Leveraging Contextual Information for Robustness in Vehicle Routing Problems

We investigate the benefit of using contextual information in data-driven demand predictions to solve the robust capacitated vehicle routing problem with time windows. Instead of estimating the demand distribution or its mean, we introduce contextual machine learning models that predict demand quantiles even when the number of historical observations for some or all customers is limited. We investigate the use of such predicted quantiles to make routing decisions, comparing deterministic with robust optimization models. Furthermore, we evaluate the efficiency and robustness of the decisions obtained, both using exact or heuristic methods to solve the optimization models. Our extensive computational experiments show that using a robust optimization model and predicting multiple quantiles is promising when substantial historical data is available. In scenarios with a limited demand history, using a deterministic model with just a single quantile exhibits greater potential. Interestingly, our results also indicate that the use of appropriate quantile demand values within a deterministic model results in solutions with robustness levels comparable to those of robust models. This is important because, in most applications, practitioners use deterministic models as the industry standard, even in an uncertain environment. Furthermore, as they present fewer computational challenges and require only a single demand value prediction, deterministic models paired with an appropriate machine learning model hold the potential for robust decision-making

Developing a Strategic Stochastic Optimization Model, Robust Solutions, and a Decision Support System for Energy-efficient Buildings

This research is being carried out in the context of the EnRiMa project (Energy Efficiency and Risk Management in Public Buildings), funded by the European Commission (EC) within the Seventh Framework Program. Energy Systems Optimization is increasing its importance due to regulations and de-regulations of the energy sector and the setting of targets such as the European Union's 20/20/20. This raises new types of dynamic stochastic energy models incorporating both strategic and operational decisions (short-term decisions have to be made from long-term perspectives) involving standard technological as well as market-oriented financial options. Thus, buildings managers are challenged by decision making processes to achieve robust optimal energy supply portfolio and they are encouraged to adopt an active role in energy markets. Moreover, those decisions must be made under inherently uncertain conditions. The goal of this paper is to develop an integrated framework for the representation and solution of such energy systems optimization problems, to be implemented in Decision Support Systems (DSSs) for robust decision making at the building level to face rising systemic economic and environmental global challenges. As the combination of operational and strategic decisions in the same model induces risk aversion in strategic decisions, the developed approach allows easy to include quantile-based measures such as Conditional Value at Risk (CVaR). Such complex energy systems need to be accurately described in a condensed way representing a large amount of variables, parameters and constraints reflecting endogenous and exogenous interdependencies, sustainability requirements and threats. Therefore, a comprehensive Symbolic Model Specification (SMS) development is a part of the research work. Using the R statistical software and programming language, an integrated framework is proposed to cover the needs of the whole decision making process, ranging from data analysis and estimation to effective representation of models and decisions to be used by both humans and machines. Such a framework provides an environment for enforcing the necessary stakeholders dialog. Furthermore, the framework allows communicating with different types of optimization software

Unpacking the Upper Echelon’s Cognitive Black Box: A Qualitative Study of Selective Attention and Decision Making in Senior Executives

In today’s volatile, uncertain, complex, and ambiguous world, senior executives face a myriad of difficult decisions. These decisions are often accompanied by a barrage of stimuli, which can complicate decision-making processes. To traverse these challenges, those in the upper echelons of leadership must manage their selective attention well, make clear sense of unfolding events, and act upon them in ways that maximize organization outcomes. However, there is a gap in research around how the upper echelons of leadership manage their selective attention in high-stimuli decision scenarios. This qualitative grounded theory research addresses this gap by studying the cognitive processes used by senior military executives to manage their limited attentional resources in such environments. Data was collected via semi-structured interviews of a purposive and snowball sampled group of 18 recently retired senior military officers who held key strategic positions during their time in service. Interviews were transcribed, coded using open and axial techniques, and analyzed to develop a grounded theory of how the upper echelons of leadership navigate information-saturated, high-stimuli environments and manage their limited attentional resources when making high-consequence decisions. Findings show that executives rely heavily on the team of people around them while taking steps to create mental space, and then doing the best they can to gather and prioritize information, given time constraints. This model suggests the top management teams play a central role in helping senior executives manage their limited attention, which can shape how senior executives are chosen and developed

Investment projects evaluation in a fuzzy environment using the simplified WISP method

This paper examines the importance of investment activity for companies and the challenges they face when evaluating investment projects in a fuzzy environment, that is when decisions have to be made based on some predictions and uncertain or imprecise data. The study focuses on the usage of a new extension of the Simplified WISP (Weighted Sum Product) method, which allows the use of triangular fuzzy numbers, as a tool for evaluating investment projects and minimizing the risk associated with such decisions. Investment projects were evaluated based on the following criteria: Net Present Value, Internal Rate of Return, Profitability Index, Payback Period, and Risk of project failure. The proposed extension of the Simplified WISP method can be used to solve other complex decision problems associated with predictions and uncertainties.The paper highlights the benefits of using this MCDM technique in investment project evaluation and the potential to improve decision-making processes. The study also discusses the challenges associated with applying MCDM techniques in a fuzzy environment and proposes solutions to overcome them. It also provides valuable insights for academics, practitioners, and policymakers interested in investment evaluation and decision-making processes

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

Advancing Alternative Analysis: Integration of Decision Science.

Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts