Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

Hybrid simulation-optimization methods: A taxonomy and discussion

The possibilities of combining simulation and optimization are vast and the appropriate design highly depends on the problem characteristics. Therefore, it is very important to have a good overview of the different approaches. The taxonomies and classifications proposed in the literature do not cover the complete range of methods and overlook some important criteria. We provide a taxonomy that aims at giving an overview of the full spectrum of current simulation-optimization approaches. Our study may guide researchers who want to use one of the existing methods, give insights into the cross-fertilization of the ideas applied in those methods and create a standard for a better communication in the scientific community. Future reviews can use the taxonomy here described to classify both general approaches and methods for specific application fields.The possibilities of combining simulation and optimization are vast and the appropriate design highly depends on the problem characteristics. Therefore, it is very important to have a good overview of the different approaches. The taxonomies and classifications proposed in the literature do not cover the complete range of methods and overlook some important criteria. We provide a taxonomy that aims at giving an overview of the full spectrum of current simulation-optimization approaches. Our study may guide researchers who want to use one of the existing methods, give insights into the cross-fertilization of the ideas applied in those methods and create a standard for a better communication in the scientific community. Future reviews can use the taxonomy here described to classify both general approaches and methods for specific application fields. (C) 2014 Elsevier B.V. All rights reserved

Extensive Sensitivity Analysis and Parallel Stochastic Global Optimization Using Radial Basis Functions of Integrated Biorefineries under Operational Level Uncertainties

This work presents a decision-making framework for global optimization of detailed renewable energy processes considering technological uncertainty. The critical uncertain sources are identified with an efficient computational method for global sensitivity analysis, and are obtained in two different ways, simultaneously and independently per product pathway respect to the objective function. For global optimization, the parallel stochastic response surface method developed by Regis & Shoemaker (2009) is employed. This algorithm is based on the multi-start local metric stochastic response surface method explored by the same authors (2007a). The aforementioned algorithm uses as response surface model a radial basis function (RBF) for approximating the expensive simulation model. Once the RBF’s parameters are fitted, the algorithm selects multiple points to be evaluated simultaneously. The next point(s) to be evaluated in the expensive simulation are obtained based on their probability to attain a better result for the objective function. This approach represents a simplified oriented search. To evaluate the efficacy of this novel decision-making framework, a hypothetical multiproduct lignocellulosic biorefinery is globally optimized on its operational level. The obtained optimal points are compared with traditional optimization methods, e.g. Monte-Carlo simulation, and are evaluated for both proposed types of uncertainty calculated

A methodology for designing flexible multi-generation systems

An FMG (flexible multi-generation system) consists of integrated and flexibly operated facilities that provide multiple links between the various layers of the energy system. FMGs may facilitate integration and balancing of fluctuating renewable energy sources in the energy system in a cost- and energy efficient way, thereby playing an important part in smart energy systems. The development of efficient FMGs requires systematic optimization approaches. This study presents a novel, generic methodology for designing FMGs that facilitates quick and reliable pre-feasibility analyses. The methodology is based on consideration of the following points: Selection, location and dimensioning of processes; systematic heat and mass integration; flexible operation optimization with respect to both short-term market fluctuations and long-term energy system development; global sensitivity and uncertainty analysis; biomass supply chains; variable part-load performance; and multi-objective optimization considering economic and environmental performance. Tested in a case study, the methodology is proved effective in screening the solution space for efficient FMG designs, in assessing the importance of parameter uncertainties and in estimating the likely performance variability for promising designs. The results of the case study emphasize the importance of considering systematic process integration when developing smart energy systems. (C) 2016 Elsevier Ltd. All rights reserved

SURROGATE SEARCH: A SIMULATION OPTIMIZATION METHODOLOGY FOR LARGE-SCALE SYSTEMS

For certain settings in which system performance cannot be evaluated by analytical methods, simulation models are widely utilized. This is especially for complex systems. To try to optimize these models, simulation optimization techniques have been developed. These attempt to identify the system designs and parameters that result in (near) optimal system performance. Although more realistic results can be provided by simulation, the computational time for simulator execution, and consequently, simulation optimization may be very long. Hence, the major challenge in determining improved system designs by incorporating simulation and search methodologies is to develop more efficient simulation optimization heuristics or algorithms. This dissertation develops a new approach, Surrogate Search, to determine near optimal system designs for large-scale simulation problems that contain combinatorial decision variables. First, surrogate objective functions are identified by analyzing simulation results to observe system behavior. Multiple linear regression is utilized to examine simulation results and construct surrogate objective functions. The identified surrogate objective functions, which can be quickly executed, are then utilized as simulator replacements in the search methodologies. For multiple problems containing different settings of the same simulation model, only one surrogate objective function needs to be identified. The development of surrogate objective functions benefits the optimization process by reducing the number of simulation iterations. Surrogate Search approaches are developed for two combinatorial problems, operator assignment and task sequencing, using a large-scale sortation system simulation model. The experimental results demonstrate that Surrogate Search can be applied to such large-scale simulation problems and outperform recognized simulation optimization methodology, Scatter Search (SS). This dissertation provides a systematic methodology to perform simulation optimization for complex operations research problems and contributes to the simulation optimization field

Digital twin applications in urban logistics:an overview

Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external factors like pollution and congestion. To counter this, smart cities deploy technologies such as digital twins (DT)s to achieve sustainability. Research suggests that DTs can be beneficial in optimizing the physical systems they are linked with. The concept has been extensively studied in many technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics applications. To do this, we survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the identification of key factors in urban logistics, we produce a conceptual model for the general design of an urban logistics DT through a knowledge graph. We provide an illustration on how the conceptual model can be used in solving a relevant problem and showcase the integration of relevant DDO methods. We finish off with a discussion on research opportunities and challenges based on previous research and our practical experience