Supply Uncertainty Research Articles

Performance of the Trade Service Regarding Supervision of Subsidized Fertilizer Distribution in Balikpapan City

This study aims to evaluate the Performance of the Balikpapan City Trade Office in supervising the distribution of subsidized fertilizers, which is an important element in supporting the agricultural sector and food security. Subsidized fertilizers play a role in providing access to farmers to obtain fertilizers at affordable prices, but challenges such as uncertainty of supply and uneven distribution are still problems. The research method used is descriptive qualitative with data collection through documentation and interviews. The results of the study indicate that the trade office has implemented integrated and comprehensive supervision standards, including planning, implementation, and monitoring of fertilizer distribution. Although there has been progress in the availability of fertilizers, the evaluation indicates the need for improvements in inter-agency cooperation and distribution systems to increase the effectiveness of supervision. With reports on the circulation of fake fertilizers, the Trade Office conducts direct supervision at kiosks and farmer groups and coordinates with related agencies to ensure effective distribution

Agility in supply chains and operational performance outcomes: investigating the contingent influence of supply uncertainty and product complexity

PurposeBorrowing from the dynamic capabilities theory and augmented by the relational view, the study investigates the criticality of supply chain agility in delivering operational performance while understanding the determinant role of key cross-firm resources. Additionally, based on the contingency theory, the interactive influence of two critical context factors, supply uncertainty and product complexity, is examined to enrich the understanding of the contingent nature of the operational performance implications.Design/methodology/approachThe study draws its conclusions from the survey data collected from a 152-respondent sample of executives from US manufacturing firms. The empirical data analyses using partial least square structural equation modeling (PLS-SEM) relate agility to operational performance enhancements while incorporating the moderating effects of contextual factors.FindingsThe study relates agility capability to operational performance enhancements, while resource specificity and resource complementarity emerge as significant determinants of the capability. Results on the contingent impact of contextual factors suggest differential influences of supply uncertainty and product complexity on the agility–performance relationship: while the former enhances, the latter detracts from the relationship.Originality/valueThe study’s contributions suggest theory extensions into supply chains as contexts, reinforcing the importance of market-responsive capabilities and the foundational nature of supply chains as repositories of vital cross-firm resources. The contingent nature of the agility–performance relationship accents the importance of market context factors.

Project portfolio selection and scheduling problem under material supply uncertainty

Project portfolio selection and scheduling problem under material supply uncertainty

Adaptive demand planning models: Utilizing reinforcement learning to address contingencies and dynamic market conditions in supply chains

There are remarkable opportunities in supply chain management associated with using the reinforcement learning (RL) approach in demand planning. As opposed to numerous other techniques, such as modeling and forecasting and applying them over a certain period, RL allows for the making and adapting these decisions in real-time, depending on the current demand and other conditions in the market. Supplementary to this, RL-based models enable supply chains to constantly adapt to shifts since they directly update knowledge from incoming data, making them less susceptible to economic shocks or other supply uncertainties. This flexibility is particularly important for contemporary supply chains in uncertain global environments where conventional, deterministic demand planning techniques cannot address changing needs. In this research, we discover how RL-based models could reduce demand volatility and react to contingencies. As such, it is best suited for industries that are required to respond flexibly and faster. This paper enlightens RL on the benefits of demand planning. It provides live examples that give insight into how it may be used to improve inventory, reduce cost, and improve decision-making. Regarding the specific research questions, this study helps investigate the methodologies, evaluation metrics, and case outcomes that shape RL's potential for changing demand planning and improving supply chain adaptability for future research on adaptive supply chain management.

Double-Layer Optimization and Benefit Analysis of Shared Energy Storage Investment Considering Life-Cycle Carbon Emission

As a crucial path to promote the sustainable development of power systems, shared energy storage (SES) is receiving more and more attention. The SES generates carbon emissions during its manufacturing, usage, and recycling process, the neglect of which will introduce a certain extent of errors to the investment of SES, especially in the context of the large-scale integration of renewable energy and dramatic increase in demand for SES capacity. To enhance the accuracy of SES investment, we propose a double-layer optimization model to compute the optimal configuration of a shared energy storage station (SESS) considering its life-cycle carbon emission. First, the service mode, settlement method, profit mechanism, and application scenarios of SESS are introduced. Second, the life-cycle assessment approach is used to calculate the life-cycle carbon emission of SESS, and the uncertainty of supply and demand is considered. Then, a double-layer optimization model that considers the economic operation of multi-microgrid systems and the optimal allocation of SESS is established. The lower-layer model’s Karush–Kuhn–Tucher (KKT) condition is derived to convert the double-layer model into a single-layer one. Finally, a combined heat and power (CHP) three-microgrid system is used to demonstrate the validity of our proposed model, and the economy of SESS investment is analyzed from multiple perspectives. The results show that considering the life-cycle carbon emission of SESS can provide more accurate guidance for investing in and measuring the carbon emission and reduction for SESS.

Constructing the Joint Probability Spatial Distribution of Different Levels of Drought Risk Based on Copula Functions: A Case Study in the Yellow River Basin

Joint multivariate distribution and calculation of return period are essential in enhancing drought risk assessment and promoting the sustainable development of water resources. Aiming to address the increasingly serious drought situation in the Yellow River Basin, this study first utilized the Soil and Water Assessment Tool (SWAT) distributed hydrological model combined with the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Soil Moisture Index (SSMI), and the Standard Water Yield Index (SWYI); the duration, peak, and severity of meteorological, agricultural, and hydrological droughts were analyzed. Based on the selected copula function, a three-dimensional joint distribution of drought duration (D), drought severity (S), and maximum severity (M) was constructed. The corresponding copula joint probability was calculated, leading to the three-dimensional joint return period and concurrent return period of meteorological drought, agricultural drought, and hydrological drought. The findings reveal several key trends: (1) Meteorological drought intensifies over time. Although drought areas eased after the 1990s, the overall drought trend continues to rise. Agricultural drought has intensified in arid regions but eased in semi-humid areas after the 2000s. Hydrological drought was severe in the upstream regions during the 1990s but eased in the 2000s, while it was particularly severe in the midstream and downstream regions during the 2000s. (2) Meteorological droughts are more severe in arid and semi-arid temperate regions and milder in semi-humid cold temperate regions. Agricultural droughts are extreme in arid and semi-arid cold temperate regions. Hydrological drought events are fewer but more severe in semi-arid temperate regions and have the lowest probability of occurrence in semi-humid cold temperate regions. (3) The overall probability of the occurrence of meteorological drought is between 55.7% and 69%; that of agricultural drought is between 73.1% and 91.7%, and that of hydrological drought is between 66.9% and 84%. Drought risk assessment provides scientific references for the analysis of the uncertainty of water supply in the basin and the formulation of effective risk management strategies.

Recent Research Advances and Challenges in Supply Chain Resilience Management

This paper explores recent research advances and challenges in supply chain resilience management, aiming to provide a comprehensive overview of current supply chain resilience theory and practice. Firstly, this paper defines the core concepts of supply chain resilience management and describes related theories, including supply chain risk management theory, dynamic capability theory and complexity theory. Next, the paper discusses in detail the latest research progress in supply chain resilience management, analysing it from strategic perspectives, technical support and digital development, enterprise case studies, and indicator systems. Supply chain vulnerability, complexity and uncertainty brought about by globalisation, challenges brought about by technological development and problems in business management are explored. In response to these challenges, the paper proposes future research directions, including dynamic capability enhancement, application of artificial intelligence and machine learning, integrated risk management, cross-industry comparative studies, and sustainable development. Ultimately, the paper summarises the key findings and proposes strategies to address them, such as risk assessment, information transparency, supplier optimisation, inventory management and technological innovation. Through these measures, companies can improve the resilience of their supply chains, ensure that they maintain stable operations in complex and uncertain environments, and achieve long-term competitive advantage.

Addressing Uncertainty in Renewable Energy Integration for Western Australia’s Mining Sector: A Robust Optimization Approach

The mining industry is a key contributor to Western Australia’s economy, with over 130 mining operations that produce critical minerals such as iron ore, gold, and lithium. Ensuring a reliable and continuous energy supply is vital for these operations. This paper addresses the challenges and opportunities of integrating renewable energy sources into isolated power systems, particularly under uncertainties associated with renewable energy generation and demand. A robust optimization approach is developed to model a multi-source hybrid energy system that considers risk-averse, risk-neutral, and risk-seeking strategies. These strategies address power demand and renewable energy supply uncertainties, ensuring system reliability under various risk scenarios. The optimization framework, formulated as a mixed integer linear programming problem and implemented in Python using the Gurobi Optimizer, integrates renewable energy sources such as wind turbines, photovoltaic arrays, and demand response programs alongside traditional diesel generators, boilers, combined heat and power units, and water desalination. The model ensures reliable access to electricity, heat, and water while minimizing operational costs and reducing reliance on fossil fuels. A comprehensive sensitivity analysis further examines the impact of uncertainty margins and the value of a lost load on the total system cost, providing insights into how different risk strategies affect system performance and cost-efficiency. The results are validated through three case studies demonstrating the effectiveness of the proposed approach in enhancing the resilience and sustainability of isolated power systems in the mining sector. Significant improvements in reliability, scalability, and economic performance are observed, with the sensitivity analysis highlighting the critical trade-offs between cost and reliability under varying uncertainty conditions.

Optimal adaptive heuristic algorithm based energy optimization with flexible loads using demand response in smart grid.

Demand response-based load scheduling in smart power grids is currently one of the most important topics in energy optimization. There are several benefits to utility companies and their customers from this strategy. The main goal of this work is to employ a load scheduling controller (LSC) to model and solve the scheduling issue for household appliances. The LSC offers a solution to the primary problems faced during implementing demand response. The goal is to minimize peak-to-average demand ratios (PADR) and electricity bills while preserving customer satisfaction. Time-varying pricing, intermittent renewable energy, domestic appliance energy demand, storage battery, and grid constraints are all incorporated into the model. The optimal adaptive wind-driven optimization (OAWDO) method is a stochastic optimization technique designed to manage supply, demand, and power price uncertainties. LSC creates the ideal schedule for home appliance running periods using the OAWDO algorithm. This guarantees that every appliance runs as economically as possible on its own. Most appliances run the risk of functioning during low-price hours if just the real time-varying price system is used, which could result in rebound peaks. We combine an inclined block tariff with a real-time-varying price to alleviate this problem. MATLAB is used to do a load scheduling simulation for home appliances based on the OAWDO algorithm. By contrasting it with other algorithms, including the genetic algorithm (GA), the whale optimization algorithm (WOA), the fire-fly optimization algorithm (FFOA), and the wind-driven optimization (WDO) algorithms, the effectiveness of the OAWDO technique is supported. Results indicate that OAWDO works better than current algorithms in terms of reducing power costs, PADR, and rebound peak formation without sacrificing user comfort.

A distributed simulation-optimization framework for many-objective water resources allocation in canal-well combined irrigation district under diverse supply and demand scenarios

To address the issues of both water resources allocation and sustainable management in agriculture areas with rising food demand, a simulation-optimization framework based on Flopy and Pymoo was proposed and developed for canal-well combined irrigation districts. The proposed framework first solved the many-objective water resources allocation problem which integrates groundwater simulation, crop production, and farmer income modules to quantitatively reveal the various trade-offs and synergies by using NSGA-III algorithm. The Entropy-TOPSIS method was then applied to recommend proper water allocation schemes. The proposed framework was further tested in Baojixia irrigation district considering various water supply and crop demand scenarios based on Copula-based uncertainty analysis. The Key findings are as follows: (1) the proposed framework could effectively optimize conjunctive water resources allocation problems of both surface water and groundwater; (2) low supply combined with high demand (p=0.17) is more likely to occur than high supply with high demand (p=0.02); (3) increased crop demand and restricted surface water negatively impact both water productivity and groundwater sustainability; and (4) the cumulative groundwater drawdown of recommend schemes is 36.9 % and 6.5 % higher under low to medium supply scenarios, while water productivity of recommend schemes decreases 28.2 % and 9.7 % with high and medium demand. This framework could provide useful insights for sustainable agricultural water management in canal-well combined irrigation district with various uncertainties in supply and demand scenarios.

Cross-border supply chain coordination of low-carbon agricultural products under the risk of supply uncertainty.

The purpose of this paper is to discuss how cross-border e-commerce enterprises can promote the sustainable development of the supply chain by optimizing the risk of supply disruption and product quality control mechanism of the cross-border supply chain of low-carbon agricultural products in the face of the problem of uneven quality and inventory shortage that prevails in the supply chain of low-carbon agricultural products under the framework of low-carbon economy. Methods: A two-level supply chain model consisting of a risk-averse cross-border e-commerce enterprise and two risk-neutral overseas suppliers is constructed to compare the optimal strategies and their coordination effects under the centralized and decentralized decision-making modes, and to deeply analyze the supply chain's operation mechanism. Further, the quality cost factor is introduced and an option contract model is designed to quantitatively analyze the impact of different decision-making scenarios and parameter changes on the overall supply chain performance. The results of the study show that under the coordination of option contract, the profit of cross-border e-commerce companies tends to decrease as the risk of supply disruption increases, while the profit of suppliers rises accordingly. Meanwhile, improving the quality of agricultural products can significantly improve the overall profitability of the supply chain. The cost-sharing mechanism is positively related to the profit of cross-border e-commerce companies, but negatively affects the profit of suppliers. In addition, the adjustment of the option price is directly associated with the increase of the specific supplier's profit and the decrease of the cross-border e-commerce enterprise's profit. Conclusion: By constructing and analyzing the option contract model considering the risk of supply disruption, this study effectively reveals the key influencing factors and their interactions in the cross-border supply chain of low-carbon agricultural products, and provides cross-border e-commerce enterprises with specific strategies to achieve coordination of the supply chain contract, improve product quality, and mitigate the risk of supply disruption, and then promote the sustainable development of the supply chain of low-carbon agricultural products.

Managing platelets supply chain under uncertainty: A two-stage collaborative robust programming approach

This study investigates the role of horizontal collaboration among blood centers in mitigating the negative impacts of fluctuations in blood platelet supply and demand during normal circumstances and after sudden-onset disasters. Specifically, four scenarios are studied: no collaboration, collaboration among blood centers, collaboration among hospitals, and collaboration among blood centers and hospitals. To formulate these scenarios, we propose a two-stage robust programming model that enables decision-makers to monitor the collaborative behavior of blood centers and hospitals before and after changes in supply and demand occur. In the first stage, a robust optimization approach is developed to cope with supply and demand uncertainties, where collaboration is limited to blood centers. In the second stage, the uncertain parameters (supply and demand) are treated as deterministic. The key decision is how to transfer platelets among hospitals located in a collaborative cluster. This research uses benchmark data from previous studies to test and validate the developed model and solution approach. Comparing the results obtained in different scenarios, we systematically select the most favorable scenario in terms of model performance under normal and emergency situations. The final results indicate that the network achieves the highest performance when blood centers and hospitals participate in the grand coalition.

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

The Influence of Big Data Analytics on Supply Chain Operations Efficiency

This paper aims to validate the feasibility of establishing a risk assessment system. Delphi and Entropy Weighting for Indicator Weighting was conducted by experts in the field of economics on the assessment data of Haier's supply chain indicators to analyze and derive a risk assessment system based on Haier's supply chain indicators. With a comprehensive analysis of risk assessment indicators using the Delphi and entropy weighting methods, the feasibility of the evaluation system for supply chain risk analysis in the context of big data gets demonstrated. The establishment of this assessment system will help Haier to analyze and respond to supply chain risks in the future, and will solve the related problems in avoiding supply chain market risk. These problems encompass difficulties related to managing substantial volumes of data and handling uncertainties in demand and supply. The template makes relevant conclusions and suggestions based on analysis, which could have effect on supply chain risk circumvention in a certain extent.

Assessing the economic viability and factors affecting farmer adoption of black soldier fly larvae as broiler feed in Bangladesh: a comparative analysis

Despite its impressive economic growth, Bangladesh faces a persistent malnutrition challenge, costing over US$1 billion annually. The broiler industry plays a vital role in addressing nutritional needs by providing essential protein through meat and eggs. However, small and medium enterprises (SMEs) in this sector struggle with high feed prices, impacting their profitability and growth. Black Soldier Fly Larvae (BSFL) offers a sustainable and cost-effective alternative to conventional poultry feed, addressing both rising costs and environmental impacts. This study examined different aspects of BSFL, such as broiler feed, and farmers' perceptions of them in some selected areas of the Sylhet region of Bangladesh. A total of 60 respondents from three subdistricts in Sylhet district were selected using the purposive sampling technique. Data were analyzed through descriptive statistics, discriminative power value, binary logistic regression model, and profitability equation. Among the respondents, 70% (42) had a favorable attitude toward BSFL, and 30% (18) had a highly favorable attitude towards BSFL. Among eight socioeconomic attributes examined for the likeliness of adopting BSFL as broiler feed, the respondents' average income (p < 0.01), years of schooling (p < 0.05), and years of broiler farming experience (p < 0.01) were found to be significant. Further, farms using 50% BSFL and 50% traditional feed had a net return of BDT (Bangladeshi Taka) 35,045.67 (USD 419.71), higher than those using traditional feed, which had a net return of BDT 29670.67 (USD 355.38). Further, farms using 50% BSFL and 50% traditional feed had a benefit–cost ratio (BCR) of 1.16, compared to other farms with a BCR of 1.11. The study also found some problems causing slow growth in the adoption rate of BSFL in the study region, such as lack of training programs, extension campaigns, the uncertainty of input supply, and financial assistance. The study suggests making adequate measures to raise awareness about the profitability of BSFL as broiler feed, conducting training at the root level, and making inputs easily available to farmers.

An integrated blockchain-enabled multi-channel vaccine supply chain network under hybrid uncertainties

The recent pandemic caused by COVID-19 is considered an unparalleled disaster in history. Developing a vaccine distribution network can provide valuable support to supply chain managers. Prioritizing the assigned available vaccines is crucial due to the limited supply at the final stage of the vaccine supply chain. In addition, parameter uncertainty is a common occurrence in a real supply chain, and it is essential to address this uncertainty in planning models. On the other hand, blockchain technology, being at the forefront of technological advancements, has the potential to enhance transparency within supply chains. Hence, in this study, we develop a new mathematical model for designing a COVID-19 vaccine supply chain network. In this regard, a multi-channel network model is designed to minimize total cost and maximize transparency with blockchain technology consideration. This addresses the uncertainty in supply, and a scenario-based multi-stage stochastic programming method is presented to handle the inherent uncertainty in multi-period planning horizons. In addition, fuzzy programming is used to face the uncertain price and quality of vaccines. Vaccine assignment is based on two main policies including age and population-based priority. The proposed model and method are validated and tested using a real-world case study of Iran. The optimum design of the COVID-19 vaccine supply chain is determined, and some comprehensive sensitivity analyses are conducted on the proposed model. Generally, results demonstrate that the multi-stage stochastic programming model meaningfully reduces the objective function value compared to the competitor model. Also, the results show that one of the efficient factors in increasing satisfied demand and decreasing shortage is the price of each type of vaccine and its agreement.

Coupling geospatial suitability simulation and life cycle carbon emissions towards uncertain optimization planning for wind-photovoltaic-hydrogen multi-energy complementary system

Building clean and modern multi-energy complementary systems is a direct path to decarbonization in energy sector. The introduction of hydrogen energy with high operational flexibility enables the multi-energy complementary system to fully accommodate renewable energy. The key issues of the optimization planning of the wind-photovoltaic-hydrogen multi-energy complementary system that need to be solved are how to finely characterize the carbon reduction potential of the system, consider the uncertainty of supply and demand in the modeling process, and incorporate the energy supply business of electric vehicles into the planning. This study constructs optimization planning model with the following three progressive stages: geospatial suitability simulation based on geographic information system, uncertainty analysis based on probabilistic scene generation technology, and capacity configuration optimization. Unlike previous studies, this model measures and optimizes the carbon emissions level of the system from a lifecycle perspective. The empirical study results indicate that carbon emissions optimization from the perspective of the entire life cycle would directly affect the system configuration results. From a lifecycle perspective, the carbon emissions accounting result is about 9 times higher than that obtained only during the operation phase. Compared to the traditional distribution system, the carbon emissions of the system constructed in this study decreased by 82.75 %. In addition, considering that the system provides charging services for electric vehicles, which can reduce carbon emissions by about 93.6 % but only increase costs by about 29.4 %. The computational results justify the proposed optimization framework. This study provides optimization models for the system, and the implementation framework can support technical reference for practice.

Interruption Cost Estimation for Value-Based Reliability Investment in Emerging Smart Grid Resources

Growing uncertainty in supply and demand in power systems causes significant challenges in maintaining supply reliability at affordable costs. Power grids are expected to undergo substantial transformations to address these challenges with upgrades and integration of emerging smart technologies that require significant investment costs. A value-based reliability assessment of these grid technologies is necessary to justify the worth of these investments. A key parameter required in such an assessment is the cost of power interruptions originating from transmission system failures. The interruption cost data available in published reports and past surveys relate to generation inadequacy since generation facilities comprise the most capital-intensive investment of an electric utility. Customer interruptions due to a lack of generation mainly occur due to generation failures during the peak demand period, whereas interruptions due to transmission component failures can occur at other periods with specific probabilities. This paper presents a methodology to estimate the cost of outages originating from transmission asset failures, which proposes a sector period model for each customer sector to obtain associated demand-normalized interruption costs. The proposed method can also be used to decide investment in grid resiliency enhancement against extreme weather that mainly impacts the grid network facilities.

The Impact of Business Uncertainty on the Performance of Small Enterprises During the Economic Crises in Sri Lanka

Small enterprises often regarded as the backbone of economies, are currently confronted with unprecedented challenges due to the uncertain conditions arising from the COVID-19 pandemic and subsequent economic crisis in Sri Lanka. Therefore, the study attempts to assess the impact of uncertainty in the business environment on the business performance of small enterprises in Sri Lanka during the economic crisis. The study is a deductive and explanatory approach in quantitative design. The primary data was collected for a structured questionnaire from 185 owners of small enterprises in Kalutare district, Sri Lanka. The data was analyzed by using Partial Least Square Structural Equation Modelling. The results revealed that demand uncertainty, supply uncertainty, price uncertainty, policy uncertainty, and behavioral uncertainty have negatively affected business performance while technological uncertainty does not have a significant impact on business performance. Thus, the study empirically exposed that the business performance of small enterprises is severely blocked by business uncertainty during the economic crisis. Therefore, small enterprises should plan business activities to make rational business decisions based on reliable information to achieve better business performance by avoiding uncertainty during the economic crisis.

Supply chain finance on corporate debt financing behavior under uncertainty

At present, the world is undergoing a major change that has not been seen in a century. How to deal with the increase in environmental uncertainty has become an important problem facing the high-quality development of Chinese enterprises. Chinese enterprises generally face financing constraints, and weak debt financing channels hinder the process of high-quality development of enterprises. How to improve debt financing capabilities has become a practical problem that Chinese enterprises urgently need to solve in response to the increase in environmental uncertainty. This paper first introduces the concept, characteristics and common patterns of supply chain finance , and also explains the definition of uncertainty environment and its impact on corporate operations and financing. By systematically combing through relevant domestic and foreign literature, this paper summarizes the results of existing research on supply chain finance, uncertainty environment and corporate debt financing behavior, laying a theoretical foundation for subsequent analysis. Secondly, this paper demonstrates the specific application of supply chain finance in an uncertain environment and its impact on corporate debt financing behavior through case analysis. By analyzing the success factors of these cases, such as risk management, support from financial institutions and technological innovation, the application value of supply chain finance in an uncertain environment is revealed. It is hoped that theoretical support and practical guidance will be provided for policymakers, financial institutions and enterprises to promote the development of supply chain finance and promote sound financing of enterprises.