaux F (1997) Introduction to stochastic programming. Springer, New York
Prékopa A (1995) Stochastic programming. Kluwer Academic, London
Birge JR (1997) Stochastic programming computation and applications. INFORMS J Comput 9:111–133
Sen S, Higle JL (1999) An introductory tutorial on stochastic linear programming models. Interfaces 29:33–61
Charnes A, Cooper WW (1959) Chance constrained programming. Manag Sci 6:73–79
Charnes A, Cooper WW (1963) Deterministic equivalents for optimizing and satisfying under chance constraints. Oper Res 11:18–39
Ahmadizar F, Ghazanfari M, FatemiGhomi SMT (2009) Application of chance-constrained programming for stochastic group shop scheduling problem. Int J Adv Manuf Technol 42:321–334
Lawrence SR, Sewell EC (1997) Heuristic, optimal, static, and dynamic schedules when processing times are uncertain. J Oper Manag 15:71–82
Singer M (2000) Forecasting policies for scheduling a stochastic due date job shop. Int J Prod Res 38:3623–3637
Yoshitomi Y (2002) A genetic algorithm approach to solving stochastic job-shop scheduling problems. Int Trans Oper Res 9:479–495
Yoshitomi Y, Yamaguchi R (2003) A genetic algorithm and the Monte Carlo method for stochastic job-shop scheduling. Int Trans Oper Res 10:577–596
Tavakkoli-Moghaddam R, Jolai F, Vaziri F, Ahmed PK, Azaron A (2005) A hybrid method for solving stochastic job shop scheduling problems. Appl Math Comput 170:185–206
Luh PB, Chen D, Thakur LS (1999) An effective approach for job-shop scheduling with uncertain processing requirements. IEEE Trans Robot Autom 15:328–339
Neumann K, Schneider WG (1999) Heuristic algorithms for job shop scheduling problems with stochastic precedence constraints. Ann Oper Res 92:45–63
Alcaide D, Rodriguez-Gonzalez A, Sicilia J (2006) A heuristic approach to minimize expected make span in open shops subject to stochastic processing times and failures. Int J Flex Manuf Syst 17:201–226
Athan TW, Papalambros PY (1996) A note on weighted criteria methods for compromise solutions in multi-objective optimization. Eng Optim 27:155–176
Zeleny M (1973) Compromise programming. In: Cochrane JL, Zeleny M (eds) Multiple criteria decision making. University of South Carolina Press, Columbia, pp 262–301
Zeleny M (1974) A concept of compromise solutions and the method of the displaced ideal. Comput Oper Res 1:479–496
Yu PL (1973) A class of solutions for group decision problems. Manag Sci 19:936–946
Zeleny M (1976) Multiple criteria decision making. Springer, Berlin
Zeleny M (1982) Multiple criteria decision making. McGraw-Hill, New York
Osyczka A (1984) Multicriterion optimization in engineering with FORTRAN programs. Wiley, New York
Eschenauer H, Koski J, Osyczka A (1990) Multicriteria design optimization procedures and applications. Springer, Berlin
Miettinen K (1999) Nonlinear multi-objective optimization. Kluwer Academic, Boston
Marler RT (2005) A study of multi-objective optimization methods for engineering applications. A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Mechanical Engineering in the Graduate College of the University of Iowa
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ekhtiari, M., Ghoseiri, K. Multi-objective stochastic programming to solve manpower allocation problem. Int J Adv Manuf Technol 65, 183–196 (2013). https://doi.org/10.1007/s00170-012-4159-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-012-4159-3