Designing Integrated Biorefineries Supply Chain: Combining Stochastic Programming Models with Scenario Reduction Methods

Abstract

This paper addresses the design and planning of integrated biorefineries supply chain under uncertainty. A two-stage stochastic mixed integer linear programming (MILP) model is proposed considering the presence of uncertainty in the residual lignocellulosic biomass availability and technology conversion factors. Nevertheless, when the scenario tree approach is applied to a large real world case study, it generates a computationally complex problem to solve. To address this challenge the present paper proposes the improvement of the scenario tree approach through the use of two scenario reduction methods. The results illustrate the impact of the uncertain parameters over the network configuration of a real case when compared with the deterministic solution. Both scenario reduction methods appear promising and should be further explored when solving large scenario trees problems.

Fostering long-term care planning in practice: extending objectives and advancing stochastic treatment within location-allocation modelling

“…Although scenario reduction techniques are recognized as key to the use of models in real decision-making processes (for instance, by Karuppiah et al (2010), Govindan and Fattahi (2017) and Paulo et al (2017)), to the best of authors' knowledge, only Leaven and Qu (2014) have applied the scenario reduction algorithms available in GAMS, in a health context, to schedule phlebotomists in a hospital laboratory.…”

Scenario Reduction for Stochastic Optimization Applied to Short-Term Trading of PV Power

“…While stochastic optimization has been widely applied in decision-making problems, the need to use representative scenarios through scenario reduction methods has received limited attention from researchers in the past [4][5][6]. Due to its importance, scenario reduction is applied in the most diverse areas of knowledge, namely in supply chain [7] and particularly in the scope of this research, in electricity markets [4]. In power systems, most applications of scenario reduction methods focus on unit commitment and short-term operation [8][9][10].…”

Scenario tree reduction for optimisation under uncertainty using sensitivity analysis

“…Despite the importance of this topic, it has received limited attention in the literature. Note that scenario reduction techniques can be applied to different areas of interest, such as supply chains (Paulo et al, 2017), electrical markets (Dupačová et al, 2003) hydro-thermal power systems (de Oliveira et al, 2010) and maintenance of units (Qian and Tang, 2017).…”