Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach

Due to the high necessity of medical face masks and face shields during the COVID-19 pandemic, healthcare centers dealing with infected patients have faced serious challenges due to the high consumption rate face masks and face shields. In this regard, the supply chain of healthcare centers should put all of their efforts into avoiding any shortages of masks and shields as these products are considered as primary ways to prevent the spread of the virus. Since, any shortages in these products would lead to irrecoverable and costly consequences in terms of the mortality rate of patients and medical staff. Therefore, healthcare centers should decide on best supplier to supply required products, considering technical, and sustainability measures. Dynamicity and uncertainty of the pandemic are other factors that add up to the complexity of the supplier selection problem. Therefore, this paper develops a novel decision-making approach using Measuring attractiveness through a categorical-based evaluation technique (MACBETH) and a new combinative distance-based assessment method to address the supplier selection problem during the COVID-19 pandemic. Due to high uncertainty, vague and incomplete information for decision-making problems during the COVID-19 pandemic, the developed decision-making approach is implemented under fuzzy rough numbers as a superior uncertainty set of the traditional fuzzy set and rough numbers. Extensive sensitivity analysis tests are performed based on parameters of the decision-making approach, impacts of weight coefficients, and consistency of results in comparison to other MCDM methods. A real-life case study is investigated for a hospital in Istanbul, Turkey to show the applicability of the developed approach. Based on the results of MACBETH method, job creation and occupational health and safety systems are two top criteria. Results of the case study for five suppliers indicate that supplier (A1) is the best supplier with a distance score of 3.308.

A GIS-based multi-criteria decision-making method for the selection of potential municipal solid waste disposal sites in Mersin, Turkey

Due to rapid urbanization and the resulting rapid population increases, an important problem for cities today is the elimination of solid waste or finding suitable places for waste storage. Municipal solid waste disposal (MSWD) site selection is one of the most important steps in urban waste management. Many criteria political, economic, social, and technological should be considered in this process. Geographic information systems (GIS) and multi-criteria decision-making (MCDM) are tools that are superior to traditional methods in the planning phase of site selection studies. In this study, suitable MSWD sites were determined in Mersin (a Turkish province) based on GIS and the analytic hierarchy process, an MCDM method. Unsuitable areas in the study were removed at the beginning of the analysis. Eleven evaluation criteria were selected: elevation, slope, permeability, distance from lineaments, groundwater level, distance from rivers and water surfaces, distance from roads, distance from settlements, distance from protected areas, and land cover. Considering the evaluation and exclusion criteria, 19.12% of the study area was deemed suitable, and 80.88% was determined unsuitable for an MSWD site. An MSWD suitability map was created as a result of the study. The outcomes indicate that 80,377 ha and 83,022 ha of the study area were classified as high and very high suitability, respectively. Based on these results, we evaluate whether the locations of existing solid waste landfills are appropriate and propose alternative solid waste landfills for each district.

Development of a multi-criteria decision-making-based assessment model for dental material selection: Engine-driven nickel-titanium instruments case study

OBJECTIVES

The aims of this study are (i) to propose specific selection criteria related to NiTi instruments for dental practitioners and (ii) to objectively assess the NiTi instruments.

MATERIALS AND METHODS

The steps of the methodology are as follows: Step 1: "Delphi method" was employed to reach a consensus on criteria defined according to the literature review and a group of panelists. Step 2: "Smart pairwise comparisons" were employed to rank the proposed criteria. Step 3: "Borda voting" was employed to determine the weights of the proposed criteria. Step 4: To determine assessment scores, "Simple Additive Weighting" was employed. Step 5: Reliability and validity checks were made by "simulation."

RESULTS

Specific criteria classified under dimensions were proposed and weighted for the NiTi instrument assessment. In this context, an assessment model was proposed and validated.

CONCLUSIONS

The proposed assessment model for NiTi instruments could aid to make the decision-making process as systematic, transparent, and reproducible as possible not only for dental practitioners but also for healthcare professionals. Also, this proposed model can represent a reference framework for further MCDM studies which can rank or classify materials in medical science.

CLINICAL RELEVANCE

The model proposed in this study can be used to aid decision-making in clinical practice by means assessing the NiTi instrumentation system alternatives for practitioners.

A q-rung orthopair fuzzy ARAS method based on entropy and discrimination measures: an application of sustainable recycling partner selection

The necessity and policy of eco-economy stimulate enterprises to attain sustainability by executing supply chain management. Generally, the evaluation process of sustainable recycling partner (SRP) selection is treated as a multi-criteria decision-making problem due to existence of numerous influencing aspects. To tackle the uncertain information during the process of SRP selection, the q-rung orthopair fuzzy sets have a good choice, which can refer to a broader range of uncertain decision-making information. Thus, this study presents a combined framework with the additive ratio assessment (ARAS) approach, notions of q-rung orthopair fuzzy set (q-ROFS) and information measures, and further implements to tackle the multi-criteria SRP selection problem with q-ROFSs setting. In this procedure, the criteria weights are evaluated with the integration of the subjective weights given by decision-experts and the objective weights obtain from the entropy and discrimination measures-based approach. For this, new entropy and discrimination measures are introduced for q-ROFSs and discussed the effectiveness of proposed measures. To elucidate the applicability of the present methodology, a case study related to sustainable recycling partner assessment is presented under q-ROFSs context. Sensitivity analysis is conducted over diverse set of criteria weights to verify the robustness of introduced framework. The results of the sensitivity analysis signify that the recycling partner SRP1 constantly secures the best rank and despites how sub-criteria weights differ. A comparison with extant methods is made to validate of the results of proposed one. The findings of the work verify that the developed framework is more valuable and well consistent with formerly proposed decision-making models.

Based on T-spherical fuzzy environment: A combination of FWZIC and FDOSM for prioritising COVID-19 vaccine dose recipients

The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.

An integrated method for hybrid distribution with estimation of demand matching degree

Timely and effective distribution of relief materials is one of the most important aspects when fighting with a natural or a man-made disaster. Due to the sudden and urgent nature of most disasters, it is hard to make the exact prediction on the demand information. Meanwhile, timely delivery is also a problem. In this paper, taking the COVID-19 epidemic as an example, we propose an integrated method to fulfill both the demand estimation and the relief material distribution. We assume the relief supply is directed by government, so it is possible to arrange experts to evaluate the situation from aspects and coordinate supplies of different sources. The first part of the integrated method is a fuzzy decision-making process. The demand degrees on relief materials are estimated by extending COPRAS under interval 2-tuple linguistic environment. The second part includes the demand degrees as one of the inputs, conducts a hybrid distribution model to decide the allocation and routing. The key point of hybrid distribution is that each demand point could be visited by different vehicles and each vehicle could visit different demand points. Our method can also be extended to include both relief materials and medical staffs. A real-life case study of Wuhan, China is provided to illustrate the presented method.

Picture fuzzy WASPAS method for selecting last-mile delivery mode: a case study of Belgrade

Background

Last-mile delivery (LMD) is becoming more and more demanding due to an increasing number of users and traffic problems in cities. Besides, medical crises (like the COVID-19 outbreak) and air pollution represent additional motives for the transition from traditional to socially and environmentally sustainable LMD mode. An emerging problem for companies in the postal and logistics industry is how to determine the best LMD mode in a multi-criteria setting under uncertainty.

Method

For the first time, an extension of the Weighted Aggregated Sum Product ASsessment (WASPAS) method under the picture fuzzy environment is presented to solve the LMD mode selection problem. The introduced picture fuzzy set (PFS) based multi-criteria decision-making (MCDM) method can be highly beneficial to managers who are in charge of LMD since it can take into account the neutral/refusal information and efficiently deal with high levels of imprecise, vague, and uncertain information. The comparative analysis with the existing state-of-the-art PFS-based MCDM methods approved the high reliability of the proposed picture fuzzy WASPAS method. Its high robustness and consistency are also confirmed. The presented method can be used to improve LMD in urban areas worldwide. Besides, it can be applied to solve other emerging MCDM problems in an uncertain environment.

Findings

A real-life case study of Belgrade is presented to fully illustrate the potentials and applicability of the picture fuzzy WASPAS method. The results show that postomates are the best mode for LMD in Belgrade, followed by cargo bicycles, drones, traditional delivery, autonomous vehicles, and tube transport.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12544-021-00501-6.

An application of multi-criteria decision-making approach to sustainable drug shortages management: evidence from a developing country

Background

Drug shortage is a significant public health problem, especially for drugs related to life threatening conditions. Almost all countries affected by variety of supply problems and spent a considerable amount of time and resources responding to shortage. The aim of present study is to determine and prioritize strategies to achieve best solutions for these considerable healthcare system challenges and to evaluate this strategies base on practical criteria.

Methods

To achieve the study objectives, the research was conducted in two phases. Determining of the strategies to control drug shortage, and comprehensive assessments of priority of possible strategies. For each phase, a self-design questionnaire was developed. The five main managerial strategies dimensions including: regulatory, financial, supply chain, information system and policy-making were set out. Forty-five alternatives were elicited from literature, and were evaluated and trimmed to 37 strategies based on experts’ opinion. The Multiple criteria decision-making (MCDM) methods were applied in second phase. Five important criteria including cost, time, labor, compliance with law and culture were weighed by Analytic Hierarchy Process (AHP) technique. Then, 37 alternatives have been rated base on the five criteria on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) technique.

Results

“Creating integrated Supply chain information system to manage medicines inventory in the country”, “Creating and using the databases to predict the shortage of medicines”, “Using track and trace system” are alternatives 20th, 24th and 25th, which related to supply chain (SC) and information system (IS) dimensions have higher priority in the experts’ point of view. The results show IS dimension has 100 percentage of priority; following that policy and supply chain have higher priority, respectively.

Conclusion

Health systems rely on consistent supplying of pharmaceuticals to support patient care. The results show that information system, policy-making and supply chain are in the top-ranking priorities. Warning system needs to be improved to the advance system via better collaboration with stakeholders, publish precise and explicit national guidelines for drug shortage management, enforce the guidelines, and improve Iran FDA’s pharmaceutical market control capability.

Recovery agenda for sustainable development post COVID-19 at the country level: developing a fuzzy action priority surface

As a response to the urgent call for recovery actions against the COVID-19 crisis, this research aims to identify action priority areas post COVID-19 toward achieving the targets of the sustainable development goals (SDGs) within the 2030 Agenda for Sustainable Development launched by the United Nations (UN). This paper applies a mixed-method approach to map the post-COVID-19 SDGs targets on a fuzzy action priority surface at the country level in Iran, as a developing country, by taking the following four main steps: (1) using a modified Delphi method to make a list of the SDGs targets influenced by COVID-19; (2) using the best-worst method, as a multi-criteria decision-making tool, to weight the COVID-19 effects on the SDGs targets achievement; also (3) to weight the impact of the SDGs targets on the sustainable development implementation; and finally (4) designing a fuzzy inference system to calculate the action priority scores of the SDGs targets. As a result, reduction of poor people proportion by half (SDG 1.2), development-oriented policies for supporting creativity and job creation (SDG 8.3), end the pandemics and other epidemics (SDG 3.3), reduction of deaths and economic loss caused by disasters (SDG 11.5), and financial support for small-scale enterprises (SDG 9.3) were identified as the highest priorities for action, respectively, in the recovery agenda for sustainable development post COVID-19. The provided fuzzy action priority surface supports the UN's SDGs achievement and implementing the 2030 Agenda for Sustainable Development in Iran. It also serves as a guideline to help the government, stakeholders, and policy-makers better analyze the long-term effects of the pandemic on the SDGs and their associated targets and mitigate its adverse economic, social, and environmental consequences.

Selection of optimum biological treatment for coking wastewater using analytic hierarchy process

The design of biological treatment process for the coking wastewater (CW) is complicated since wastewater treatment demand is gradually increasing lacking the systematic strategy in efficiency evaluation and advisable selection. Therefore, this study develops a holistic approach by means of the analytic hierarchy process (AHP) that uses numerical representation to rank the preferences of each participating alternatives for evaluation of the advanced biological technologies in CW treatment. Based on survey results, six types reactor combinations were selected as the alternatives, which were further classified as two group according to COD load. The AHP methodology consists of weighting and ranking procedures considering technical, economic, environmental and administration factors defined as criteria layers. Eighteen indicators were chosen as sub-criteria layers. Inclusively beneficial and sustainable biological processes were assessed and ranked along the AHP implementation. The results placed technical indicators to the top position among the criteria layers in the weighting descending order 'technical indicators > economic indicators > environmental indicators > administrative indicators', whereas the weight of indicators in sub-criteria layers fitted in the range of 0.005 to 0.151. The inclusive priority calculation integrating all weight indices of criteria and sub-criteria layers resulted in the anaerobic-anoxic-oxic (A/A/O) combination rising in the hierarchy of the low load group, whereas the oxic-hydrolytic-oxic (O/H/O) process was prioritized in the high load group. The accuracy and objectivity of AHP application was also supported by sensitivity and variability analyses that examines a range for the weights' values and corresponding to alternative scenarios.

A multi-criteria decision support framework for municipal solid waste landfill siting: a case study of New South Wales (Australia)

Sanitary waste disposal and site selection for establishing landfills are challenging problems for environmental planners. This paper aims to take environmental, socio-economic, geological, geomorphological, hydrological and ecological factors into consideration to provide a decision support framework for landfill siting. Analytical hierarchy process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) are coupled to develop an efficient multi-criteria decision-making method to be utilized in a Geographic Information System (GIS) environment for evaluating the suitability for landfill siting. As the first attempt to employ DEMATEL effectively in a landfill site selection problem, the proposed method is tested with landfill siting scenarios in New South Wales (NSW), Australia. Regional analysis is also performed to identify the potentially most suitable statistical divisions for landfill siting in NSW. The top two ranked zones covering 0.7% and 22% of the study area, respectively, are considered as the optimal areas for establishing landfills, while the bottom two ranked zones are not recommended for further consideration. Further detailed analysis is also conducted on the existing landfills, which shows that 1.0% and 37.0% of them are ranks 1 and 2, respectively. The scenario-based analysis implies that, among the contributing factors; geological and economic factors are highly important.

Optimal Selection of Sewage Treatment Technologies in Town Areas: A Coupled Multi-Criteria Decision-Making Model

In recent years, the development of sewage treatment technologies has made many treatment options available in towns. Selecting the most appropriate alternative (MAA) can make the best use of existing resources to achieve the optimal effect, which has become a topical issue in academic circles. The Liao River basin in China is an important area for agricultural cultivation and animal husbandry, but it also suffers from water shortages and pollution. In this study, the fuzzy set theory (FST), the Analytic Hierarchy Process (AHP), and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) were combined as a scientific and effective multi-criteria decision-making (MCDM) model to optimize the sewage treatment technologies in town areas of the Liao River basin. It was found that compared with natural treatment technologies (such as constructed wetlands, stabilization ponds, etc.) and combination technologies (i.e., a combination of various technologies), single small-scale sewage treatment plant technologies (such as activated sludge process or sequencing batch reactor with small daily capacity) were more suitable for those areas. The indicator of construction costs was critical in this model, and the fluctuation of its weight might change the MAA. This study aims to provide a decision support framework for the future optimization of sewage treatment technologies in towns by combining economic, environmental, and social issues, rather than just focusing on the technical aspects.

Introducing the coupled stepwise areal constraining and Mahalanobis distance: a promising MCDM-based probabilistic model for landfill site selection

This study sets out to propose a new ensemble of probabilistic spatial modeling and multi-criteria decision-making comprised of stepwise areal constraining and Mahalanobis distance algorithms in order to assess areal suitability for landfilling. The Ardak watershed was selected as the study area due to encountering several cases of open garbage dumps and uncontrolled landfills which are one of the main sources of river water pollution in the upstream of the Ardak dam. The results revealed that the proposed algorithm successfully assists in inventory-irrespective probabilistic modeling of landfill siting which is mainly indebted to the role of areal constraining in providing training and validation samples for the Mahalanobis distance model. The latter also showed a robust pattern recognition results from which a discernible differentiation of the area was attained while the spatial dependencies between the environmental factors were taken into account. Mahalanobis distance also gave an outstanding performance in terms of goodness of fit (area under the success rate 89.367) and prediction power (area under the success rate 89.252). Based on a five-point scale classification scheme, about 2.7% and 2.6% of the study area, respectively, have high and very high suitability for landfilling, while the remaining area is shared between very low-to-moderate suitability classes. According to the current trail of literature regarding landfill site selection which mostly relies on mere areal filtering, a probabilistic model would give invaluable inferences regarding the pattern of suitability/susceptibility of the area of interest and causative role of the influential factors. Graphical Abstract.

Prioritization of the treatment and disposal methods of wastes containing polychlorinated biphenyl by fuzzy multi-criteria decision-making and risk assessment

A total of 209 different types of polychlorinated biphenyls (PCBs) with various properties have been produced from 1930 to 1970s in which they have been banned due to their toxic effects. Total produced PCBs in the world are around 15 to 20 million tons, and up to now, 5.4 million tons of PCB-containing or PCB-contaminated equipment/materials has been eliminated. The remaining 10-15 million tons still needs to be removed or managed. Moreover, PCBs are pollutants still being unintentionally formed. These pollutants can be treated or disposed of various methods. However, there is a lack of knowledge about the selection of the treatment and disposal methods and their environmental, technological, cost, and social/ergonomic evaluation perspectives and the risk assessment during method selection. In this study, a projection was presented for the management of PCBs with an integrated multi-criteria decision-making (MCDM)-risk analysis focusing on these questions. Treatment (physical, chemical, biological) and disposal (incineration, landfill, supercritical water oxidation/gasification (SCWO/G), and pyrolysis/gasification) methods for the management of PCB-containing waste have been prioritized by fuzzy-analytical hierarchy process (F-AHP) in terms of environmental, technology, cost, and social/ergonomic criteria. Risk analysis was also made in terms of these criteria for considered alternatives, and compliance with risk and MCDM was evaluated. As a result of the study, priority methods among alternatives were determined as chemical treatment and SCWO/G. It has been determined that the weight values of the main criteria of environmental, technology, cost, and social/ergonomics were close to each other, but the emission criterion of the sub-criteria was determined to be of higher priority. In the risk analysis, chemical treatment and SCWO/G methods have been found to carry a more acceptable risk. Thus, it has been evaluated that these methods provide more superiority than other methods.

Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method

This study presents a fuzzy logical decision-making algorithm based on block theory to effectively determine discontinuous rock slope reliability under various wedge and planar slip scenarios. The algorithm was developed to provide rapid response operations without the need for extensive quantitative stability evaluations based on the rock slope sustainability ratio. The fuzzy key-block analysis method utilises a weighted rational decision (multi-criteria decision-making) function to prepare the ‘degree of reliability (degree of stability-instability contingency)’ for slopes as implemented through the Mathematica software package. The central and analyst core of the proposed algorithm is provided as based on discontinuity network geometrical uncertainties and hierarchical decision-making. This algorithm uses block theory principles to proceed to rock block classification, movable blocks and key-block identifications under ambiguous terms which investigates the sustainability ratio with accurate, quick and appropriate decisions especially for novice engineers in the context of discontinuous rock slope stability analysis. The method with very high precision and speed has particular matches with the existing procedures and has the potential to be utilised as a continuous decision-making system for discrete parameters and to minimise the need to apply common practises. In order to justify the algorithm, a number of discontinuous rock mass slopes were considered as examples. In addition, the SWedge, RocPlane softwares and expert assignments (25-member specialist team) were utilised for verification of the applied algorithm which led to a conclusion that the algorithm was successful in providing rational decision-making.

Geographic information system-based investment system for photovoltaic power plants location analysis in Turkey

The motivation of this research, development, demonstration, deployment, and diffusion (RD³&D) study is to present the progress of designing the GIS-based location selection module of autonomous investment decision support system and its experimental application for photovoltaic power plants (PVPPs) in Antalya, Burdur, and Isparta planning region Turkey. The other motivation of this RD³&D study is to start investigating in combinations the applicability and usability of weighted linear combination with 4 subjective weighting approaches (rank sum weight method (RS), inverse or reciprocal weights method (RR), rank order centroid (ROC), point allocation (PA)) for 5 main criteria, 14 sub-criteria, and 79 value ranges. The results show that 38.48% of the planning region is unsuitable, 61.52% is suitable. Only 2.07% of this region is very highly suitable according to RS. 7.13%, 9.22%, and 5.58% are respectively very highly suitable according to RR, ROC, and PA. Similarities between RS, RR, ROC, and PA methods are presented such as RS-RR: 0.7834, RS-ROC: 0.8510, and RS-PA: 0.6384 with covariance and correlation analysis. A backward-looking performance verification and validation analysis is also performed with 7 PVPPs for only 4 decisive success factors (capacity factor, annual energy/land use, project cost/capacity, project cost/energy). This study is thus able to evaluate the optimal locations for future investments, as well as the suitability conditions of the available investments. This study will contribute to provide some useful recommendations for decision makers to identify and assess the hotspots which are suitable for PVPPs in the planning region.

Evaluating renewable energy sources for implementing the hydrogen economy in Pakistan: a two-stage fuzzy MCDM approach

Hydrogen can play a crucial role in increasing energy security and reducing greenhouse gases in Pakistan. Hydrogen can only be a clean and sustainable fuel if it is generated from renewable energy sources (RES). Thus, it is important to evaluate viability of RES for hydrogen production. This study developed a two-stage fuzzy MCDM (Multi-criteria decision-making) approach to select the most efficient RES. In the first stage, fuzzy analytical hierarchy process (AHP) obtained the relative weights of four criteria for the selection of best RES. These criteria included commercial potential, environmental impacts, economic benefits, and social acceptance. In the second stage, data envelopment analysis (DEA) measured the relative efficiency of RES using weights of criteria as outputs, and the cost of RES-based electricity generation as input. The results indicated that wind and solar are the most efficient sources of hydrogen production in Pakistan. Municipal solid waste (MSW) and biomass can also be considered a feedstock for the hydrogen economy. Geothermal reported to be the less efficient source and thus is not recommended at present. Sensitivity analysis confirmed the robustness of results obtained using the developed framework.

GIS-based MCDM modeling for landfill site suitability analysis: A comprehensive review of the literature

One of the cheapest and proper methods for the ultimate disposal of Municipal Solid Waste (MSW) is landfilling. However, determining the location of landfill sites is a difficult and complex task due to depending on social, environmental, technical, economic, and legal factors. To solve the aforementioned challenges related to the landfill site suitability analysis, the combinations of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) have been studied by academia and applied by experts over the years. This notice is apparent by the large number of academic papers which have been announced in the near future. To provide a framework of the existing literature, and to guide colleagues, a state-of-the-art of recent papers is crucial. The goal of this study is to review all scientific papers in GIS-based MCDM modeling for landfill site suitability analysis in academic journals. A total of 106 studies published between 2005 and 2019 are recorded and surveyed. The studies are then investigated and classified by a generated taxonomy including following categories: GIS software, application area, uncertainty, MCDM techniques, cell sizes in GIS, and criteria. Based on the review conducted, it is observed that while Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) are the most widely used MCDM methods for weighting the criteria and ranking the alternatives, respectively. On the other hand, while environmental dimension is the most commonly preferred main criteria, surface water comes first in the sub-criteria pool. Criteria analysis shows that surface and ground water, geology, land use, distance to fault zone, distance to urban areas, and distance to road and slope are the most commonly used criteria groups among others. These classifications and observations are helpful for identifying research gaps in the current literature and provide insights for future modeling and research efforts in the field.

Identifying critical success factors to facilitate reusable plastic packaging towards sustainable supply chain management

The objective of this research is to identify critical success factors (CSFs) of the reusable plastic packaging (RPP) system and to establish their interrelationship in the context of manufacturing industries. Fourteen CSFs were shortlisted by reviewing the literature and considering expert team inputs. To explore the mutual influence between the identified CSFs the Interpretive Structural Modeling (ISM) approach was applied. To strengthen link interpretation, ISM methodology was supported by the Total Interpretive Structural Modeling (TISM) approach. Further, to identify the factors with high driving power the Matrice d'Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) analysis was employed. It was concluded that three CSFs namely, 'top management commitment' 'lean support,' and 'optimized inventory management' were the critical ones with high influential power. This study aims to help the decision-makers in developing effective strategies for the implementation of the RPP system in the case sector for achieving sustainability.

Intuitionistic fuzzy analytical hierarchical processes for selecting the paradigms of mangroves in municipal wastewater treatment

Municipal wastewater discharge is widespread and one of the sources of coastal eutrophication, and is especially uncontrolled in developing and undeveloped coastal regions. Mangrove forests are natural filters of pollutants in wastewater. There are three paradigms of mangroves for municipal wastewater treatment and the selection of the optimal one is a multi-criteria decision-making problem. Combining intuitionistic fuzzy theory, the Fuzzy Delphi Method and the fuzzy analytical hierarchical process (AHP), this study develops an intuitionistic fuzzy AHP (IFAHP) method. For the Fuzzy Delphi Method, the judgments of experts and representatives on criterion weights are made by linguistic variables and quantified by intuitionistic fuzzy theory, which is also used to weight the importance of experts and representatives. This process generates the entropy weights of criteria, which are combined with indices values and weights to rank the alternatives by the fuzzy AHP method. The IFAHP method was used to select the optimal paradigm of mangroves for treating municipal wastewater. The entropy weights were entrained by the valid evaluation of 64 experts and representatives via online survey. Natural mangroves were found to be the optimal paradigm for municipal wastewater treatment. By assigning different weights to the criteria, sensitivity analysis shows that natural mangroves remain to be the optimal paradigm under most scenarios. This study stresses the importance of mangroves for wastewater treatment. Decision-makers need to contemplate mangrove reforestation projects, especially where mangroves are highly deforested but wastewater discharge is uncontrolled. The IFAHP method is expected to be applied in other multi-criteria decision-making cases.

Administration of temozolomide: Comparison of conventional and metronomic chemotherapy regimens

PURPOSE

We compare the Maximum Tolerated Dose (MTD) and Metronomic Chemotherapy (MC) protocols for temozolomide administration. We develop an innovative methodology for characterizing optimal chemotherapy regimens.

METHODS

We use a PK/PD model based on Faivre et al. (2013) for the pharmacokinetics of temozolomide, as well as the pharmacodynamics of its efficacy. For toxicity, which is measured by the nadir of the normalized absolute neutrophil count, we formalize the myelosuppression effect of temozolomide with the physiological model of Panetta et al. (2003b). We introduce a multi-criteria tool for comparing protocols along their efficacy and toxicity dimensions.

RESULTS

We show that the toxicity of the MC regimen proposed by Faivre et al. (2013) can greatly be reduced without affecting its efficacy, while the standard MTD protocol efficacy cannot be improved without impairing its toxicity. We also show that for any acceptable toxicity level, the optimal protocol remains closely related to standard MTD.

CONCLUSIONS

Overall, our new method enables a rich comparison between protocols along multiple dimensions. We can rank protocols for temozolomide administration. It is a first step toward building optimal individual protocols.

Optimization of multi-pathway production chains and multi-criteria decision-making through sustainability evaluation: a biojet fuel production case study

Selection of optimal technologies for novel biobased products and processes is a major challenge in process design, especially when are considered many alternatives available to transform materials into valuable products. Furthermore, such technological alternatives vary in their technical performances and cause different levels of economic and environmental impacts throughout their life cycles. Additionally, selection of optimal production pathways requires a shift from the traditional materials management practices to more sustainable practices. This contribution provides a method for optimizing multi-product network systems from a sustainability perspective by applying the GREENSCOPE framework as a sustainable objective function. A case study is presented in which the four GREENSCOPE target areas (i.e., efficiency, energy, economics, and environment) are evaluated by 21 preselected indicators as part of a multi-objective optimization problem of a biojet fuel production network. The biojet fuel production network evaluated in this study consists of four main elements: (1) feedstocks management, (2) conversion technologies, (3) co-products upgrading, and (4) auxiliary sections for in situ production of raw materials and utilities. For the sustainability objective function, the 21 indicators are analyzed considering multiple perspectives of stakeholders to study their influence on the decision-making process. It is, different sets of weighting factors are assigned to each of the four target areas. Hence, this sustainability evaluation from different stakeholders' perspectives allows identifying optimal networks, specific target areas with great potential for improvements, and processing steps with great influence in the entire network performance. As a result, diverse optimal network arrangements were obtained according to the multiple stakeholders' perspectives. This evidences that a win-win situation for all sustainability aspects considered can hardly be reached. Finally, this contribution demonstrated the applicability of the proposed methodology for sustainability evaluation, optimization, and decision-making in the context of a multi-product material facility by developing a multi-objective optimization model.

Multi-criteria decision-making on assessment of proposed tidal barrage schemes in terms of environmental impacts

For tidal range power plants to be sustainable, the environmental impacts caused by the implement of various tidal barrage schemes must be assessed before construction. However, several problems exist in the current researches: firstly, evaluation criteria of the tidal barrage schemes environmental impact assessment (EIA) are not adequate; secondly, uncertainty of criteria information fails to be processed properly; thirdly, correlation among criteria is unreasonably measured. Hence the contributions of this paper are as follows: firstly, an evaluation criteria system is established from three dimensions of hydrodynamic, biological and morphological aspects. Secondly, cloud model is applied to describe the uncertainty of criteria information. Thirdly, Choquet integral with respect to λ-fuzzy measure is introduced to measure the correlation among criteria. On the above bases, a multi-criteria decision-making decision framework for tidal barrage scheme EIA is established to select the optimal scheme. Finally, a case study demonstrates the effectiveness of the proposed framework.

Transshipment hub selection from a shipper's and freight forwarder's perspective

Transshipment hub selection becomes increasingly important to the global logistics community. From the perspectives of shippers and freight forwarders, a selection must align with cost control strategy and sustain service reliability across cooperative service providers. This paper assesses the selection with the options of both sea and air transports, and from the influence of country of origin of the company. Critical factors of transshipment hub selection, both qualitative and quantitative, are identified through focus group discussions. Relative importance of these factors is determined based on collective views of logistics stakeholders. The competitiveness of transshipment hubs is then assessed using an AHP approach. Our analysis is based on the historical implementation of direct transportation link policy between Mainland China and Taiwan. With this empirical work, the finding suggests that even the spawn of other nearby ports with shorter transport distance and closer proximity to cargo sources, there remain overriding factors such as customs regulations & government policies and connectivity that a transshipment hub is preferred.

Datasets for supplier selection and order allocation with green criteria, all-unit quantity discounts and varying number of suppliers

This data article provides detailed optimization input and output datasets and optimization code for the published research work titled "Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability" (Hamdan and Cheaitou, 2017, In press) [1]. Researchers may use these datasets as a baseline for future comparison and extensive analysis of the green supplier selection and order allocation problem with all-unit quantity discount and varying number of suppliers. More particularly, the datasets presented in this article allow researchers to generate the exact optimization outputs obtained by the authors of Hamdan and Cheaitou (2017, In press) [1] using the provided optimization code and then to use them for comparison with the outputs of other techniques or methodologies such as heuristic approaches. Moreover, this article includes the randomly generated optimization input data and the related outputs that are used as input data for the statistical analysis presented in Hamdan and Cheaitou (2017 In press) [1] in which two different approaches for ranking potential suppliers are compared. This article also provides the time analysis data used in (Hamdan and Cheaitou (2017, In press) [1] to study the effect of the problem size on the computation time as well as an additional time analysis dataset. The input data for the time study are generated randomly, in which the problem size is changed, and then are used by the optimization problem to obtain the corresponding optimal outputs as well as the corresponding computation time.