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Strategic Capacity Expansion of a Multi-item Process with Technology Mixture under Demand Uncertainty: An Aggregate Robust MILP ApproachTopics: Industrial Engineering; Linear Programming; Mathematical Modeling; Optimization; Stochastic Optimization

Keyword(s):Capacity Expansion, Machine Requirement Planning, Work Shifts, Robust Optimization.

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Abstract: This paper analyzes the optimal capacity expansion strategy in terms of machine requirement, labor force, and work shifts when the demand is deterministic and uncertain in the planning horizon. The use of machines of different technologies are considered in the capacity expansion strategy to satisfy the demand in each period. Previous work that considered the work shift as a decision variable presented an intractable nonlinear mix-integer problem. In this paper we reformulate the problem as a MILP and propose a robust approach when demand is uncertain, arriving at a tractable formulation. Computational results show that our deterministic model can find the optimal solution in reasonable computational times, and for the uncertain model we obtain good quality solutions within a maximum optimal gap of $10^{-4}$. For the tested instances, when the robust model is applied with a confidence level of 99\%, the upper limit of the total cost is, on average, 1.5 times the total cost of the deterministic model.(More)

This paper analyzes the optimal capacity expansion strategy in terms of machine requirement, labor force, and work shifts when the demand is deterministic and uncertain in the planning horizon. The use of machines of different technologies are considered in the capacity expansion strategy to satisfy the demand in each period. Previous work that considered the work shift as a decision variable presented an intractable nonlinear mix-integer problem. In this paper we reformulate the problem as a MILP and propose a robust approach when demand is uncertain, arriving at a tractable formulation. Computational results show that our deterministic model can find the optimal solution in reasonable computational times, and for the uncertain model we obtain good quality solutions within a maximum optimal gap of $10^{-4}$. For the tested instances, when the robust model is applied with a confidence level of 99\%, the upper limit of the total cost is, on average, 1.5 times the total cost of the deterministic model.

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Weston, J.; Escalona, P.; Angulo, A. and Stegmaier, R. (2017). Strategic Capacity Expansion of a Multi-item Process with Technology Mixture under Demand Uncertainty: An Aggregate Robust MILP Approach.In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-218-9, pages 181-191. DOI: 10.5220/0006202201810191

@conference{icores17, author={Jorge Weston. and Pablo Escalona. and Alejandro Angulo. and Raúl Stegmaier.}, title={Strategic Capacity Expansion of a Multi-item Process with Technology Mixture under Demand Uncertainty: An Aggregate Robust MILP Approach}, booktitle={Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,}, year={2017}, pages={181-191}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0006202201810191}, isbn={978-989-758-218-9}, }

TY - CONF

JO - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, TI - Strategic Capacity Expansion of a Multi-item Process with Technology Mixture under Demand Uncertainty: An Aggregate Robust MILP Approach SN - 978-989-758-218-9 AU - Weston, J. AU - Escalona, P. AU - Angulo, A. AU - Stegmaier, R. PY - 2017 SP - 181 EP - 191 DO - 10.5220/0006202201810191