Annals of Operations Research最新文献

The combined reserve of physical and production capacity by the government and private enterprise is currently one of the most widely adopted emergency relief reserve modes. However, the incomplete evaluation of the supplier’s resilience and the ignorance of its relationship with the implementation of production capacity reserve may hinder the timely and sufficient supply of relief during a disaster. To address the limitations, a relief reserve allocation approach considering the supplier’s resilience in a multi-type relief and multi-supplier scenario is proposed. The approach consists of two parts, first, a disaster simulation-based method considering all the node enterprises’ performance and the network structure is developed to evaluate the overall resilience of a supply chain network. Then, the overall resilience of the supply chain network is used to indicate the corresponding 1st-tier supplier’s resilience, and the correlation between the 1st-tier supplier resilience, the implementation of production capacity reserve, and the final delivered relief is identified to construct a stochastic programming model for the optimal allocation scheme of physical and production capacity reserve among alternative suppliers. A random sampling solution method is designed to solve the model. Numerical analysis and sensitivity analysis are conducted to verify the effectiveness of the model and solution method, and several managerial insights are proposed accordingly.

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 analyzes, which is novel in the literature, combined aimed and unaimed as reported by Lanchester (Aircraft in warfare: the dawn of the fourth arm, Tiptree Constable and Co. Ltd., London, 1916) warfare with variable kill rates. Each group possesses nine kinds of military equipment, i.e. planes, helicopters, artillery, tanks, armored personnel carriers, guided missiles, manpowered handheld offensive weapons, submarines, and air defense systems. Each group’s artillery, tanks and armored personnel carriers are attacked by the opposing group’s artillery in unaimed as reported by Lanchester (Aircraft in warfare: the dawn of the fourth arm, Tiptree Constable and Co. Ltd., London, 1916) warfare, and also attacked by the opposing group’s other eight kinds of military equipment in aimed as reported by Lanchester (Aircraft in warfare: the dawn of the fourth arm, Tiptree Constable and Co. Ltd., London, 1916) warfare. Simulations show how one 5% stronger group gradually eliminates the opposing group. If the first group lacks planes and helicopters, it loses most of its military equipment. It degrades especially the opposing group’s artillery, tanks, armored personnel carriers and submarines. It degrades partly the opposing group’s planes, helicopters, guided missiles. It eliminates the opposing group’s manpowered handheld offensive weapons. This article is useful for military analysts, budget allocators, and others composing groups where multiple kinds of military equipment impact each other multifariously. Recommendations include prioritizing air equipment for dominance, with limitations noted for modern factors like drones and munitions.

In this work we propose a bi-objective variant of the well-known 0/1 Knapsack Problem, that finds application in cases in which some item pairs may be seen as mutually conflicting. Previous variants considered in this scenario proposed to either avoid all conflicts, or to deal with them by considering the payment of appropriate penalty costs. We propose a different approach where the maximization of the profit and the minimization of the accepted conflicts are considered two different objective functions. We aim at identifying all Pareto-optimal solutions, so that a decision maker may choose a posteriori the optimal trade-off. We propose an exact resolution method based on the (epsilon )-constraint approach. Computational results on a wide set of instances show that our approach can be used in practice to identify and analyze their Pareto front.

This paper reassesses the role of gold, silver, and palladium as hedging and safe-haven instruments against exchange rate risk, across both daily and intra-day frequencies. A novel, parsimonious testing framework is introduced to identify and model time-varying correlations between precious metals and currency returns, addressing common sources of overfitting and spurious dynamics in multivariate volatility modelling. Using data spanning key market stress episodes—such as the COVID-19 pandemic and the first phase of the Ukraine—the analysis examines the conditions under which precious metals provide effective hedges. The results highlight substantial heterogeneity across currencies and frequencies, with silver performing best at high frequencies and gold offering the most capital-efficient hedges for emerging markets during daily stress episodes. A newly added analysis quantifies the consequences of modelling spurious dynamic correlation, demonstrating that incorrect assumptions can significantly reduce hedging effectiveness. Findings yield stylized, empirically grounded recommendations for investors and policymakers seeking to manage currency risk using precious metals.

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.

An extension of the concept of power domination, which is motivated by the context of the monitoring of electric networks, is introduced. Theoretical properties, models and algorithms are presented together with computational results.

Recirculating aquaponics, the integration of aquaculture and hydroponics, presents a promising solution to global food security, resource scarcity, and environmental degradation. This paper examines a techno-economic analysis for large-scale commercial aquaponics investments, introducing a Bayesian Network simulation methodology designed to mitigate financial risks and improve investment efficiency, profitability, and sustainability of aquaponic operations. The methodological approach presented here effectively captures the intricacies of such large-scale investments. Furthermore, this paper demonstrates the application of scenario extraction from simulated data, enabling the comparison of outcomes under a variety of conditions. These conditions, which might include low and high market demand, the influence of certification price premiums, and various marketing strategies, can be flexibly combined to create multiple scenarios. The design of the simulation model allows for flexible generation and combination of these conditions, providing a wide range of potential scenarios for comparison and analysis. Indeed, this approach's flexibility and versatility make it an effective decision support system, particularly useful in complex domains characterized by many variables and high levels of uncertainty and risk. While the focus here is on aquaponics, the methodology can be applied to other domains, improving decision-making processes, and leading to more informed and robust conclusions.

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.