Abstract
The purpose of this work is to examine a comprehensive production planning problem with uncertain demand. The problem is a combination of two NP-hard optimization problems: assembly line balancing and capacitated lot-sizing.
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References
Camussi NB, Cerdá J, Cafaro DC (2021) Mathematical formulations for the optimal sequencing and lot sizing in multiproduct synchronous assembly lines. Comput Ind Eng 152:107006
Roberts SD, Villa CD (1970) On a multiproduct assembly line-balancing problem. AIIE Trans 2:361–364
Göcen H, Erel E (1998) Binary integer formulation for mixed-model assembly line balancing problem. Comput & Ind Eng 34:451–461
Vilarinho PM, Simaria AS (2002) A two-stage heuristic method for balancing mixed-model assembly lines with parallel workstations. Int J Prod Res 40:1405–1420
Chutima P, Chimklai P (2012) Multi-objective two-sided mixed-model assembly line balancing using particle swarm optimisation with negative knowledge. Comput & Ind Eng 62:39–55
Kucukkoc I, Zhang DZ (2016) Mixed-model parallel two-sided assembly line balancing problem: a flexible agent-based ant colony optimization approach. Comput & Ind Eng 97:58–72
Delice Y, Kızılkaya Aydğan E, Özcan U, İlkay MS (2017) A modified particle swarm optimization algorithm to mixed-model two-sided assembly line balancing. J Intell Manuf 28:23–36
Kucukkoc I, Li Z, Karaoglan AD, Zhang DZ (2018) Balancing of mixed-model two-sided assembly lines with underground workstations: a mathematical model and ant colony optimization algorithm. Int J Prod Econ 205:228–243
Lopes TC, Sikora CGS, Michels AS, Magatão L (2020) An iterative decomposition for asynchronous mixed-model assembly lines: combining balancing, sequencing, and buffer allocation. Int J Prod Res 58:615–630
Lopes TC, Sikora CGS, Michels AS, Magatão L (2020) Mixed-model assembly lines balancing with given buffers and product sequence: model, formulation comparisons, and case study. Ann Oper Res 286:475–500
Lopes TC, Michels AS, Lüders R, Magatão L (2020) A simheuristic approach for throughput maximization of asynchronous buffered stochastic mixed-model assembly lines. Comput & Operat Res 115:104863
Tiacci L, Mimmi M (2018) Integrating ergonomic risks evaluation through OCRA index and balancing/sequencing decisions for mixed model stochastic asynchronous assembly lines. Omega 78:112–138
Zhang B, Xu L, Zhang J (2020) A multi-objective cellular genetic algorithm for energy-oriented balancing and sequencing problem of mixed-model assembly line. J Cleaner Prod 244:118845
Manne A (1958) Programming of economic lot-sizes. Manage Sci 4:115–136
Dzielinski BP, Gomory RE (1965) Optimal programming of lot sizes, inventory and labor allocations. Manage Sci 11:874–890
Dixon PS, Silver EA (1981) A heuristic solution procedure for the multi-item, single-level, limited capacity, lot-sizing problem. J Oper Manag 2:23–39
Barany I, Van Roy TJ, Wolsey LA (1984) Strong dormulations for multi-item capacitated lot sizing. Manage Sci 30:1255–1261
Leung JMY, Magnanti TL, Vachani R (1989) Facets and algorithms for capacitated lot-sizing. Math Program 45:331–359
Pochet Y, Wolsey LA (1991) Solving multi-item lot-sizing problems using strong cutting planes. Manage Sci 37:53–67
Miller AJ, Nemhauser GL, Savelsbergh MW (2003) On the polyhedral structure of a multi-item production planning model with setup times. Math Program 94:375–405
Venkatachalam S, Narayanan A (2016) Efficient formulation and heuristics for multi-item single source ordering problem with transportation cost. Int J Prod Res 54:4087–4103
Behnamian J, Ghomi SMTF, Karimi B, Moludi MF (2017) A Markovian approach for multi-level multi-product multi-period capacitated lot-sizing problem with uncertainty in levels. Int J Prod Res 55:5330–5340
Wu T, Liang Z, Zhang C (2018) Analytics branching and selection for the capacitated multi-item lot sizing problem with nonidentical machines. INFORMS J Comput 30:236–258
Wu T, **ao F, Zhang C, He Y, Liang Z (2018) The green capacitated multi-item lot sizing problem with parallel machines. Comput & Oper Res 98:149–164
Cunha JO, Kramer HH, Melo RA (2019) Effective matheuristics for the multi-item capacitated lot-sizing problem with remanufacturing. Comput & Oper Res 104:149–158
Brandimarte P (2006) Multi-item capacitated lot-sizing with demand uncertainty. Int J Prod Res 44:2997–3022
Tempelmeier H (2011) A column generation heuristic for dynamic capacitated lot sizing with random demand under a fill rate constraint. Omega 39:627–633
Meistering M, Stadtler H (2017) Stabilized-cycle strategy for capacitated lot sizing with multiple products: fill-rate constraints in rolling schedules. Prod Oper Manag 26:2247–2265
Guillaume R, Thierry C, Zieliński P (2017) Robust material requirement planning with cumulative demand under uncertainty. Int J Prod Res 55:6824–6845
Curcio E, Amorim P, Zhang Q, Almada-Lobo B (2018) Adaptation and approximate strategies for solving the lot-sizing and scheduling problem under multistage demand uncertainty. Int J Prod Econ 202:81–96
Alimian M, Saidi-Mehrabad M, Jabbarzadeh A (2019) A robust integrated production and preventive maintenance planning model for multi-state systems with uncertain demand and common cause failures. J Manuf Syst 50:263–277
Noyan N (2012) Risk-averse two-stage stochastic programming with an application to disaster management. Comput & Oper Res 39:541–559
Rockafellar T, Uryasev S (2000) Optimization of conditional value-at-risk. J Risk 2:21–41
Schultz R, Tiedemann S (2006) Conditional value-at-risk in stochastic programs with mixed-integer recourse. Math Program 105:365–386
Fábián CI (2008) Handling CVaR objectives and constraints in two-stage stochastic models. Eur J Oper Res 191:888–911
Jiang R, Guan Y (2018) Risk-averse two-stage stochastic program with distributional ambiguity. Oper Res 66:1390–1405
Fernández E, Hinojosa Y, Puerto J, Saldanha-da Gama F (2019) New algorithmic framework for conditional value at risk: application to stochastic fixed-charge transportation. Eur J Oper Res 277:215–226
Rendeki S, Nagy B, Bene M, Pentek A, Toth L, Szanto Z, Told R, Maroti P (2020) An overview on personal protective equipment (PPE) fabricated with additive manufacturing technologies in the era of COVID-19 pandemic. Polymers 12(11):2703
Tarfaoui M, Nachtane M, Goda I, Qureshi Y, Benyahia H (2020) Additive manufacturing in fighting against novel coronavirus COVID-19. Int J Adv Manuf Technol 110:2913–2927
Forouzandeh P, O’Dowd K, Pillai SC (2021) Face masks and respirators in the fight against the COVID-19 pandemic: an overview of the standards and testing methods. Saf Sci 133:104995
Vanhooydonck A, Van Goethem S, Van Loon J, Vandormael R, Vleugels J, Peeters T, Smedts S, Stokhuijzen D, Van Camp M, Veelaert L, Verlinden J, Verwulgen S, Watts R (2021) Case study into the successful emergency production and certification of a filtering facepiece respirator for Belgian hospitals during the COVID-19 pandemic. J Manuf Syst 60:876–892
Markowitz HM, Todd GP (2000) Mean-variance analysis in portfolio choice and capital markets. Wiley, New York
Pochet Y, Wolsey LA (2006) Production planning by mixed integer programming. Springer, New York
Otto A, Otto C (2014) How to design effective priority rules: Example of simple assembly line balancing. Computers & Industrial Engineering 69:43–52
Chen G, Daskin MS, Shen ZJM, Uryasev S (2006) The alpha-reliable mean-excess regret model for stochastic facility location modeling. Nav Res Logist 53:617–626
Gregoriou GN (2009) TheVaR Implementation Handbook. McGraw-Hill
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Li, Y. (2022). A Joint Optimization of ALBP and Lot-Sizing Under Demand Uncertainty. In: Assembly Line Balancing under Uncertain Task Time and Demand Volatility. Engineering Applications of Computational Methods, vol 8. Springer, Singapore. https://doi.org/10.1007/978-981-19-4215-0_6
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DOI: https://doi.org/10.1007/978-981-19-4215-0_6
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