Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

A hierarchical model for strategic and operational planning in blood transportation with drones.

Blood transportation is a critical aspect of the healthcare systems, ensuring whole blood and blood products are delivered to patients in a timely and efficient manner. However, transportation of blood and other medical supplies can be challenging, especially in urban areas with limited infrastructure and heavy traffic. Drones have become increasingly important in recent years as a means of delivering medical supplies, including blood, due to their ability to provide fast, reliable, and cost-effective transportation. This study proposes two mathematical programming models in the hierarchical structure to improve decision-making for strategic and operational planning in the blood supply chain network. The limited information available in strategic planning presents risks to the blood supply chain, making it imperative to address uncertainties. To tackle this challenge, a novel approach called Scenario-based Robust Bi-objective Optimization has been proposed. The first model employs this approach to efficiently handle demand uncertainty by simultaneously maximizing the covered demand and minimizing costs. The model is subsequently solved using the augmented ε-constraint method. The second model is a routing-scheduling operational model that aims to minimize the sum of operations time, taking into account time windows for blood collection centers and hospitals. The developed hierarchical model is implemented in a three-level supply chain of Tehran province under three crisis scenarios in different parts. The findings and analysis of this implementation suggest that it is beneficial to set up drone stations in cost-effective and central locations to avoid costly network design. Furthermore, utilizing the minimum number of feasible drones enhances operational time and results in cost savings and increased efficiency. Overall, this study highlights the potential of using drones for blood transportation in urban settings, which can have significant implications for improving the quality of healthcare delivery.

Viable healthcare supply chain network design for a pandemic.

The recent COVID-19 pandemic revealed that healthcare networks must have a flexible and effective structure. In this study, we develop a viable healthcare network design for a pandemic using a multi-stage stochastic approach. We propose a multi-level network that includes health centers, computed tomography scan centers, hospitals, and clinics. Patients have conditions to returning to normal life or quarantining at home. Three objectives are defined: maximizing the probability of patient recovery, minimizing the costs of all centers in the network, and minimizing the Coronavirus death rate. We investigate a real case study in Iran to demonstrate the model's applicability. Finally, we compare the healthcare supply chain network design in a pandemic with a normal situation to advise how the network can continue to remain viable.

Green dynamic multimodal logistics network design problem considering financing decisions: a case study of cement logistics.

Logistics network is one of the most important parts of supply chains with significant share in achieving sustainability across them. In this paper, we investigate a new multi-objective mixed integer linear programming model for the design of multimodal logistics network. A bi-objective mathematical model is introduced and two conflicting objectives including the minimization of total cost and the total environmental impact are taken into account. Effective environmental life cycle assessment-based method is incorporated in the model to estimate the relevant environmental impacts. Due to budget constraints, financing decisions for facility construction are considered in the proposed model. To cope with the model objective functions, the augmented ε-constraint method is applied. Computational analysis is also provided by using a cement multimodal rail-road logistics network case study to present the significance of the proposed model. Results show that utilizing the proposed multi-period optimization model influences the location of multimodal terminals and their construction time. Also, the results show that the use of the proposed model enhances the efficiency of terminals. On the other hand, computational results indicate that preferences of decision-makers and the importance of environmental objective have significant impacts on the topology of transportation network.

A decision support system to reorganize medical service network in pandemic.

The advent of the Covid-19 pandemic has posed severe challenges to health care networks in various countries. The overcrowding of hospitals and the lack of medical staff and beds in multiple wards are among the main problems of governments. A new virus wave also exacerbates these problems. Also, the lack of information and the variability of the incidence rate and severity of the disease in different waves make it difficult to estimate the number of patients accurately. In this respect, this study develops a mixed-integer linear programming model to reorganize the medical service network. A fuzzy approach is employed to estimate the number of patients in each period. The result obtained from the model, apart from preventing the shortage of hospital beds, demonstrates a 60% reduction in visits to these centers.

A novel two-phase robust portfolio selection and optimization approach under uncertainty: A case study of Tehran stock exchange.

Portfolio construction is one of the most critical problems in financial markets. In this paper, a new two-phase robust portfolio selection and optimization approach is proposed to deal with the uncertainty of the data, increasing the robustness of investment process against uncertainty, decreasing computational complexity, and comprehensive assessments of stocks from different financial aspects and criteria are provided. In the first phase of this approach, all candidate stocks' efficiency is measured using a robust data envelopment analysis (RDEA) method. Then in the second phase, by applying robust mean-semi variance-liquidity (RMSVL) and robust mean-absolute deviation-liquidity (RMADL) models, the amount of investment in each qualified stock is determined. Finally, the proposed approach is implemented in a real case study of the Tehran stock exchange (TSE). Additionally, a sensitivity analysis of all robust models of this study is examined. Illustrative results show that the proposed approach is effective for portfolio selection and optimization in the presence of uncertain data.

A sustainable municipal solid waste system design considering public awareness and education: A case study.

The municipal solid waste management (MSWM) system design considered here is effective in reducing supply chain costs and environmental risks. One of the practical approaches that governments use to encourage MSWM practices is educating households with regard to reducing waste generation and increasing recycling and composting rates. This research aims to utilize a new multi-objective stochastic optimization model to design a MSWM system, taking public awareness and educating the public into consideration. Further, the efficiency induced by educating the public is also taken into account using its corresponding factors in the model. In this article, two types of truck for collecting the mixed and separated waste are appraised. Due to the unpredictability of some of the parameters of the MSWM models, allowances are made for uncertainty. The proposed model is applied to a real case in Tehran, Iran. To show the impact of educating households, the model is compared with a similar model that does not take educating the public into account. The results show a reduction of 40% in the total cost and an increment of 17% in the social impact as opposed to the model that does not take educating the public into consideration. This acknowledges that educating the public will improve the results obtained.

A fuzzy optimization approach to the capacitated node-routing problem for municipal solid waste collection with multiple tours: A case study.

In many countries, municipal solid waste management is considered a very important challenge, and the most relevant costs in this field are dedicated to the collection process. Therefore, this study aimed to propose a mathematical model with multiple depots and multiple intermediate facilities to minimize fixed and variable costs of waste collection. Intermediate facilities are used in the developed countries in their waste collection network, because these facilities reduce the long-term costs of waste management and increase the quality of the waste collection process. Also, in reality, the amount of waste generated per day is not deterministic, so, to cope with the issue of uncertainty in the amount of waste, a fuzzy optimization approach was considered. Furthermore, a system where vehicles that could collect the wastes in multiple tours, with a maximum number of tours for each vehicle, was also considered. Due to the high complexity of this model, a genetic algorithm was elaborated. Further, the efficiency of the proposed algorithm was confirmed by comparison with the exact solution in small dimensions. It should be noted that the initial solution of this algorithm was obtained by a proposed heuristic algorithm. Finally, a case study on the vehicle routing of municipal solid waste was conducted in a district of Tehran, Iran. Moreover, the solutions of the model were validated by comparing the results of the proposed model and the current real-life situation. The contractors could improve vehicle routes and reduce costs by implementing the results of the proposed model, without any additional cost.

A decision-making model for performance evaluation and profit sharing in a diagnostic laboratory network.

RATIONALE, AIMS, AND OBJECTIVES: Creating networked business models is one of the innovative approaches that have the ability and potential for meeting market needs. The purpose of this study is to provide a decision-making model for a fair profit sharing among the members of a diagnostic laboratory network while providing a distinctive value for the patients. METHODS: To identify the members of the network of laboratories, a suitable approach to calculate members' efficiency scores is proposed. Then, the network members are classified into three groups based on their performance scores. The three groups help administrators identify eligible members, members who need to improve their performance in order to meet the minimum requirements, and members who do not qualify for admission to the network. Since the performance of the members should play a significant role in the fair profit-sharing mechanism, the fair allocation of profits among network members is done by the use of Shapely value based on the efficiency scores of members. RESULTS: The results show that for such a fair mechanism, the efficiency and sample size (the number of samples [blood and urine] taken from the patients by the laboratories), as the two effective factors, have a decisive role in the share of profit of laboratory units of the network. In the Laboratory Services Network, members receive a number of samples according to their performance. As a result, the sample size received has a direct impact on the net income of each member. CONCLUSION: In conclusion, it is evident that the use of Shapely value may help managers in the process of sharing profits among network members in a fair way, thereby improving network performance. In this way, incentive strategies may be created for the members of the network, and long-term survival of the network may be achieved.

Performance assessment of medical diagnostic laboratories: A network DEA approach.

RATIONALE, AIMS, AND OBJECTIVES: The main purpose of this paper is to measure the efficiency and ranking of medical diagnostic laboratories by applying a network data envelopment analysis (NDEA). METHODS: In this study, each medical diagnostic laboratory is considered as a decision-making unit (DMU), and an NDEA model is utilized to calculate the efficiency of each medical diagnostic laboratory. Therefore, we design a series of four-stage system composed of three main laboratory processes (the pretest process, the test process, and the posttest process). We also consider sustainability criteria in order to cover social, economic, and environmental problems of health care organizations. RESULTS: The results show that three of the 22 considered laboratories are efficient. Therefore, the NDEA approach can lead to performance scores and ultimately real ranking. Also, the average efficiency scores show that the decrease of the reception unit's efficiency results in a decrease of the efficiency of each laboratory. Therefore, the laboratories can increase the number of patients. Along with the intermediate values of the reception unit and the sampling unit, the efficiency of the reception unit increases, which results in an increase for the overall efficiency of each laboratory. CONCLUSION: The proposed model can appropriately help the administrators and managers to identify inefficient units in their laboratory and ultimately improve the laboratory performance.

The Scarce Drugs Allocation Indicators in Iran: A Fuzzy Delphi Method Based Consensus.

Almost all countries are affected by a variety of drug-supply problems and spend a considerable amount of time and resources to address shortages. The current study aims to reach a consensus on the scarce drug allocation measures to improve the allocation process of scarce drugs in Iran by a population needs-based approach. To achieve the objective, two phases were conducted. Firstly, a set of population-based indicators of health needs were identified by reviewing the literature and were scrutinized by fifty academics/executives who were specialists in pharmaceutical resource allocation. In the second phase, a structured process, based on the Delphi technique requirements, was performed to finalize the indicators. The yield of literature review step was about 20 indicators, which was based on availability of data in Iran, 16 indicators were added to the next step and formed the initial questionnaire. Based on the results of the first questionnaire, only 3 indicators were rejected and 13 indicators were added to the Delphi phase. Then, in Delphi phase, the consensus was built after three Rounds. In addition to the burden of endemic, special, rare, and incurable diseases, traumatic diseases and total population of each province were the main measures. Furthermore, total mortality rates and the number of pharmacies in each province were on the border; hence, the monitoring team made the decision about inclusion or exclusion of such indicators. Other measures were in the range of 'important' ones. To reach a higher effective and efficient process of resource allocation, the paper suggests the use of a population needs-based approach in Iran's pharmaceutical sector. The scarce drug allocation indicators extracted in this study can make a considerable contribution to preventing, controlling, and mitigating drug shortages.

An efficient method for kidney allocation problem: a credibility-based fuzzy common weights data envelopment analysis approach.

Given the perennial imbalance and chronic scarcity between the demand for and supply of available organs, organ allocation is one of the most critical decisions in the management of organ transplantation networks. Organ allocation systems undergo rapid revisions for the sake of improved outcomes in terms of both equity and medical efficiency. This paper presents a Data Envelopment Analysis (DEA)-based model to evaluate the efficiency of possible patient-organ pairs for kidney allocation in order to enhance the fitness of organ allocation under inherent uncertainty in such problem. Eligible patient-kidney pairs are regarded as decision making units (DMUs) in a Credibility-based Fuzzy Common Weights DEA (CFCWDEA) approach and are ranked based on efficiency scores. Using a common set of weights for all DMUs ensures a high degree of fairness in the assessment and ranking of DMUs. The proposed model is also the first allocation method capable of coping with the vague and intervallic medical and nonmedical allocation factors by the aid of fuzzy programming. Verification and validation of the proposed approach are performed in two steps using a real case study from the Iranian kidney allocation system. First, the superiority of the proposed deterministic model in enhancing allocation outcomes is demonstrated and analyzed. Second, the applicability of the proposed fuzzy DEA method is demonstrated using a series of data realizations for different credibility levels.