Precision nutrition for type 2 diabetes in Benin: leveraging linear goal programming to optimize diets with emphasis on adequacy, affordability, accessibility, and culture.

Background: Nutrition and diet are critical to managing Type 2 diabetes (T2D). Low-income households often face challenges maintaining a healthy and balanced diet due to food insecurity, availability, and cost. To address this issue, we used a linear goal programming (LGP) model to develop nutritionally adequate, affordable, accessible, and culturally acceptable diets for persons with T2D in Benin, a French-speaking sub-Saharan country. The goal was to help persons with T2D manage their condition more effectively. Methods: We compiled a robust list of local commonly consumed foods in Benin, and calculated their nutritional value using West African food composition tables and food costs per serving from a market survey. Using mathematical optimization techniques, we designed dietary plans that meet the daily nutrient intake recommended by the World Health Organization (WHO) to prevent chronic diseases in normal adults. While adhering to dietary constraints of T2D, we developed optimized diet plans with varying energy levels that meet all nutrient requirements while considering availability, acceptability, and budgetary constraints. Results: Fifty-two food items and recipes were evaluated to create six low-cost daily menus. Menu 1 was the most affordable at CFA 1,127 (USD 1.88), providing 1890 kcal of energy, while Menu 6 was the most expensive at CFA 1,227 (USD 2.05), providing 1749 kcal. All the menus met the daily WHO minimum requirements for carbohydrates, fat, cholesterol, and fiber content, while other nutrients such as protein, vitamin C, and iron reached the upper limits of the acceptable value range. Conclusion: Linear goal programming can be an effective tool in helping to obtain optimized adequate, accessible, and culturally acceptable diets at minimal cost by interpreting and translating dietary recommendations into a nutritional model, based on local market prices.

Site selection optimization for 100% renewable energy sources.

The increase in the use of Renewable Energy Sources (RES) provides many advantages such as reducing the environmental problems and sustainability. In this study, a long-term optimum RES settlement strategic plan is conducted for 81 provinces in Turkey by considering real data. Biomass energy, solar energy, hydroelectric energy, geothermal energy, and wind energy are considered RES sources. Energy consumption until the 2050 year is estimated with the regARIMA method, and then a weighted goal programming model was developed in which the outputs of the regARIMA method and risk analysis are integrated. The results of the regARIMA method are tested, and the test results indicate that an R2 value close to 1 indicates that the model is suitable, a low and negative MPE value is neutral, and a MAPE value below 4% indicates high accuracy of the model. Using GAMS 23.5 optimization software program, the developed weighted goal programming model is solved optimally. In this integrated model developed, the objectives of minimizing the installation time, minimizing the investment cost, minimizing the annual cost, maximizing the carbon emission reduction, maximizing the usage time, and minimizing the risk are considered. When the results obtained regarding the number of installations according to the model are examined, the decisions are made for 53% wind energy, 23% biomass energy, 13% hydroelectric energy, 9% solar energy, and 2% geothermal energy. Computational results show that the effective solutions are obtained by minimizing the sum of goals values, covering all provinces in Turkey, and considering real data.

A decision support framework to evaluate the sustainability performance of urban road transportation.

This study proposes a decision support framework (DSF) based on two data envelopment analysis (DEA) models in order to evaluate the urban road transportation of countries for sustainable performance management during different years. The first model considers different years independently while the second model, which is a type of network model, takes into account all the years integrated. A multi-objective programming model under two types of uncertainties is then developed to solve the proposed DEA models based on a revised multi-choice goal programming (GP) approach. The efficiency scores are measured based on the data related to several major European countries and the factors including the level of freight and passenger transportation, level of greenhouse gas emissions, level of energy consumption, and road accidents which are addressed as the main evaluation factors. Eventually, the two proposed models are compared in terms of interpretation and final achievements. The results reveal that the efficiency scores of countries are different under deterministic/uncertain conditions and according to the structure of the evaluation model. Furthermore, efficiency changes are not necessarily the same as productivity changes. The high interpretability (up to 99.6%) of the models demonstrates the reliability of DSF for decision-making stakeholders in the transport sector. Furthermore, a set of managerial analyses is conducted based on different parameters of the performance evaluation measures for these countries including the productivity changes during the period under consideration, resilience of the countries, detection of the benchmark countries, ranking of different countries, and detection of the patterns for improving the transportation system.

Using the deterministic approach model for project portfolio selection problem (PPSP) solutions.

Selection of projects using a robust technique is rare as most of the techniques are not considered useful due to the limitation on the number of projects that can be selected as well as cost saving projects not being selected. This study investigated the validity of a hybrid model - integrated analytical hierarchy process-goal programming (AHP-GP) - to avoid project portfolio selection problems delaying community development. The proposed model includes two steps: AHP to determine the project criteria, the relative importance of weights, and priority preferences, while the GP model was formulated to select the optimal projects. An empirical study on government agencies was carried out to validate the proposed model, and the results compared against GP as a standalone to solve the same problem. The results proved that the hybrid model (AHP-GP) was better than the GP model. AHP-GP has proved to be a robust mechanism most suitable for managerial use due to its ability to handle multi-criteria decision-making (MCDM) situations. This study showed that the hybrid model can select more projects and will create more jobs in the communities concerned compared to the single model (GP). The novelty of this study is the introduction of an integrated model formed from two distinct models as a deterministic approach to solving project portfolio selection problems.

Identifying the most Efficient Detailed Trajectories toward Healthy Diets-A Graph-Based Analysis.

BACKGROUND: Much effort has been devoted to defining healthy diets, which could lower the burden of disease and provide targets for populations. However, these target diets are far removed from current diets, so at best, the population is expected to move slowly along a trajectory. OBJECTIVE: Our aim was to characterize the different possible trajectories toward a target diet and identify the most efficient one for health to point out the first dietary changes being the most urgent to implement. METHODS: Using graph theory, we have developed a new method to represent in a graph all stepwise change trajectories toward a target healthy diet, with trajectories all avoiding risk of nutrient deficiency. Then, we have identified and characterized the trajectory with the highest value for long-term health. Observed male and female average diets are from the French representative survey INCA3, and target diets were set using multicriteria optimization. The best trajectories were found using the Dijkstra algorithm with the Health risk criteria based on epidemiological data. RESULTS: Within ∼2.6M diets in the graphs, we found optimal trajectories that were rather similar for males and females regarding the most efficient changes in the first phase of the pathways. In particular, we found that a 1-step increase in the consumption of whole/semirefined bread (60 g) was the first step in all healthiest trajectories. In males, the subsequent decrease in red meat was immediately preceded by increases in legumes. CONCLUSIONS: We show simple practical dietary changes that can be prioritized along an integral pathway that is the most efficient overall for health when transiting toward a distant healthy diet. We put forward a new method to analyze dietary strategy for public health transition and highlight the first critical steps to prioritize.

Corrigendum: Bi-objective goal programming for balancing costs vs. nutritional adequacy.

[This corrects the article DOI: 10.3389/fnut.2022.1056205.].

Cost and water footprint trade-off in a supply chain optimization model.

Abstract: Water footprint (WF) is an appropriate tool to help any water-intensive industrial system to adapt to climate change. WF is a metric where the direct and indirect freshwater consumption of a country, firm, activity, or product are quantified. Most of existing WF literature emphasizes the assessment of products, not the optimal decision making in the supply chain. To address this research gap, a bi-objective optimization model is developed for supplier selection in a supply chain that minimizes costs and WF. Apart from determining the sources of raw materials to use in producing the products, the model also determines the actions to be taken by the firm in case of supply shortages. The model is demonstrated using three illustrative case studies which show that WF embedded in the raw materials can influence the actions to be taken when addressing issues on raw material availability. The WF becomes significant in the decisions in this bi-objective optimization problem when it is given a weight of at least 20% (or the weight of the cost is at most 80%) for case study 1 and at least 50% for case study 2. When the assigned weight in cost reaches the point where WF becomes significant, the increase in the assigned weight in WF has an inverse impact on the total cost. Case study 3 demonstrates the stochastic variant of the model. Supplementary Information: The online version contains supplementary material available at 10.1007/s10098-023-02549-5.

Designing a dairy supply chain network considering sustainability and resilience: a multistage decision-making framework.

The crucial role of sustainable development and resiliency strategies is undeniable in today's competitive market space, especially after the Coronavirus outbreak. Hence, this research develops a multistage decision-making framework to investigate the supply chain network design problem considering the sustainability and resiliency dimensions. In this way, the scores of the potential suppliers based on the sustainability and resilience dimensions were calculated using the MADM methods, and then, these scores were applied as inputs in the proposed mathematical model (the second stage), which determined which supplier should be selected. The proposed model aims to minimize the total costs, maximize the suppliers' sustainability and resiliency, and maximize the distribution centers' resiliency. Then, the proposed model is solved by the preemptive fuzzy goal programming method. Overall, the main objectives and aims of the current work are to present a comprehensive decision-making model that can incorporate the sustainability and resilience dimensions into the supplier selection and supply chain configuration processes. In general, the main contributions and advantages of this work can be summarized as follows: (i) this research simultaneously investigates the sustainability and resiliency concepts in the dairy supply chain, (ii) the current work develops an efficient multistage decision-making model that can evaluate the suppliers based on the resilience and sustainability dimensions and configure the supply chain network, simultaneously. Based on the obtained results, the responsiveness and facilities reinforcement indicators are the most important indicators for the resilient aspect. On the other hand, reliability and quality are the most important indicators of sustainability aspect. Also, the results show that a large percentage of supply chain costs are related to purchasing and production costs. Besides, according to the outputs, the total cost of supply chain increases by enhancing the demand. Supplementary Information: The online version contains supplementary material available at 10.1007/s10098-023-02538-8.

Evaluation of smart long-term care information strategy portfolio decision model: the national healthcare environment in Taiwan.

A globally aging population results in the long-term care of people with chronic illnesses, affecting the living quality of the elderly. Integrating smart technology and long-term care services will enhance and maximize healthcare quality, while planning a smart long-term care information strategy could satisfy the variety of care demands regarding hospitals, home-care institutions, and communities. The evaluation of a smart long-term care information strategy is necessary to develop smart long-term care technology. This study applies a hybrid Multi-Criteria Decision-Making (MCDM) method, which uses the Decision-Making Trial and Evaluation Laboratory (DEMATEL) integrated with the Analytic Network Process (ANP) for ranking and priority of a smart long-term care information strategy. In addition, this study considers the various resource constraints (budget, network platform cost, training time, labor cost-saving ratio, and information transmission efficiency) into the Zero-one Goal Programming (ZOGP) model to capture the optimal smart long-term care information strategy portfolios. The results of this study indicate that a hybrid MCDM decision model can provide decision-makers with the optimal service platform selection for a smart long-term care information strategy that can maximize information service benefits and allocate constrained resources most efficiently.

Train maintenance personnel shift scheduling: case study.

In this study, the shift scheduling problem of the personnel who maintain the 324 trains operating on the lines of the Ankara Metro, which carry approximately 10 million passengers per month, is discussed. In the problem, daily personnel needs and personnel qualifications are evaluated together according to the fault prediction. Firstly, a total of 1721 fault data for 181 days is analyzed, and the prediction is made according to 8 prediction methods. Then, a 30-day fault prediction is made with the prediction method, which has the least error rate. According to the number of faults obtained as a result of the prediction, the daily personnel requirement is determined. Then, according to the type of fault, the personnel are classified as electronics, electromechanics, and mechanics. It is aimed at distributing the personnel evenly to the shifts. The goal programming model is used to solve the problem, allowing us to add more than one goal to the model. When the solution results of the problem are compared with the current schedule, it is seen that the monthly total working hours are distributed equally, and the shifts are distributed fairly, and a necessary balance is achieved in the shifts according to the personnel qualifications. It is thought that this study, in which personnel qualifications and fault prediction are evaluated together, will contribute to the literature on railway maintenance personnel scheduling.

Designing reverse logistics network for healthcare waste management considering epidemic disruptions under uncertainty.

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.

Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era.

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.

Iron Bioavailability Should be Considered when Modeling Omnivorous, Vegetarian, and Vegan Diets.

BACKGROUND: To lower environmental impact of human food consumption, replacement of animal proteins with plant-based proteins is encouraged. However, the lower iron bioavailability of plant-based foods is rarely considered when designing healthy and sustainable diets by using diet modeling. The estimated absorbable iron content of vegetarian and vegan menu plans might therefore be too optimistic. OBJECTIVE: The main aim of this study was to investigate and compare the impact of various methods to estimate absorbable iron intake on the nutritional adequacy of omnivorous, vegetarian, and vegan menu plans designed for women of reproductive age. METHODS: A diet model was developed to design menu plans consisting of a selection of meals that best complied with nutritional requirements. Meals used for modeling were created based on food intake data from the National Health and Nutrition Examination Survey (NHANES). For each meal, absorbable iron concentrations were estimated by using 2 constant absorption factors (18% and 10%) and 2 diet-dependent absorption equations (Conway and Hallberg). For each absorption method and diet type, we used the diet model to design the optimal menu plan. Retrospectively, menu plans were evaluated by estimating the absorbable iron content by using the other absorption methods. RESULTS: Retrospective diet-dependent absorbable iron estimates were consistently lower than estimates based on constant absorption factors. Using diet-dependent estimates increased absorbable iron by optimizing enhancer and inhibitor concentrations. CONCLUSION: Iron bioavailability should be considered when modeling diets.

A multi-objective optimization approach for integrated risk-based internal audit planning.

Annual audit planning is a multi-criteria decision-making problem faced by internal audit departments of all organizations. Due to the constrained audit resources, the planning process primarily involves the analysis and evaluation of complex factors for selecting auditable units that maximize the full potential of internal audit. Previous research on internal audit planning only focused on the goal of risk minimization and applied ranking methods to prioritize alternatives. In order to enable internal audit activities to add more value to the organization, the integrated risk-based internal audit planning is proposed to assist audit department in achieving multiple objectives in addition to risk management. Meanwhile, a multi-stage framework is proposed to support the development of such value-added internal audit plan. The new framework integrates the risk assessment of auditable units with the selection of audit activities and resource allocation through a combined analytic hierarchy process (AHP), fuzzy comprehensive evaluation (FCE) and weighted multi-choice goal programming (WMCGP) approach. The model considers both qualitative and quantitative decision criteria. A real-life case study of the development of an integrated risk-based annual audit plan is presented, and sensitivity analysis is performed to illustrate the validity of the proposed approach. The results indicate that the proposed framework is a useful tool for internal audit planning and the implications of the study can be extended to various selection and allocation problems.

A goal programming model for two-stage COVID19 test sampling centers location-allocation problem.

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.

Evaluating sustainable efficiency of decision-making units considering undesirable outputs: an application to airline using integrated multi-objective DEA-TOPSIS.

Sustainable development has gained significant attention in the literature due to the increased global awareness of environmental sustainability during the last decade. Sustainable development has three aspects, including economic, social, and environmental. The challenge of sustainable development is to establish a balance between these three aspects. Assessing the efficiency of a company contributes comprehensive information to improve its overall performance. Despite numerous studies in this field, the literature lacks studies that simultaneously consider all three aspects of sustainable development, especially the social aspect. The main objective of this paper is to calculate the technical, social, and environmental efficiency scores. We also introduce a new efficiency called sustainable efficiency that merges all three sustainable development aspects in one efficiency score. This study applies two existing data envelopment analysis (DEA) models to evaluate technical, social, environmental, and sustainable efficiencies. These models, namely the three-step method and the modified three-step method, are computationally intensive. Also, this paper introduces two new DEA models, namely the common weight goal programming DEA and the common weight DEA, to assess the efficiencies with much fewer computations. Each model produces results that are different from one another. Therefore, the TOPSIS approach is applied to provide an overall result by integrating the results obtained from the four presented models. For this purpose, the implementation of four TOPSIS models is required. To illustrate the capability and validity of the developed models in efficiency calculation, a case of Iranian airlines is presented. The selected airlines are evaluated in different aspects, and final results are obtained by applying TOPSIS. The findings show that using TOPSIS to combine the results of several DEA models leads to a fully ranking of airlines in four aspects of technical, social, environmental, and sustainable efficiencies. Also, it is recommended to managers to probe pairwise comparison between different efficiencies of airlines in order to find and improve the weak ones.

Multicriteria decision making and goal programming for determination of electric automobile aimed at sustainable green environment: a case study.

In this paper, we consider the problem of automobile selection for transportation in inner city using a hybrid multicriteria decision making approach. The electric automobiles that are a relatively new concept in the world of the automotive industry, are widely viewed as attractive among its alternatives day by day. Fuel-vehicles produce a lot of carbon emissions that are ejected into our natural atmosphere, leaving us vulnerable to things like pollution and greenhouse gases. So, electric vehicle and automobiles have emerged as a more efficient alternative and these vehicles have been a great step forward to help positively the environment with zero emissions and total energy consumption in their lifecycle. Many companies focus on electric vehicle production with the development of electric vehicle technology. Therefore, the selection process emerges among the various electric automobile technologies for the users. The selection process includes several conflicting factors which are such as economic, technical and technological factors. In the present study, we propose a hybrid approach for electric automobile selection that combines analytic hierarchy process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS) and goal programming (GP) is used to determine the weights to assign to the factors that go into these selection decisions and TOPSIS method is used for preference ranking. These weights founded by AHP are inputted into a GP model to determine the best alternative among the electric automobiles. Finally, the study used three methods TOPSIS, AHP- TOPSIS and AHP-GP for better comparison and evaluation. The most suitable electric automobile is selected among their alternatives by using analytic methods and goal programming.

A goal programming-based fuzzy best-worst method for the viable supplier selection problem: a case study.

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.

A new integrated circular economy index and a combined method for optimization of wood production chain considering carbon neutrality.

The wood industry is potentially advantageous to applying the concepts of circular economy for sustainable development and can contribute to the commitment of carbon neutrality. This study developed an integrated circular economy index based on five different quantitative indicators for assessment of the wood production chain: heat recovery rate, CO2 sequestration rate, fossil fuel substitution rate, renewable electricity usage rate, and revenue increase from the by-products. A combination of best-worst method (BWM) and linear goal programming (LGP) techniques was investigated to develop an optimal circular economy model of wood processing chain for reduction in CO2 emission. The integrated circular economy index and the combined method were tested in a case-study of a rubberwood processing chain in Vietnam. The proposed model suggests that the woodchips and biomass from the harvesting and processing of rubberwood could be collected and treated using microwave thermolysis techniques; the enzyme hydrolysis technique is appropriate for bioethanol and biomethane recovery from the sawdust; and the hot air technique is preferable in the drying process. The proposed model could result in a significant reduction of the total net carbon emission from +552,750 tons CO2eq to -1,145,940 tons CO2eq per year. This could support the achievement of Vietnam's zero CO2 emission goal and hopefully contribute to the country's commitment to carbon emission neutrality by the year 2050.

National IoMT platform strategy portfolio decision model under the COVID-19 environment: based on the financial and non-financial value view.

The Internet of Medical Things (IoMT) is an emerging technology in the healthcare revolution which provides real-time healthcare information communication and reasonable medical resource allocation. The COVID-19 pandemic has had a significant effect on people's lives and has affected healthcare capacities. It is important for integrated IoMT platform development to overcome the global pandemic challenges. This study proposed the national IoMT platform strategy portfolio decision-making model from the non-financial (technology, organization, environment) and financial perspectives. As a solution to the decision problem, initially, the decision-making trial and evaluation laboratory (DEMATEL) technology were employed to capture the cause-effect relationship based on the perspectives and criteria obtained from the insight of an expert team. The analytic network process (ANP) and pairwise comparisons were then used to determine the weights for the strategy. Simultaneously, this study incorporated IoMT platform resource limitations into the zero-one goal programming (ZOGP) method to obtain an optimal portfolio selection for IoMT platform strategy planning. The results showed that the integrated MCDM method produced reasonable results for selecting the most appropriate IoMT platform strategy portfolio when considering resource constraints such as system installation costs, consultant fees, infrastructure costs, reduction of medical staff demand, and improvement rates for diagnosis efficiency. The decision-making model of the IoMT platform in this study was conclusive and significantly compelling to aid government decision makers in concentrating their efforts on planning IoMT strategies in response to various pandemic and medical resource allocations.