Annals of Operations Research最新文献

Accurate forecasting of electricity consumption in residential buildings is essential for effective energy planning, real-time load balancing, and demand-side management. As residential demand becomes increasingly variable due to the proliferation of smart appliances and renewable energy systems, robust and adaptive forecasting models are crucial. To address this need, this study introduces a hybrid forecasting model that combines a deep Temporal Convolutional Network (TCN) with a Fuzzy Wavelet Neural Network (FWNN). The TCN component captures long-range temporal dependencies in time-series data, while the FWNN integrates fuzzy logic and wavelet transforms within neural network architecture, allowing the model to effectively handle uncertainty, imprecision, and nonlinearity commonly observed in electricity consumption behaviors. To further improve predictive accuracy, the model’s hyperparameters are fine-tuned using the Aquila Optimization metaheuristic algorithm. The proposed model is evaluated on two real-world datasets containing minute-level and hourly electricity consumption records. Comparative experiments with several established baseline models show that the suggested hybrid approach consistently outperforms its rivals across key performance metrics. These results underscore the model’s accuracy and robustness, positioning it as a promising candidate for integration into modern residential energy management systems.

Facility location problems on networks deal with locating facilities (e.g., parks, schools, or health facilities) to serve a set of clients (e.g., citizens). Many existing facility location studies assume that it is possible to (re-)locate predetermined or build new facilities. However, this assumption is not realistic for situations when relocating or building facilities would be too expensive or impossible. Recognizing this challenge, Berman et al. (Ann Oper Res 40:1–16, 1992) first propose the facility network addition modification problems (FNAMPs) on networks that aim to add a given number of new edges (e.g., constructing new roadways or bridges) to improve the client accessibility to the facilities by minimizing the total accessibility or maximum accessibility cost objectives based on clients’ distances to the facility. Yet, all existing approaches for FNAMPs are only heuristics, provide no solution quality guarantees, and fail to scale to even moderate-sized network instances. In this paper, we revisit a special case of FNAMPs with binary demands. We develop approximation algorithms and efficient heuristics for this special case under the two cost objectives. We then consider the strategic aspects of the special case of FNAMPs in which clients’ locations are private information. We design scalable strategyproof mechanisms to address FNAMPs under the two cost objectives while incentivizing clients to report their locations truthfully. We conduct extensive experiments on synthetic and real-world networks to demonstrate the effectiveness and efficiency of all proposed methods against various baselines as well as the strategyproofness of the proposed methods.

This paper introduces the special issue on Ensemble Learning for Operations Research and Business Analytics. Its main purpose is to provide summaries for the 14 contributing research papers that were accepted for inclusion in this special issue. We first define an updated and extended taxonomy of ensemble learner architectures to characterize and differentiate ensemble learning algorithms. Subsequently, we characterize the special issue contributions in two ways: with respect to the operations research (OR) application they address and contribute to, and methodologically with respect to the newly defined taxonomy. Finally, we present an ambitious agenda for future research on ensemble learning for OR and business analytics.

The article investigates the existence of a stable coalition structure in coalition partition games dealing with a system of distinct representatives. The system of distinct representatives, or the transversal, is a coalition of agents whose members represent different groups. An agent gains utility if he/she belongs to a transversal. The transversal value of a cooperative game with coalition structure is introduced to solve the coalition formation problem. It is demonstrated that any cooperative game has a partition that is simultaneously Nash stable, permutation stable, and total payoff maximization for the transversal value. The transversal value expresses the players’ payoff functions in the workgroup formation game and the game of chairpersons. A player’s payoff in the workgroup formation game is the monetary reward for the projects the player participated in. In the game of chairpersons, each player is interested in being the group’s leader. It is demonstrated that there exist stable coalition structures with a punctuality property in such games.

A systematic survival analysis approach is developed to integrate the fundamental concepts from conventional statistics and the more explanatory, algorithmic, and computational techniques offered by optimization and machine learning. The proposed methodology enables the discovery of the combinations of significant features associated with time to an event of interest and produces combinatorial patterns in large-scale datasets. The combinatorial survival patterns are obtained as algorithmic solutions of a mixed integer linear programming problem. Application of the developed pattern-based survival analysis approach is demonstrated in benchmark medical datasets.

Long-term carpooling is a convenient and stable way for demanders who travel to their destinations for a long time and have similar travel time. How to match drivers and riders effectively is a very important problem in long-term carpooling. This paper proposes a stable matching method for long-term carpooling. Firstly, the stable matching problem of long-term carpooling is described, and the relevant definitions of stable matching are given. Secondly, a mixed-integer programming model is constructed with the objective of maximizing the total utility. Then, a heuristic algorithm based on knowledge rules and Benders decomposition is proposed. Finally, numerical experiments on different scales validate the feasibility and effectiveness of the proposed method. The results show that the price of stability is relatively small compared with system optimum. On this basis, we explore how certain parameters such as the stability constraints, objective function, cost-sharing method, vehicle capacity and maximum detour ratio, might affect the matching scheme.

Uncertainty in demand, product deterioration, and sales termination due to food safety concerns can lead to substantial profit losses for members of the perishable product supply chain. The implementation of effective risk mitigation measures is crucial for maintaining the integrity of the fresh perishable product supply chain. Taking into account the retailer’s aversion to uncertainty, we have developed a mean-variance model to investigate the retailer’s risk prevention strategies, specifically the setting of import termination thresholds. Our analysis indicates that establishing an import termination threshold can be advantageous for both the retailer and the supplier, provided it is set within an optimal range. The retailer determines the import termination threshold by weighing uncertainties in demand, product deterioration, and sales termination risks. Furthermore, the retailer’s risk tolerance for these elements is complementary. Both the product delivery schedule and the trading period have a direct impact on the retailer’s decisions regarding the import termination threshold and profits, as they influence the rate of product deterioration. However, their effect on the supplier’s profits is nonmonotonic. When the product’s non-deterioration rate is high or the trading period or product delivery time is short, the retailer tends to set a lower import termination threshold. Numerical results further reveal a misalignment between the retailer’s and supplier’s preferences concerning market base, primarily due to the distinct risks they face. While the retailer generally benefits from higher market demand, the supplier may experience negative effects under certain conditions. These findings offer guidance and strategies for members of the perishable product supply chain to mitigate various risks, thereby enhancing profitability and sustainability during operations.

With the growing emphasis on low-carbon agriculture and increased public awareness, controlling fertilizer use and methane emissions from farmland through low-carbon policies has become essential for promoting sustainable operations in China’s agricultural product (AP) supply chain. This paper constructs a game-theoretic model to study the dynamics of low-carbon policies and their impact on supply chain performance. The study reveals key findings: (i) Under carbon tax policies, the optimal order quantity in a centralized decision-making framework exceeds that of a decentralized framework, and carbon emission reductions by agricultural firms are higher in the centralized setting. (ii) Carbon trading does not always benefit green agricultural product planting firms. When the unit carbon trading price falls below a certain threshold, the income of both green and conventional agricultural product planting firms is inversely related to the unit carbon trading price. (iii) A revenue-sharing contract facilitates the coordinated development of the supply chain, enabling both agricultural planting firms and sellers to achieve a win–win outcome under mixed carbon policy constraints.This study enriches the body of research on low-carbon supply chain operations for agricultural products, providing a theoretical foundation for decision-making by members of the green agricultural product supply chain under low-carbon policies. Additionally, it offers countermeasures, policy recommendations, and strategies for the development and governance of green agricultural product supply chains, serving as a reference for governments to craft more effective and targeted policies.

River exploration during, before, or after floods enables operators in civil protection and disaster control to better prepare for or even prevent disasters. While typically, this river exploration is done by boat, truck, helicopter, or even not at all, autonomous flying drones equipped with a camera can enhance this process. Moreover, interaction between a truck and a drone can enable the drone to be used flexibly and extend its short range. Thus, the Bavarian Red Cross equipped a truck with a drone for river coverage. Based on this real case, we introduce a truck drone arc covering problem (TD-ACP) for the application of river coverage. We formulate the TD-ACP as a mixed-integer linear program and introduce valid inequalities that strengthen the formulation and allow us to solve realistic-sized instances to optimality. In a real-world case study involving an actual river, we demonstrate that using drones for river coverage can reduce coverage time by 56.3% compared to boats and by 28.1% compared to trucks. Additionally, we propose a manual planning heuristic that is straightforward for practitioners to apply and achieves an optimality gap of 4.0% on this specific river.