A Hybrid Stochastic Optimization Model for Lot Sizing and Scheduling Problem
DOI:
https://doi.org/10.48047/CU/54/02/2007-2018Keywords:
LotSizing,Scheduling, Stochastic Optimization, Mixed-Integer Linear Programming (MILP), Supply Chain Management, Uncertainty ManagementAbstract
In modern supply chain management, lot sizing and scheduling problems play a crucial role in optimizing production processes while managing fluctuating demand and production constraints. Traditional deterministic approaches fail to account for uncertainties in demand, leading to inefficiencies, excess inventory, and increased operational costs. This paper proposes a hybrid
stochastic optimization model that combines stochastic programming and mixed-integer linear programming (MILP) to address the Lot Sizing and Scheduling Problem (LSSP). The hybrid model leverages probabilistic demand scenarios and robust optimization techniques to derive cost-effective production plans that ensure feasibility under uncertainty. The proposed model improves the balance between cost, service level, and robustness, making it applicable to various manufacturing environments.
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References
● Birge, Z. and Louveaux, S. (1997). Principles on stochastic programming.
● Chaharsooghi, S. K., Honarvar, M., Modarres, M., and Kamalabadi, I. N. (2011). De-veloping a two stage stochastic programming model of the price and lead-time decisionproblem in the multi class make-to-order firm. Computers & Industrial Engineering,61(4):1086–1097.
● Curcio, E., Amorim, P., Zhang, Q., and Almada-Lobo, B. (2018). Adaptation and ap-proximate strategies for solving the lot-sizing and scheduling problem under multistagedemand uncertainty. International Journal of Production Economics, 202:81–96.
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