Scenario Reduction Algorithms in Stochastic Programming
Computational Optimization and Applications
Journal of Global Optimization
A Dynamic Stochastic Stock-Cutting Problem
Operations Research
Solving Real-World Linear Programs: A Decade and More of Progress
Operations Research
Order Allocation for Stock Cutting in the Paper Industry
Operations Research
Risk Aversion via Excess Probabilities in Stochastic Programs with Mixed-Integer Recourse
SIAM Journal on Optimization
Reel and sheet cutting at a paper mill
Computers and Operations Research
A stochastic approach to hotel revenue optimization
Computers and Operations Research
Fix and Relax Heuristic for a Stochastic Lot-Sizing Problem
Computational Optimization and Applications
Convexity and decomposition of mean-risk stochastic programs
Mathematical Programming: Series A and B
Production Planning by Mixed Integer Programming (Springer Series in Operations Research and Financial Engineering)
On a stochastic sequencing and scheduling problem
Computers and Operations Research
Solving a combined cutting-stock and lot-sizing problem with a column generating procedure
Computers and Operations Research
Stochastic Programs with First-Order Dominance Constraints Induced by Mixed-Integer Linear Recourse
SIAM Journal on Optimization
Scenario tree modeling for multistage stochastic programs
Mathematical Programming: Series A and B
Robust supply chain design under uncertain demand in agile manufacturing
Computers and Operations Research
A robust transportation signal control problem accounting for traffic dynamics
Computers and Operations Research
Models for robust tactical planning in multi-stage production systems with uncertain demands
Computers and Operations Research
Generating scenario trees: A parallel integrated simulation-optimization approach
Journal of Computational and Applied Mathematics
A note on second-order stochastic dominance constraints induced by mixed-integer linear recourse
Mathematical Programming: Series A and B
Production planning in furniture settings via robust optimization
Computers and Operations Research
Risk-averse two-stage stochastic programming with an application to disaster management
Computers and Operations Research
Cutting stock optimization and integral production planning for centralized wood processing
Mathematical and Computer Modelling: An International Journal
A note on scenario reduction for two-stage stochastic programs
Operations Research Letters
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We present two-stage stochastic mixed 0---1 optimization models to hedge against uncertainty in production planning of typical small-scale Brazilian furniture plants under stochastic demands and setup times. The proposed models consider cutting and drilling operations as the most limiting production activities, and synchronize them to avoid intermediate work-in-process. To design solutions less sensitive to changes in scenarios, we propose four models that perceive the risk reductions over the scenarios differently. The first model is based on the minimax regret criteria and optimizes a worst-case scenario perspective without needing the probability of the scenarios. The second formulation uses the conditional value-at-risk as the risk measure to avoid solutions influenced by a bad scenario with a low probability. The third strategy is a mean-risk model based on the upper partial mean that aggregates a risk term in the objective function. The last approach is a restricted recourse approach, in which the risk preferences are directly considered in the constraints. Numerical results indicate that it is possible to achieve significant risk reductions using the risk-averse strategies, without overly sacrificing average costs.