Supply Uncertainty Research Articles

Global warming’s grip on agriculture: Strategies for sustainable production amidst climate change using regression based prediction

The intersection of climate change and food production is emerging as a critical area of research, focusing on both the potential benefits and the significant challenges posed by changing climate conditions. Elevated levels of carbon dioxide alongside rising global temperatures could theoretically boost crop yields, benefiting both human and animal consumption. This study examines the impact of various climate variables—temperature, humidity, precipitation, and soil moisture—on the primary production of essential foods such as rice, wheat, livestock, milk, eggs, vegetables, and fruits. Utilizing data from different countries spanning from 2000 to 2020, drawn from world development indicators, this research employs econometric analysis coupled with deep learning-based cluster analysis. Additionally, it projects future production trends up to 2100 using the moving average time series forecasting method. The findings reveal a direct correlation between climate variables and the production levels of vegetables and other food items, highlighting the immediate effects of climatic changes on agriculture. The study also points out the uneven distribution of these climate impacts, with developing countries facing more severe challenges due to their limited resources and adaptive capacities. This uneven impact contributes to increased uncertainty in food supply and affects market stability. Furthermore, concerns about food safety are intensifying under the influence of climate change, although some regions have implemented effective food conservation and control measures to mitigate these risks. This research underscores a complex landscape where the risks and benefits of climate change on food production are not uniformly distributed, but rather are influenced by a myriad of factors including geographic location, economic conditions, and the level of technological advancement in food safety practices. The nuanced understanding of these dynamics is crucial for developing targeted strategies to enhance food security in the face of a changing climate.

Designing a resilient-sustainable integrated broiler supply chain network using multiple sourcing and backup facility strategies dealing with uncertainties in a disruptive network: A real case of a chicken meat network

Increasing supply and demand uncertainty, coupled with unforeseen disruptions, pose challenges to the resilience of today's critical sectors in the global food industry, including the broiler supply chain. This study introduces a resilient model to enhance the sustainability and resilience of the broiler supply chain in the face of uncertainties and disruptions. The model integrates backup facilities and employs multiple sourcing strategies to reinforce resilience. Using mixed integer linear programming with bi-objective, multi-period, and multi-product features, the model aims to minimize carbon dioxide (CO2) emissions from transportation while maximizing overall supply chain profit. The goal programming, and the ε-constraint methods optimize decision-making and yield Pareto solutions, achieving a balanced approach to conflicting objectives. Also, robust optimization and stochastic programming provide practical solutions for handling uncertainties. Validation and sensitivity analysis confirm that the proposed model optimizes the broiler supply chain, enhancing resilience, sustainability, and profitability.

Neuroevolution reinforcement learning for multi-echelon inventory optimization with delivery options and uncertain discount

The advanced information technology has enabled supply chain to make centralized optimal decision, allowing to make a global optimal solution. However, dealing with uncertainty is important in inventory management. Besides demand and supply uncertainties, supplier discounts also often arise unexpectedly. Further, suppliers or third-parties typically offer various delivery options in which trade-off occurs between cost and lead time. Thus, this study introduces new problem namely Multi-Echelon Inventory Optimization with Delivery Options and Uncertain Discount (MEIO-DO-UD). As a solution, Neuroevolution Reinforcement Learning (NERL) framework is developed for minimizing total system cost. The environment is modeled via System Dynamics (SD) and actor is presented by integration of Artificial Neural Network and Evolutionary Algorithm (EA), creating an effective decision-making model under dynamic uncertainty. The experimental study has been conducted where two different supply chain networks are given namely serial and divergence. Three EA algorithms are compared namely Differential Evolution (DE), Memetic Algorithm (MA), and Evolution Strategy (ES). Furthermore, NERL is also compared with the EA-optimized classical continuous review model namely (s,Q). The result shows that regardless what EA type is used, the proposed NERL always outperforms EA-optimized (s,Q) model. The more complex the problem, the further improvement can be made i.e. cost reduction up to 58%, followed by the fill rate improvement. The result also shows that NERL can avoid overfitting. Managerial implications are highlighted where NERL provides the more stable inventory level among all supply chain partners and bull-whip effect can be damped.

A multi-period optimal distribution model of emergency resources for responding to COVID-19 under uncertain conditions

Ideally, optimal emergency resource allocation would have been vital for effective relief work during the COVID-19 outbreak. However, the suddenness of the epidemic and uncertainty of its spread added some difficulties to distributing emergency resources. First, this study introduces triangular fuzzy numbers to describe the uncertainty of supply and demand of emergency resources, and interval numbers to describe the time required for resource transportation under disaster conditions. To minimize the total delivery time and difference in the total satisfaction rate, this study constructs an optimal model for emergency resource distribution under uncertain conditions that considers both efficiency and equity. Subsequently, an improved genetic algorithm (IMGA) is proposed to obtain the optimal decision scheme. Finally, a case study on emergency resource distribution during the COVID-19 pandemic is conducted for model verification. The results demonstrate that the proposed model can improve the efficiency and effect of emergency resource distribution. The model allocates some emergency resources to each demand site during each emergency period, which can help avoid large losses caused by extreme shortages of resources at a certain demand point. The emergency resource allocation scheme considers the transportation time and degree of impact, which is beneficial for enhancing the flexibility of decision-making and practical applicability of distribution operations. A comparative analysis of the algorithms shows that the proposed IMGA is an effective method for managing emergency resource distribution optimization problems because it has higher solving efficiency, better convergence, and stronger stability. These findings can provide decision support for the optimal distribution of large-scale, multiperiod emergency resources during the COVID-19 pandemic.

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.

Conjunctive surface water and groundwater management in a multiple user environment

The conjunctive use of surface water and groundwater is practiced worldwide as a measure to address water supply uncertainty. This paper focuses on a scenario in which an aquifer is open to multiple users and examines the impact of such an open-access environment on the stabilization function of conjunctive management. We construct a non-cooperative stochastic dynamic game model where multiple users utilize groundwater intake from a common aquifer as a complement to fluctuating surface water. We also propose a simpler baseline applicable to dynamic environments to compute the benefits of the stabilization function when users are unable to adjust groundwater intake to surface water uncertainties. We then apply the model to the real-world case of the water supply environment of the Cao’e River in China. Simulation results show that open access leads to diminishing stabilization values as higher pumping costs due to declining stocks lead to a weakening of users’ incentive to utilize groundwater flexibly. Furthermore, the stabilization function itself is destabilized as the number of users increases. This is because greater groundwater intake and a decline in stocks amplify variations in pumping costs caused by different patterns of surface-water fluctuation.

111 Current and future usage of soybean meal in swine diets in China

Abstract The objective of this presentation was to introduce the utilization tendency and research progress of soybean meal (SBM) in swine diets in China. As for the utilization tendency of SBM in China, it not only brings great pressure to the food security of China, but also directly pushes the high production cost of animal products. In 2022, 117 million tons of protein feed was consumed by the animal husbandry in China, accounting for nearly 1/3 of the total protein feed consumption in the world. Thus, the application of low-protein diet has been promoted with the strong support of the Ministry of Agriculture and Rural Affairs in China since 2018. With the related techniques, including popularizing low protein diet, developing new protein feed resources, promoting precise nutrition, and feeding technology, even though the compound feed output has increased by more than 84 million tons in 2022 in China, the SBM consumption in feed has reduced 6%. Recently, the geopolitical challenges, regional unrest and climate change tendency have increased the great uncertainty of protein feed supply, which requires us to deeply understand the significance of SBM reduction and substitution in animal feed. As for the research progress, the available energy contents of SBM were determined on growing pigs. Barrows [n = 24; initial body weight (BW) = 36 ± 1.4 kg] were randomly allotted to 4 diets containing SBM with either corn basal or corn-soybean meal basal, and total feces and urine collection method and indirect calorimetry method were used to determine the available energy of SBM. The results showed that the average digestible energy (DE), metabolizable energy (ME), net energy (NE), and retained energy (RE) values in SBM [17.36, 16.52, 10.62, and 5.06 MJ/kg dry matter (DM), respectively] determined using the corn-SBM-basal diet were not different from the those determined using the corn-basal diet. Moreover, the best fitted prediction equations developed based on 22 SBM samples produced from soybeans from China (n = 6), USA (n = 6), Brazil (n = 7) or Argentina (n = 3) were: DE = 38.44 - 0.43 CF - 0.98 GE + 0.11 ADF (R2 = 0.67, P < 0.01) and ME = 2.74 + 0.97 DE - 0.06 CP (R2 = 0.79, P < 0.01). The in vitro digestion kinetics of SBM from different sources showed that the release of amino acids in the 0-10 min time interval was significantly negatively correlated with the NE of SBM (R2 = -0.34).

Robust optimization of transactive flexibility oriented scheduling of joint electricity and gas supplies

The power system with flexibility provision will lead to the expansion of the utilization of renewable energy resources. Providing flexibility is one of the basic challenges for power system operators and developers, which is addressed in this study. Moreover, the security of the gas supply system is important for power system operators, because the operation of gas-fired units depends significantly on the gas supply. In this study, the availability of pipelines has been considered as a gas supply uncertainty. Then, an optimal uncertainty set is proposed for security constraint unit commitment (SCUC) which is calculated for managing the uncertainty of wind and gas supply in different scenarios of risk. Robust-based optimization has been implemented to obtain the optimal uncertainty set in which both feasibility and economic optimal robustness have been guaranteed. A new robust flexibility-based SCUC model has been applied using a demand response program (RFSCUCDRP). The transactive flexibility between flexible providers, such as thermal units and DR, and uncertainty producers, such as wind resources, has been determined in the proposed model. The problem has been implemented on the 6-bus and IEEE-118-bus standard test systems with different risk percentages. The operation costs of the 6-bus system with and without robust optimization are 89,094 $ and 119,475 $, respectively. It is concluded that robust optimization is ultimately economically better.

Evaluating Supply Chain Performance through Financial Metrics: A Study in a Manufacturing Industry

This empirical study is based on primary data emphasizing the importance of evaluating supply chain performance through financial metrics, each activity within the supply chain directly impacts both the top line and bottom line of a business. In today's challenging business environment, organizations must prioritize monitoring their financial performance to ensure enduring resilience. The economy of Bangladesh has encountered turbulence due to disruptions such as the aftermath of the COVID-19 pandemic and the ongoing global political unrest. These occurrences have significantly impacted the business landscape, resulting in uncertainties in both demand and supply, shifts in consumer purchasing patterns, and notable increases in the prices of essential commodities and raw materials, therefore, the overall cost of business has risen. This study investigates the consequences of various disruptions, both internal and external, on the economy, adversely affecting organizations. The ability of an organization to withstand such challenges depends on maintaining robust financial and operational performance. Given the persistent global upheaval, there is a compelling need for an innovative approach to enhance supply chain optimizations, overall operational efficiency, and meticulous budget control and variance analysis aligned with financial planning. This is crucial at a substantial level to ensure positive entries in the profit and loss account and maintain a healthy cash flow statement. This study is done based on twenty-four (24) manufacturing sectors of Bangladesh. To address these challenges, the study advocates an Integrated Supply Chain Performance Measurement (ISCPM) model. This model contributes to academia through a novel approach, demonstrating its effectiveness by employing ten attributes to evaluate supply chain performance, with a specific focus on the financial performance termed Financial Health (FH), an attribute to measure supply chain performance in an organization. The study further elaborates Financial Health attributes into five key Performance Measurement Index (PMI) which are Economic Performance, Cost, Budget Variance, Inventory, and Planning Variance —within the context of manufacturing operations, providing insights into various facets of their impact. The study significantly contributes to the industry for practitioners by bringing synergy into the framework and performance measurement attributes, ultimately adding value to the manufacturing industry.

Impact of uncertainty ascribed to defective products on supply chains

ABSTRACT Traditional inventory management has considered when and how much to order against the uncertainty of demand. Although many studies on inventory management have presumed that quality of products received from manufacturers is perfect, defective products would be, in actual, contained in the procurement to retailers. The inclusion of defective products causes the uncertainty in supply. The retailer does not necessarily require being responsible for all the loss by overstock and shortage of products due to the uncertainty ascribed to the inclusion of defective products. The purpose of this paper is to investigate the impact of the uncertainty ascribed to the inclusion of defective products on a supply chain. This paper first has formulated a simple supply chain model with defective products composed of the manufacturer and retailer. The model has made it clear how the uncertainty ascribed to the inclusion of defective products has an impact on the order quantity and respective profits. Then, this paper has discussed a method of assessing a latent loss as a monetary value. Combining contract techniques and game theory, this paper has proposed a contracting method that the manufacturer compensates the retailer for the latent loss to maintain a supply chain partnership.

IMPROVING INVENTORY MANAGEMENT PRACTICES IN TRANSFORMING THE EFFICIENCY OF HOUSEHOLD EQUIPMENT STORES AT ROYAL MARKET IN SERANG

The purpose of this study is to investigate the inventory management practices utilized in household equipment stores located in Pasar Royal, Serang City, as well as the difficulties that store owners encounter when managing their inventory. This study used qualitative research methods, including conducting in-depth interviews, observing, and analyzing documents from within the store. The findings revealed that the retailers utilized various inventory management practices, ranging from straightforward to more organized approaches. Store owners must contend with several obstacles, including inaccurate demand predictions, supply uncertainty, and a need for more efficient inventory management systems. On the other hand, certain stores have implemented improvement strategies, such as Just-in-Time (JIT) methods and the utilization of inventory management software, which have accomplished the goal of increasing both the overall store performance and the operational efficiency of the store. This study emphasizes the importance of better inventory management practices in achieving efficiency transformation in home improvement stores

A reinforcement learning approach for selecting infill drilling locations considering long-term production planning in mining complexes with supply uncertainty

Simultaneous stochastic optimisation frameworks provide a method for optimising long-term production schedules in mining complexes that aim to maximise net present value and manage risk related to supply uncertainty. The uncertainty and local variability related to the quality and quantity of material in the mineral deposits are modelled with a set of stochastic orebody simulations, an input into the simultaneous stochastic optimisation framework. Infill drilling provides opportunities to collect additional information associated with the mineral deposits, which can inform future production scheduling decisions. A framework is developed for optimising infill drilling locations with a criterion that seeks areas that directly affect long-term planning decisions and requires the use of geostatistical simulations. Actor-critic reinforcement learning is applied to identify infill drilling locations in a copper mining complex using this criterion. The case study demonstrates that adapting production scheduling decisions given additional information has the potential to improve the associated production and financial forecasts and identifies a stable area for infill drilling.

Multistage adaptive distributionally robust optimization for the medical supplies distribution problem with uncertain demand in humanitarian aid

Production, inventory, distribution, and dispensing of relief resources are critical operational functions in humanitarian aid. To design an efficient humanitarian relief network, it is beneficial to study these operational functions in an integrated way. Accounting for the demand uncertainty of medical supplies, we propose a multistage adaptive distributionally robust model for the medical supplies distribution network design that considers simultaneously the issues of production, inventory, distribution and dispensing of medical resources, as well as the life-loss due to the delays in treatment. The objective is to dynamically match the supply and demand of medical supplies so as to minimize the total cost consisting of the production cost, holding cost, dispensing cost, and life-loss cost related to the unmet demand. We also introduce a safety-stock and production capacity model to efficiently predetermine the initial supply of medical supplies and maximum available production abilities under the given demand information. To obtain tractable formulations, we approximate the developed models using an enhanced linear decision rule (ELDR) and a simplified ELDR (SELDR), respectively. Using a set of real-world COVID-19 data, we show that (i) both the ELDR and SELDR can yield feasible solutions extremely close to the optimal solution of the multistage adaptive distributionally robust model, whereas the SELDR is about one order of magnitude faster than the ELDR; and (ii) accounting for the safety-stock and production capacity model yields significant improvements of the obtained solution, which can also inform the decision-maker about at least how many initial supply of vaccines and the maximum available production abilities should be set to counter the risk of demand uncertainty. We also analyze the impact of some model parameters to gain managerial implications.

Unleashing the power of manufacturing flexibility: enhancing performance in Bangladesh's ready-made garment industry

PurposeThis research aims to investigate how manufacturing flexibility can address the challenges of an ever-changing and unpredictable business environment in Bangladesh’s labor-intensive ready-made garment (RMG) industry, which is underserved and situated in a developing country.Design/methodology/approachUsing Partial Least Square Structural Equation Modeling, this study empirically evaluated the relationships between manufacturing flexibility, environmental uncertainty and firm performance. The analysis utilized 320 survey responses from potential RMG experts, representing 95 organizations.FindingsThe study achieved a decision-making model for implementing manufacturing flexibility in the RMG industry of Bangladesh with acceptable model fit criterion. The research pinpointed that workforce flexibility plays the maximum mediating among different types of manufacturing in coping with demand and supply uncertainty in the RMG sector.Research limitations/implicationsThe study made valuable contributions to theoretical and practical knowledge in the context of manufacturing flexibility in Bangladesh’s RMG and other underserved labor-intensive sectors in developing economies. It suggests that managers should shift from defensive and risky business strategies to more aggressive and proactive approaches by utilizing workforce flexibility resources adaptively to enhance manufacturing capabilities and align with dynamic market demand. Additionally, the study offers recommendations for future research to build upon its findings.Originality/valueThis study is unique in its approach because it presents a decision model for implementing manufacturing flexibility in a labor-intensive industry in a developing economy, specifically the RMG industry in Bangladesh, whereas previous research has primarily focused on high-tech industries in developed economies.

Becoming a freelancer or contractor? Drivers’ contractual mode and schedule decisions in a dual sourcing market

In the ride-hailing market, the platforms could recruit contractors subject to prescriptive schedules to reduce the labor supply uncertainty and balance the demand and supply, and the drivers are entitled to become a freelancer or a contractor and choose the schedule they would like to provide the service. This paper proposes an analytical framework to capture the drivers’ decisions of becoming a freelancer and contractor as well as how to arrange their work schedules in a dual sourcing market. The utility structures for two kinds of drivers, freelancers and contractors, are defined and a time-expanded network is adopted to portray the drivers’ work schedule choices. With this setting, the drivers’ simultaneous contractual mode and work schedule selection model is formulated and the algorithm is designed. The numerical examples are conducted to investigate the characteristics of the equilibrium solutions. The results show that a dual-sourcing market is always advantageous to contractors and conditionally beneficial to the platform, freelancers and customers. When there is low potential demand, higher fixed salary is preferred for the platform to attract contractor and complement the labor supply. When the market is thriving, the effect of the profits’ increment by dual-sourcing is not obvious. This work could provide new insights on how to manage a dual-sourcing market for the platform.

Impact of supply risk on cost pass-through

ABSTRACT We study the cost pass-through rate in a supply chain setting where a buying firm sources from either a single unreliable supplier or dual unreliable suppliers. Numerous papers studying cost pass-through rates have assumed that supply is always certain and ample, which is not usually the case in practice. We find that the cost pass-through rate should depend on the supply uncertainty, its risk-sharing levels with upstream suppliers, and different sourcing strategies. In particular, the cost pass-through rate increases in supply risk-sharing levels, but decreases under single sourcing, compared to dual sourcing.

An integrated model of supply chain resilience considering supply and demand uncertainties

AbstractThe complexity of the global supply chain has increased dramatically over the past few decades as a result of uncertainty caused by various factors. This paper studies the optimal strategy for supply chain resilience models considering supply disruptions and demand fluctuations. We present two‐stage stochastic programming models based on different scenarios, including a risk‐neutral model that considers the expected total cost, a risk‐averse model that considers the conditional value‐at‐risk measure, and a responsiveness model that considers the service level. We also propose multiobjective mathematical programming that considers all three models simultaneously and suggests the solution approach. Finally, we present the results of computational experiments and demonstrate how to cope with uncertainty through flexibility and redundancy. We offer a set of nondominated solutions from the multiobjective model and derive managerial insights, which suggest a decision‐making strategy between the disruption risk, expected total cost, and service level.

A Comprehensive Review of the Thermohydraulic Improvement Potentials in Solar Air Heaters through an Energy and Exergy Analysis

Supply disruptions, uncertainty, and unprecedented price rises of fossil fuels due to the recent pandemic and war have highlighted the importance of using renewable sources to meet energy demands. Solar air collectors (SACs) are major types of solar energy systems that can be utilized for space and water heating, drying, and thermal energy storage. Although there is sufficient documentation on the thermal analyses of SACs, no comprehensive reviews of the exergetic performance or qualitative insight on heat conversion are available. The primary objective of this article is to provide a comprehensive review on the optimum conditions at which the thermal performance of diverse types of solar air collectors is optimized. The effect of operating parameters such as temperature rise, flow rate, geometric parameters, solar radiation, and the Reynolds number on the thermal performance of SACs in terms of thermal hydraulic performance, energy, and exergy efficiencies has been reviewed adaptively. Beyond the operating parameters, a deep investigation is outlined to monitor fluid dynamics using analytical and computational fluid dynamics (CFDs) methodologies in the technology of SACs. In the third phase, thermodynamic irreversibility due to optical losses, thermal losses between absorber and environment, heat losses due to insulation, edge losses, and entropy generation are reported and discussed, which serve as the fundamental tools for optimization purposes.

PERANCANGAN STRATEGI MITIGASI RISIKO DAN KEY RISK INDICATORS PADA BLOOD SUPPLY CHAIN PMI KABUPATEN GUNUNGKIDUL

The concept of blood supply chain is distribute blood products safely, cheaply, and accessible to patients. There are four main activities in the blood supply chain, namely blood collection, blood processing, laboratory testing, and storage and distribution of blood products. The current development of PMI Gunungkidul Regency in carrying out its duties is still hampered by several things caused by risks, one of which is supply and demand uncertainty. In 2021, the blood supply at PMI will still experience a crisis of decreasing by 20%-30%, while the demand for blood from hospitals continues to exist and fluctuates. Based on the results of House of Risk Phase 1, there are 10 dominant risks that affect the blood supply chain. The results of the House of Risk Phase 2 resulted in preventative action to handle the 10 dominant risks. Apart from that, the results of the key risk indicators (KRI) are used as an early warning system for PMI Gunungkidul Regency. Implementation of preventive actions that have been designed and implemented key risk indicators, namely the number of blood bag supplies per month with a limit value of 79 blood bags and 399 blood bags.

Voltage Regulation and Power Factor Enhancement for EV Charging Station by Using Optimization Algorithm

A Cuk converter-based Power Factor (PF) and voltage regulation enhancement model for an electric vehicle (EV) charging circuit is created and developed, utilizing an optimization technique. Using the Cuk converter's performance measures in Continuous Conduction Mode (CCM), the charging system may produce a steady output current with better PF. This project implements an optimization algorithm-based correction approach for power factor and voltage regulation. The Jaya optimization algorithm, which is a basic metaheuristic algorithm, uses less algorithm-specific parameters. The use of an optimization algorithm aids in the dynamic adjustment of converter operation to maintain the appropriate power factor and voltage levels under non-linear situations such as load fluctuations and input changes. The algorithm evaluates the output and modifies the system's parameters to optimize performance for real-time EV applications, taking into account factors such as changing load requirements, input supply uncertainty, and desired voltage levels. The suggested method is created and tested in MATLAB/Simulink. The suggested algorithm is then confirmed experimentally as a hardware prototype.