Uncertain Demand Research Articles

Divesting or keeping overseas subsidiary production under rising production costs and uncertain market demands in host countries

This study examines whether overseas manufacturing subsidiaries are divested or kept under rising production costs and uncertain market demands in their host countries. We predict that even under increasing production costs, a subsidiary will not be divested but kept under high market demand uncertainty. A multinomial logit analysis of Korean overseas manufacturing subsidiaries finds that an overseas manufacturing subsidiary exposed to high labor cost growth in its host country is not divested but kept alive under high market demand uncertainty via cross-border production volume adjustments with other in-network subsidiaries in different countries. It also shows that the moderating impact of demand uncertainty on production shifts over divestment is pronounced more for the subsidiaries with low cross-country labor cost correlation, high product compatibility, and high ownership share. These findings imply that the longevity of subsidiary operations is shaped by geographic and organizational characteristics determining production shift conditions.

Renewable compensation policies and conventional energy investment: A theoretical model

Many jurisdictions are simultaneously expanding natural gas and renewable capacities, largely supported by renewable compensation policies (RCPs). However, RCPs' impacts on firms' incentives for conventional capacity investment remain unclear. This paper develops a two-stage theoretical model to investigate this interaction within an imperfect competition and uncertain demand context. Firms initially invest in conventional energy capacity, followed by competing to supply electricity from conventional and previously owned renewables. Conventional output is compensated at market prices, but renewable output is subject to two common RCPs: feed-in tariffs (FiT) and feed-in premiums (FiP). The illustrative numerical example shows that increasing the proportion of renewable output compensated by a FiT from 20% to 80% increases the market-level conventional investment by 18%, leading to an increase in consumer surplus but decreasing firms' profits. These results exemplify the unintended effects of RCPs, encouraging the adoption of conventional generation capacity. The model presented in this paper provides a theoretical foundation for understanding the relationship between RCPs and conventional energy capacity investment—critical for carbon-intensive nations transitioning to renewables while maintaining reliable electricity supply through conventional generation.

Research on optimization of multimodal hub-and-spoke transport network under uncertain demand

In the cargo transportation system, the hub-and-spoke transport network can make full use of the scale effect between logistics hubs and reduce logistics costs. Joint transportation of multiple modes of transportation can give full play to the advantages of different modes of transportation, which not only reduces logistics costs but also improves transportation efficiency. Therefore, this paper combines the advantages of multimodal transportation and hub-and-spoke network, and establishes an optimization model of multimodal hub-and-spoke transport network under demand uncertainty. The model takes into account hub capacity constraints and customer satisfaction with respect to transportation time, and to facilitate the model solution, we utilize the fuzzy expected value method and the fuzzy chance constraints based on credibility to clarify the uncertain variables in the model. We use mixed coding to describe the selection of hubs, assignment of nodes, and choice of transportation modes in this study and use the NSGA-II algorithm with local reinforcement to solve the problem. Finally, numerical experiments are designed to verify the validity of the model and algorithm through sensitivity analysis of relevant parameters, determine the reasonable number of hubs and confidence level, and obtain the influence of the change of hub capacity limit and the ratio of single and double hub transit on the research objectives. The results show that: the NSGA-II algorithm with local reinforcement can significantly improve the convergence speed of the algorithm; There is benefit inversion between economic cost and time cost, and the pursuit of economic cost minimization and time cost minimization, respectively, will lead to different choices of the number of hubs; Increasing the ratio of goods transfer between hubs is beneficial for fully utilizing the scale effect between hubs, achieving the goal of reducing economic costs, but at the same time, it will increase time costs.

Forecasting Analysis of Raw Material Needs for Manjakani and Pepet Key in the Manufacture of Madurese Herb Products

Demand for Madurese herbal products at PT. XYZ is increasing, especially for women's herbal medicine products, namely fragrant rapet herbal medicine. Uncertain demand means that the supply of raw materials for fragrant rapet herbal medicine, namely manjakani and pepet key, must be precise. Therefore, to meet the demand for herbal medicine, it is necessary to predict the need for raw materials for fragrant rapet herbal medicine. This article aims to predict the need for raw materials in the form of pepet and manjakani keys using various forecasting techniques, including average MA, WMA, ES and ES with trends. Forecasting results with the lowest MAD, MSE, and MAPE values—which show very minimal forecasting errors—will be selected for further examination. The results of the research show that for raw material needs for manjakani, the best forecast result value is 24.43 kg with the smallest percentage error value, namely MAPE of 11.255%, while for key raw material needs for pepet, the forecast result value is 13.59 kg with the smallest error percentage, namely MAPE of 14.851%.

Resilient and robust management policy for multi‐stage supply chains with perishable goods and inaccurate forecast information: A distributed model predictive control approach

AbstractAn efficient supply chain (SC) management requires that decisions are taken to minimize the effects of parametric uncertainties and unpredictable external disturbances. In this article, we consider this problem with reference to a multi‐stage SC (MSSC) whose dynamics is characterized by the following elements of complexity: perishable goods with uncertain perishability rate, an uncertain future customer demand that is only known to fluctuate inside a given compact set. The problem we face is to define a resilient and robust Replenishment Policy (RP) such that at any stage the following requirements are satisfied: the fulfilled demand is maximized, overstocking is avoided, the bullwhip effect (BE) is mitigated. These objectives should be pursued despite the mentioned uncertainties and unexpected customer demand behaviors violating the bounds of the compact set. Robustness is here intended with respect to uncertainty on the perishability rate, and resiliency as the ability to quickly react to the mentioned unforeseen customer demands. We propose a method based on a distributed resilient robust model predictive control (DRRMPC) approach. Each local robust MPC (RMPC) involves solving a Min‐Max constrained optimization problem (MMCOP). To drastically reduce the numerical complexity of each MMCOP, we parametrize its solution by means of B‐spline functions.

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm.

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model's effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

Two-stage approach for COVID-19 vaccine supply chain network under uncertainty using the machine learning algorithms: A case study

Healthcare systems worldwide have been facing a scarcity in COVID-19 vaccine rationing as a severe challenge. This research presents a two-stage optimization mathematical model to design a COVID-19 vaccine supply chain network under uncertain demand and lead time. The first stage aims to minimize the lead time by focusing on the suppliers' goals. In contrast, four objective functions, namely minimization of the gap of service level, the waiting time in the queue, and costs, and maximization of the related assignment, are considered in the second stage considering the government's goals. The popular supervised machine learning approaches are adopted to estimate COVID-19 vaccine demand. The boosted neural networks method as a superior mechanism is selected to estimate the demand as input parameters to the model. Additionally, since the lead time is an inherently uncertain parameter, a fuzzy method by Gaussian membership function is employed. Then, a rolling horizon planning mechanism along with an iterative procedure is proposed to prevent the shortage of vaccines. The applicability of the proposed model and solution method is demonstrated based on a real-case study from Iran. The results indicate that when demand exceeds supply, priority grouping becomes essential. Additionally, fairly allocating vaccines among people is a severe challenge during an epidemic, and preferences among the government and suppliers can make the problem more complex. The computational results illustrate that implementing the proposed model and approaches achieves the goals and reduces the shortage in the network compared to the existing condition.

A stochastic programming model for free-floating shared bike redistribution considering bike gathering

We study a stochastic free-floating shared bike redistribution problem, which first gathers bikes randomly scattered around the hotspots of a city and then transships the gathered bikes among the hotspots to achieve a better supply–demand balance. In the literature, the bike gathering operation and the associated stochasticity, although significant for shared bike redistribution practice, are rarely considered. To fill the gap, we propose a two-stage stochastic programming model that explicitly incorporates the bike-gathering cost with two formulations. In the planning stage, the deployment of gathering sites and the allocation of gathering staff are determined. In the operation stage, the gathering, transshipment and replenishment of shared bikes are optimized based on the decisions of the planning stage and the realization of the uncertain supply and demand of free-floating shared bikes in space. With a case study based on real data from the Mobike company in Beijing, China, we illustrate the real-world applications of our proposed model, highlight the importance of considering the cost of bike gathering when planning free-floating shared bike redistribution, and gain managerial insights for better field practices.

Contract adoption of motivating green technological innovation in a supply chain

AbstractGreen technological innovation has been widely recognized around the world; however, consumer environmental awareness may result in uncertain market demand growth and lead to manufacturers’ risk‐averse attitude towards green technological innovation. This paper focuses on the contract adoption problem of a risk‐averse manufacturer who is responsible for green technological innovation, through a resell contract (RC) or agency contract (AC) with a retail platform. Stackelberg models are built, and the conditional value at risk method is used to measure the manufacturer's risk‐averse behavior. The differences between two contracts are compared. We find that when the commission rate is less than some threshold, the green technological level under AC is greater than that under RC regardless of the fixed cost coefficient, otherwise the green technological level under RC is higher. Further, there is the dual excellent region for AC and RC where the manufacturer can not only obtain a higher level of green technology but also achieve better utility. Finally, consumer surplus and social welfare under two contracts are performed through numerical experiments.

A simulation-optimization approach for economic assessment of an imperfect serial production management under uncertainty

ABSTRACT One of the critical quandaries in real-world production management systems is uncertainty in the product demand, which is closely associated to the product price. This uncertain behavior of demand affects production decisions in a system. In this context, this study develops a decision support framework for an imperfect serial production management system under uncertainty. The proposed framework is analyzed considering price-dependent uncertain demand and random defective rate in the system, thus considering both internal and external uncertainties of the system. Three distinct production models are formulated considering different types of uncertain relations among the product demand and market price. Closed form solutions of the decisions are obtained through hybrid of simulation and analytical optimization and are tested through an experimental case analysis. @RISK of Palisade Suite is utilized for simulation and to capture the uncertainty in defective proportion and demand elasticity parameters by creating several possibilities.

A Competitive Newsvendor Problem with Product Substitution under the Carbon Cap-and-Trade System

This study investigates the competitive issues of the newsvendor problem with product substitution under the carbon cap-and-trade system. Building on existing research, this paper introduces the carbon cap-and-trade system under uncertain market demand and considers that the original equipment manufacturer (OEM) can choose to procure raw materials from the contract manufacturer (CM), with both final products being substitutable. Furthermore, we explore the different substitution relationships between OEM and CM products under both pure competitive and co-opetitive modes. For this problem, decision models are developed for various scenarios, and optimal solutions satisfying given conditions are provided. We find that in one-way substitution, under pure competition, an increase in the OEM’s (or CM’s) green investment and substitution rate only leads to an increase in OEM’s (or CM’s) yields, while an increase in the OEM’s (or CM’s) green investment does not necessarily reduce CM’s (or OEM’s) yields. In the co-opetitive mode, an increase in the substitution rate and green investments of both manufacturers may lead to an increase in the yields of both manufacturers. Furthermore, an increase in carbon trading prices does not necessarily inhibit the manufacturer’s yields. Moreover, we find that under the same competition mode, under certain conditions, two-way substitution between OEM and CM can bring better profits to both manufacturers and the entire supply chain. When the two modes are in the same substitution scenario, and the CM cannot substitute for the OEM, the optimal decisions and total supply chain profits of the two modes are equal. Finally, through numerical analysis, we find that neither mode is necessarily optimal when CM can substitute for OEM. Additionally, it is observed that when the same mode is in different substitution scenarios, total supply chain profits may be enhanced in the presence of product substitution.

Horizontal subcontracting and the global factory

The focus of this article is the examination of the factors that trigger the addition of horizontal subcontracting to the structure of global factories. Horizontal subcontracting takes place when one firm is contracted to produce a given item, fills as much of the order as they are able to based on their available capacity, and subsequently subcontracts the remainder to a qualitatively identical firm. We find that similar entrepreneurial backgrounds of contract manufacturers lead to strong business ties in the same industry and stage of production; low-cost, used production machinery and inexpensive factory rent lead to low barriers to entry; tightening labor markets, limited capital access and lumpy or uncertain demand lead to the need to avoid underused internal capacity, and the widespread use of digital sourcing platforms by buyers enables contract manufacturers to receive orders both small and much larger than their internal production capacity. These factors combine to produce many networked small manufacturers who use horizontal subcontracting to ensure sustained demand. Currently a largely China-based phenomenon, horizontal subcontracting is likely to spread beyond China, offering a new source of vitality to low-cost manufacturing in global factories, keeping global factories viable into the future.

Location and Distribution Decision of Emergency Logistics Network under Uncertain Demand Distributions

To ensure the supply of materials after urgent events, including recent public health crisis, it is crucial to formulate a reasonable preventive strategy for the location and distribution of emergency materials within the logistics network. In this paper, a class of multi-category emergency material storage distribution models under scenarios with uncertain post-event demand distribution is studied. Based on historical small-sample post-event demand information, the model constructs a two-stage material storage and scheduling distributionally robust optimization model. By applying a dual method, the non-linear distributionally robust optimization is transformed into linear optimization, and an improved Lagrangian L-shaped algorithm is designed to solve the two-stage model. Finally, the robustness of the model and algorithm is verified by implementing the method to a numerical example, and the sensitivity of the location and distribution decisions to material shortage under different levels of urgent events is analyzed.

Logistics distribution scheduling algorithm based on artificial intelligence

In order to improve the quality of urban logistics distribution services and meet customer needs to the greatest extent, improve the utilization rate of service resources and reduce the cost of logistics and distribution services, the author proposes an urban logistics distribution scheduling algorithm based on artificial bee colony. Uncertain dynamic customer demands often occur during the execution of logistics distribution services, resulting in the inability to achieve optimal cost by executing the distribution service according to the original plan, in response to such problems with dynamic customer needs, the author conducted a problem analysis, the corresponding mathematical model is established and transformed into a static problem for rescheduling. The result shows: Using the scheduling algorithm to solve the example 100 times, when the vehicle 1 and vehicle 3 can meet their own load, they can serve customers with new needs, which can make the delivery cost the lowest. In the case of a small number of customers, the algorithm can achieve high accuracy, and when the number of customers reaches a certain scale, the stability of the algorithm will decrease slightly. ConclusionThe algorithm achieves the purpose of promptly responding to changes in customer demand and quickly adjusting distribution services.

Offline planning and online operation of zonal-based flexible transit service under demand uncertainties and dynamic cancellations

This paper introduces a comprehensive framework for planning and operating a zonal-based flexible transit (FT) service, a public transit mode designed to accommodate uncertain demand patterns. The framework addresses both offline planning based on stochastic demand distributions and cancellations, as well as online routing considering real-time orders and cancellation behaviour. Offline interzonal route planning is formulated as a two-stage recourse problem, while the intrazonal routing problem is modelled using a Markov decision process (MDP) that incorporates online information. To solve the problem, a service reliability-based decomposition method is employed to divide the problem into three mixed-integer subproblems. The first subproblem focuses on designing interzonal routes up to a specific demand level, taking into account a designated cancellation probability as determined by reliability measures. An insertion heuristic is developed for this subproblem to improve the solution efficiency. The second subproblem allocates passengers from certain categories to vehicles based on the passenger volume designated by reliability measures. Lastly, the third subproblem refines the vehicle intrazonal route according to the passenger assignment from the previous subproblem. The reliability measures are optimised iteratively until no further improvements are observed in consecutive iterations. The proposed FT service’s performance is evaluated using numerical simulations based on real New York City (NYC) taxi demand data, illustrating the effectiveness of the integrated planning and operational approach in accommodating uncertainties in on-demand transit systems.

Resilient day-ahead microgrid energy management with uncertain demand, EVs, storage, and renewables

Managing microgrid energy presents a complex challenge due to unpredictable renewable sources, fluctuating demand, and diverse equipment like batteries, distributed generators, and electric vehicles. This paper introduces a novel two-step optimization model, the Robust Day-Ahead Scheduling for Enhanced Resilience, tailored for microgrid operations. The model addresses the integration of electronic generation, uncertain demand patterns, and small-scale renewable resources. Detailed formulations optimize microgrid energy use, including strategic battery usage, efficient electric vehicle charging, balancing device utilization, and distributed generation dispatch. This multi-faceted approach aims to minimize costs over 24 h, including energy loss, power purchases, reduced power usage, generator operation, and battery/EV expenses. Employing a column-and-constraint generation (C&CG) algorithm ensures efficient problem solving. The proposed model achieved a significant reduction in operational costs, outperforming existing methods by at least 8%. Notably, it minimized energy purchases, energy losses, and load shedding while improving voltage stability, showcasing its effectiveness in enhancing microgrid performance and resilience.

A multi-objective optimization-based framework for extending reservoir service life in a changing world

Reservoirs are an essential component of water resources systems, improving water supply reliability and security. With long-term climate and water demand changes, the performance of reservoirs may deteriorate, necessitating interventions such as infrastructure upgrades and/or water demand management. These interventions often require substantial financial investment, have long implementation time or impede economic development. Therefore, a more cost-effective and less disruptive alternative is needed to extend the service life of existing reservoir systems. Adapting reservoir operation policies to cater for future changes is such an alternative. By adapting reservoir operation policies to future water availability and demand changes, the capacity of existing systems can be fully utilized to avoid or postpone more expensive and disruptive interventions. However, there is a lack of an integrated framework for estimating the potential of service life extension by re-optimizing operation policies to cater to uncertain changes in future conditions. In this study, we develop a multi-objective optimization-based framework to allow operation policies to adapt to uncertain future changes in water availability and demand by optimizing operation policies for a range of plausible future conditions, thus enabling the service life of existing reservoir systems to be maximized albeit uncertainty in future changes. The developed framework is demonstrated using a reservoir system in Northern Australia. Results show that the service life of the reservoir can be extended by up to 40 years considering uncertain future water availability and demand. In addition, significant changes in operation policies and system responses with time are identified, providing valuable information for developing long-term reservoir planning strategies.

Maximizing profitability through cloud-enabled Reinforcement Learning for UAV coverage in real-time e-business applications

Real-time e-business applications are vital for operational efficiency, but connectivity challenges persist, particularly in remote or crowded areas. Drone Base Station (DBS) architecture, proposed for Beyond fifth Generation (B5G) and Sixth Generation (6G) multi-cell networks, offers on-demand hotspot coverage, addressing connectivity gaps in remote or crowded environments. DBSs provide a promising solution to meet the demanding requirements of high data rates, real-time responsiveness, low latency, and extended network coverage, particularly for real-time e-business applications. A critical challenge in this context involves efficiently allocating the needed number of DBSs to the different hotspot service areas, referred to as cells, to optimize the operator’s total profit under unpredictable user demands, varying area-specific service costs, and price dependence real-time e-service. The objective is to achieve the highest total revenue while minimizing the cost (cost savings) throughout the multi-cell system. This challenge is formulated as a profit-maximization discount return problem, integrating the coverage constraint, the variable cell-dependent operational cost, the e-service-based price and the uncertain demands of users across cells. Traditional optimization methods fail due to environmental uncertainty, which leads to the need to reformulate the problem as a Markov Decision Problem (MDP). We introduce a cloud-based Reinforcement Learning (RL) algorithm for DBS dispatch to address the MDP formulation. This algorithm dynamically adjusts to uncertain per-cell user distributions, considering variable operating costs and service-dependent prices across cells. Through extensive evaluation, the RL-based dispatch approach is compared with reference drone dispatch algorithms, demonstrating superior performance in maximizing operator profit through cost savings by optimizing DBS dispatch decisions based on learned user behaviors, variable operational costs, and e-service types.

Green supply chain for steel raw materials under price and demand uncertainty

The scientific formulation of procurement and inventory plans is an important topic in the steel industry, when considering both economic and environmental impacts in an uncertain market environment. Robust optimization methods offer an effective solution to this problem. This study has investigated a multi-echelon and multi-period front-end supply chain centered on a steel enterprise under uncertain purchase prices and demand. The goal is to minimize the costs associated with mining, processing, transportation, procurement, and inventory along with the corresponding carbon emission costs. To address this issue, we present a robust rolling horizon model (RRHM) that considers the inventory capacity constraints of steel enterprise and incorporates ore grade requirements. Subsequently, the RRHM was reformulated into a solvable mixed-integer linear programming model, and the relationship between the robust model and a specific deterministic model was established. Finally, the model was validated by generating instances based on real scenarios. The results demonstrate that the robust rolling horizon model proposed in this paper outperforms both the Sample Average Approximation (SAA) strategy and the (s, S) strategy in terms of cost and robustness. In addition, a sensitivity analysis of the key parameters was conducted. The findings reveal that the unit inventory, mining, and carbon emission costs have a limited impact on the total cost, highlighting the robustness of the model. These findings provide valuable insights into the green and sustainable development of the steel industry, enabling the formulation of effective strategies to mitigate the impacts of uncertainties.

Newsboy theory for real-time multi-source risk operation of reservoir group: A case study of Pearl River Basin

Operations of reservoir groups face significant challenges due to uncertainties in inflow and water demand arising from various risk factors, including extreme climate, demand fluctuations, and operational experience. Traditional risk operations have rarely addressed uncertainties from multiple sources over current and future periods comprehensively. This study innovatively applies economic Newsboy theory to reservoir risk operations to fill this gap. Specifically, taking the reservoir group in the Pearl River Basin as the research object and comprehensively considering multiple risk sources from inflow uncertainty and demand uncertainty in both current and subsequent operating periods, a Newsboy-based model of real-time risk operation is established, and the influence law of multiple risk sources on the real-time salty-tide risk process of reservoirs in this basin is then revealed. The results demonstrate that the optimized risk process, fluctuating between 0 and 0.053, offers increased stability and lower risk compared to the measured process, which fluctuates between 0 and 0.093. Under single risk source with uncertain inflow, the greater the runoff forecast error, the greater the risk of suppressing salt tide at Wuzhou section. Under single risk source with uncertain water demand, with the increase of inflow, opportunity risk of suppressing salty tide gradually changes into excess risk. And the inflow corresponding to the minimum risk is 2200 m3/s at Wuzhou section. Under multiple risk sources with uncertain inflow and demand, the risk increases significantly compared with single risk source, and the impact of demand uncertainty is more significant on risk. Specifically, multi-source risk is 1.78 times of the single-source risk with inflow uncertainty, and is 1.07 times of the single-source risk with demand uncertainty. The study findings advance real-time risk operation theories and methodologies for reservoirs.