A Commodity Production Model with Operational Flexibility of Investing Optional Capacity on Offshore Platforms

We study the problem of operational flexibility on capacity investment of an oil producer. Our decision-maker operates only on land fields and has option to extend operations to offshore oil platforms. The operational flexibility arises from the ability to invest on offshore fields. Our main goal is to integrate offshore platforms from the chemical and petroleum engineering literature, and capacity investment from operations management literature. We use a mixed integer programming solution approach and set a basic model to analyse the value of operational flexibility. Our main contribution is to provide an operational flexibility option to the problem of oil drilling

Equações Diferenciais Estocásticas na Engenharia Química

The generation process of a mathematical model should present as a result the simulation, which must represent the experimental data. Several phenomena in Nature show erratic fluctuations that are phenomenological.  An expressive example is a path that pollen develops through a river surface, the Brownian movement. Chemical Engineering encompasses several issues that are evaluated considering stochastic processes, such as process optimization and control, diffusion, and chemical kinetics. In the present work, fundamental concepts that are related to stochastic differential equations (SDE) are exposed, as well as some classic examples and one in Chemical Engineering. An open-source tool by Python was used, viewing to generate the sample paths, the Python open tool, specifically the PyPI sdeint algorithm.  O processo de geração de um modelo matemático deve ter como resultado uma simulação que represente o conjunto de dados experimentais. Diversos fenômenos na Natureza apresentam flutuações erráticas que são fenomenológicas. Um exemplo significativo é a trajetória que desenvolve um pólen que se movimenta na superfície de um rio, o movimento browniano. A Engenharia Química engloba diversos itens que são avaliados por processos estocásticos como otimização e controle de processos, difusão e cinética de reações químicas. No presente trabalho são apresentados conceitos fundamentais relacionados com equações diferenciais estocásticas, alguns exemplos clássicos e um em engenharia química. Para gerar as trajetórias amostrais foi usada a ferramenta aberta Python, especificamente a biblioteca sdeint do PyPI.  &nbsp

Which Echo Chamber? Regions of Attraction in Learning with Decision-Dependent Distributions

As data-driven methods are deployed in real-world settings, the processes that generate the observed data will often react to the decisions of the learner. For example, a data source may have some incentive for the algorithm to provide a particular label (e.g. approve a bank loan), and manipulate their features accordingly. Work in strategic classification and decision-dependent distributions seeks to characterize the closed-loop behavior of deploying learning algorithms by explicitly considering the effect of the classifier on the underlying data distribution. More recently, works in performative prediction seek to classify the closed-loop behavior by considering general properties of the mapping from classifier to data distribution, rather than an explicit form. Building on this notion, we analyze repeated risk minimization as the perturbed trajectories of the gradient flows of performative risk minimization. We consider the case where there may be multiple local minimizers of performative risk, motivated by real world situations where the initial conditions may have significant impact on the long-term behavior of the system. As a motivating example, we consider a company whose current employee demographics affect the applicant pool they interview: the initial demographics of the company can affect the long-term hiring policies of the company. We provide sufficient conditions to characterize the region of attraction for the various equilibria in this settings. Additionally, we introduce the notion of performative alignment, which provides a geometric condition on the convergence of repeated risk minimization to performative risk minimizers

Underground mine scheduling under uncertainty

17 USC 105 interim-entered record; under review.The article of record as published may be found at http://dx.doi.org/10.1016/j.ejor.2021.01.011Underground mine schedules seek to determine start dates for activities related to the extraction of ore, often with an objective of maximizing net present value; constraints enforce geotechnical precedence between activities, and restrict resource consumption on a per-time-period basis, e.g., development footage and extracted tons. Strategic schedules address these start dates at a coarse level, whereas tactical schedules must account for the day-to-day variability of underground mine operations, such as unanticipated equipment breakdowns and ground conditions, both of which might slow production. At the time of this writing, the underground mine scheduling literature is dominated by a deterministic treatment of the problem, usually modeled as a Resource Constrained Project Scheduling Problem (RCPSP), which precludes mine operators from reacting to unforeseen circumstances. Therefore, we propose a stochastic integer programming framework that: (i) characterizes uncertainty in duration and economic value for each underground mining activity; (ii) formulates a new stochastic variant of the RCPSP; (iii) suggests an optimization-based heuristic; and, (iv) produces implementable, tactical schedules in a practical amount of time and provides corresponding managerial insights.National Institute of Occupational Safety and HealthNational Agency for Research and Development (ANID

A mixed integer linear programming model for the optimal operation of a network of gas oil separation plants

Inspired from a real case study of a Saudi oil company, this work addresses the optimal operation of a regional network of gas oil separation plants (GOSPs) in Arabian Gulf Coast Area to ultimately achieve higher savings in operating expenditures (OPEX) than those achieved by adopting single-surface facility optimisation. An originally tailored and integrated mixed integer linear programming (MILP) model is proposed to optimise the crude transfer through swing pipelines and equipment utilisation in each GOSP, to minimise the operating costs of a network of GOSPs. The developed model is applied to an existing network of GOSPs in the Ghawar field, Saudi Arabia, by considering 12 different monthly production scenarios developed from real production rates. Compared to rule-based current practice, an average 12.8% cost saving is realised by the developed model