Artificial Intelligence and its Potential Adverse Impacts on the Philippine Economy

Recent developments in artificial intelligence (AI) and deep learning techniques are expected to reshape the nature of the working environment in many economic sectors through the automation of many white collar jobs. This technological breakthrough poses threats of job obsolescence in several industries, particularly for a labor abundant country such as the Philippines. With human capital as one of its largest resources, the services sector is a major contributor to the country’s economy, contributing around 60% of the total gross domestic product and employing about 22.8 million workers (Philippine Statistics Authority, 2017)

Preparing for Shortages of Future COVID-19 Drugs: A Data-Based Model for Optimal Allocation

Drugs for the treatment of Covid-19 are currently beign tested, and those that are apporved for use are likely to be in short supply due to the global scale of the pandemic. This policy brief proposes a model for optimally allocating future Covid-19 drugs to patients to minimize deaths under conditions of resource scarcity. A linear programming model is developed that estimates the potential number of deaths that may result from Covid-19 under two scenarios: with antivirals and without antivirals. It takes into account patient risk level, the severity of their symptoms, resource availability in hospitals (i.e. hospital beds, critical care units, ventilators), observed mortality rates, and share of the Philippine population. Based on simulations, the model can make actionable recommendations on how to prioritize the allocation of the drugs

Development of P-graph approach for designing polygeneration systems

Polygeneration systems reduce the use of resources while at the same time produces power and other products such as heat and cooling services. The traditional way of designing process systems such as polygeneration plants uses mixed integer linear programming (MILP). Another way of designing polygeneration systems besides MILP is to use graph-theory based approach such as P-graph. P-graph is used in designing network systems such as chemical and manufacturing plants, reaction kinetics, transportation, work allocation, and supply chains. This thesis developed the P-graph methodology for designing polygeneration systems starting from a simple trigeneration system progressing to a polygeneration system with biochar production. The progression of complexity of the design problem was done incrementally. The objective function of each design was to maximize the profit of the polygeneration system. The result of the P-graph design of each case study resulted in two solution structures where the optimal design of each polygeneration design was based in economic potential. For the simple trigeneration system the optimal design has an annual profit of 48,680.32 €, for polygeneration system with purified water production the optimal design has an annual profit of 306,838.90 €, for the polygeneration systems with biomass as part of feed the optimal design has an annual profit of 84,418.61 €, and for polygeneration systems with biochar production has an annual profit of 695,980.60 €. Therefore, it is possible to design polygeneration systems with more than two main products using P-graph where the objective function of each design was based on maximizing the profit. It is also possible for P-graph to generate more than one solution for each design compared with conventional methods such as MILP. However, P-graph is limited to designs with linear models compared with other optimizing programs such as LINGO where it can be used for non-linear models

P-graph approach to optimal operational adjustment in polygeneration plants under conditions of process inoperability

Polygeneration plants are inherently more efficient, and generate reduced emissions, in comparison to equivalent stand-alone production systems. These benefits arise from process integration opportunities within the plant. However, such integration also creates interdependencies among process units, which may lead to cascading failures in the event of partial or complete inoperability of key system components. In such cases, the major operational concern is to maximize operating profits (or minimize losses relative to the baseline state) by reallocating process streams; process units may be run at partial load or shut down completely, as needed. In previous work, it has been proposed to determine the optimal operational adjustments using mixed-integer linear programming (MILP). In this note, we propose an alternative methodology for determining the optimal adjustments based on P-graphs, and demonstrate it using a case study. © 2014 Elsevier Ltd

P-graph approach to human resource reallocation in industrial plants under crisis conditions

P-graph methodology was originally proposed as a systematic approach to process network synthesis (PNS). However, this graph theoretic approach has also been applied to a broad range of problems with similar structure as PNS. In particular, recent work has demonstrated the similarity of PNS problems to input-output (IO) optimization problems; the latter class of problems has been applied for physical flows at scales ranging from process plant level to supply chain level. IO models have also been proposed to plan the allocation of human resource in organizations. In this work, a P-graph based approach to reallocation of human resources in an industrial plant during a transient crisis is presented. The model determines how personnel can be reassigned to allow a plant to operate at an alternative temporary steady state when the plant becomes short-handed due to a disruptive external event. This methodology is demonstrated using a representative case study involving an instant coffee plant. Results show that in the occasion that a reduction in available workforce is experienced, workforce is allocated in more critical areas and productivity is maximized by minimizing interaction with less critical departments

P-graph approach to criticality analysis in integrated bioenergy systems

The use of integrated bioenergy systems (IBS) is a prospective solution to address the emergent global demand for clean energy. The sustainability of IBS compared to stand-alone biomass processing facilities is achieved through integration of process units or component plants via their bioenergy products, by-products, wastes, and common utilities. However, such increased component interdependency makes the resulting integrated energy system vulnerable to capacity disruptions. IBS in particular are vulnerable to climate change-induced events (e.g., drought) that reduce the availability of biomass feedstocks in bioenergy production. Cascading failure due to such supply-side disruptive event is an inherent risk in IBS and may pose a barrier to the commercial-scale adoption of such systems. A previous study developed a risk-based criticality index to quantify the effect of a component’s disruption within integrated energy systems. This index is used to rank the component’s relative risk in the network based on the ripple effects of its disruption. In this work, a novel P-graph approach is proposed as an alternative methodology for criticality analysis of component units or plants in an IBS. This risk-based metric can be used for developing risk management polices to protect critical facilities, thereby increasing the robustness of IBS against disruptions. Two case studies on determining the criticality index of process units in an integrated biorefinery and component plants in a bioenergy park are used to demonstrate the effectiveness of this method. © 2017, Springer-Verlag Berlin Heidelberg

Optimizing human resource allocation in organizations during crisis conditions: A p-graph approach

The onset of climate change is expected to bring about more severe weather patterns which may lead to floods, drought, and even the outbreak of new types of diseases. These can potentially disrupt the operations of industries and firms as infrastructure can be damaged and the availability of resources and workforce are compromised. It is thus important to develop models which will assist in the efficient management of resources during times of crisis. Process systems engineering techniques have previously been used for the design and optimization of complex systems during crisis conditions. This work presents the development of a P-graph model for the optimal allocation of human resources within a firm when the available workforce has been limited due to a climate change-induced crisis. The model identifies an optimal strategy for maximizing firm productivity by prioritizing highly critical areas. © 2017, Springer Science+Business Media Singapore

A risk-based criticality analysis in bioenergy parks using P-graph method

The adoption of bioenergy parks is a prospective solution to increase the sustainability of stand-alone biomass processing plants. Production and resource efficiency, lower carbon emissions, and economic sustainability are achieved by synergistic exchanges of material and energy resources between components plants. However, such increased plant interdependency and the resulting integrated energy system is vulnerable to capacity disruptions. Cascading failure due to such disruptive event is an inherent risk in bioenergy parks and may pose as a barrier in implementing such system. The extent of risk originating from disrupted critical component plants in the network exhibited to be higher. A previous study developed a novel risk-based criticality index, based on input-output models, to quantify the effect of a component plant\u27s disruption within a bioenergy park. This index is used to rank the plant\u27s relative risk in the network based on its disruption consequence. In this work, a P-graph approach is proposed as an alternative methodology for criticality analysis of component plants in a bioenergy park. The P-graph framework is initially developed for solving process network synthesis, but recently being used to solve similarly structured problems. This risk-based metric can also be used for developing risk management measures to protect critical infrastructures, thereby increasing the robustness of bioenergy parks against disruptions. A case study is then presented to demonstrate the effectiveness of this method. Copyright © 2016, AIDIC Servizi S.r.l

P-graph approach to optimizing crisis operations in an industrial complex

Industrial complexes allow for efficient and sustainable production of various goods, but at the same time they are also vulnerable to cascading failures caused by disruptions in process capacity or resource availability. Climate change in particular may cause significant perturbations in the supply of important process inputs such as water, energy, or feedstocks. Thus, for industrial complexes, proper risk management strategies must be developed as part of overall climate change adaptation and resilience measures. Rigorous modeling approaches are needed to ensure that economic losses resulting from a disruption are minimized. In this paper, a P-graph-based methodology is used to determine optimal adjustments to crisis conditions in order to minimize manufacturing losses; this graph theoretic methodology has traditionally been used for process network synthesis problems but has recently proven to be useful for structurally analogous problem domains. Two case studies on the reallocation of production capacities and product streams in an aluminum production complex and a biomass processing complex are used to illustrate the methodology. © 2015 American Chemical Society

A fuzzy linear programming enterprise input–output model for optimal crisis operations in industrial complexes

Industrial complexes may be subject to significant risk of cascading failure caused by various disruptions and emerging economies are potentially more susceptible to the impacts as less established policies are in place to deal with these issues. In particular, there is a need to develop adaptation strategies to ensure the resilience of industrial activities to various perturbations that may result from climate change. The inherent complexity of such systems makes decision-making for risk management a non-trivial task that is best facilitated with the aid of mathematical models. Enterprise input–output models have been used extensively to model production systems at different scales. In this work, a fuzzy linear programming enterprise input–output model is developed to determine optimal adjustments in production levels of multi-product systems when a crisis is induced by a loss of resource inputs. The model allows for adjustments that are equitable for different decision-makers that may comprise an industrial complex or a supply chain. Capabilities of the model are illustrated with a case study on the effect of water shortage on an aluminum production system. © 2015 Elsevier B.V