Optimal Decision Research Articles

Automated monitoring of natural parameters of renewable energy production systems

The paper discusses the urgent issues of the need to automate the monitoring of natural parameters of renewable energy production systems. Monitoring involves the collec- tion and analysis of various parameters of the natural environment that affect the efficiency and environmental friendliness of systems powered by renewable energy. Based on analytical materials and practical work related to techno-economic and ecological studies concerning the assessment of water reserves in indi- vidual wells, the study focused on the development of technologies and technical means for monitoring. It was found that, in practice, hydrogeological research on groundwaters use outdated methods and technical means for measuring and storing data. A local automated system for monitoring well parameters was developed and constructed. It ensures the operation of the hydrothermal heat pump system at the experimental site of the Institute of Renewable Energy of the National Academy of Sciences of Ukraine. This system was tested in our research. The benefits of this study are revealing the need to create an intelligent monitoring system with the possibility of automated decision-making regarding the management of renewable energy generation systems, depending on changes in the am- bient parameters. A network automated system for monitoring natural parameters was developed, consisting of several wireless sensor subsystems that communicate with each other via wireless connection and transmit information to the gateway. The gateway, in turn, is connected to the computer with a graphical interface for interaction with the system. The results of work indicate that the proposed structures for automated water monitoring are simple and easy to implement. Consequently, the study revealed that one of the prospects is the development of automated monitoring systems with intelligent features, capable of making optimal individual decisions regard- ing the management of renewable energy generation systems under variable environmental parameters. As a result, the data obtained from this study have significant scientific and practical implications for advancing the design methodology of geothermal heat pump wells, focusing on long-term forecasts and assessments of their ecological and economic efficiency.

Case Article—Optimizing Transportation and Equity in a District Zoning Problem for ORville Public Schools

This case asks students to solve a multiobjective optimization decision problem applied to school district zoning. In the case, residential areas in a school district must be assigned to middle schools having capacity limits. The operational considerations on the decision are the weighted distances traveled between students’ residences and their assigned middle school campuses. The ethical considerations pertain to equity in education and are measured using the numbers of economically disadvantaged students assigned to each middle school. This problem, including its two objectives, is motivated by a real zoning problem faced by a school district. Publicly available data from the school district and its rezoning efforts are used to populate the case. To complete the case, students model and solve an optimization problem both with and without the ethical considerations included. In doing so, they observe how ethical considerations can change the outputs generated by the optimization model. Evidence is provided from student surveys to indicate how completion of the case impacts perceptions of the importance of and understanding of how to include ethical considerations in optimization modeling. Results indicate statistically significant increases in those perceptions. Supplemental Material: The Teaching Note and DataAndMetricsDemo Excel workbook are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-Materials .

Product Design Incorporating Competition Relations: A Network-Based Design Framework Considering Local Dependencies

Abstract System design has been facing the challenges of incorporating complex dependencies between individual entities into design formulations. For example, while the decision-based design framework successfully integrated customer preference modeling into optimal design, the problem was formulated from a single entity’s perspective, and the competition between multiple enterprises was not considered in the formulation. Network science has offered several solutions for studying interdependencies in various system contexts. However, efforts have primarily focused on analysis (i.e., the forward problem). The inverse problem still remains: How can we achieve the desired system-level performance by promoting the formation of targeted relations among local entities? In this study, we answer this question by developing a network-based design framework. This framework uses network representations to characterize and capture dependencies and relations between individual entities in complex systems and integrate these representations into design formulations to find optimal decisions for the desired performance of a system. To demonstrate its utility, we applied this framework to the design for market systems with a case study on vacuum cleaners. The objective is to increase the sales of a vacuum cleaner or its market share by optimizing its design attributes, such as suction power and weight, with the consideration of market competition relations, such as inter-brand triadic competition involving three products from different brands. We solve this problem by integrating an exponential random graph model (ERGM) with a genetic algorithm. The results indicate that the new designs, which consider market competition, can effectively increase the purchase frequency of specific vacuum cleaner models and the proposed network-based design method outperforms traditional design optimization.

Efficient and robust sequential decision making algorithms

AbstractSequential decision‐making involves making informed decisions based on continuous interactions with a complex environment. This process is ubiquitous in various applications, including recommendation systems and clinical treatment design. My research has concentrated on addressing two pivotal challenges in sequential decision‐making: (1) How can we design algorithms that efficiently learn the optimal decision strategy with minimal interactions and limited sample data? (2) How can we ensure robustness in decision‐making algorithms when faced with distributional shifts due to environmental changes and the sim‐to‐real gap? This paper summarizes and expands upon the talk I presented at the AAAI 2024 New Faculty Highlights program, detailing how my research aims to tackle these challenges.

Optimal product pricing and lot sizing decisions for an omni-channel supply chain

In an omni-channel supply chain, companies offer customers multiple channels to interact with their brand. This allows for greater outreach and sales, resulting in increased customer loyalty and trust. Furthermore, there has been little discussion on the simultaneous consideration of strategic and tactical decisions under return policies in omni-channel supply chains. To maximize total profit, this research proposes a two-echelon supply chain, comprising a manufacturer and multiple retailers, including traditional, electronic and omni-channels. The non-deterministic demand function takes into account the sales price, refund price, delivery time and order quantities. A heuristic algorithm based on analysis of Karush–Kuhn–Tucker optimality conditions used to solve a nonlinear programming model. This research presents an analysis of the factors influencing the decision variables and profit of the entire supply chain and offers retailers a unique way to evaluate their performance and make operational decisions regarding price, delivery time and order quantities.

Outcome Disparities in Patients with Early-Stage Laryngeal Cancer Depending on Localization, Tobacco Consumption, and Treatment Modality.

Background/Objectives: Laryngeal squamous cell carcinoma (LSCC) is among most frequent malignancies of the head and neck. Recent oncologic research focusses on advanced rather than on early stages. Thus, we aimed to improve the knowledge concerning prognostic factors and survival in early glottic (GC) and supraglottic cancer (SGC). Methods: We retrospectively investigated patients diagnosed in 2007 to 2020 with stage I or II GC (ICD-10-C32.0) or SGC (ICD-10-C32.1, C32.8 or C32.9). For precise discrimination of GC and SGC, pathology reports about biopsy and definitive excision were closely examined and information on clinical characteristics and risk factors were collected before analyzing patterns of risk factors for overall survival (OS) in multivariate Cox regression analyses (mvCox). Results: The cohort included 220 patients with early GC (n = 183) and SGC (n = 37). The GC patients showed significantly improved 5-year OS compared to SGC patients (83.6% vs. 64.9%; p = 0.004), whereas survival according to UICC stage (I vs. II) was not different (p = 0.177). Surgical resection was superior to definitive radiotherapy (RT) for 5-year OS (p < 0.001). Cumulative tobacco consumption of greater than 10 pack years drastically impaired OS (p = 0.024), especially in patients receiving RT (p < 0.001). Supraglottic localization, smoking, and re-resection after initial R1 status consistently were independent prognostic factors in mvCox. Conclusions: Our cohort of early LSCC patients demonstrates significant negative impact of supraglottic localization, older age, tobacco consumption, poor tumor differentiation, and re-resection on OS. Further research is required as there is still lack of evidence on optimal decision-making and therapeutic strategies.

Research on the technology path selection of China's manufacturing industry under the high-quality development strategy

As the foundation and leading force of national economic development, manufacturing determines a country's comprehensive strength and international competitiveness, and is the key to achieving transformation, upgrading and high-quality development. Based on the perspective of global value chain embedding, this paper explores the issue of technology path selection and high-end development in my country's manufacturing industry. Against the background of Internet development, this paper analyzes the basis for technology path selection in my country's manufacturing industry to achieve intelligent transformation and high-quality development. By constructing an analytical framework for technology path selection from two aspects of internal and external factors, the theoretical basis for technology path selection in my country is proposed. Guided by high-quality development, this paper studies the internal mechanism of technology path selection that can promote the high-end, intelligent, service-oriented and green development of manufacturing industry, and discusses the technology path selection for manufacturing industry to achieve green and sustainable development. Based on an in-depth analysis of the impact and mechanism of global value chain embedding, Internet development, productive services and environmental constraints on technology path selection, this paper proposes the optimal decision equation for R&amp;D resource allocation in my country based on factor endowment conditions, thereby promoting the transformation of manufacturing industry from factor-driven to innovation-driven, accelerating the process of achieving high-end, intelligent, service-oriented and green development, which is of great significance to promoting the high-quality development of manufacturing industry.

Analysis of Thermal Comfort of the Building Envelope Using an Earth-Air Heat Exchanger

The constructive elements of a building influence the demand for electrical energy, and optimal decisions can be assessed with the support of software that measures this building's demand more accurately. The objective of this research is to assess the thermal behavior of a residential dwelling located in bioclimatic zone 2 in the southern region of Brazil, seeking energy sources that contribute to improving thermal comfort. The study utilizes parameters that reduce the thermal demand for cooling and heating by installing an earth-air heat exchanger (EAHE) in the prolonged occupancy enviroments (POEs) within the building. The results obtained in the EnergyPlus software show that the adaptive comfort of the model with the Earth-Air Heat Exchanger (EAHE), compared to without it, reduces cold discomfort by 5.4% and increases thermal comfort in the building by 5.2%. Analyzing the annual temperatures of the spaces with the EAHE, it is observed that the outlet temperature of the EAHE duct remains at an average of 23.6°C and 23.8°C for the living room and bedrooms, respectively. From an energy perspective, the electrical energy consumption of the building was reduced by 29.8% when using the EAHE.

Demand-Side Flexibility in Power Systems, Structure, Opportunities, and Objectives: A Review for Residential Sector

The integration of renewable energy sources (RESs) is rapidly increasing within energy systems worldwide. However, this shift introduces intermittency and uncertainty on the supply side. To hedge against RES intermittency, demand-side flexibility introduces a practical solution. Therefore, further studies are required to unleash demand-side flexibility in power systems. This flexibility is relevant across various sectors of power systems, including residential, industrial, commercial, and agricultural sectors. This paper reviews the key aspects of demand-side flexibility within the residential sector. To achieve this objective, a general introduction to demand flexibility across the four sectors is provided. As a contribution of this paper, and in comparison with previous studies, household appliances are classified based on their flexibility and controllability. The flexibility potential of key residential demands, including heat pumps, district heating, electric vehicles, and battery systems, is then reviewed. Another contribution of this paper is the exploration of demand-side flexibility scheduling under uncertainty, examining three approaches: stochastic programming, robust optimization, and information-gap decision theory. Additionally, the integration of demand flexibility into short-term electricity markets with high-RES penetration is discussed. Finally, the key objective functions and simulation software used in the study of demand-side flexibility are reviewed.

Innovation in Agriculture Advantages of a Remote Irrigation Monitoring and Control System

Globally, the irrigation of crops is the largest consumptive user of water. Water scarcity is increasing worldwide, resulting in tighter regulation of its use for agriculture. This necessitates the development of irrigation practices that are more efficient in the use of water but do not compromise crop quality and yield. Precision irrigation already achieves this goal, in part. The goal of precision irrigation is to accurately supply the crop water need in a timely manner and as spatially uniformly as possible. However, to maximize the benefits of precision irrigation, additional technologies need to be enabled and incorporated into agriculture. This Search discusses how Advantages of a remote irrigation monitoring and control system irrigation management will enable significant advances in increasing the efficiency of current irrigation approaches. From the literature review, it is found that precision irrigation can be applied in achieving the environmental goals related to sustainability. The demonstrated economic benefits of precision irrigation in field-scale crop production is however minimal. It is argued that a proper combination of soil, plant and weather sensors providing real-time data to an adaptive decision support system provides an innovative platform for improving sustainability in irrigated agriculture. The review also shows that adaptive decision support systems based on model predictive control are able to adequately account for the time-varying nature of the soil–plant–atmosphere system while considering operational limitations and agronomic objectives in arriving at optimal irrigation decisions. It is concluded that significant improvements in crop yield and water savings can be achieved by incorporating model predictive control into precision irrigation decision support tools. Further improvements in water savings can also be realized by including deficit irrigation as part of the overall irrigation management strategy. Nevertheless, future research is needed for identifying crop response to regulated water deficits, developing improved soil moisture and plant sensors, and developing self-learning crop simulation frameworks that can be applied to evaluate adaptive decision support strategies related to irrigation.

DEVELOPMENT OF METHODOLOGY AND STUDY OF THE INFLUENCE OF TECHNICAL, ORGANIZATIONAL AND TECHNOLOGICAL FACTORS ON THE DURATION OF THE PROCESS OF LIQUIDATION AND LOCALIZATION OF THE DEVELOPMENT OF DESTRUCTIONS

Analysis of the influence of technical, organizational and technological factors on the duration of the process of liquidation and localization of the development of destruction is an important stage of research into the process of liquidation of the consequences of accidental destruction. In order to compare options for liquidation and localization of the development of destruction of an emergency building, three options for reinforcement systems were analyzed: metal typical, wooden individual and combined. The work uses the method of analysis of hierarchies - a mathematical tool of a systematic approach to complex decision-making problems. The method does not impose the "correct" option, but allows you to interactively find an alternative (rational reinforcement option) that best matches the requirements for solving optimization tasks. To reduce the subjectivity of making a justified optimization decision in the conditions of complex multi-criteria optimization problems, a solution to the local problem of finding the optimal alternative according to the duration criterion, or a group of alternatives, taking into account the ratio of advantages based on the vector criterion, is proposed. To indicate the influence of advantages in quantitative expression, a pairwise comparison scale was used, which consists in a qualitative and quantitative assessment of relative advantage: equal in importance - 1; weak advantage – 2; average degree of preference ‒ 3; preference above the average degree ‒ 4; moderately stronger advantage ‒ 5; strong advantage ‒ 6; much stronger advantage ‒ 7; the most significant advantage is 8; absolute advantage ‒ 9. Initial data for choosing a temporary reinforcement option were obtained by comparing the estimated duration of dismantling and restoration works and optimizing the sequence of their execution for three options using wooden individual, metal typical and combined temporary reinforcement systems. It was established that it is impossible to ensure complete unification of reinforcement elements using only typical elements, therefore it is effective to use combined reinforcement systems using typical metal and individual wooden elements.

Spare parts provisioning strategy of warranty repair demands for capital-intensive products

The inventory cost of stocking spare parts is a nonnegligible expenditure of providing after-sales service for the manufacturers making capital-intensive products, such as electric vehicles. Especially, for warranty repair service, it is important to manage the spare stock appropriately to satisfy the warranty claims of customers as well as reduce the associated inventory costs. In this paper, we investigate the spare parts inventory issue related to a critical component for under-warranty units of a product. In particular, under the free-replacement warranty policy, failed component will be replaced by a new one by consuming the spare stock. According to the field claim data, we find that the general trend of warranty claims is nonstationary, which will be affected by the product sales and under-warranty failures. Thus, we first propose a model to forecast the time-varying warranty repair demand by explicitly considering the randomness from two major sources, that is, product sales and under-warranty failures. Under the assumptions of Poisson sales process and exponential failure distribution, the closed-form expressions of mean and variance of cumulative warranty repair demand over time are obtained. Because the number of warranty claims in each period is a one-time data, the associated distribution information is unavailable. Then, based on the properties of the demand statistics, we derived a worst-case upper bound for the associated inventory cost and formulate a three-phase finite-horizon spare parts inventory model, which can be used to appropriately address the time-varying warranty claims. Finally, numerical experiments are conducted to investigate the key parameters affecting the optimal decisions where a case study based on real data is presented.

Optimal decisions in a dual-channel remanufacturing supply chain with reference quality effect under WTP differentiation

Optimal decisions in a dual-channel remanufacturing supply chain with reference quality effect under WTP differentiation

Analysing the pedigree to identify undesirable losses of genetic diversity and to prioritize management decisions in captive breeding: a case study.

When prevention of species extinction is the priority, captive breeding is a key component in conservation programmes, allowing the recording of pedigree information in studbooks. The genealogical information registered in Cuvier's gazelle studbook between 1975 and 2023 was analysed to (a) assess if the implemented mating policy was successful in preserving the genetic background of the founders (1 male:3 females) in the present population, and b) improve future management and breeding decisions. Although the maternal contribution of one founder female was lost and the mean inbreeding of the total live population was high (0.305 ± 0.095), the breeding policy applied produced better results than expected from a population starting from four founders. It was successful in keeping the individual increase in inbreeding low (0.047 ± 0.021), and, notably, the inbreeding tended to decrease during the last three decades of the breeding programme, ensuring the viability of this highly inbred population. Historical dissemination of individuals among the zoos of Europe and North America caused population structuring and genetic differentiation of the live North American population. However, it did not risk the viability of the captive population. The average relatedness coefficients allowed the identification of individuals with underrepresented genotypes, which is relevant to plan future mating guidelines to keep the founders' representation balanced in the next generations. This study highlights the importance of keeping long-term pedigree information to monitor changes in the genetic diversity of captive populations, which is crucial to implement optimal mating decisions and assuring their long-term viability within an ex situ conservation programme.

The evolution of two‐stage production and order equilibrium in a random yield supply chain with demand information updating

AbstractThe mismatch between production, order, and demand seriously affects supply chain performance. However, most research focus on the mismatch between the retailer's order and customer's demand, which ignores the influence of the supplier's random yield on supply chain members' decision‐making. This paper investigates a two‐stage optimization problem within a two‐echelon supply chain, featuring a supplier with random yield and a retailer updating demand information in real‐time. Faced with a long production lead time, the retailer can either place advance orders at the production season's onset (first‐stage advance order) or opt for instant orders at the beginning of the sales season (second‐stage instant order). To ensure timely order fulfillment, the supplier initially employs a cost‐effective regular production mode with random yield during the production season. If yields are insufficient during sales, a pricier emergency production mode with guaranteed output becomes available. Utilizing a dynamic programming approach, we formulate the two‐stage optimization problem to derive optimal production and order decisions. Our analysis uncovers how realized random yield and stochastic market signals influence emergency production and instant order quantities in the second stage. We compare expected profits in scenarios with perfect and imperfect market signals, probing the members' preferences regarding order strategies. An intriguing finding emerges: as instant wholesale prices rising, the supplier's preferred order strategy diverges from the retailer. By strategic adjustments to the instant wholesale price, we demonstrate the potential for unanimous agreement on preferred order strategies among supply chain members — a quality enhancing the chain's flexibility and performance. Moreover, we extend the model to hybrid order strategies and identify conditions for unanimous preference among the three strategies. To bolster our theoretical findings, we provide numerical examples, lending practical support to our study.

Sustainable and efficient separation of ternary multi-azeotropic mixture butanone/ethanol/water based on the intensified reactive extractive distillation: process design, multi-objective optimization, and multi-criteria decision-making

It is of great significance and necessity to develop a sustainable and efficient separation scheme of butanone (MEK), ethanol (EtOH), and water, which are usually formed during the cinepazide maleate synthesis process. To the best of our knowledge, the systematic separation of this ternary multi-azeotropic mixture remains comparatively less studied, compounded by the significant challenges posed by its intricate composition comprising four azeotropes and three distillation boundaries. Therefore, a systematic approach by integrating the process design, multi-objective optimization, and multi-criteria decision-making (MCDM) methods was proposed to achieve the sustainable separation. Initially, three separation schemes, i.e., triple-column extractive distillation (TCED), double-column reactive extractive distillation (DCRED), and side stream intensified double-column reactive extractive distillation (SS-DCRED), were mainly explored via the thermodynamic and kinetic analysis. Subsequently, the multi-objective particle swarm optimization (MOPSO) algorithm was employed to optimize the process based on three objectives namely the total annual cost (TAC), CO2 emissions, and process route index (PRI). Finally, an efficient MCDM method combining CRITIC and TOPSIS was employed to rank the Pareto front solutions of the corresponding process design. Based on the comparison results, the optimal TCED process exhibits the lowest PRI with a decrease of 50.61% and 55.06% compared to that of the SS-DCRED and the DCRED, respectively. It also shows the best performance simultaneously considering economic, environmental, and safety criteria. Of note, the SS-DCRED separation process exhibited the lowest TAC, with a decrease of 60.67% and 11.45% compared to that of the TCED and the DCRED, respectively.

Research on Collaborative Strategy of Dual-Channel Supply Chain System for Product and Service Selection Driven by Experiential Demand

With the intensification of dual-channel competition, manufacturers are increasingly inclined to invest in offline showroom services to improve consumers' shopping experience, making it an emerging trend. Based on this background, this paper makes an in-depth study of how manufacturers choose the business strategy of physical showrooms in the dual-channel e-commerce environment, discusses the advantages and disadvantages of different service models, and analyzes the impact of these choices on consumer behavior and market competition. Through this research, this paper aims to provide valuable strategic suggestions for manufacturers to optimize their dual-channel service models. Four Stackelberg game models are constructed: 1) no service model (N), 2) manufacturers invest in service online products (MSM), 3) retailers invest in services online products (RSM), and 4) retailers invest in services online products (RSR). Then the optimal decision under the above four strategies is compared and analyzed, and the optimal choice of the manufacturer and the optimal service model of the whole supply chain are obtained.

Edge-Intelligence-Powered Joint Computation Offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

Prediction Model of In-Hospital Death for Acute Exacerbation of Chronic Obstructive Pulmonary Disease Patients Admitted to Intensive Care Unit: The PD-ICU Score

Introduction: Patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) admitted to intensive care unit (ICU) are exposed to poor clinical outcomes, and no specific prognostic models are available among this population. We aimed to develop and validate a risk score for prognosis prediction for these patients. Methods: This was a multicenter observation study. AECOPD patients admitted to ICU were included for model derivation from a prospective, multicenter cohort study. Logistic regression analysis was applied to identify independent predictors for in-hospital death and establish the prognostic risk score. The risk score was further validated and compared with DECAF, BAP-65, CURB-65, and APACHE II score in another multicenter cohort. Results: Five variables were identified as independent predictors for in-hospital death in APCOPD patients admitted to ICU, and a corresponding risk score (PD-ICU score) was established, which was composed of procalcitonin >0.5 μg/L, diastolic blood pressure <60 mm Hg, need for invasive mechanical ventilation, disturbance of consciousness, and blood urea nitrogen >7.2 mmol/L. Patients were classified into three risk categories according to the PD-ICU score. The in-hospital mortality of low-risk, intermediate-risk, and high-risk patients was 0.3%, 7.3%, and 27.9%, respectively. PD-ICU score displayed excellent discrimination ability with an area under the receiver-operating characteristic curve (AUC) of 0.815 in the derivation cohort and 0.754 in the validation cohort which outperformed other prognostic models. Conclusion: We derived and validated a simple and clinician-friendly prediction model (PD-ICU score) for in-hospital mortality among AECOPD patients admitted to ICU. With good performance and clinical practicability, this model may facilitate early risk stratification and optimal decision-making among these patients.

Optimal supply chain green innovation and subsidy strategy: producer subsidy versus consumer subsidy

PurposeConsidering two types of subsidies for producers (supplier and manufacturer) and one for consumers based on product greenness and sales quantity, this study aims to formulate optimal supply chain green innovation and subsidy strategies, and to achieve this goal with the support of information systems.Design/methodology/approachThis study introduces a composite green-product supply chain where suppliers focus on green innovation for component greenness and manufacturers focus on green innovation for manufacturing process greenness. Game theory modeling is applied to investigate the differences of product greenness, supply chain members’ profit and social welfare under different government subsidy strategies.FindingsIncreasing the unit greenness subsidy coefficient can boost product greenness and supply chain members’ profits, but does not always raise social welfare. When the government exclusively offers subsidies to producers, subsidies should be allocated to suppliers when there is a significant disparity in supply chain green innovation costs. Conversely, it is more beneficial to subsidize manufacturers. Consumer subsidies have the potential to enhance both environmental and economic performance in the supply chain compared with producer-exclusive subsidies, but may not always maximize social welfare when supply chain members have low unit costs associated with green innovation.Originality/valueThis study examines the optimal decisions for green supply chain innovation and government subsidy strategies. Supply chain members and the government can use the information system to collect and evaluate the cost of upstream and downstream green innovation, and then develop reasonable collaborative green innovation and subsidy strategies.