Manpower Planning with Annualized Hours Flexibility: A Fuzzy Mathematical Programming Approach

• Autorzy: Hasan Md. G. , Hasan S. S.

Identyfikatory

DOI

10.7494/dmms.2016.10.1-2.5

• Języki publikacji: EN

Abstrakty

EN

We have considered the problem of annualized hours (AH) in workforce management. AH is a method of distributing working hours with respect to the demand over a year. In this paper, the basic Manpower planning problem with AH flexibility is formulated as a fuzzy mathematical programming problem with flexible constraints. Three models of the AH planning problem under conditions of fuzzy uncertainty are presented using different aggregation operators. These fuzzy models soften the rigidity of the deterministic model by relaxing some constraints with the use of flexible programming. Finally, an illustration is given with a computational experiment performed on a realistic-scale case problem of an automobile company to demonstrate and analyze the effectiveness of the fuzzy approach over a deterministic model.

Słowa kluczowe

EN

annualized hours; fuzzy mathematical programming; manpower planning; workforce management; flexible working

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Decision Making in Manufacturing and Services
• Rocznik: 2016
• Tom: Vol. 10, no. 1-2
• Strony: 5--29
• Opis fizyczny: Bibliogr. 48 poz., tab., wykr.

Twórcy

autor

Hasan Md. G.

• Aligarh Muslim University, Department of Statistics and Operations Research, Aligarh, India

autor

Hasan S. S.

• Aligarh Muslim University, Department of Statistics and Operations Research, Aligarh, India

Bibliografia

• [1] Azmat, C.S., Widmer, M., 2004. A case study of single shift planning and scheduling under annualized hours: A simple three-step approach. European Journal of Operational Research, 153(1), pp. 148–175.
• [2] Azmat, C.S., Hürlimann, T., Widmer, M., 2004. Mixed integer programming to schedule a single-shift workforce under annualized hours. Annals of Operations Research, 128(1–4), pp. 199–215.
• [3] Bellman, R.E., Zadeh, L.A., 1970. Decision making in a fuzzy environment. Management Sciences, 17, pp. 141–164.
• [4] Cadenas, J.M., Verdegay, J.L., 2006. A primer on fuzzy optimization models and methods. Iranian Journal of Fuzzy Systems, 3(1), pp. 1–21.
• [5] Campbell, G.M., Diaby, M., 2002. Development and evaluation of an assignment heuristic for allocation cross-trained workers. European Journal of Operational Research, 138, pp. 9–20.
• [6] Corominas, A., Pastor, R., 2000. Manpower planning and scheduling in services with seasonal demand. In: Machuca, J.A.D., Mandakovic, T., (eds.), Proceedings of the I World Conference on Production and Operations Management, August 27th–September 1st, 2000, Sevilla, Spain.
• [7] Corominas, A., Pastor, R., 2010. Replanning working time under annualised working hours. International Journal of Production Research, 48(5), pp. 1493–1515.
• [8] Corominas, A., Lusa, A., Pastor, R., 2002. Using MILP to plan annualised working hours. The Journal of the Operational Research Society, 53(10), pp. 1101–1108.
• [9] Corominas, A., Lusa, A., Pastor, R., 2004. Planning annualised hours with a finite set of weekly working hours and joint holidays. Annals of Operations Research, 128(1–4), pp. 217–233.
• [10] Corominas, A., Lusa, A., Pastor, R., 2005. Annualised hours a real flexibility tool. OR Insight, 18(1), pp. 10–14.
• [11] Corominas, A., Lusa, A., Pastor, R., 2007a. Planning production and working time within an annualised hours scheme framework, Annals of Operations Research, 155(1), pp. 5–23.
• [12] Corominas, A., Lusa, A., Pastor, R., 2007b. Using a MILP model to establish a framework for an annualised hours agreement. European Journal of Operational Research, 177(3), pp. 1495–1506.
• [13] Corominas, A., Lusa, A., Olivella, J., 2012. A detailed workforce planning model including non-linear dependence of capacity on the size of the staff and cash management. European Journal of Operational Research, 216(2), pp. 445–458.
• [14] Czyzyk, J., Mesnier, M.P., Moré, J.J., 1998. The NEOS Server. IEEE Journal on Computational Science and Engineering, 5(3), pp. 68–75.
• [15] Dolan, E., 2001. The NEOS Server 4.0 Administrative Guide. Technical Memorandum ANL/MCSTM250, Mathematics and Computer Science Division, Argonne National Laboratory.
• [16] Filho, E.V.G., Marçola, J.A., 2001. Annualized hours as a capacity planning tool in maketo-order or assemble-to-order environment: an agricultural implements company case. Production Planning and Control, 12(4), pp. 388–398.
• [17] Grabot, B., Letouzey, A., 2000. Short-term manpower management in manufacturing systems: new requirements and DSS prototyping. Computers in Industry, 43(1), pp. 11–29.
• [18] Gropp, W., Moré, J.J., 1997. Optimization Environments and the NEOS Server. In: Buhmann, M.D., Iserles, A. (eds.), Approximation Theory and Optimization, Cambridge University Press, pp. 167–182.
• [19] Hertz, A., Lahrichi, N., Widmer, M., 2010. A flexible MILP model for multiple-shift workforce planning under annualized hour. European Journal of Operational Research, 200(3), pp. 860–873.
• [20] Hung, R., 1999a. A multiple-shift workforce scheduling model under annualized hours. Naval Research Logistics, 46(6), pp. 726–736.
• [21] Hung, R., 1999b. Scheduling a workforce under annualized hours. International Journal of Production Research, 37(11), pp. 2419–2427.
• [22] Itoh, T., Ishii, H., Nanseki, T., 2003. A model of crop planning under uncertainty in agricultural management. International Journal of Production Economics, 81–82, pp. 555–558.
• [23] Jiafu, T., Dingwei, W., Fung, R.Y.K., Yung, K.L., 2004. Understanding of fuzzy optimization: theories and methods. Journal of Systems Science and Complexity, 17(1), pp. 117–136.
• [24] Lai, Y.J., Hwang, C.L., 1992. Fuzzy Mathematical Programming, Lecture Notes in Economics and Mathematical Systems 394, Springer-Verlag, Berlin.
• [25] Lusa, A., Pastor, R., 2011. Planning working time accounts under demand uncertainty. Computers & Operations Research, 38(2), pp. 517–524.
• [26] Lusa, A., Corominas, A., Muñoz, N., 2008a. A multistage scenario optimisation procedure to plan annualised working hours under demand uncertainty. International Journal of Production Economics, 113(2), pp. 957–968.
• [27] Lusa, A., Corominas, A., Olivella, J., Pastor, R., 2009. Production planning under a working time accounts scheme. International Journal of Production Research, 47(13), pp. 3435–3451.
• [28] McMeekin, J., 1995. Why Tesco’s new composite distribution needed annual hours. International Journal of Retail and Distribution Management, 23(9), pp. 36–38.
• [29] Miller, W.A., Leung, L.C., Azhar, T.M., Sargent, S., 1997. Fuzzy production planning model for fresh tomato packing. International Journal of Production Economics, 53(3), pp. 227–238.
• [30] NEOS Server: State-of-the-Art Solvers for Numerical Optimization, http://www.neos-server.org/neos/, accessed on 2016 June 21.
• [31] Official governmental site on the 35-hour workweek, http://travail-emploi.gouv.fr/, accessed on 2016 June 21.
• [32] Pendharkar, P.C., 1997. A fuzzy linear programming model for production planning in coal mines. Computers & Operations Research, 24, pp. 1141–1149.
• [33] Rabbani, M., Jolai, F., Manavizadeh, N., Radmehr, F., Javadi, B., 2012. Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach. International Journal of Industrial Engineering Computations, 58, pp. 709–722.
• [34] Rodriguez, M., 2003. Flexible working patterns using annualised hours. Work Study, 52(3), pp. 145–149.
• [35] Sanderson, T., 2016. Improving Productivity through workforce agility. Available at: http://www.smart-workforce.com/resource/uploads/publications/thomas_sanderson.pdf, accessed on 2016 June 21.
• [36] Selim, H., Ozkarahan, I., 2008. A Supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. International Journal of Industrial Engineering Computations, 36, pp. 401–418.
• [37] Sureshkumar, M.R., Pillai, V.M., 2012. Planning annualized hours when spike in demand exists. International Journal of Industrial Engineering Computations, 3, pp. 313–320.
• [38] Sureshkumar, M.R., Pillai, V.M., 2013. An efficient method to reduce relative capacity shortage using annualized hours planning. The International Journal of Advanced Manufacturing Technology, 65, pp. 571–580.
• [39] Tsai, C.-C., Chu, C.-H., Barta, T.A., 1997. Modeling and analysis of a manufacturing cell formation problem with fuzzy mixed-integer programming. IIE Transactions, 29(7), pp. 533–547.
• [40] Ull Hasan, M.G., Ali, I., Hasan, S.S., 2016. Annualized hours planning with fuzzy demand constraint. ProbStat Forum, 09, pp. 50–56.
• [41] Van den Hurk, H., 2007. Arbeidsvoorwaarden in 100 vragen: voor OR’en en HR-medewerkers, Kluwerm, Alphen aan den Rijn, The Netherlands (in Dutch).
• [42] Van der Veen, E., Boucherie, R.J., Van Ommeren, J.C.W., 2012. Optimal staffing under an annualized hours regime using cross-entropy optimization, Memorandum 1982, Department of Applied Mathematics, University of Twente, Enschede.
• [43] Van der Veen, E., Hans, E.W., Veltman, B., Berrevoets, L.M., Berden H.J.J.M., 2014. A case study of cost-efficient staffing under annualized hours. Health Care Management Science, 18(3), pp. 279–288.
• [44] Werner, B., 1987. An interactive fuzzy programming system. Fuzzy Sets and Systems, 23, pp. 131–147.
• [45] Wikipedia, 2016, 35-hours workweek, [online] Available at https://en.wikipedia.org/wiki/35-hourworkweek, accessed on 2016 June 21.
• [46] Zadeh, L.A., 1965. Fuzzy sets. Information and Control, 8, pp. 338–353.
• [47] Zimmermann, H.J., 1976. Description and optimization of fuzzy systems. International Journal of General Systems, 2, pp. 209–215.
• [48] Zimmermann, H.J., 1996. Fuzzy set theory and its applications (3rd ed.), Boston, Kluwer Academic Publishers.