Annals of Operations Research最新文献

This paper aims to analyze the main contributions of Marco Tucci, with whom we had the great pleasure of guest-editing this special issue of the Annals of Operations Research. Unfortunately, Marco passed away in December 2023. Therefore, this special issue is dedicated to Marco, and this note summarizes his main contributions.

Home Health Care services aim to provide comprehensive care and support to patients in the comfort of their homes, ensuring a quality of service comparable to that of hospitals while also addressing additional objectives such as cost management and enhancing living conditions. Previous literature, exemplified by the paper authored by Euchi et al. (4OR 20(3):351–389, 2022), delineates the Home Healthcare Routing and Scheduling Problem (HHCRSP), presenting a taxonomy of its characteristics and constraints, along with an overview of state-of-the-art decision-making solutions. This study proposes an update to this research, highlighting the significant evolution of HHCRSP as it adapts to technological advancements and accommodates variant objectives and constraints across past, present, and future challenges. Through exhaustive literature reviews, this paper meticulously constructs a framework that delineates the intricate and diverse paths of HHCRSP’s evolution, fostering a deeper understanding of the impacts of emerging challenges such as digitization and sustainability. It offers invaluable insights for academic researchers and industry professionals, facilitating better alignment with the evolving landscape for consistently improved performance.

It is not uncommon that supply chain partners carry out cooperative advertising in green production involving green and dirty products (i.e., substitute products). Besides the advertising decisions, they need to jointly make many other decisions, such as substitute products’ production quantities, wholesale prices, and retail prices. Practice and literature have shown that manufacturers-retailers’ joint decision-making is of paramount importance yet challenging. This decision-making difficulty is compounded by governments’ carbon tax policies and financial subsidies. To facilitate firms in making decisions, this study examines the joint decision-making mechanism involving local governments’ carbon taxes and subsidies. To overcome the limitations of the relevant literature addressing one product and relatively fewer decisions, we include both dirty and green products and consider diverse decisions, including technology selection, production quantities, wholesale prices, and retail prices for both products. Additionally, we consider the retailers’ advertising investment decisions for both products and the manufacturers’ ratios of advertising investment paid to retailers. Capitalizing on decision interactions, we develop a Stackelberg game-based bilevel optimization model. Caused by the large number of decisions and their interactions, solving the game model analytically is barely possible. Consequently, we propose an algorithm of nested particle swarm optimization (NPSO). We perform numerical examples to show how the game model and the NPSO can help firms make complex joint decisions with many interactions. We also carry out sensitivity analysis based on which managerial insights are drawn.

In 2010, the European Commission set out the development of an economy based on knowledge and innovation as one of the priorities of its Europe 2020 strategy for smart, sustainable, and inclusive growth. This culminated in the ‘Youth on the Move’ flagship initiative, aimed at enhancing the performance and international attractiveness of Europe’s higher education institutions and raising the Union’s overall education and training levels. Therefore, it is relevant to assess the performance of the ‘Youth on the Move’ initiative via the creation of composite indicators (CIs) and, ultimately, monitor the progress made by European countries in creating a positive environment supporting learner mobility. For this reason, we make use of the CI-building ‘Benefit-of-the-Doubt’ approach, in its robust and conditional setting to account for outliers and the human development of those nations, to exploit the European Commission’s Mobility Scoreboard framework between 2015/2016 and 2022/2023. Furthermore, we incorporate the value judgements of experts in the sector to construct utility scales and compute weight restrictions through multi-criteria decision analysis. This enables the conversion of ordinal scales into interval ones based on knowledgeable information about reality in higher education. In the end, the results point to a slight performance improvement, but highlight the need to improve the ‘Recognition of learning outcomes’, ‘Foreign language preparation’, and ‘Information and guidance’.

This paper proposes a modeling and solution approach for the integrated planning of the planting and harvesting of sucrose cane and energy-cane considering multiple harvesters. An integer linear bi-objective optimization model is proposed, which seeks to find a trade-off between the maximization of the production volumes of sucrose and fiber and the minimization of the operational costs. The model considers the technical constraints of the mill, such as the milling capacity and meeting the monthly demand. A MIP-heuristic based on relax-and-fix and fix-and-optimize strategies with exact decomposition is appropriately proposed to determine approximations to Pareto optimal solutions to this problem. These approximations are used as incumbents for a branch-and-bound tree to generate potentially Pareto optimal solutions. The results reveal that the MIP-heuristic efficiently solves the problem for real and semi-random instances, generating approximate solutions with a reduced error and a reasonable computational effort. Moreover, the different solutions quantify the trade-off between cost and production volume, opening up the possibility of increasing sucrose and fiber content or decreasing the costs of solutions found. Thus, the proposed bi-objective approach, the solution technique and the different Pareto optimal solutions obtained can assist mill managers in making better decisions in sugarcane production.

We develop two alternate approaches to arbitrage-free, market-complete, option pricing. The first approach requires no riskless asset. We develop the general framework for this approach and illustrate it with two specific examples. The second approach does use a riskless asset. However, by ensuring equality between real-world and risk-neutral price-change probabilities, the second approach enables the computation of risk-neutral option prices utilizing expectations under the natural world probability ({mathbb{P}}). This produces the same option prices as the classical approach in which prices are computed under the risk neutral measure ({mathbb{Q}}). The second approach and the two specific examples of the first approach require the introduction of new, marketable asset types, specifically perpetual derivatives of a stock, and a stock whose cumulative return (rather than price) is deflated. These two asset types are designed specifically for hedgers who don’t have access to sovereign riskless rates or may be hesitant to utilize interbank rates such as SOFR.

This study investigates the relationship between climate change risks, namely transition and physical risks, and their predictive effects on Environmental, Social, and Governance (ESG) stock prices. We assessed the performance of various machine learning models by analyzing daily time series data from January 2006 to July 2022. Our results indicate that incorporating climate risk variables significantly enhances the accuracy and effectiveness of these models in predicting ESG stock market prices, highlighting the crucial role of climate-related factors in financial modeling. To better understand the dependencies between the variables, we employ a novel copula-based dependence measure (qda) to quantify the deviation from independence in the dependency structure. In addition, we utilized explainable artificial intelligence (XAI) techniques such as SHAP plots to interpret the complex machine learning algorithms used in this study. These techniques reveal the significant impacts of variables, such as inflation, recession, pollution levels, and climate risk indices, on the SP 500 ESG index. From a policy perspective, our findings emphasize the need for policymakers to integrate climate change risks into stock market regulations and guidance, thereby enhancing market resilience and supporting informed decision-making among investors.

This paper introduces a multistage stochastic mixed-integer programming model designed for a goal-based investing (GBI) problem, incorporating the option of goal postponement. Our model allows individuals to defer the fulfillment of their goals within a predefined timeframe. We emphasize the advantages of incorporating goal postponement into the GBI framework, including its ability to accommodate stage-preference ambiguity, address mistiming issues, and enhance utility for individuals. Theoretical results of a GBI problem with goal postponement are presented, and to tackle large-scale multistage GBI problems, we employ a decomposition algorithm known as stochastic dual dynamic integer programming (SDDiP). Numerical results demonstrate that the option to postpone a goal proves especially advantageous when goals are exposed to high inflation rates, and SDDiP emerges as a computationally efficient approach for handling large-scale GBI problems.