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- Designing integrated biorefineries supply chain: combining stochastic programming models with scenario reduction methodsPublication . Paulo, Helena; Cardoso, Teresa; Relvas, Susana; Barbosa-Povoa, AnaThis 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.
- Assessment of financial risk in the design and scheduling of multipurpose plants under demand uncertaintyPublication . Vieira, Miguel; Paulo, Helena; Pinto-Varela, Tânia; Barbosa-Póvoa, Ana PaulaIndustrial companies are seeking for highly flexible strategic and operational solutions to face the requirements of current dynamic markets. The aim of this work is to provide a decision support assessment for the design and scheduling of a multipurpose plant under demand uncertainty, allowing the assessment of alternative risk profile solutions. A general two-stage mixed-integer linear programming (MILP) model is proposed with the goal to maximise the annualised profit of the plant operation under a set of scenarios while minimising the associated financial risk. Considering the long-term investment perspective, the Conditional Value at Risk (CVaR) measure is used to evaluate the likelihood that a specific loss or gain will exceed a certain value at risk. A bi-objective model is formulated using the augmented ε-constraint method to generate an approximation to the Pareto-optimal curve, illustrating the trade-offs between plant profit (with the corresponding design and scheduling decisions) and the associated financial risk. Addressing a set of propositions regarding a case-study, the conclusions highlight the advantages of the risk measure integration in support of the decision-making process, discussing the managerial insights in the assessment of diverse financial outcomes for the solution optimisation.
- Risk assessment for the design and scheduling optimization of periodic multipurpose batch plants under demand uncertaintyPublication . Vieira, Miguel; Paulo, Helena; Vilard, Corentin; Pinto-Varela, Tânia; Povoa, AnaThe aim of the present work is to provide an integrated decision support approach for the design and scheduling of multipurpose batch plants under demand uncertainty allowing the assessment of alternative risk profile solutions. Based on two-stage mixed integer linear programming (MILP) model, the goal is to maximize the annualized profit of the plant operation under a set of scenarios while minimizing the associated financial risk, evaluated by the Conditional Value at Risk (CVaR) using the augmented ε-constraint method. Considering a literature example, the conclusions highlight the advantages of the proposed approach for the decision-support in industrial plant design and scheduling solutions by considering the explicit risk measure assessment towards expected financial outcomes.