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)

Development of a rough set-based decision support system for life cycle impact assessment and interpretation

Life cycle assessment (LCA) is a methodological framework for assessing the environmental impacts of products or processes during their entire lifetime. It consists of four phases: (1) goal and scope definition, (2) inventory analysis (LCI), (3) impact assessment (LCIA) and (4) interpretation. LCA involves the simultaneous evaluation of multiple criteria or multiple goals. A systematic way of dealing with this problem is provided by Decision Analysis techniques, particularly through the use of multiple criteria decision analysis (MCDA) methods. MCDA methods include the multi-attribute utility/value theory (MAUT/MAVT), outranking methods and the analytical hierarchy process (AHP). Thus recognizing the benefits, most of the existing LCIA and interpretation methods patterned their frameworks to MCDA methods. However, these require decision makers (DM) to express their preferences into importance weights or parameters, which are necessary for the chosen preference model - a task, which is tedious. Hence, an alternative approach is recommended in this study. The use of rough set methodology has been successfully applied to multiple criteria or multiple attribute problems in engineering, medicine, banking, economics, and financial and market analysis. It is capable of finding patterns in data and dealing with uncertainties and inconsistencies, which may be due to a DMs limited discriminatory power. It only requires previously expressed decisions made by the DM to infer the DMs adapted preference model in terms of decision rules. This study thus presents the development of a decision support system (DSS) utilizing a two-step procedure of Pareto optimality and rough set methodology for impact assessment and interpretation. This alternative methodology has shown comparability in results with AHP and was found to predict accurately the decisions of experts to a degree of 83%. The model, which is founded on the decision rules derived from the assessment of a panel of experts on a set of power generating technologies, encapsulates the environmental concerns considered and the state of knowledge of the experts during the time of the survey. Thus this model can be utilized to rank and evaluate new technologies against four other systems, which are stored in the models database, based on the same arguments utilized for assessing the training data examples

Design of robust water exchange networks for eco-industrial symbiosis

The field of industrial ecology promotes the establishment of resource exchange networks in eco-industrial parks (EIPs) as an approach toward resource conservation. Previous studies have shown that full blown resource integration can be encouraged through the exchange of common utilities such as energy and water. Different approaches such as mathematical programming, pinch analysis and game theory have been used to identify the optimal network designs, which can simultaneously reduce the utilization of freshwater resources and the generation of wastewater streams. Since water exchange in an EIP involves multiple independently operating plants, information exchange between the participants is not completely transparent and multiple future scenarios are expected to happen as the fate and plans of other participants are not completely divulged. These future scenarios may bring about changes in the capacity or characteristic of industrial processes and may also involve the entry of additional companies and the closure of previously operating ones. Such aspects have not been fully addressed in previous studies. A robust optimization model is thus developed in this work to determine the optimal network design which can effectively operate in anticipation of multiple probable scenarios. Case studies are solved to demonstrate the capability of the model. © 2013 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved

Optimal human resource planning with P-graph for universities undergoing transition

[[abstract]]This study deals with the problem of human resources planning due to the additional workforce requirements of research activities. In practice, planning of human resource expansion is seldom done in a systematic manner, thus leading to sub-optimal results. In this work, a P-graph model is developed as a decision support tool to aid in planning expansion of staffing levels for Higher Education Institutions. Many higher education institutions in the developing world are undergoing the transition from being teaching-intensive to becoming increasingly research-oriented. This shift is widely recognized as an important adjustment to the need for universities to play a greater role in creating knowledge capital, which is an essential component to fuel the next phase of growth of developing countries. Research universities are an essential resource for facilitating eco-innovation in industry. New targets that involve increased research output tend to put strain on institutional processes and resources that were previously configured to meet the demands of teaching-intensive organizations. The model formulation is based on an input-output framework that reflects interdependencies among different employee categories. The use of the model is illustrated with a representative case study of a typical Higher Education Institution in the Philippines

An inverse optimization approach to inducing resource conservation in eco-industrial parks

The exchange of wastes among plants within an eco-industrial park (EIP) creates potential for significant gains in sustainability through efficient use of resources and reduction of environmental discharges. If the establishment of such resource conservation networks (RCNs) is not economically optimal, intervention of an EIP authority will be necessary in order to induce companies to act in an environmentally responsible manner. This conflict of interest between the EIP authority and the industrial plants results in a Stackelberg game, which may be represented as a bi-level optimization model. In this work, a bi-level linear integer programming model for optimizing waste exchange in an EIP is developed. Then, an inverse optimization approach is used to solve it. An auxiliary model is used to determine the best set of incentives and disincentives to induce the plants in the EIP to form an optimal RCN. The methodology is demonstrated using an illustrative case study. © 2012 Elsevier B.V

Fuzzy P-graph for optimal synthesis of polygeneration systems

Polygeneration systems have been utilized to simultaneously generate a number of energy and utility products such as heat, power, cooling and treated water. Its implementation has proven to increase fuel efficiency and to reduce the associated carbon footprint in products in comparison to stand-alone production systems. The polygeneration system consists of interdependent process units whose design capacities will depend on the expected product demands. Because of the multiple product streams generated and the associated demands, it is necessary to design a system which aims to simultaneously meet potentially conflicting product demand targets. Fuzzy optimization has initially been used to identify the optimal solution which simultaneously satisfices multiple product demand targets. However, real life decision-making may require an evaluation of alternative solutions. This aspect can be addressed by the P-graph methodology which is able to provide both optimal and sub-optimal network designs. This work thus proposes the development of a fuzzy P-graph model for the design of a polygeneration system. The capabilities of the model are demonstrated in a case study. The model results identify both optimal and sub-optimal design options which generate products within the defined demand targets and which can be further evaluated for other parameters such as robustness for final decision-making. Copyright © 2017, AIDIC Servizi S.r.l

An extended P-graph approach to process network synthesis for multi-period operations

An extension of the P-graph approach for multi-period process network synthesis (PNS) is proposed in this work. A modification of a previously published approach enables partial load operational lower limit for process units to be considered via the addition of fictitious streams. A simple case study is presented to illustrate the advantages of this modified approach. © 2015 Elsevier Ltd

Modelling the economic impact and ripple effects of disease outbreaks

The Coronavirus Disease 2019 (COVID-19) outbreak has had alarming effects on human lives and the economies of affected countries. With the world’s manufacturing hubs experiencing a period of extended factory closures, the economic impact transcends territorial borders via global supply chains. This paper provides a roadmap on how to evaluate the vulnerability that cascades through the supply chain due to a disease outbreak at the firm level, national level, and global scale. The final extent of losses is not yet known, but the development of economic models combined with epidemiological models and network analysis techniques can yield more realistic estimates to select appropriate strategies in a timely manner. © 2020, Springer Nature Singapore Pte Ltd

A linear program for optimizing enhanced weathering networks

We develop a linear programming (LP) model for optimizing enhanced weathering (EW) networks. EW is a negative emissions technology (NET) that involves mining and grinding of naturally occurring CO2-reactive rocks, and subsequently applying the resulting powder to soil to provide ample surface area for contact with the atmosphere and thus accelerate carbon fixation. Use of EW as a carbon management strategy at scale will result in the need to properly match sources (rock crushing plants) with sinks (application sites); the result is a special case of a supply chain optimization problem. The model proposed here can determine optimal matches of sources and sinks in EW-based CMNs, considering material flow and temporal constraints. A case study is solved to illustrate the model. © 201

Fuzzy p-graph for optimal synthesis of cogeneration and trigeneration systems

Cogeneration systems provide an efficient means of producing electricity and heat, while trigeneration systems extend the concept by producing an additional output in the form of cooling. Such systems are more efficient than stand-alone production of separate product streams due to the inherent opportunities for Process Integration. Additional advantages include operational flexibility, which can be achieved by varying the outputs of component process units, or switching them on and off selectively as the need arises. Various Process Systems Engineering tools such as Mathematical Programming and P-graph have been used for the synthesis of such plants, most of which work on deterministic assumptions. In this work, a fuzzy P-graph approach is developed for the optimal synthesis of cogeneration and trigeneration systems. This approach assumes that product demand and fuel availability are specified as fuzzy constraints, or fuzzy ranges, instead of exact values. Two case studies are used to illustrate how the fuzzy P-graph method identifies sets of optimal and near-optimal designs that meet the specified fuzzy constraints. © 2018 Elsevier Lt