Our Research
Published Papers
(A simple)
Modelling De Novo Programming Within Simon’s Satisficing Theory
Published in European Journal of Operational Research, 2024
H-index: 305; Q1
De novo programming (DNP) is an efficient technique for optimal system design. This paper explores the ability to link the DNP technique with Simon’s satisficing theory to deal with a system design that is satisfactory rather than optimal. To achieve this aim, the ideal vector is replaced by an aspiration-level vector, and the solutions are determined by minimising the Lp-distance metric between the aspiration level and the feasible objective region.
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De novo programming (DNP) is an efficient technique for optimal system design. This paper explores the ability to link the DNP technique with Simon’s satisficing theory to deal with a system design that is satisfactory rather than optimal. To achieve this aim, the ideal vector is replaced by an aspiration-level vector, and the solutions are determined by minimising the Lp-distance metric between the aspiration level and the feasible objective region. To generate a satisficing solution, we develop two models (weighted DNP (W-DNP) and Chebyshev DNP (C-DNP)) based on goal programming techniques. To achieve equilibrium between the solutions obtained from W-DNP and C-DNP, an extended DNP (E-DNP) model is proposed. Moreover, to deal with uncertainty and give decision makers more flexibility to incorporate their preferences, we consider the concept of penalty function (PF) with DNP and propose DNP type models with penalty functions (DNP-PFs). An illustrative example is adopted to show the usefulness of the proposed approach over the standard DNP. We also conduct a hypothetical application to Italian offshore wind farm locations to assess and validate the proposed formulations for solving real-world problems. To check the stability of the obtained results, the impact of the weights on the obtained solution is detected with a weight–space analysis. The results confirm the proposed methodologies and show that they can assist decision makers in determining the optimal location under uncertain aspiration levels.
XOR-Best Worst Method and its Assessment to COVID-19
Sectorial Impact
Published in Annals of Operation Research, 2023
H-index: 125; Q1
The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 has had significant detrimental effects on human well-being, psychologically, socially, economically, and operationally. As the fight against the pandemic continues, the design of efficient policies for the transitional phase remains fraught with complexity and uncertainty. Like many countries around the globe, the Italian government needs to repair the damage done to its socio-economic system by COVID-19 and the response to it.
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The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 has had significant detrimental effects on human well-being, psychologically, socially, economically, and operationally. As the fight against the pandemic continues, the design of efficient policies for the transitional phase remains fraught with complexity and uncertainty. Like many countries around the globe, the Italian government needs to repair the damage done to its socio-economic system by COVID-19 and the response to it. In this regard, policymakers require reliable decision-making supports that improve and validate their decisions and policies. As part of this effort, they must effectively measure and mitigate the impact of the pandemic, including determining which sectors have been most impacted. Due to the high level of uncertainty surrounding this health crisis, in this study, we first develop a new technique for dealing with decision-making problems under uncertainty using exclusive-or logic, called the XOR-Best Worst Method. Then, the proposed technique is adopted to assess the impact of COVID-19 on seven relevant sectors (tourism, transport, industrial, financial, agriculture, education, healthcare) by considering social, operational, and economic dimensions. The principal findings show that Italy’s tourism, industrial, and healthcare sectors have been most impacted by COVID-19, by 20.29%, 18.86%, and 15.10% respectively from social-economic and operational point of view. These results indicate that most of sustainable development goals of the United Nations agenda for 2030, “No poverty,” “Zero hunger,” and “Decent work and economic growth,” have been strongly impacted in Italy due to the pandemic and that there is an urgent need for support and recovery.
XOR-Analytic Network Process and Assessing the Impact of COVID-19 by Sector
Published in Computers & Industrial Engineering, 2023
H-index: 161; Q1
The consequences of any extreme event can deteriorate any system at all levels: socially, economically, and operationally. The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), provides a good example of the tremendous impact that can be produced by such extreme events. To effectively measure and mitigate the impact of the COVID-19 pandemic and relaunch the Moroccan economy, policymakers need to determine which sectors have been most impacted.
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The consequences of any extreme event can deteriorate any system at all levels: socially, economically, and operationally. The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), provides a good example of the tremendous impact that can be produced by such extreme events. To effectively measure and mitigate the impact of the COVID-19 pandemic and relaunch the Moroccan economy, policymakers need to determine which sectors have been most impacted. Due to the high level of uncertainty and complexity surrounding this health crisis, this study first develops a new technique for dealing with decision problems under uncertainty using exclusive-or (XOR) logic, called the XOR-analytic network process (XOR-ANP). Then, the proposed technique is adopted to assess the impact of COVID-19 on seven relevant sectors (tourism, transport, industrial, financial, agriculture, education, and healthcare) by considering social, operational, and economic dimensions. The key findings show that COVID-19 has a significant impact on Moroccan’s tourism, healthcare, and transport sectors, with respect to social-economic and operational dimensions by 30.99%, 21.81%, and 17.88%, respectively. These results indicate that most of the United Nations Sustainable Development Goals for 2030, such as “Healthy Lives”, “Decent Work” and “Economic Growth” have been severely impacted, thus, assistance and recovery are urgently needed
XOR-Data Envelopment Analysis and its Application to Renewable Energy Sector
Published in Expert Systems with Applications, 2022
H-index: 271; Q1
The conventional data envelopment analysis (DEA) method suffers from its inability to incorporate the decision-makers’ preferences and cope with the uncertainty that exists in real-life decision problems. Exclusive-or (XOR for short) is an uncertain logic that describes a situation in which there is only one choice between two or more competitive actions and neither is strong enough to overcome the others. In this paper, a new research thread of the DEA paradigm, named XOR-DEA, is proposed to deal with decision-making problems under xorness (or XOR input/output data).
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The conventional data envelopment analysis (DEA) method suffers from its inability to incorporate the decision-makers’ preferences and cope with the uncertainty that exists in real-life decision problems. Exclusive-or (XOR for short) is an uncertain logic that describes a situation in which there is only one choice between two or more competitive actions and neither is strong enough to overcome the others. In this paper, a new research thread of the DEA paradigm, named XOR-DEA, is proposed to deal with decision-making problems under xorness (or XOR input/output data). To incorporate decision-makers’ preferences in the optimization process, three types of preferences are proposed: positive, negative, and neutral. To cope deeply with uncertainty, a new concept of “the output mechanism of the XOR function” is developed to support the analyst in controlling this phenomenon based on two channels: controlled and uncontrolled. Moreover, to enrich the analysis of practical applications, a new visual analytic material is designed to detect the behavior of the XOR functions during the optimization process. To show the models’ applicability, an illustrative example and an application of ranking renewable energy technologies are presented.
XOR-Analytic Hierarchy Process and its Application in the Renewable Energy Sector
Published in Omega, 2019
H-index: 167; Q1
Uncertainty is a ubiquitous and inherent feature of the decision-making process. This paper proposes a new method called the XOR-analytical hierarchy process (XOR-AHP) to solve multi-criteria decision-making problems in uncertain and imprecise environments. In particular, the method derives a priority vector from an XOR comparison matrix, an XOR weighting (XOR-W) technique based on mathematical programming that allows decision makers (DMs) to set multiple judgments for a particular evaluation using XOR logic.
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Uncertainty is a ubiquitous and inherent feature of the decision-making process. This paper proposes a new method called the XOR-analytical hierarchy process (XOR-AHP) to solve multi-criteria decision-making problems in uncertain and imprecise environments. In particular, the method derives a priority vector from an XOR comparison matrix, an XOR weighting (XOR-W) technique based on mathematical programming that allows decision makers (DMs) to set multiple judgments for a particular evaluation using XOR logic. To incorporate DMs’ preferences in this process, three types of XOR matrices are proposed: optimistic, pessimistic, and neutral. How the new model offers an alternative way to support DMs under uncertain conditions and in imprecise environments is illustrated by considering a hypothetical application (ranking and selecting North African countries for RE investments in the case of the Desertec project).
