Factors Associated with Alternate Level of Care Status Designation: a Case-Control Study and Model to Optimize Care Trajectories.

Background: As health-care demand is growing, our health-care system will require the optimization of the care trajectories. Patients with an alternate level of care (ALC) status could be a target for flow optimization. We aimed to characterize ALC patients and risk factors for ALC status, and to propose an integrated model to analyze the trajectory of ALC patients and discuss solutions to reduce their burden. Methods: A case-control design was used to compare 60 ALC and 60 non-ALC patients admitted to the geriatric unit of the Centre hospitalier de l'Université de Montréal in 2021, collecting medical and sociodemographic data. Based on our model, univariate statistical analyses were computed to compare groups and identify risk factors for ALC status. Results: ALC patients were less independent (22% performed five to six activities of daily living vs. 43%, p = .03). Both groups were comparable in terms of mobility and neurocognitive disorders. ALC patients were more likely to receive a new diagnosis of a neurocognitive disorder or new behavioural or psychological symptoms (37% vs. 15%, p = .008). Up to 25% of ALC patients were admitted despite presenting no active medical condition (vs. 3% of non-ALC patients, p = .002). Conclusions: The optimization of the care trajectory of ALC patients is mainly based on pre-hospital and post-hospital factors. A proportion of ALC admissions might be avoidable with additional investment in home care resources and relocation procedures. Fluidity of ALC trajectory may benefit from improved orientation at discharge procedures. Full optimization of ALC trajectories requires a systemic understanding of the health-care system.

Postoperative intensive care allocation and mortality in high-risk surgical patients: evidence from a low- and middle-income country cohort.

BACKGROUND: The escalation of surgeries for high-risk patients in Low- and Middle-Income Countries (LMICs) lacks evidence on the positive impact of Intensive Care Unit (ICU) admission and lacks universal criteria for allocation. This study explores the link between postoperative ICU allocation and mortality in high-risk patients within a LMIC. Additionally, it assesses the Ex-Care risk model's utility in guiding postoperative allocation decisions. METHODS: A secondary analysis was conducted in a cohort of high-risk surgical patients from a 800-bed university-affiliated teaching hospital in Southern Brazil (July 2017 to January 2020). Inclusion criteria encompassed 1431 inpatients with Ex-Care Model-assessed all-cause postoperative 30-day mortality risk exceeding 5%. The study compared 30-day mortality outcomes between those allocated to the ICU and the Postanesthetic Care Unit (PACU). Outcomes were also assessed based on Ex-Care risk model classes. RESULTS: Among 1431 high-risk patients, 250 (17.47%) were directed to the ICU, resulting in 28% in-hospital 30-day mortality, compared to 8.9% in the PACU. However, ICU allocation showed no independent effect on mortality (RR = 0.91; 95% CI 0.68‒1.20). Patients in the highest Ex-Care risk class (Class IV) exhibited a substantial association with mortality (RR = 2.11; 95% CI 1.54-2.90) and were more frequently admitted to the ICU (23.3% vs. 13.1%). CONCLUSION: Patients in the highest Ex-Care risk class and those with complications faced elevated mortality risk, irrespective of allocation. Addressing the unmet need for adaptable postoperative care for high-risk patients outside the ICU is crucial in LMICs. Further research is essential to refine criteria and elucidate the utility of risk assessment tools like the Ex-Care model in assisting allocation decisions.

A fuzzy interval dynamic optimization model for surface and groundwater resources allocation under water shortage conditions, the case of West Azerbaijan Province, Iran.

The allocation of water in areas which face shortage of water especially during hot dry seasons is of utmost importance. This is normally affected by various factors, the management of which takes a lot of time and energy with efforts falling infertile in many cases. In recent years, scholars have been trying to investigate the applicability of fuzzy interval optimization models in attempts to address the problem. However, a review of literature indicates that in applicating such models, the dynamic nature of the problem has mostly been overlooked. Therefore, the aim of the present study is to provide a fuzzy interval dynamic optimization model for the allocation of surface and groundwater resources under water shortage conditions in West Azerbaijan Province, Iran. In so doing, an optimization model for the allocation of water resources was designed and then was validated by removing surface and groundwater resources and analyzing its performance once these resources were removed. The model was then applied in the case study of ten regions in West Azerbaijan Province and the optimal allocation values and water supply percentages were determined for each region over 12 periods. The results showed that the increase in total demand has the greatest effect while the increase in groundwater industrial demand has the least effect on the supply reduction rate. The increase of uncertainty up to 50% in the fuzzy interval programming would lead to subsequent increases in groundwater extraction by up to 19% and decreases in water supply by up to 10%. The increase of uncertainty in the fuzzy interval dynamic model would cause an increase in groundwater extraction to slightly more than 10% and a decrease in water supply to 0.05%. Therefore, implementing the fuzzy interval dynamic programming model would result in better gains and would reduce uncertainty effects. This would imply that using a mathematical model can result in better gains and can provide better footings for more informed decisions by authorities for managing water resources.

Can the allocation of primary health care system resources affect efficiency? A spatial Dubin model study in China.

BACKGROUND: The primary health care (PHC) system plays an important role in China's health care system, but there are challenges such as irrational allocation of health resources and inefficient operation, which need to be improved. The purpose of this study was to explore the impact of resource allocation on the efficiency of the PHC system in China. METHODS: The data in 31 provinces were collected from the China Statistical Yearbook 2017-2021 and the China Health Statistical Yearbook 2017-2021. The comprehensive health resource density index (CHRDI) was constructed based on the entropy method and the health resource density index (HRDI), which was used to analyze the allocation of primary health resources in each province. The adjusted efficiency of the PHC system in each province was calculated by the bootstrap data envelopment analysis (DEA). Finally, the spatial Dubin model was used to explore the effect of the CHRDI on efficiency. RESULTS: From 2016 to 2020, the allocation of primary health resources in 31 provinces showed an increasing trend, and the average efficiency after correction showed a decreasing state year by year. The spatial direct effect and spatial spillover effect coefficients of CHRDI were 0.820 and 1.471, which positively affect the efficiency. Per capita Gross Domestic Product (GDP), urbanization rate, and the proportion of the elderly were the factors affecting the efficiency of the PHC system. CONCLUSIONS: The allocation of primary health resources in all provinces in China has improved each year, but there are still great differences, and efficiency must be further improved. Pay attention to the spatial spillover effect of the level of resource allocation and formulate differentiated measures for different regions. Attention should also be paid to the impact of population aging and economic development on the utilization of primary health resources by increasing health needs and choices.

Different policies constrained agricultural non-point pollutants emission trading management for water system under interval, fuzzy, and stochastic information.

Formulating suitable policies is essential for resources and environmental management. In this study, an agricultural pollutants emission trading management model driven by water resources and pollutants control is developed to search reasonable policies for agricultural water resources allocation under multiple uncertainties. Random-fuzzy and interval information in water resources system that have directly impact on the effectiveness of management schemes is reflected through interval two-stage stochastic fuzzy-probability programming. The model was root from regional agricultural water resources system in Jining City, China under considering the relationship among effective precipitation, crop water demand, and pollutants emission. Two types policies (water consumption-control and pollutants emission-control) are designed for searching the related interaction on water resources management and water quality improvement. The results indicated that water resources policies would be of water and environmental double benefits, and a large rainfall would reduce irrigation amount from water sources and lead to a larger pollutants emission trading. The results will help for defining scientific and effective water resources protection and management policies and analyzing the related interacted effects on water consumption, pollutants control and system benefit.

Platform economy development and energy efficiency inequality: evidence from China.

Alleviating energy efficiency inequality between regions is critical for achieving green sustainable development and environmental equality. This study constructs platform economy development index and adopts Theil index to measure energy efficiency inequality. By using panel data from 30 regions in China spanning from 2013 to 2020, this study investigates the influence of platform economy development on energy efficiency in inequality through a two-way fixed-effects model. The results show that (1) platform economy development can alleviate energy efficiency inequality between regions. After the robustness tests, the results still support the findings. (2) When the level of industrial agglomeration, marketization and environmental decentralization is high, platform economy development is more effective in alleviating energy efficiency inequality. (3) Platform economy development can optimize energy resources allocation, promote energy utilization technology flow and narrow the green finance development gap, thus alleviating energy efficiency inequality. Governmental departments should promote platform economy development and green finance, and optimize renewable energy allocation.

Research on Restricting Factors and Countermeasures of Balanced Development between Fairness and Efficiency of Basic Medical and Health Service in China / 中国医学伦理学

This paper analyzed the problems existing in China’s basic medical and health services from the perspective of fairness and efficiency, and the main restricting factors of balanced developmen. The main factors were: the far-reaching influence of "dual economic structure" between urban and rural areas, the government’s low-efficiency intervention in the allocation of health resources, the efficiency of hierarchical diagnosis and treatment system has not been fully released, the level of basic medical security needs to be improved, and the reform of the management and operation mechanism of medical institutions is lagging. It is proposed to adhere to the value concept of appropriate balance between fairness and efficiency, give full play to the functions of the government and the market, and take concrete measures to improve the financing mode, optimize the allocation of resources and improve the service system.

Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection.

In this paper, we present an efficient statistical method (denoted as 'Adaptive Resources Allocation CUSUM') to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples.

A research of the evaluation of preschool education resource allocation level and spatio-temporal differences: Based on repeated indicators method and Theil index.

Based on the implementation of the third stage of "Three-year Action Plan for Preschool Education", this research took preschool education resources of 31 provinces (cities), autonomous regions (excluding Hong Kong, Macao and Taiwan) and municipalities in China from 2003 to 2019 as research samples. At the same time, according to the data of Educational Statistics Yearbook of China (2003-2019) and China Educational Finance Statistics Yearbook (2004-2020), this research collected the data of preschool education human resources, material resources, and financial resources. As for the data analysis, this research was based on the methodology of entropy method, repeated indicators method, and the Theil index and so on, and evaluated the state of preschool education resources allocation in China from 2003 to 2019. Simultaneously, according to the state of preschool education resources allocation, this research analyzed the spatio-temporal differences of preschool education resources allocation between various provinces and various areas, then analyzed the causes of spatio-temporal differences. After that, suggestions were put forward according to the analysis. The results show that, since the implementation of the third stage of "Three-year Action Plan for Preschool Education", the condition of preschool education resources allocation has been generally improved. However the preschool education resources allocation still presents a low-level and uneven development trend. Preschool education resources have formed a "preschool education resource advantage belt" in Beijing, Tianjin and the Yangtze River Delta; Comparing with material resources, the development of human resources in preschool education lags behind in China; In terms of each regions in China, eastern China has the most significant difference in the preschool education resources allocation, which is followed by western China, and inner China has the least significant difference; The main factor affecting the regional preschool education resources allocation difference is the inter-provincial differences in eastern China, inner China, and western China; And the main factors affecting the differences in preschool education resources in eastern, inner, and western China are respectively financial resources, human resources, and financial resources. At the same time, financial resources in eastern China and human resources in inner China tend to expand. Based on this, this study proposes methods to narrow the regional differences in preschool education resources allocation, so as to provide references for realizing preschool education equity and preschool education sustainable development.

A Framework and System Design for Medicines Resources Allocation: A Multi-Stakeholder Assessment of Processes and Electronic Platform Needs.

Utilizing resources effectively is becoming more critical, especially with the ever-increasing healthcare cost. Little is known about the current practices used by healthcare organizations for the procurement, allocation, and utilization of medical resources. Moreover, the available literature needed to be enriched to bridge the link between resource utilization and allocation processes' performance and outcomes. This study investigated the processes that major healthcare facilities in Saudi Arabia apply to procure, allocate and utilize medicines resources. The work explored the role of electronic systems and provided a system design and conceptual framework to enhance the availability and utilization of resources. A three-part multi-method, multi-field (healthcare and operational), and multi-level exploratory and descriptive qualitative research design were used to collect the data that was analyzed and interrupted to feed the "future state" model. The findings demonstrated the current state procedure and discussed the challenges and the experts' opinions on developing the framework. The framework includes various elements and perspectives and is designed based on the results of the first part and was further validated by experts who were optimistic about the inclusiveness of this framework. Some major technical, operational, and human factors were perceived as obstacles by the subjects. Decision-makers can adopt the conceptual framework to gain insights into interrelated objects, entities, and processes. The findings of this study can imply future directions for research and practices.

Fuzzy Logic System Assisted Sensing Resource Allocation for Optical Fiber Sensing and Communication Integrated Network.

With the development of information transmission, there is an increasing demand for state monitoring of fiber-optic communication networks to improve the security and self-healing ability of the network. Distributed optical fiber sensing is one of the most attractive methods because it can achieve real-time detection of the whole network without additional sensing heads. However, when the sensing network is introduced into the communication network, the failure probability should be efficiently suppressed with limited sensing resources. In this paper, the fuzzy logic system is used to evaluate the impact of different sensing resource allocation on optical cable network quality. The link failure probability and path failure probability under the condition of uniform and non-uniform sensing resource allocation are simulated and analyzed, respectively. As shown in the analysis results, the failure probability under non-uniform allocation is significantly lower than under uniform allocation. In this paper, we discussed and addressed the allocation of the optical fiber sensing and communication integrated (OFSCI) network with the limited sensing resource for the first time. The results are helpful to develop an allocation strategy for optical fiber sensing and a communication integrated network with a higher robustness.

Facing the Impact of the COVID-19 Pandemic: How Can We Allocate Outpatient Doctor Resources More Effectively?

The COVID-19 pandemic caused significant damage to global healthcare systems. Previous studies regarding COVID-19's impact on outpatient numbers focused only on a specific department, lacking research data for multiple departments in general hospitals. We assessed differences in COVID-19's impact on outpatient numbers for different departments to help hospital managers allocate outpatient doctor resources more effectively during the pandemic. We compared the outpatient numbers of 24 departments in a general hospital in Beijing in 2019 and 2020. We also examined an indicator not mentioned in previous studies, monthly departmental patient reservation rates. The results show that, compared with 2019, 2020 outpatient numbers decreased overall by 33.36%. Ten departments' outpatient numbers decreased >33.36%; however, outpatient numbers increased in two departments. In 2020, the overall patient reservation rate in 24 departments was 82.22% of the 2019 reservation rate; the rates in 14 departments were <82.22%. Moreover, patient reservation rates varied across different months. Our research shows that COVID-19's impact on different departments also varied. Additionally, our research suggests that well-known departments will be less affected by COVID-19, as will departments related to tumor treatment, where there may also be an increase in patient numbers. Patient reservation rates are an indicator worthy of attention. We suggest that hospital managers classify departments according to changes in outpatient numbers and patient reservation rates and adopt accurate, dynamic, and humanized management strategies to allocate outpatient doctor resources.

Research on the Equity and Influencing Factors of Medical and Health Resources Allocation in the Context of COVID-19: A Case of Taiyuan, China.

COVID-19 has killed millions of people worldwide. As a result, medical and health resources continue to be strained, posing a great threat to people's safety and economic and social development. This paper built the index system of influencing factors of medical and health resources containing the economy, population and society, and then classified Taiyuan into three types of regions by cluster analysis. The Gini coefficient, Theil index and agglomeration degree were then used to analyze the spatial distribution of medical and health resources allocation, and its influencing factors were studied by grey relational analysis. It was found that the population allocation of medical and health resources in Taiyuan was better than area allocation. Population has the greatest influence on the allocation of medical and health resources, followed by society and the economy. The more developed the regional economy, the more diversified the main influencing factors, and the more adjustment and control choices of medical and health resources allocation. Suggestions for optimal allocation were put forward in order to fully utilize the limited medical and health resources, effectively respond to the epidemic needs, promote the sustainable development of resources, protect the health of residents, and improve social benefits.

Exploring the efficiency of primary health care provision in rural and sparsely populated areas: a case study from Mongolia.

Mongolia is facing serious challenges in the health sector and the macro-economic sphere that have important implications for health financing and the completion of universal health coverage. In this context, improving the efficiency of primary health care facilities is a critical issue. We study the efficiency of Soum Health Centres (SHCs) that provide primary care in rural Mongolia. Based on activity and resources data collected for all SHCs of Mongolia in 2017 and 2018 we estimate bias-adjusted efficiency scores. A double bootstrap truncated regression procedure is then used to study the factors associated with SHCs' efficiency. On average, SHCs could potentially engage in the same activity while reducing overall resource use by ∼23%. A comparatively higher population density and dependency ratio in the districts where they are located tend to favour SHCs' efficiency. Conversely, the higher the poverty rate in the soum, the lower the efficiency. We find a positive association between SHCs' efficiency and the proportion of doctors in the health workforce. The human resources allocation process and the capitation formula currently used to pay SHCs should be adjusted based on the size and socioeconomic/demographic characteristics of the population living in the catchment area of SHCs.

Ethical issues in oncology practice: a qualitative study of stakeholders' experiences and expectations.

BACKGROUND: Clinical Ethics Support Services (CESS) have been established to support healthcare professionals in addressing ethically sensitive issues in clinical practice and, in many countries, they are under development. In the context of growing CESS, exploring how healthcare professionals experience and address clinical ethics issues in their daily practice represents a fundamental step to understand their potential needs. This is even more relevant in the context of extremely sensitive diseases, such as cancer. On this basis, we carried out a qualitative study conducting in-depth semi-structured interviews with stakeholders of a major comprehensive cancer centre in Italy, with the twofold aim of investigating what ethical issues arise in the context of clinical oncology and how they are addressed, as well as stakeholders' expectations about a potential CESS to be implemented within the Institution. METHODS: The study was conducted within the theoretical framework of Grounded Theory. Participants were healthcare professionals and other key stakeholders working within the cancer centre. The semi-structured interview aimed at exploring common ethical aspects of oncology, investigating stakeholders' professional experience in dealing with clinical ethics issues, their expectations and requests regarding ethics support services. Transcripts of the interviews were coded and analysed according to the principles of Grounded Theory. RESULTS: Twenty-one stakeholders were interviewed. Our analysis showed a wide consensus on the identification of ethically relevant issues, above all those concerning communication, end-of-life, and resource allocation. The absence of institutional tools or strategies to address and manage ethical issues at the patient bedside emerged, and this is reflected in the widespread request for their development in the future. The ideal support service should be fast and flexible in order to adapt to different needs and clinical cases. CONCLUSIONS: The interviewees showed a limited degree of 'ethical awareness': despite having reported many issues in clinical practice, they could hardly identify and describe the ethical aspects, while  complaining about a lack of ethical resources in their management. To build a truly effective support service, it therefore seems appropriate to take such context into consideration and address the emerged needs. Ethical sensitivity seems to be key and it becomes even more relevant in critical clinical areas, such as the therapeutic pathways of terminally ill patients.

A distributed constraint multi-agent model for water and reclaimed wastewater allocation in urban areas: Application of a modified ADOPT algorithm.

Distributed Constraint Optimization (DCOP)-based approaches, as the distributed version of constraint optimization, provide a framework for coordinated decision making by a team of agents. In this paper, an agent-based DCOP model is developed to allocate water and reclaimed wastewater to demands considering the conflicting interests of involved stakeholders. One of the well-known DCOP algorithms, ADOPT1, is modified to incorporate an agent responsible for monitoring and conserving water resources. This new algorithm considers the social characteristics of agents and a new form of interaction between agents. For the first time in the literature, a real-world water and reclaimed wastewater allocation problem is formulated as a DCOP and solved using the Modified ADOPT (MADOPT) algorithm. To evaluate the MADOPT algorithm, it is applied to a water and reclaimed wastewater allocation problem in Tehran, Iran. The results illustrate the applicability and efficiency of the proposed methodology in dealing with large-scale multi-agent water resources systems. It is also shown that agents' selfishness and social relationships could affect their water use policies.

A Copula-based interval linear programming model for water resources allocation under uncertainty.

Water scarcity tends to be aggravated by increase in water demand with the trend of socio-economic development. Thus, non-stationary characteristics of water demand should be identified in water resources allocation (WRA) to alleviate the potential influences from water shortages. In this study, a Copula-based interval linear programming model was established for regional WRA. Through combining correlation analysis and an interval linear programming model, this model can: 1) identify interactions between water demand and socio-economic development levels based on Copula functions, 2) explore variations in water shortage with consideration of multiple risk tolerance levels of decision-makers based on Copula sampling, and 3) obtain desired strategies for WRA through an interval linear programming model. Also, Dalian City in China was selected as a case study area to verify the effectiveness of the model for WRA to five water users (i.e., agricultural sector, industrial sector, public service sector, domestic residents, and ecological environment). Considering multiple tolerance levels of decision-makers to water shortage risk, three scenarios (i.e., S1 to S3), indicating 20%, 40%, and 60% of their low, medium, and high tolerance levels, were proposed. The results showed that the correlation between the amount of water demand and indicators of socio-economic development can be described by Clayton and Gaussian Copula functions. The total water supply of Dalian in 2030 would increase by 2.06%-2.65%, compared with the one in 2025. The allocation of water resources across districts was influenced by varied water demand, energy consumption, and risk tolerance levels. Compared with the amount of water allocation in 2025, the contribution of transferred water sources would increase by 6.71% and 7.04% under S1 and S2 in 2030, respectively, and decrease by 14.31% under S3. With the increase of risk tolerance levels of decision-makers, the amount of water supply in Dalian City would gradually decrease.

Avoiding Crisis Conditions in the Healthcare Infrastructure: 2 Case Studies in Statewide Collaboration.

In fall 2020, COVID-19 infections accelerated across the United States. For many states, a surge in COVID-19 cases meant planning for the allocation of scarce resources. Crisis standards of care planning focuses on maintaining high-quality clinical care amid extreme operating conditions. One of the primary goals of crisis standards of care planning is to use all preventive measures available to avoid reaching crisis conditions and the complex triage decisionmaking involved therein. Strategies to stay out of crisis must respond to the actual experience of people on the frontlines, or the "ground truth," to ensure efforts to increase critical care bed numbers and augment staff, equipment, supplies, and medications to provide an effective response to a public health emergency. Successful management of a surge event where healthcare needs exceed capacity requires coordinated strategies for scarce resource allocation. In this article, we examine the ground truth challenges encountered in response efforts during the fall surge of 2020 for 2 states-Nebraska and California-and the strategies each state used to enable healthcare facilities to stay out of crisis standards of care. Through these 2 cases, we identify key tools deployed to reduce surge and barriers to coordinated statewide support of the healthcare infrastructure. Finally, we offer considerations for operationalizing key tools to alleviate surge and recommendations for stronger statewide coordination in future public health emergencies.

An optimization model for water resources allocation in Dongjiang River Basin of Guangdong-Hong Kong-Macao Greater Bay Area under multiple complexities.

In this research, an interval two-stage stochastic fuzzy-interval credibility constraint programming (ISFICP) method was developed for water resources allocation among multiple water users under complexities and uncertainties. The method could reflect the multiple complexities of water resources management, also trade-offs between the system benefits and violation risks. Dongjiang River (DJR) Basin, which supplies water to several core cities in south China such as Guangzhou, Shenzhen, and Hong Kong, was applied as the real demonstrative case. The water resources system of DJR Basin is particularly complex due to it is the primary source water for Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Through considering multiple complexities and uncertainties of the water resources system, such as natural, economic, and social conditions, ISFICP was developed to obtain potential water-allocation schemes. Probabilistic distribution, fuzzy-interval sets (FIS), and discrete intervals were introduced to represent the multiple uncertainties associated with the multiple complexities. The results indicated that the model could provide practical schemes for local decision-makers under multiple scenarios such as flow levels, credibility levels, and recycling rates.

Considering economic-environmental joint benefits of water-land resources allocation for supporting sustainable agricultural system development in northeastern China.

Water and land are crucial natural resources for agricultural development. It is necessary to allocate water and land resources effectively in order to achieve the maximum economic profits and the minimum environmental costs. In this research, an inexact two-stage fractional programming model was developed for the allocation of water and land resources, which is integrated interval-parameters (IPP), two-stage stochastic programming (TSP), fractional programming (FP). This model could optimally allocate water and land resources at the same time under the scenario of the maximum economic profit and the minimum environmental cost; it was proved to be beneficial in (1) dealing with the conflicts between economic development and environmental protection and give insights in trade-off among the agricultural system; (2) allocating water and land resources for five crops under multiple flow level simultaneously; and (3) describing the uncertain inputs as interval-parameters to reduce model uncertainties. The developed model was applied to the northeast region of China. The optimal allocation schemes of water and land resources, the maximum economic profits, and the minimum environmental costs were obtained. The results showed that economic profits in the agricultural system in the northeast region of China would not definitely be connected with the allocation of water and land resources, and solid waste pollution would bear the largest environmental cost. The developed model could help decision-makers to get a deeper understanding of the agricultural system and manage water and land resources in an efficient and environment-friendly way.