Uncertain Demand Research Articles

Dual-Stackelberg game-based trading in community integrated energy system considering uncertain demand response and carbon trading

Given the uncertainties in integrated demand response and carbon trading mechanism, this paper utilizes a dual-Stackelberg game framework to propose trading strategies among multiple entities within such a system. The innovation of this study lies in its development of a collaborative optimization method that encompasses extensive competition between the supply and demand sides, considering the synergistic incentives of carbon trading on the source-side and uncertain responses on the load-side. Firstly, a typical system framework is constructed involving various participants, clarifying energy and carbon market mechanisms and incorporating users’ psychological characteristics in demand response uncertainty. Secondly, a dual-Stackelberg game model is established, and the existence and uniqueness of game equilibrium are demonstrated. Finally, the model's effectiveness is validated through various scenarios. The results indicate that the equilibrium benefits of each entity are 6643.81, 7747.16, 8029.01 yuan, and 9326.62 yuan. The carbon trading mechanism can effectively constrain and reduce the system's carbon emissions by 10.20 %. Furthermore, accounting for user uncertainties in integrated demand response behavior leads to an 18.54 % increase in the peak-to-valley difference in electricity demand. It is concluded that the presented methodology provides innovative insights for addressing trading challenges within hierarchical integrated energy markets.

Robust optimization of multi-objective multi-cycle remanufacturing supply chain network considering incentive compatibility theory under improved algorithm

In recent years, industrial production and transportation have generated a significant amount of carbon emissions, leading to a heightened focus on environmental resource conservation. Concurrently, with the development of internet technologies, an increasing number of enterprises are leveraging big data and artificial intelligence for production and logistics. Within this context, the challenge of how enterprises can utilize big data services to reduce costs while minimizing carbon emissions has become a pressing concern. Meanwhile, remanufacturing, as a sustainable solution, has garnered substantial attention. However, remanufacturing supply chain networks become exceptionally complex and challenging when faced with uncertainties in demand and recovery quantities, necessitating government intervention. Thus, to examine the impact of government intervention on the optimization of green remanufacturing supply chain networks, this paper, based on incentive compatibility theory, establishes an optimization function with objectives of minimizing total cost, reducing carbon emissions, and maximizing return on data investment in scenarios marked by uncertain market demand and recovery quantities. It constructs multi-objective fuzzy optimization models for various scenarios. The paper employs improved robust optimization to address uncertainties related to demand and recovery quantities. It also utilizes the Improved Multi-Objective Genetic Algorithm (IMOGA) to address multi-objective optimization, addressing the limitations of Genetic Algorithms (GA) and Multi-Community particle swarm optimization (MPSO). Case study results indicate that robust optimization effectively handles uncertainties in the model, aligning better with real-world conditions. High-tech enterprises often prioritize economic objectives, and government intervention measures can compensate for their limited carbon reduction capabilities. In most cases, the improved multi-objective genetic algorithm (IMOGA) outperforms MPSO and GA in terms of performance. In the context of the evolving internet landscape, high-tech enterprises should harness big data and artificial intelligence to establish green remanufacturing supply chain networks. Simultaneously, government guidance to reduce carbon emissions is instrumental in enhancing corporate competitiveness and industrial upgrading.

Risk Aversion and Double Marginalization*

In vertical markets, eliminating double marginalization with a two‐part tariff may not be possible due to risk aversion. Under uncertain demand, contracts with large fixed fees expose the downstream firm to more risk than contracts that are more reliant on variable fees. In equilibrium, contracts may thus rely on variable fees, giving rise to double marginalization. Counterintuitively, however, we show that increased demand risk or risk aversion can actually mitigate double marginalization. We also characterize several sufficient conditions under which increased risk or risk aversion is guaranteed to exacerbate double marginalization. We conclude by discussing potential applications and extensions.

Firms' diverse market beliefs can facilitate information sharing and improve profit performance

AbstractA supplier sells a product through a retailer to the market with uncertain demand. The retailer has a signal useful for updating the forecast of market uncertainty, while the supplier can offer a payment to acquire the retailer's signal, termed information sharing. Due to differential means of market access and methods of data analysis, the supplier and the retailer hold diverse beliefs about market conditions. A firm is more confident about market conditions as it perceives the market to be less uncertain. The supplier can be either aware or unaware of the retailer's market belief. In the former case, the supplier correctly predicts the retailer's belief‐based response and makes decision accordingly. In the latter case, the supplier infers the retailer's market belief from the retailer's decision about signal disclosure. We unveil the concrete circumstances where the supplier gains access to the retailer's signal, which would not occur when they held the same accurate market belief. Moreover, with the actual profit performance as the measure, the firms can benefit from holding diverse market beliefs, albeit not simultaneously. The supplier's knowledge of the retailer's market belief can facilitate information sharing but can have detrimental effects on the firms' actual profit performance. Given the opportunity, the retailer may report a market belief that is less confident than its real market belief in communicating with the supplier, which can deter information sharing but has intricate effects on the firms' profits.

Inventory Control for Eyeglass Supply Using the P Model Based on Sales Products Sales Forecasting (Case Study: Merry Optic Bandung)

Inventory is a resource owned by the company to be used in the production process to meet consumer demand. Companies must be able to control inventory appropriately in order to avoid excess or shortage of inventory by using inventory control. Inventory control is a necessary part of a company that requires an appropriate inventory policy to meet uncertain needs. Based on this background, this study discusses the single item inventory model in the form of photochromic glasses at Merry Optik to find the optimal total inventory cost. In meeting the uncertain needs of the company, the Additive Decomposition forecasting method is used in order to find out the forecast sales data pattern in the future. Uncertain demand causes the inventory system to be probabilistic, so it is necessary to carry out probabilistic inventory control. The P model of the case of back orders was chosen because the range of ordering periods is fixed and the company can buy inventory when it runs out before the time the inventory order is made so that buyers can wait until the inventory arrives. By using Model P for the case of back orders, the company can obtain the period between orders, the total cost of inventory, and the optimal level of service. Based on the results of this study, a pattern of sales forecast data is obtained which repeats every 12 months. Companies must order glasses within a period of 32 days between orders so that it is optimal and able to provide a reduction in the total inventory cost of IDR 21,828,771 with a service level of 95%. Companies can save on inventory costs if they use shorter periods between orders. The total cost of inventory can be more optimal if the company reduces the cost of storing inventory in the warehouse.

Cost Prediction in Blockchain-Enabled Pharmaceutical Supply Chain under Uncertain Demand

Cost prediction can provide a pharma supply chain industry with completing their projects on schedule and within budget. This paper provides a new multi-function Blockchain Technology-enabled Pharmaceutical Supply Chain (BT-enabled PSC) mathematical cost model, including PSC costs, BT costs, and uncertain demand. The purpose of this study is to find the most appropriate algorithm(s) with minimum prediction errors to predict the costs of the BT-enabled PSC model. This paper also aims to determine the importance and cost of each component of the multi-function model. To reach these goals, we combined four Supervised Learning algorithms (KNN, DT, SVM, and NB) with two Evolutionary Computation algorithms (HS and PSO) after data generation. Each component of the multi-function model has its importance, and we applied the Feature Weighting approach to analyze their importance. Next, four performance metrics evaluated the multi-function model, and the Total Ranking Score determined predictive algorithms with high reliability. The results indicate the HS-NB and PSO-NB algorithms perform better than the other six algorithms in predicting the costs of the multi-function model with small errors. The findings also show that the Raw Materials cost has a more substantial influence on the model than the other components. This study also introduces the components of the multi-function BT-enabled PSC model.

Waste collection routing: a survey on problems and methods

Waste collection is a vital service performed all over the world, which heavily relies on vehicle routing. Due to regulations and local conditions, the problems and their characteristics often differ greatly. This literature survey aims to review the current state of the art overlap in waste collection and vehicle routing literature. The most notable papers are categorized according to their underlying problem type, examined and brought into relation based on their common problem characteristics. The problem types comprise general, node and arc routing problems, with vehicle routing problems being the most common, followed by arc and location routing problems. Besides the use of intermediate facilities, which is naturally very common in waste collection literature, the authors point out other interesting characteristics found in the literature and in practical problems, such as uncertain demand, personnel planning aspects, alternative collection systems or vehicle types, and characteristics related to risk or sustainability. Additionally, the authors highlight prominent scopes and objectives as well as recent developments in this area. Overall, this survey provides a selective overview and calls attention to research gaps and possible future research directions.

Optimal Exit Policy with Uncertain Demand*

In a framework where entrants must make sunk investment decisions with uncertain returns and have private demand information, we show that the relationship between innovation and exit value is non‐monotone and features an inverted U‐shaped pattern. Consumer surplus is maximised at the lowest exit value that incentivises the investment. These insights are applied to optimal merger policy. An entrant is more willing to innovate to be acquired afterwards, even if it has no bargaining power. This innovation‐for‐buyout effect implies that an entrant is less likely to leave the market under a lenient than a strict merger policy.

Who benefits from postponement in multi-period supply channel optimization?

Duopolistic price-setting supply channels competing in a bilevel framework have been extensively studied in single-period (static) settings. However, such supply channels typically face uncertain and time-varying demand; and thus require a dynamic analysis. Dynamic channel optimization while addressing uncertain demand has received limited attention due to the highly nested structure of the ensuing equilibrium problems. The level of complexity rises when demand is dependent on current and previous prices. We consider a decentralized (non-cooperative) supply channel whose members, a manufacturer and a retailer, competing in a Stackelberg framework, must address the demand for a perishable commodity within a multi-period discrete-time setting. In the first part of the paper, we propose a constructive theorem providing an explicit solution algorithm to obtain equilibrium states at each period. Next, we prove that the resulting equilibria are subgame perfect. In the second part, we allow the retailer (follower) to postpone the supply and pricing decisions until demand uncertainty is resolved in each period. Using subgame perfection of the equilibria, we propose solution algorithms that use the delayed information obtained by the postponement. Our comparative theorems and simulated scenarios indicate that postponement strategies are always beneficial for the follower, and, for a centralized (cooperative) channel. Whereas in a decentralized channel, due to vertical competition, there may be scenarios wherein postponement strategies, i.e., access to extra information, turn out to be detrimental to the manufacturer (leader).

Liquidity regulation and banks: Theory and evidence

This paper theoretically and empirically investigates the effects of liquidity regulation on the banking system. We document that the current quantity-based liquidity rule has reduced banks' liquidity risks. However, the mandated liquidity buffer appears to crowd out bank lending and lead to a migration of liquidity risks to banks that are not subject to liquidity regulation. These findings motivate a model of liquidity regulation with endogenous liquidity premiums and heterogeneous banks. The model shows that the current liquidity rule can improve upon the unregulated equilibrium but can also have distortionary effects because of the dual role of the liquidity buffer as an implicit tax and a costly mitigator of liquidity risks. The fixed quantity mandate can interact with the uncertain liquidity demand, amplifying the volatility in the liquidity premium. A central bank committed liquidity facility could improve the current quantity-based regulation by introducing a price-based mechanism.

A robust optimization approach for inventory management with limited-time discounts and service-level requirement under demand uncertainty

A multi-period inventory management problem is considered for a retailer offering limited-time discounts and having a joint service-level requirement over the discount periods under demand uncertainty. In each period, the retailer, only knowing a reference value and the bounds of the uncertain demand, has to determine the order quantity of the product before demand realizations to maximize the total profit over the planning horizon while achieving the required service level over the discount periods. A budgeted uncertainty set is adopted to accommodate demand uncertainty and a joint chance constraint is used to formulate the service-level requirement. The retailer problem is formulated as an affinely adjustable robust chance-constrained model and is transformed into a linear program. A double-layer iterative approach is proposed to solve the problem. The outer layer uses a posteriori method to update the budget coefficient. The inner layer solves the affinely adjustable robust chance-constrained model with the updated budget coefficient, and obtains the updated order quantities. A case study based on real data demonstrates that the proposed approach outperforms other approaches by better balancing the average realized profit and the realized service level. Moreover, the results show that the retailer can obtain a larger average realized profit by gradually than abruptly changing the prices.

An application of deep reinforcement learning and vendor-managed inventory in perishable supply chain management

This article delves into the challenging supply chain management domain, explicitly addressing the intricate issue of perishable inventory allocation within a two-echelon supply chain. The approach outlined here leverages deep reinforcement learning with a keen understanding of the inherent stochasticity arising from uncertain demands and variable supply conditions. The examined supply chain encompasses two retailers and a central distribution center operating under a vendor-managed inventory system. The primary goal of this research is to combat the prevalent problems of wastage and shortages frequently encountered at the retail level in such supply chains. The study employs the Advantage Actor-Critic (A2C) algorithm, tailored to handle the continuous action space inherent in inventory allocation. To rigorously evaluate this approach, empirical data from a real-world blood supply chain in Tabriz is used for numerical experiments. This practical case study involves a single distribution center and two hospitals. The outcomes of these experiments affirm the effectiveness of the A2C algorithm, showcasing its ability to address the complex inventory allocation problem successfully. Furthermore, the research highlights that the algorithm outperforms existing supply chain policies, underscoring the pivotal role of optimal allocation in enhancing efficiency and operational excellence in perishable supply chains.

Dynamic fitness-distance balance-based artificial rabbits optimization algorithm to solve optimal power flow problem

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (dFDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the dFDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (dFDBARO) algorithm. Moreover, the practicability of the proposed dFDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed dFDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the dFDBARO algorithm is available at https://www.mathworks.com/matlabcentral/fileexchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm.

Capacity acquisition and PPE distribution planning during the COVID-19 pandemic

As demand for personal protective equipment (PPE) surged during the COVID-19 pandemic and healthcare facilities faced supply shortages, the availability and distribution of PPE, being critical in fighting the pandemic, required pooling resources from multiple sectors. We explore such a resource pooling problem and propose to build up capacity by acquiring distribution and storage capacity from private carriers in return for compensation. We present a capacity acquisition and PPE distribution planning model under sufficient supply to optimally acquire storage and distribution capacity from third parties to fulfill demand. We then consider insufficient supply and propose a priority-based model based on pandemic severity. Finally, we model uncertain demand through two-stage stochastic programming with recourse and solve it using Benders decomposition and sample average approximation. We build a test case for the province of Ontario, Canada and provide detailed analysis under varying supply and demand scenarios.

A Two-Phase Pattern Generation and Production Planning Procedure for the Stochastic Skiving Process

The stochastic skiving stock problem (SSP), a relatively new combinatorial optimization problem, is considered in this paper. The conventional SSP seeks to determine the optimum structure that skives small pieces of different sizes side by side to form as many large items (products) as possible that meet a desired width. This study studies a multiproduct case for the SSP under uncertain demand and waste rate, including products of different widths. This stochastic version of the SSP considers a random demand for each product and a random waste rate during production. A two-stage stochastic programming approach with a recourse action is implemented to study this stochastic N P -hard problem on a large scale. Furthermore, the problem is solved in two phases. In the first phase, the dragonfly algorithm constructs minimal patterns that serve as an input for the next phase. The second phase performs sample-average approximation, solving the stochastic production problem. Results indicate that the two-phase heuristic approach is highly efficient regarding computational run time and provides robust solutions with an optimality gap of 0.3% for the worst-case scenario. In addition, we also compare the performance of the dragonfly algorithm (DA) to the particle swarm optimization (PSO) for pattern generation. Benchmarks indicate that the DA produces more robust minimal pattern sets as the tightness of the problem increases.

FUZZY LOGIC IN DISTANCE MEASUREMENT: COMPARATIVE ANALYSIS

Measuring distance plays a pivotal role across a multitude of domains, such as robotics, automation, and navigation systems. Fuzzy logic has increasingly garnered substantial interest as a means to augment the accuracy and versatility of distance measurement systems. This comprehensive review delves deep into the examination and juxtaposition of diverse techniques for measuring distance by harnessing the power of Fuzzy logic. The primary objective is to meticulously assess the efficacy of Fuzzy logic when contrasted with alternative methodologies and affirm their dominance regarding precision, resilience, and adaptability. Fuzzy logic holds the potential to revolutionize distance measurement technology and contribute to safer and more efficient operations in fields such as robotics, automation, and navigation systems. Future research should focus on fine-tuning fuzzy logic parameters and exploring new applications to further harness their potential. Fuzzy logic outperforms conventional control methods in distance measurement due to their remarkable adaptability, robustness, human-like decision-making capabilities, ease of integration, capacity to handle non-linearity, and real-time adaptability. While traditional methods have their merits, they often struggle to match the versatility and reliability of Fuzzy logic in a world where complex and uncertain environments demand precision and adaptability. As technology continues to evolve and applications become more demanding, Fuzzy logic remains at the forefront of enhancing the accuracy and efficiency of distance measurement systems across various domains. Keywords: Fuzzy Logic, Distance Measurement, Ultrasonic sensors, Non-linearity, Measurement accuracy.

UPPER BOUND FOR THE COMPETITIVE FACILITY LOCATION PROBLEM WITH DEMAND UNCERTAINTY

We consider a competitive facility location problem with two competing parties operating in a situation of uncertain demand scenario. The problem to find the best solutions for the parties is formulated as a discrete bi-level mathematical programming problem. In the paper, we suggest a procedure to compute an upper bound for the objective function on subsets. The procedure could be employed in implicit enumeration schemes capable to compute an optimal solution for the problem under study. Within the procedure, additional constraints iteratively augment the high-point relaxation of the initial bi-level problem, what strengthens the relaxation and improves the upper bound’s quality. New procedure to generate such cuts allows to construct the strongest cuts without enumerating the parameters encoding them.

Data Envelopment Analysis Approaches for Multiperiod Two-Level Production and Distribution Planning Problems

This paper deals with two-level production and distribution planning problems in supply chain management where the leader is a distributor and the follower is a manufacturer. Assuming that the distributor can observe the input–output data in the production process, we formulated the data envelopment analysis (DEA) production problem corresponding to the production planning problem of the manufacturer. This paper proposes a novel data envelopment analysis (DEA) approach to solve a challenging multiperiod two-level production and distribution planning problem in supply chain management. The innovative idea behind the proposed approach is to allow the distributor to observe the input–output data regarding the production activities of the manufacturer, even if the distributor cannot fully comprehend all parameters of the manufacturer’s production cost minimization problem. This approach addresses the challenge of uncertain demands by employing a two-stage model with simple recourse and considering the usage of the input–output data. The paper demonstrates the validity of the proposed DEA approaches through computational experiments and discusses the accuracy, reliability, and importance of the input–output data. The proposed approach provides a practical and effective solution to the multiperiod two-level production and distribution planning problem in supply chain management, and can help decision-makers improve the efficiency and effectiveness of their operations. The innovative idea of allowing the distributor to observe the input–output data about the production activities of the manufacturer is a significant contribution to the field of supply chain management and has the potential to advance the state of the art in this area.

Coordinated design of multi-stakeholder community energy systems and shared energy storage under uncertain supply and demand: A game theoretical approach

Shared energy storage plays an important role in achieving sustainable development of renewable-based community energy systems. In practice, the independent or disordered planning of community energy systems and shared storage systems can lead to suboptimal design without considering the complex interactions between neighboring energy systems. Therefore, a coordinated design approach for community energy systems and shared energy storage is proposed, and a pricing mechanism for storage sharing based on bounded rationality theory is developed. A Stackelberg game is introduced to enable consideration of storage sharing among energy systems at the design phase. The uncertainties of solar irradiation, wind speed, and electrical demand on both the supply and demand sides are also considered. Then, an uncertainty model is formed to optimize the system configurations, operation strategies, and storage sharing strategies of each stakeholder, simultaneously. Furthermore, the influence of the profit margin, storage investment cost, and carbon tax on the storage rental price and system design strategies are discussed. Illustrative examples highlight the feasibility and applicability of the shared storage pricing mechanism and coordinated design approach. This paper provides references for the government with respect to the problem of coordinated design for multi-stakeholder distributed energy systems and storages in a market-oriented environment.

T-S fuzzy model based event-triggered change control for product and supply chain systems

Product and supply chain systems are a class of complex nonlinear systems due to the uncertain demand fluctuation and unexpected events, and Takagi–Sugeno (T-S) fuzzy systems are suitable for modelling such product and supply chain systems with different production rates. According to the uncertainty customer demands, different subsystems which describe the corresponding production-inventory processes are orchestrated by the T-S fuzzy rule. Change control is designed to handle the supply chain blocking and event breaking caused by uncertain factors. Then, sufficient conditions are given such that the exponential stability with a prescribed bullwhip effect attenuation level can be guaranteed, and the control synthesis problem is transformed into the solvability of linear matrix inequalities for the product and supply chain systems. An example is introduced to verify the validness and efficiency of the proposed approach.