Page 29 - IJOCTA-15-1
P. 29

Significance of stochastic programming in addressing production planning under uncertain demand...


            [16] C¸avga, S. H., & Aydin, N. (2023). Stochastic goal  American Society of Mechanical Engineers.
                programming for the nurse scheduling problem
                with uncertain demand. GMBD, 9(3), 490-507.
                https://doi.org/10.30855/gmbd.0705082
                                                              [26] Malakov, I., & Zaharinov, V. (2021). Choos-
            [17] Kleywegt, A. J., Shapiro, A., & Homem-
                                                                  ing the optimal size range for the ”pipe clamp”
                de-Mello,  T.   (2002).  The  sample   aver-
                                                                  product. Recent Advances in Soft Comput-
                age   approximation   method   for  stochas-
                                                                  ing and Cybernetics. 197-213. https://do
                tic  discrete  optimization.  SIAM  Journal
                                                                  i.org/10.1007/978- 3- 030- 61659- 5_1
                on Optimization,  12(2),  479-502. h t t p s :
                                                                  7
                //doi.org/10.1137/S1052623499363220
                                                              [27] Ozt¨urk, B. (2019). Prin¸c boru ba˘glantı elemanının
            [18] Kafali, M., Aydin, N., Gen¸c, Y., & C¸elebi,     seri ¨uretim enerji t¨uketiminde optimizasyon.
                U. B. (2022). A two-stage stochastic model        Technological Applied Sciences (NWSATAS),
                for workforce capacity requirement in ship-       14(3), 68-79. http://dx.doi.org/10.12739/NW
                building.  Journal  of  Marine   Engineering      SA.2019.14.3.2A0170
                                                                                                       ¨
                &   Technology,  21(3),  146-158.  h t t p s :  [28] Oz¨ulk¨u,  D.,  Ka¸ctıo˘glu,  S. (2019). Uretim
                //doi.org/10.1080/20464177.2019.1704977           planlamada    bulanık   do˘grusal  program-
                                                                  lama y¨ontemi:  metal sekt¨or¨unde bir uygu-
            [19] Z., & Aydın, N. (2022). A stochastic model
                                                                          ˙
                                                                                             ¨
                                                                  lama.   Istanbul  Ticaret  Universitesi  Fen
                for facility locations using the priority of fuzzy
                                                                  Bilimleri Dergisi,  22(43),  67-93.  h t t p s :
                AHP. Sigma Journal of Engineering and Natural
                                                                  //doi.org/10.55071/ticaretfbd.1149499
                Sciences, 40(3), 649-662.
                                                              [29] Gulsun, B., Tuzkaya, G., Tuzkaya, U. R., & Onut,
            [20] Kucukkoc, I. (2023). Scheduling of distributed   S. (2009). An aggregate production planning
                additive manufacturing machines considering       strategy selection methodology based on linear
                carbon emissions.An International Journal of      physical programming. International Journal of
                Optimization and Control:   Theories & Ap-        Industrial Engineering, 16(2), 2009, 135-146.
                plications (IJOCTA), 14(1), 20-31. h t t p s :  [30] Mukherjee, I., Ray, P. K., Tuzkaya, U. R.
                / / d o i . o r g / 1 0 . 1 1 1 2 1 / i j o c t a . 1 4 44  (2006). A review of optimization techniques
                                                                  in metal cutting processes. Computers & In-
                                                                  dustrial Engineering, 50(1-2), 15-34. https:
            [21] Ersoy, B., Da¸sba¸sı, B., & Aslan, E. (2023).
                                                                  //doi.org/10.1016/j.cie.2005.10.001
                Mathematical modelling of fiber optic cable with  [31] Nystr¨om, D., S¨oderstr¨om, P. (2009). Productivity
                an electro-optical cladding by incommensurate     increase valve and pipe assembly: An investiga-
                fractional-order differential equations. An Inter-  tion of how to improve the manufacturing process
                national Journal of Optimization and Control:     in a large variant production environment. BSc
                Theories & Applications (IJOCTA), 14(1), 50-      Thesis. Chalmers University of Technology.
                61. https://doi.org/10.11121/ijocta.1369      [32] Toksarı, M. D., & Erol, U. (2017). Chance-
                                                                  constrained approach for production-distribution
                                                                  problem with stochastic demand and waste rates
            [22] Karagoz, S., Aydin, N., & Simic, V. (2022).
                                                                  in supply chain management. Erciyes University
                A novel stochastic optimization model for re-
                                                                  Journal of Science, 33(2), 102-115.
                verse logistics network design of end-of-life ve-
                                                              [33] Sarit, M., Mishra, V., & Kundu, S. (2023). A
                hicles: A case study of Istanbul. Environmental
                                                                  Novel Approach With Monte-Carlo Simulation
                Modeling & Assessment, 27(4), 599-619. https:
                                                                  And Hybrid Optimization Approach For Inven-
                //doi.org/10.1007/s10666-022-09834-5
                                                                  tory Management With Stochastic Demand.Arxiv
                                                                  Preprint, Arxiv:2310.01079.
            [23] Aydin, N. (2012). Sampling based progressive  [34] Atalay, K. D., & Apaydın, A. (2011). Determin-
                hedging algorithms for stochastic programming     istic equivalents of chance-constrained stochastic
                problems. PhD Thesis. Wayne State University.     programming problems. Anadolu University of
                                                                  Sciences & Technology-B: Theoretical Sciences,
                                                                  1(1).
            [24] Aydin, N., Murat, A., & Mordukhovich, B.
                                                              [35] Zhang, J., Zhang, L. W., & Wu, Y. (2012). A
                S. (2024). Sample intelligence-based progressive
                                                                  smoothing SAA method for a stochastic math-
                hedging algorithms for the stochastic capaci-
                                                                  ematical program with complementarity con-
                tated reliable facility location problem. Artifi-
                                                                  straints. Applied Mathematics, 57 (5), 477-502. ht
                cial Intelligence Review, 57(6), 135. https://
                                                                  tps://doi.org/10.1007/s10492-012-0028-5
                doi.org/10.1007/s10462- 024- 10755-w
                                                              [36] Bastin, F., Cirillo, C., & Toint, P. L. (2006).
                                                                  An adaptive Monte Carlo algorithm for com-
            [25] Wiseman, P., & Mayes, A. (2018). A study         puting mixed logit estimators. Computational
                on dynamic pipe clamp design. Conference          Management Science, 3 (1), 55-79. https:
                on Pressure Vessels and Piping, Vol. 51630.       //doi.org/10.1007/s10287-005-0044-y
                                                            23
   24   25   26   27   28   29   30   31   32   33   34