Demand Uncertainty Research Articles

Optimization of pediatric vaccines distribution network configuration under uncertainty

Millions of young people are not immunized in low income (LI) and lower middle income (LMI) countries because of low vaccine availability resulting from inefficiencies in cold supply chains. We create supply chain network design and distribution models to address the unique characteristics and challenges facing vaccine supply chains in LI and LMI countries. The models capture the uncertainties of demand for vaccinations and the resulting impacts on immunization, the unique challenges of vaccine administration (such as open vial wastage), the interactions between technological improvements of vaccines and immunizations, and the trade-offs between immunization coverage rates and available resources. The objective is to maximize both the percentage of fully immunized children and the vaccine availability in clinics. Our research examines how these two metrics are affected by three factors: number of tiers in the supply chain, vaccine vial size, and new vaccine technologies. We tested the model using Niger’s Expanded Program on Immunization, which is sponsored by the World Health Organization. We make many observations and recommendations to help LMI countries increase their immunization coverage.

A responsive closed-loop supply chain network design under demand uncertainty

A closed-loop supply chain (CLSC) is important in developing a green industrial supply chain and a sustainable economy. This study addresses the challenge of designing a closed-loop supply chain network (CLSCN) under demand uncertainty by integrating forward and reverse logistics. A continuous approximation (CA) approach is proposed to solve a responsive CLSCN model. An inventory-sharing strategy is applied in the continuous approximation (CA) approach to further solve uncertain demand in a CLSCN. We conduct sensitivity analysis to demonstrate how the optimal design characteristics and the responsive supply chain network (SCN) design depend on various scenarios. A responsive CLSCN's network density is higher than a responsive SCN's. The changes in network density of the distribution center (DC) are not significantly affected by increased risks of uncertainties. The proposed model has demonstrated substantial cost savings compared to the traditional responsive CLSCN model.

How can entrepreneurial obstacles improve entrepreneurs’ psychological well-being? The role of emotional ambivalence

ABSTRACT Entrepreneurial obstacles present both threats to survival and opportunities for learning. By integrating the double-edged nature of obstacles into the evaluative space model of emotion, this study examines the effects of entrepreneurial obstacles on entrepreneurs’ emotional ambivalence and their psychological well-being. Through survey among 324 new ventures in China, we find that although entrepreneurial obstacle is directly detrimental to psychological well-being, it can also trigger entrepreneurs’ emotional ambivalence which has a U-shaped relationship with psychological well-being. Moreover, demand uncertainty increases entrepreneurs’ ambivalent emotional response to entrepreneurial obstacles. This study identifies a self-healing path between entrepreneurial obstacles and entrepreneurs’ psychological well-being.

Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization

Modern supply chain systems face significant challenges, including lack of transparency, inefficient inventory management, and vulnerability to disruptions and security threats. Traditional optimization methods often struggle to adapt to the complex and dynamic nature of these systems. This paper presents a novel blockchain-based zero-trust supply chain security framework integrated with deep reinforcement learning (SAC-rainbow) to address these challenges. The SAC-rainbow framework leverages the Soft Actor–Critic (SAC) algorithm with prioritized experience replay for inventory optimization and a blockchain-based zero-trust mechanism for secure supply chain management. The SAC-rainbow algorithm learns adaptive policies under demand uncertainty, while the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. An experiment using real-world supply chain data demonstrated the superior performance of the proposed framework in terms of reward maximization, inventory stability, and security metrics. The SAC-rainbow framework offers a promising solution for addressing the challenges of modern supply chains by leveraging blockchain, deep reinforcement learning, and zero-trust security principles. This research paves the way for developing secure, transparent, and efficient supply chain management systems in the face of growing complexity and security risks.

How power structure and markup schemes impact supply chain channel efficiency under price-dependent stochastic demand

Although considerable attention has been separately given to factors such as power structures, price-dependent demand, and markup pricing schemes, there has been limited exploration of the combined effects of these factors on supply chain efficiency and the leader’s advantage. We propose a game theoretic model in which a manufacturer sells a single product to a newsvendor retailer who sets both optimal order quantity and selling price under uncertain price-dependent demand. Furthermore, we examine a supply network wherein a single retailer fulfills orders using a global manufacturer for regular orders and a local manufacturer to clear any shortages. Through numerical analysis, we show that the retailer always prefers to charge a percentage markup. In a two-player game, channel efficiency is higher when the retailer is the leader under linear demand; however, under iso-elastic demand, the manufacturer being a leader brings a higher channel efficiency. When a local manufacturer is involved as a second manufacturer, channel efficiency is higher when the retailer remains a follower, as this induces more fierce wholesale price competition between the two manufacturers. Additionally, when demand uncertainty is high in the two-player game with linear demand, the retailer as a follower can achieve higher profits, whilst high uncertainty under iso-elastic demand decreases both players’ profits. Moreover, it becomes advantageous for the retailer to have a local manufacturer as demand uncertainty increases, even when the local manufacturer announces the wholesale price first.

Modeling a robust 2-service community healthcare network design problem with central hospital congestion

We address a more realistic variant of the community healthcare network design problem, considering a 2-service network framework and hospital congestion structures. Our objective is to minimise the total expected cost through strategic location of central hospitals and allocation of community health stations. Notably, each community health station is allocated to exactly two central hospitals. We also account for uncertainties in resident medical demands, which are challenging to predict accurately due to their volatility. To tackle this, we propose an adaptive distributionally robust optimisation approach that confines uncertain variables to an ambiguity set reflecting demand distribution characteristics. To enhance tractability, we reformulate the distributionally robust model as a mixed-integer linear programme within the specified ambiguity set. Numerical experiments on a real case demonstrate the validity and superiority of the proposed model. Finally, the impact of uncertainty is discussed in depth and some managerial insights for healthcare managers are summarised.

A risk-averse distributionally robust optimisation approach for drone-supported relief facility location problem

Drone-supported last-mile humanitarian logistics applications play a crucial role in the rapid and efficient delivery of essential relief items, such as medicine, blood, and vaccines, during disaster and emergency situations. This paper explores a novel drone-supported relief facility location problem (DSRFLP) aimed at establishing an effective drone-supported last-mile humanitarian logistics system. The problem involves making joint decisions for both pre-disaster and post-disaster phases while considering the characteristics of drone-based delivery operations and uncertain demands. In the pre-disaster phase, we make the decisions regarding the locations of drone-supported relief facilities, inventory prepositioning of relief items, assignment of drones to the opened facilities, and allocation of drones to disaster demand sites. In the post-disaster phase, we make decisions related to delivery quantities. To tackle the challenge of incomplete demand distribution information in chaotic disaster environments, we establish a distributionally robust optimisation (DRO) model to handle the uncertainty of demands. This model adopts worst-case mean Conditional Value-at-Risk as the risk measurement, reflecting the risk-averse attitude of humanitarian organisers. In this paper, three ambiguity sets (box, ellipsoidal, and polyhedral) are considered to describe the ambiguity distributions of demands. To overcome the computational challenge, we reformulate the DRO model under three ambiguity sets into two mixed-integer linear programming models and one second-order cone programming model, which can be efficiently solved by off-the-shelf solvers. Furthermore, we validate our proposed DRO model through a small-scale example and a large-scale case study based on the Lushan earthquake in China. The computational outcomes underscore the superior performance of the proposed DRO model to mitigate the impact arising from incomplete probability distributions. We propose managerial implications and insights to support the decision-making of humanitarian organisations based on the experimental results. Finally, we propose two extended models to incorporate multiple relief items and equity constraints in priority settings and conduct numerical experiments to adapt to various real-world disaster scenarios.

Effective hydrogen supply chain management framework considering nonlinear multi-stage process uncertainties

The large amount of carbon emitted due to industrial development is posing serious climate change. Under these circumstances, hydrogen energy is being used as a major energy source and is attracting attention as an alternative energy to fossil fuels, characterized by high carbon emissions. While the use of hydrogen has the obvious benefit of a low carbon footprint, the hydrogen production process possesses a high carbon footprint difference depending on the source of utilized electricity. This study considers several situations arising in an electrolysis-based hydrogen supply chain network (HSCN). A multi-stage stochastic programming (SP) is modeled to consider the uncertainty of demand and transportation capacity generated in HSCN. Moreover, a typical stochastic model has a chain rule between the decision variables resulting from nonlinearity and uncertainty, and these features make it difficult to derive an effective solution. Thus, in this study, a new methodology, Weighted Scenario Sample Average Approximation (WSSAA), is proposed to derive an effective solution for the multistage SP model considering various scenarios under the hydrogen supply chain. While a general stochastic programming approach fails to generate feasible solutions with volatile HSCN uncertainties, the proposed WSSAA framework provides feasible decisions considering multiple scenarios. The effectiveness of the proposed model applied with WSSAA is demonstrated through comparative experiments over the models used in existing studies.

An AQ-SCS Optimization Assisted Optimal Expansion Planning with Fixed and Switchable Capacitors for Distribution System

Distribution System has given renewable-based DGs like Solar PV and Wind turbines a lot of attention because of growing worries about global warming and the depletion of fossil fuels. This work proposes a unique hybridized optimization technique for a distribution system expansion plan utilizing an innovative theoretical framework. Two stages are used to tackle the issue with the distribution system expansion plan: master optimization and sub-optimization for every state of the system. The developed Aquila based Sand Cat Swarm (Aq-SCS) optimization method considers the distribution system factors, like DG type, size/capacity, location, PQ power, fixed capacitor, switchable capacitor, and the SPV/wind capacity with load demand uncertainties. The sub-algorithm is employed in conjunction with the DG allocation strategy produced by the master algorithm to deduce the state-dependent operating tactics for each individual DG unit with respect to active and reactive power. The suggested Aq-SCS optimization approach is created by combining the properties of the SCSO with the AOA models. The main objective of achieving the minimal possible cost for the DSEP is verified, and the cycle continues unless the best outcome (least cost) is reached. The analysis of the suggested approach is carried out in 4 cases, such as (i) Without DG and capacitor (ii) With capacitor and no DG (iii) With DG and no capacitor (iv) With both capacitor and DG. The analysis is made in IEEE 33 bus system. The Aq-SCS approach outperforms the conservative approaches SSI-CS, WHO, AQO, and SCSO, according to the analysis conducted in MATLAB/Simulink utilizing an IEEE-33 bus system with five system states.

Dynamic investment strategies for a folk sports tourism destination under uncertain demand

More research should shed light on discovering the optimal investment strategy for folk sports tourism destination (FSTD) projects. Therefore, in this paper, we develop a dynamic game model of FSTD considering the dynamic characteristics of FSTD investment, the mode of division of labor and cooperation between public and private operators, and the uncertainty of consumer demand. Public capital is responsible for constructing infrastructures such as venues, and private capital is responsible for services such as catering and accommodation. To promote the development of the FSTD project, the higher-level government subsidizes public investment. Consumer demand for the program is affected by factors such as the size of the two types of capital, the price and quality of services, and demand uncertainty. The study finds that the subsidy leads to an increase in the quantity of public investment and consumption demand, but private sector investment and the prices of both public and private projects are unaffected by the subsidy; the public sector’s net return varies in an inverted U-shape with the rate of subsidy, but the private sector’s net return rises monotonically. Demand disturbances widen the gap in the net returns of operators between the subsidized and unsubsidized scenarios.

Towards Dynamic Pricing Based Autonomous Valet Parking Management Under Demand Uncertainty

Towards Dynamic Pricing Based Autonomous Valet Parking Management Under Demand Uncertainty

Probabilistic photovoltaic generation and load demand uncertainties modelling for active distribution networks hosting capacity calculations

With the increasing integration of photovoltaic (PV) systems in active distribution networks (ADNs), accurate modelling of PV power generation and the network demand has become essential, especially for system operators (SO). However, existing studies have focused on deterministic representations of hourly profiles for PV generation and load consumption, which cannot thoroughly evaluate the existing uncertainties of PV power output and load demands. In this study, uncertain parameters load demand and PV power output profile will be modelled with forecasted values, and their profile will be obtained over probability density functions (PDFs). Firstly, a vast quantity of realistic load and PV generation profiles are produced over a day with 15-minute resolution, with a scenario generation method using the Monte Carlo methodology. Afterward, the generated scenarios are reduced to a set of scenarios to represent the span of all generated scenarios. A fully local reactive power regulation strategy is used in this study to evaluate the hosting capacity of the ADN. This proposed method is tested on modified 33-bus and 69-bus distribution test systems by using practical solar generation and load data. The proposed methodology results in the hosting capacity improvement by 20% besides the existing Q-Voltage and PF-Power local voltage control methods, where it has the flexibility to be implemented to any distribution feeder.

A multi-objective two-stage stochastic unit commitment model for wind and battery-integrated power systems

This paper presents a mathematical programming framework for modeling the operations of power systems with high wind power penetration and BESS, accounting for wind and power demand uncertainties. For this, we introduce a multi-objective two-stage stochastic unit commitment model aimed at constructing on/off schedules that balance operating costs and CO2 emissions. Under the realistic limitations imposed by BESS high variable operating cost, we focus on assessing and analyzing the operational impact of BESS for two different integration schemes: wind farm-coupled and stand-alone. The proposed method of solution consists in an augmented ϵ-constrained algorithm with an integrated normalized euclidean distance-based bisection routine to produce more equispaced Pareto optimal solutions. We applied the proposed framework to a modified version of the New England IEEE-39 bus test system for three disjoint cases of analysis: without BESS integration, with BESS integrated into a wind farm, and with a stand-alone BESS integrated into the grid. The results demonstrate the efficacy of the proposed framework and show substantial benefits in terms of CO2 emissions reductions, particularly in the stand-alone configuration, which, although limited in extent of use, promotes the direct injection of wind power into the grid, the decommitment of polluting thermal units, enhance the use of free or low emissions technologies, and contributes as a provider of spinning reserves.

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.

Coordinated optimal operation of integrated electrical and transportation network considering source-load uncertainties in severe weather scenarios

The large-scale penetration of electric vehicles and the emerging dynamic wireless charging technology strengthen the coupling between distribution networks and transportation networks. However, the randomness of travels in the transportation network, the fluctuation of the output of clean energy such as wind power and photovoltaic, and the sudden change of weather will have a significant impact on the safe and stable operation of the two networks. In this context, based on the background of wireless charging technology, a cooperative optimal operation scheduling model of electric transportation integrated network is proposed, which considers the uncertainty of traffic demand and photovoltaic prediction error, and regards it as the uncertainty factor of load side and source side of power grid. The Information gap decision theory (IGDT) is introduced to model the bilateral uncertainty of the network, and the corresponding risk avoidance model and risk speculation model are obtained. Furthermore, considering the effect of inclement weather scenarios on the uncertain factors of road traffic and charging facilities, a modified integrated network model of electric traffic under inclement weather scenarios is proposed. Finally, a case study is carried out on a test system consisting of a 12-node transportation network and a 20-node power distribution network. The results show that the information gap decision theory can effectively deal with the changes of dual networks under multiple uncertain scenarios, form a risk decision-making model, and provide theoretical support for different decision-making methods.

Optimal coordinated energy management in active distribution networks considering battery energy storage and price‐responsive demand

AbstractContemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology.

Pre-Disaster Optimal Operation Strategy for Hydrogen-Fuel-Based Isolated Power System with Disaster Uncertainties

The increasing occurrence of severe weather phenomena presents substantial difficulties for electricity distribution systems. This study investigates the integration of hydrogen production plants (HPPs) into power distribution networks to bolster resilience against the increasing frequency of extreme weather events. It highlights the innovative use of hydrogen, generated from renewable sources, as an energy storage medium to ensure a stable power supply during disruptions. By employing stochastic optimization, the study aims to effectively manage hydrogen production and utilization, considering uncertainties in disaster scenarios and energy demands. It addresses critical research gaps, such as the lack of focus on pre-disaster preventive scheduling using hydrogen, the under-explored application of stochastic optimization in such contexts, and the predominance of real-time response strategies over pre-emptive measures. This approach significantly advances current understanding by proposing novel strategies that leverage hydrogen production and sophisticated optimization to enhance the resilience of power networks against extreme weather events. In some scenarios, using our method can reduce the cost of pre-disaster prevention by approximately 15.68% while ensuring that the disaster recovery effect remains unchanged.

GIS-Based Optimization for Gas Distribution Route Design: A Case Study of PT Gagas Energi Indonesia

PT Gagas Energi Indonesia is a company engaged in the trading and natural gas business, one of which is Gaslink C-Cyl. Route determination for Gaslink C-Cyl distribution is carried out subjectively based on the proximity of locations between customers, thus creating inefficient routes, affecting the number of vehicles needed, and causing high transportation costs. Based on this problem, this study aims to minimize transportation costs by determining the optimal route and number of vehicles needed using the Multi-Trip Vehicle Routing Problem Time Windows Simultaneous Pickup and Delivery (MTVRPTW-SPD). This problem is solved using Geographic Information System (GIS) based optimization. To evaluate GIS-based optimization performance, comparison between existing condition and optimization are done for 50 demand points. Then, two scenario was developed, 100 and 200 demand points to explain the decision implication related to demand uncertainty. Based on the optimization process for 50 demand points gives better solution than existing condition in term of number of fleets needed, total distance travelled, and total transportation cost which result 3 units, 46,5%, and 43,5% respectively.

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.