Annals of Operations Research最新文献

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.

In order to realize corporate sustainability, it is crucial for firms to embed environmental, social and governance (ESG) concerns into their business practices when embracing corporate social responsibility (CSR). The evaluation of business performance thus far provides limited support for the promotion of corporate sustainability since it rarely takes the nonfinancial information of ESG into account. Although much research has been carried out on the association between CSR and business performance, little attention has been given to incorporating CSR within the production system from an efficiency perspective. Using the well-known two-stage (profitability–marketability) data envelopment analysis (DEA) as a backdrop, this paper presents an extended framework that incorporates CSR, profitability and marketability into a parallel-series (mixed) structure of network DEA. A directional network slacks-based measure model, which allows the presence of shared resources and nonpositive data, is formulated to better evaluate firms’ business performance. The proposed model is illustrated with an empirical application to overall and three divisional efficiencies of 71 firms that were awarded Top 100 Excellence in CSR in Taiwan. The empirical results indicate that most sampled firms do not achieve full efficiency, highlighting considerable room for improvement. Among the dimensions of profitability, CSR, and marketability, CSR efficiency is relatively the lowest, yet it shows a strong positive association with overall efficiency, underscoring its crucial role in promoting sustainable business operations. In contrast, a negative relationship between profitability and CSR efficiencies suggests potential trade-offs between financial performance and social responsibility. The proposed network DEA framework enables firms to assess their competitiveness across these dimensions and set appropriate improvement targets, thereby supporting more sustainable business practices. These findings also provide valuable insights for investors seeking to make informed, ESG-oriented investment decisions.

Restless multi-armed bandits (RMAB) are a popular framework for modeling resource allocation and scheduling problems arising in various applications. Such applications can be modeled as Markov decision processes (MDP), but optimal or sub-optimal solution through dynamic programming suffer from high complexity. RMAB provides a heuristic solution, where the solution complexity scales linearly with the number of alternatives. However, these heuristic solutions are derived under the assumption that the model of all arms are known perfectly. In this paper, we consider RMAB with uncertainty in the rewards and dynamics of the arms. In such a setting, using a robust MDP solution is not possible due to high computational complexity. So, we consider a certainty equivalence approach and bound the additional loss in performance due to model inaccuracy. Our bounds are directly in terms of the model uncertainty of each arm and we illustrate their use via examples.

Multi-class labelling in the absence of ground truth is a known hard problem in the computational intelligence paradigms. This problem is amplified in the case of e-commerce due to both high volume and high velocity of information. Specifically, it is hard to find labels for mass online reviews, rendering it unsuitable for supervised learning. Till date, the most sought solution is manual labelling, which remains a labour-intensive and time-consuming task. The purpose of this study is to develop an end-to-end approach for identifying the sources of quality-stimulated customer dissatisfaction and automatically assigning them in the context of e-commerce. The above objective is achieved by using a novel ensemble-based semi supervised pseudo-labelling technique on a large corpus of Amazon.com reviews. As a first step, a subset is manually labelled, followed by an ensemble approach of retaining commonly labelled (pseudo) class to iteratively label the entire dataset. We then apply Large Language Models (LLMs) and Deep Learning (DL) architectures on the (pseudo) labelled data to accomplish a multi-class classification problem. We contrast and showcase statistically significant improvement to the baseline machine learning models, where the pre-trained transformer models demonstrate best performance. Our approach proposes a roadmap to streamline automatically identifying sources of quality-related dissatisfaction in e-commerce channels using an amalgamation of ensemble and sophisticated computational techniques. We believe that our approach, if adopted, can bolster grievance redressal for online customers.

In various popular group decision making problems, e.g., proposal funding review, decision makers are required to provide both score values and sorting labels for alternatives. In this study, this kind of preference structure is formally defined as preference-sorting structure, which is a hybrid preference representation system extending the cardinal preference (score) model by combining multiple ordered categories in the process of preference modeling. In some cases, due to time and capacity limitations, the alternatives evaluated by a decision maker may constitute only a subset of the total alternatives. Several appealing axioms are introduced to characterize an “ideal” distance functions for preference-sorting structures in both complete and incomplete scenarios, respectively. Each set of axioms imply the existence of a unique distance function for complete and incomplete scenarios where the distance function for incomplete preference-sorting structures generalizes that for complete structures. Furthermore, based on the unique distance function for incomplete preference-sorting structures, an axiomatic distance approach with desirable properties is developed to aggregate individual incomplete preference-sorting structures. Through theoretical proof and simulation analysis, it is demonstrated that the group preference-sorting structure derived from the axiomatic distance approach exhibits a strong correlation with both the median score and median sorting.

This editorial introduces the special issue Complexity, Nonlinearity and High Frequency Financial Data Modeling: Lessons from Computational Approaches in Annals of Operations Research, which brings together 19 contributions exploring advanced methods and applications in the analysis of financial markets. The collected works reflect the growing importance of complexity and nonlinear dynamics in understanding modern financial systems, marked by high volatility, interdependence, and structural shifts. The papers are organized thematically into five main areas: (i) complexity and nonlinearity in financial markets, (ii) advanced forecasting and econometric modeling, (iii) network theory, causality, and information flows, (iv) banking, credit risk, and economic growth, and (v) continuous-time and structural model reviews. There is an additional section on methodological innovations, which include time–frequency and multi-scale analysis, recent developments of nonlinear and regime-switching models, machine learning, and complex network approaches. A heartfelt tribute is dedicated to the late Marco Tucci, co-editor of this special issue, whose vision and scholarly contributions significantly shaped its content. Sadly, Marco passed away while we were in the process of compiling this special issue. The editorial concludes by highlighting common methodological threads, synthesizing key insights, and outlining promising avenues for future research in complexity-informed financial modeling.