Annals of Operations Research最新文献

In this study, a reliability model grounded in Markov reward processes is introduced to evaluate the performance behavior of k-out-of-n systems with competing risks. These risks stem from two failure modes: one occurs when the system reaches an absorbing condition, and the other arises once the number of transfers from a perfect to an imperfect functioning state reaches a specified threshold. Identifying the optimal component configuration and the best redundancy strategy is crucial for designing reliable and economical k-out-of-n systems. For this purpose, a two-stage optimization model is constructed with the objective of minimizing the expected overall cost with specified reliability requirements. Its first stage involves searching for the optimal values of k and n, while the second stage focuses on determining the optimal constraint regarding the number of transfers. By employing the theory of aggregate stochastic processes, necessary model parameters are defined and their analytical formulas are derived. Meanwhile, corresponding simulation procedures are presented to verify the accuracy of the obtained formulas. Subsequently, a 1-D search algorithm for addressing our two-stage optimization model is provided. A numerical example in the context of offshore oil platform power systems is showcased to elucidate the reliability model and two-stage optimization model.

This paper examines the effects of consumer privacy information on firms’ strategies, profits, and competition within an online marketplace, analyzing a setting with an online platform, two retailers, and consumers where information sharing is governed by revenue-sharing contracts. We establish the optimal information-sharing strategy for the online platform and explore the equilibrium outcomes under various consumer privacy choices. Our analysis reveals several key findings. First, sharing all information with retailers is not optimal for the online platform, as sharing only partial information benefits both the platform and retailers. This aligns their profit incentives and allows for greater capture of the consumer surplus. Second, the online platform and retailers prefer a scenario where consumers can protect their privacy, which leads to higher profits. However, consumers are worse off when information is shared with both retailers, as personalized pricing reduces the consumer surplus. Our results suggest that any policy interventions regarding consumer privacy should be carefully considered, accounting for their impact on industry profits and consumer welfare. Overall, this study elucidates the interplay between consumer privacy choices and information-sharing strategies, providing insights for the development of privacy protection regulations.

We examine how greenwashing affects the strategies and outcomes of companies and consumers. We develop a two-stage game, where a monopolist sets price and invests in environmental quality in the first stage, and competes with a new entrant in the second stage. The incumbent company is genuinely environmentally friendly, while the new entrant may use deceptive green marketing. We assume that only inexperienced consumers can be influenced by greenwashing, and consider two important dynamic factors, i.e., a change in competitive structure and a learning effect in the market. We investigate the conditions under which greenwashing is profitable for the new entrant, the ways in which the incumbent company responds to it, and the impact of greenwashing on the environment and consumers. We find that greenwashing can be mutually beneficial for both firms thanks to higher market potential and the incumbent’s first-period actions. Customers always suffer from greenwashing, and in rare cases, greenwashing can be beneficial to enhance environmental quality.

Climate change and the pressing need for green transformation represent one of the most critical challenges of global economic and environmental systems. In response, this special issue examines how operations research (OR) can drive sustainable supply chain governance through technological innovation and institutional power. Comprising 42 rigorously reviewed articles, this collection explores diverse themes such as low-carbon transformation, circular economy strategies, energy system optimization, digitalization, and socio-institutional mechanisms. These studies introduce innovative OR applications, such as AI and blockchain implementations, game-theoretic frameworks, and policy assessment mechanisms, to navigate intricate sustainability trade-offs and facilitate multi-stakeholder decision-making within green supply chains. By integrating theoretical advances with practical insights, this issue offers valuable contributions for researchers, practitioners, and policymakers aiming to achieve sustainability in supply chain operations. It underscores the imperative of interdisciplinary collaboration and systemic approaches to foster resilient, inclusive, and ecologically responsible supply chains in the era of climate urgency.

Due to the constant growth in urban population and the development of e-commerce, the demand for goods and transport of merchandise in densely populated areas have increased significantly. This often leads to traffic congestion, noise and environmental pollution. As a result, conventional vehicles can no longer access urban areas, encouraging multilevel distribution policies and favoring the adoption of alternative fuel vehicles (AFVs) which are capable of reducing the disruptive effects of last-mile distribution. We introduce the two-echelon vehicle routing problem with time windows, satellite synchronization, grey zones and covering options (2E-MTVRPTW-SS-GZ-CO) that incorporates relevant constraints encountered in last-mile logistics such as the inclusion of grey zones, time windows, spatial and temporal synchronization at satellites, multiple trips for second-level vehicles, and the deployment of Customer-to-Parcel (C2P) stations. This problem arises in the research of new sustainable delivery schemes for urban distribution and aims to reduce last-mile transportation costs. This study proposes a literature review on the subject and a mixed integer linear programming (MILP) formulation to solve small instances of the described problem. Moreover, a multi-step matheuristic based on a greedy randomized adaptive search procedure (GRASP) is also developed to solve larger instances. Finally, different distribution policies are studied to demonstrate the impact of grey areas, synchronization, and C2P stations on transportation costs.

Bi-objective portfolio optimization, which simultaneously seeks to minimize risk and maximize expected return, has been a central topic in financial research for several decades. This problem is typically formulated as a parametric quadratic programming model, whose set of optimal solutions defines the efficient frontier and its construction remains a major challenge, particularly in large-scale scenarios. To address this issue, we propose the parametric direct support method (PDSM) to solve the bi-objective portfolio selection problem under linear constraints. By adapting the support concept to the special structure of the problem, the novel approach computes iteratively and efficiently all the pivot points and traces out the entire efficient frontier. In contrast to classical approaches, our PDSM method takes into account the sparsity structure of the solutions when dealing with large-scale problems. This provides a key advantage to the proposed approach, which operates on small linear systems at each iteration, thereby significantly accelerating computation and reducing CPU time. We provide a theoretical analysis of the algorithm, establish its finite termination, and evaluate its computational complexity. Extensive numerical experiments on financial datasets and randomly generated instances demonstrate the scalability and superior performance of PDSM in solving large-scale portfolio optimization problems.

In disaster preparedness, strategically placing relief supplies is crucial to guarantee timely and adequate relief efforts. Important decisions in this process include determining optimal warehouse locations, assessing logistical resources, and strategically allocating critical supplies to distribution points. The inherent uncertainty surrounding a potential disaster amplifies the complexity of these decisions. We formulate a scenario-based multi-objective optimization model that integrates the advanced placement and allocation of relief supplies, extending the general form of a cooperative covering location problem. The proposed model maximizes demand coverage while minimizing underlying storage and logistics costs. Furthermore, the model accounts for diverse disruption scenarios using stochastic programming, treating the disaster impact of individual factors as random variables. Given large-scale disaster situations, our model evaluates the effect of potential disruptions, enabling the assessment of optimal network solutions. Based on an empirical case study focusing on the German national food stockpiling system, we demonstrate the feasibility of the introduced methodology in developing efficient stockpiling and preparedness strategies while facilitating the identification of vulnerabilities to enhance overall resilience. Our results show that incorporating stochastic factors, such as warehouse availability and operability, route failure, coverage radius limitations, and demand volatility, can significantly impact the optimal network configuration and influence demand coverage and total costs. Furthermore, the methodology proves to be both scalable and feasible when applied to a large-scale scenario.