ABSTRACT
This paper determines the optimal communication by the policymakers in the wake of the Covid-19 crisis. The authors have developed a conceptual framework for optimal communication from the available literature and the opinion of the experts. Further, a hybrid methodology based on Fuzzy AHP and Goal programming has been used for the analysis. Using the conceptual framework it was revealed that there are 72 configurations from which optimal one has to be chosen by the policymakers for communicating optimally during pandemic emergencies like the Covid-19 outbreak. The analysis using hybrid methodology highlighted that FRTD is the optimal configuration out of the 72 possibilities. Considering this option would minimize the effect of the Covid-19 crisis by helping policymakers communicate to the maximum people at the minimum delay.
ABSTRACT
Population growth and recent disruptions caused by COVID-19 and many other man-made or natural disasters all around the world have considerably increased the demand for medical services, which has led to a rise in medical waste generation. The improper management of these wastes can result in a serious threat to living organisms and the environment. Designing a reverse logistics network using mathematical programming tools is an efficient and effective way to manage healthcare waste. In this regard, this paper formulates a bi-objective mixed-integer linear programming model for designing a reverse logistics network to manage healthcare waste under uncertainty and epidemic disruptions. The concept of epidemic disruptions is employed to determine the amount of waste generated in network facilities; and a Monte Carlo-based simulation approach is used for this end. The proposed model minimizes total costs and population risk, simultaneously. A fuzzy goal programming method is developed to deal with the uncertainty of the model. A simulation algorithm is developed using probabilistic distribution functions for generating data with different sizes; and then used for the evaluation of the proposed model. Finally, the efficiency of the proposed model and solution approach is confirmed using the sensitivity analysis process on the objective functions' coefficients.
ABSTRACT
Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants.
ABSTRACT
The Covid-19 epidemic, has caused a large-scale congestion in many ports around the world. This increases the cost of port docking, as well as delays the loading and unloading of goods, which affects the price and timely supply of many products. Although scholars have carried out in-depth discussion and analysis on the port congestion problem from different perspectives, there is still no appropriate model and algorithm for the large-scale comprehensive port docking problem. This paper presents a new mixed integer programming model for optimal docking of ships in ports that is comprehensive enough to include four essential objectives. It discusses the generalization and application of the model from the perspectives of the shortest overall waiting time of ships, the balance of tasks at each berth, completion of all docking tasks as soon as possible and meeting the expected berthing time of ships. We demonstrate the results of our models using relevant examples and show that our model can obtain the optimal docking scheme based on different perspectives and relevant objectives. We also show that the scale of the exact solution can reach tens of thousands of decision variables and more than a million constraints. This fully reflects the possibility that the model can be put into use in any real life scenario. This model can not only effectively improve the docking efficiency of the port, but is also suitable for the complex queuing problem of multi window and the same type of service.
ABSTRACT
Health inequity, which leads to unfair and preventable disparities across individuals in opportunities to achieve optimal health, has been brought back into the national spotlight by global COVID-19 pandemic. As artificial intelligence (AI) is increasingly being applied within the health domain, this work aims to develop a new fairness-aware framework, based on machine learning (ML) fairness metrics, AI technologies, and optimization, to help clinical researchers, healthcare providers, and policy makers identify, quantify, reduce, and eventually eliminate potential biases in data-based decision making and implement evidence-based practice to improve patient outcomes. The ultimate goal is to enhance diversity, equity, and inclusion (DEI) in population health in support of better health outcomes for all. We developed a set of health equity metrics to identify and quantify disparities between research sample learnt by AI models and the real-world population that eventual research findings will be applied to. These health equity metrics were derived from existing fairness metrics applied in other areas such as machine learning. Unlike reference-group based metrics measuring bias against a golden truth defined by researchers, these scalable metrics quantify bias against target populations who should have equal opportunity for selection. This research proves that equity metrics could be effectively applied to multiple health domains and shed light on clinical and policy implications.We applied our novel health equity assessment framework, embedded with the proposed equity metrics, to three use cases in population health: randomized clinical trials (RCTs) in Chapter 2, clinical trial recruitment planning in Chapter 3, and healthcare utilization including prescription drugs and vaccines in Chapter 4. To turn health data into usable information that can be understood by observers, we present key equity evaluation results both analytically and visually.In RCTs (Chapter 2), equity metrics, which act as representativeness metrics, enable users to determine overrepresentation, underrepresentation, or exclusion of subgroups with respect to a target population indicating potential limitations of RCTs. Additional statistical tests quantify the significance of observed subgroup inequities with consideration of study sizes and estimation errors of ideal rates. These metrics can measure the level of inequity for all possible protected subgroups of patients defined using multiple protected attributes and provide a single visualization that incorporates and compares these subgroup measures. For clinical trials recruitment planning (Chapter 3), a goal-programming-based multi-objective optimization approach, integrating quantitatively defined enrollment goals, was designed to make equitable enrollment plans for RCTs. The method can prospectively produce equitable enrollment plans in the experiment design stage and retrospectively evaluate inequities in clinical trial enrollment during and after the experiment. It provides opportunities for researchers to demonstrate validity of investigation and to examine disparities across subgroups defined over subjects' characteristics of interest.Furthermore, equity metrics can be used as measures of effects of demographic and socioeconomic determinants on healthcare access and utilization (Chapter 4). They enable users to find differences in healthcare services associated with vulnerable subpopulations such as overprescription and underprescription to medications and insufficient accessibility and utilization of healthcare services. The findings suggest that different determinants exist regarding to the resources/service of different health needs. This method can be valuable assistance in decisions regarding healthcare and provides an opportunity to promote equitable access to healthcare and improved health outcomes. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
ABSTRACT
The rapid growth of technology, environmental concerns, and disruptions caused by the COVID-19 pandemic have led researchers to pay more attention to an emerging concept called the fifth industrial revolution (I5.0). Despite the high importance of the I5.0, the literature shows that no study investigated the supply chain network design problem based on the I5.0 pillars. Hence, this research develops a multi-stage decision-making framework to configure a closed-loop supply chain based on I5.0 dimensions to cover this gap. In the first stage, the score of technologies that utilized in the supply chain is calculated using the analytic hierarchy process method. Afterwards, in the second stage, a mathematical model is proposed to configure the supply chain. Then, Furthermore, an efficient solution method, named the fuzzy lexicographic multi-choice Chebyshev goal programming method, is developed to obtain the optimal solution. In general, the main contributions of the current study can be divided into two major parts as follows: (i) the current study is the first research that incorporates the dimensions of the I5.0 into the supply chain network design problem, and (ii) this work develops a novel and efficient solution method. In this regard, the major problems and challenges that existed include the limitation of available resources in relation to Industry 5, especially in the field of the supply chain, as well as quantifying the elements of Industry 5.0 in the form of a mathematical programming model. © 2023
ABSTRACT
As consumers become ever more conscious of environmental issues, socially responsible corporate practices, and government regulations, companies are increasingly motivated to incorporate reverse logistics (RLs) into their operations, thus raising the question of provider selection. In previous studies, the food industry generally lacked a systematic reference method for RLs provider selection, especially during the post-COVID-19 pandemic. This study aims to develop a comprehensive approach that combines a technique for order preference by similarity to ideal solution (TOPSIS) and multi-segment goal programming (MSGP) models to select optimal RLs providers. Furthermore, this method will enable decision makers (DMs) to evaluate and select the best RLs provider considering the limited resources of the business. This approach allows DMs to consider both qualitative and quantitative criteria, set multiple target segmentation expectations, and achieve optimal RLs provider selection. This study also provides case studies of applications by food manufacturers. The main finding is that considering multiple criteria in making a decision produces better results than using a single criterion. © 2023 by the authors.
ABSTRACT
Since the COVID-19 outbreak has led to drastic changes in the business environment, researchers attempt to introduce new approaches to improve the capability and flexibility of the industries. In this regard, recently, the concept of the viable supply chain, which tried to incorporate the leagile, resiliency, sustainability, and digitalization aspects into the post-pandemic supply chain, has been introduced by researchers. However, the literature shows that there is lack of study that investigated the viable supplier selection problem, as one of the crucial branches of viable supply chain management. Therefore, to cover this gap, the current work aims to develop a decision-making framework to investigated the viable supplier selection problem. In this regard, owing to the crucial role of the oxygen concentrator device during the COVID-19 outbreak, this research selects the mentioned product as a case study. After determining the indicators and alternatives of the research problem, a novel method named goal programming-based fuzzy best-worst method (GP-FBWM) is proposed to compute the indicators' weights. Then, the potential alternatives are prioritized employing the Fuzzy Vlse Kriterijumsk Optimizacija Kompromisno Resenje method. In general, the main contributions and novelties of the present research are to incorporate the elements of the viability concepts in the supplier selection problem for the medical devices industry and to develop an efficient method GP-FBWM to measure the importance of the criteria. Then, the developed method is implemented and the obtained results are analyzed. Finally, managerial and theoretical implications are provided. Supplementary Information: The online version contains supplementary material available at 10.1007/s00500-022-07572-0.
ABSTRACT
Bi-level programming is an efficient tool to tackle decentralized decision-making processes in supply chains with upper level (i.e., leader) and lower level (i.e., follower). The leader makes the first decision while the follower makes the second decision. In this research, a bi-level programming formulation for the problem of location-inventory-routing in a two-echelon supply chain, including a number of central warehouses in the first echelon and retailers in the second echelon with perishable products under uncertain demand, is proposed. The total operational costs at both levels are minimized considering capacity constraints. Due to the uncertain nature of the problem, a scenario-based programming is utilized. The economic condition or unforeseen events such as COVID-19 or Russia-Ukraine war can be good examples for uncertainty sources in today's world. The model determines the optimal locations of warehouses, the routes between warehouses and retailers, number of received shipments and the amount of inventory held at each retailer. A revised solution method is designed by using multi-choice goal programming for solving the problem. The given revised method attempts to minimize the deviations of each decision maker's solution from its ideal value assuming that the upper level is satisfied higher than the lower level. Base on some numerical analysis, the proposed solution technique is more sensitive to the upper bounds of the goals rather than the lower bounds. © 2022 Elsevier B.V.
ABSTRACT
Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers' reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results. © 2022 Elsevier Ltd
ABSTRACT
The COVID19 virus, which first appeared in Wuhan, China, and has become a pandemic in a short time, has threatened the health system in many countries and put it into a bottleneck. Simultaneously, the second wave's expectation spread it necessary to plan the health services correctly. In this study, a location-allocation problem in the two-echelon system, which considers different test sampling alternatives, is examined to obtain test sampling centers' location-allocation. The problem is modeled as a goal programming model to create a network that tests samples at a minimum total distance, establishes a minimum number of test sampling centers, and reaches the distance of PCR test laboratories at minimum total distances. The proposed model is applied as a case study for the two cities located in Turkey, and the obtained locations and inventory levels of each location are presented. Besides, different scenarios are examined to understand the structure of the model. As a result, only testing in hospitals will increase the risk of contamination. Since testing at all points will not be possible administratively, it will be ensured that the most appropriate location-allocation decisions are taken by considering all the proposed model's objectives.
ABSTRACT
Global crises such as COVID-19 pandemic and the Russian-Ukrainian war pose many challenges for closed-loop supply chain networks (CLSCN) due to the lack of supplies of raw materials and returned products. Therefore, this research focused on developing a multi-objective MILP mathematical model for the design and planning of CLSCN to help overcome these challenges considering the uncertainty in both the supplying capacity of the raw materials and the return rate of the used products.The developed models aim to maximize total profit, minimize total cost, and maximize overall cus-tomer service level (OCSL) using the e-lexicographic procedure.The effect of variation in both the supply capacity and return rate of the used products on the design and performance of the CLSCN have been studied. It is recommended to optimize the profit then the total cost with a maximum allowable deviation of 5%, and finally optimize the OCSL.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).
ABSTRACT
PurposeThe recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.Design/methodology/approachThis paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.FindingsAmong various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.Originality/valueThis study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.
ABSTRACT
Introduction: Food security is attracting more attention in Malaysia not only at the national level that concern toward the enhancement of food self-sufficiency but also at the individual level which concerns more on nutrition and health. The economic recession triggered by the COVID-19 pandemic has brought the food and nutrition security challenge to the higher priority. In this study, we assessed the feasibility of encouraging a healthy eating plan by taking into account two important elements, food cost and nutrient intake, to help tackle the food and nutrition insecurity challenges at the individual level. Method and materials: This study used a goal programming model with dietary intake data from Malaysian Adult Nutrition Survey reports to develop food plans that can improve nutrition quality without increasing food cost. Missing data, such as nutrient compositions and food prices, were collected separately from existing governmental and non-governmental sources. Benchmark nutrient intakes were derived from Malaysian Dietary Guidelines and Malaysian Recommended Nutrient Intakes reports, whereas benchmark costs were estimated by mapping food prices to dietary intake. The cost of healthier diets was also assessed to examine the acceptability of dietary changes for the low-income population. Results: The results showed that healthier diets following national dietary guidelines are achievable with reasonable food choices shift without changing the cost of meal plan. Greater intake of milk and vegetables (for more calcium) and smaller intake of seafood and egg products (for less protein) will contribute to raise diet quality and achieve more adequate nutrition. However, the cost attached to healthier food plan is still likely to be burdensome for the food-insecure and low-income population. Conclusions: Our results suggest that policymakers should implement income-relevant laws to cut poverty and improve the population's dietary intake. Income growth as a result of better skills and education is needed to ensure that the real incomes of Malaysian are well sustained, and increased to help low-income population make better and healthier food choices.
ABSTRACT
PurposeThe recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.Design/methodology/approachThis paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.FindingsAmong various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.Originality/valueThis study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.
ABSTRACT
The stock fund diversification process is a tedious task due to the erratic nature of the stock market. On the other hand, work is more challenging due to high annual return expectations with low risk. This research work explores the potential of goal programming (GP) and K-means algorithm as an integrated K-means-GP approach for fund diversification, where K-means is used to create groups of stock based on their performance. Then GP is used to diversify total funds into various groups of stocks to achieve a high annual return. The experimental work has been done in 30 stocks of DOW30 of the years 2017–2018, 2018–2019, and 2019–2020. A comparative study was carried with three different cases based on individual year data and an average of two and three years of data. The empirical results show that: the K-means-GP approach outperformed the GP approach for stock fund diversification;the annual return is higher in the case of the K-means-GP approach using three years of average data with 12.59% of annual return against the expected annual return of 20%. Due to COVID-19, few stocks perform in the negative direction, and hence the annual return is being affected after fund diversification. Copyright © 2022 Inderscience Enterprises Ltd.
ABSTRACT
The COVID-19 pandemic required managerial and structural changes inside hospitals to address new admission demands, frequently reducing their care capacity for other diseases. In this regard, this study aims to support the recovery of hospital productivity in the post-pandemic context. The major challenge will be to make use of all the resources the institution has obtained (equipment, beds, temporarily hired human resources) and to increase production to meet the existing repressed demand. To support evidence-based decision-making at a major university hospital in Rio de Janeiro, hospital managers and operations research analysts designed an approach based on multiple methodologies. Besides multimethodology, one important novelty of this study is the application of a productivity frontier function to future scenario planning through the quantitative DEA methodology. Concept maps were used to structure the problem and emphasize stakeholders' perspectives. In sequence, data envelopment analysis (DEA) was applied, as it combines benchmarking best practices and assigns weights to inputs and outputs. To guarantee that the efficiency measurement considers all inputs and outputs before any inclusion of expert judgment, the scope was redirected to full dimensional efficient facet, if any, or to maximum efficient faces. The results indicate that production scenarios proposed by stakeholders based on the Ministry of Health parameters overestimate the viable production framework and that the scenario that maintains temporary human resource contracts is more compatible with quality in health provision, teaching, and research. These findings will serve as a basis for decision-making by the governmental agency that provided temporary contracts. The present methodology can be applied in different settings and scales.
ABSTRACT
The stock fund diversification process is a tedious task due to the erratic nature of the stock market. On the other hand, work is more challenging due to high annual return expectations with low risk. This research work explores the potential of goal programming (GP) and K-means algorithm as an integrated K-means-GP approach for fund diversification, where K-means is used to create groups of stock based on their performance. Then GP is used to diversify total funds into various groups of stocks to achieve a high annual return. The experimental work has been done in 30 stocks of DOW30 of the years 2017-2018, 2018-2019, and 2019-2020. A comparative study was carried with three different cases based on individual year data and an average of two and three years of data. The empirical results show that: the K-means-GP approach outperformed the GP approach for stock fund diversification;the annual return is higher in the case of the K-means-GP approach using three years of average data with 12.59% of annual return against the expected annual return of 20%. Due to COVID-19, few stocks perform in the negative direction, and hence the annual return is being affected after fund diversification.
ABSTRACT
Curriculum planning is an important but complex and challenging decision-making problem at universities. There is a growing interest in curriculum planning problem. However, the body of research on curriculum planning process using analytical methods is still small. Additionally, prior research focused on planning of an individual curriculum or making study plan for students. Curriculum planning at the program level is an under-researched topic. A robust model has not been constructed to address curriculum selection and credit allocation problems simultaneously. To help educational leaders make the most appropriate curriculum plan corresponding to their goals with the highest level of utility achieved, this study presents a new decision support framework with integrated approach. In the proposed framework, based on the competency weights derived from the analytical hierarchy process method, the importance of each potential curriculum is evaluated using the fuzzy comprehensive evaluation method. An exploratory estimation is made to calculate the contribution values of competency development by each curriculum taught at different levels. Finally, multichoice goal programming with utility function determines the curriculum to be provided and corresponding credits to minimize the aggregate deviations from predefined goals with multiple aspirations. An application to curriculum planning of an undergraduate supply chain management program is presented to validate the flexibility and practicality of the proposed approach. The implications of the study are not restricted to curriculum planning of supply chain management program.
ABSTRACT
Global crises such as COVID-19 pandemic and the Russian-Ukrainian war pose many challenges for closed-loop supply chain networks (CLSCN) due to the lack of supplies of raw materials and returned products. Therefore, this research focused on developing a multi-objective MILP mathematical model for the design and planning of CLSCN to help overcome these challenges considering the uncertainty in both the supplying capacity of the raw materials and the return rate of the used products. The developed models aim to maximize total profit, minimize total cost, and maximize overall customer service level (OCSL) using the ɛ-lexicographic procedure. The effect of variation in both the supply capacity and return rate of the used products on the design and performance of the CLSCN have been studied. It is recommended to optimize the profit then the total cost with a maximum allowable deviation of 5%, and finally optimize the OCSL.