Annals of Operations Research最新文献

The Board Packing Problem (BoPP) considers a rectangular board divided in cells with m rows and n columns. In this problem, a subset from a set of rectangles with different costs may be allocated on the cells, and in turns, each cell has an associated revenue obtained if a rectangle is placed on it. The objective of the BoPP is to allocate rectangles on the board, covering cells in order to maximize the total profit, measured as the revenues of the selected cells where the rectangle is placed minus the cost of purchasing such rectangles. The revenue of a cell is collected only once, and only if a rectangle is covering the cell. We propose a Variable Neighborhood Descent (VND) approach for solving the BoPP. Two constructive procedures are proposed for generating the initial solution for the VND: a totally greedy approach and a greedy randomized method to favor diversity. The experimental comparison analyses the contribution of each component of the final algorithm and then performs a competitive testing to evaluate the performance of the algorithm when comparing it with the best method found in the state of the art. The superiority of the proposal is supported by non-parametric statistical tests.

Promoting low-carbon innovation (LCI) in firms through government subsidies has emerged as a widely-used policy instrument for achieving sustainable development, directly contributing to the United Nations Sustainable Development Goals (SDGs), including SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Considering a complementary supply chain consisting of a supplier and a manufacturer, where the manufacturer assembles components produced in-house and by the supplier into one final product. The government aims to enhance social welfare or LCI product innovation levels. To this end, the government has four potential subsidy strategies: supplier subsidy, manufacturer subsidy, consumer subsidy, and low-carbon infrastructure subsidy. Our research shows that first, although subsidizing low-carbon infrastructure can enhance social welfare, it offers minimal improvement in product LCI. Second, subsidies directed towards suppliers yield limited advancements in social welfare and LCI. Third, when consumers are price-sensitive, subsidizing the manufacturer can maximize both LCI and social welfare. Conversely, when consumers are LCI-sensitive, allocating subsidies toward the supplier is more beneficial. In summary, our research extends the existing literature on complementary supply chain management by identifying the optimal subsidy for the government to achieve its goals, offering tangible insights into practical pathways for realizing SDG 7, 9, 12, and 13 through targeted policy design.

This study considers the impact of supply and demand uncertainty on last-mile distribution in a disaster-relief supply chain with multiple Staging Areas (SAs) and one Point of Distribution (POD). The SAs function as temporary warehouses for receiving and organizing relief supplies that are subsequently transported to the POD where they are distributed to meet the stochastic demands of disaster survivors. The goal of this paper is to evaluate the effectiveness of policies for dispatching vehicles from the SAs to the POD in an effort to minimize unsatisfied demand at the POD. Prior literature suggests that continuously dispatching vehicles is an effective and pragmatic policy, but it has not been tested on a relief network with multiple Staging Areas. This study establishes criteria under which continuous dispatching (CD) is an optimal policy and examines its performance through computational experiments. We solve problem instances using approximate dynamic programming since the stochastic dynamic programming model cannot be solved directly. Our findings suggest that the CD policy does not perform as well as in previous studies, but may be the best available option from a practical perspective.

Gerrymandering—the deliberate manipulation of electoral district boundaries for political advantage—is a persistent challenge in U.S. elections. In this work, we introduce and analyze Votemandering, a strategic blend of gerrymandering and targeted political campaigning devised to gain more seats by circumventing fairness measures. Votemandering leverages accurate demographic and socio-political data, bolstered by advancements in technology and data analytics, to influence voter decisions in pursuit of subtle gerrymandering strategies. We formulate votemandering as a Mixed Integer Program (MIP) that performs fairness-constrained gerrymandering over multiple election rounds. We analyze the influence of various redistricting constraints and parameters on votemandering efficacy. We explore the interconnectedness of gerrymandering, substantial campaign budgets, and strategic campaigning, illustrating their collective potential to generate biased electoral maps. A case study of Wisconsin State Senate redistricting reveals significant votemandering potential. Our findings underscore the need for reforms in the redistricting process beyond enforcing thresholds for specific fairness metrics.

The purpose of this paper is to investigate the impact of sentiment metrics derived from social media on stock market attributes, including returns and trading volumes, within three liquidity classes in the Russian emerging market. In this paper, we conduct a textual analysis of users' posts from two Russian-language online platforms: Tinkoff Pulse and MFD. We compare different artificial intelligence algorithms to solve the problem of classifying large textual data. This allows us to determine that the Stacking ensemble method with unoptimised hyperparameters of the metaclassifier gives the best results when classifying text data (processed with TF-IDF), achievincy of about 62% for the test set. With this method, we construct a series of sentiment metrics for retail investors to study their impact on stock returns and trading volumes. The results show that investor sentiment is a significant factor explaining stock returns in most quantiles. In the process of analysing retail investor sentiment, it was found that attention and divergence indices affect retail investor behaviour for different quantiles of stock returns and trading volumes. For example, attention indicators have quite different effects on stock returns for different quantiles, suggesting the presence of several heterogeneous groups of retail investors. Young traders tend to rely on the opinions of Tinkoff Pulse users regarding first-tier (blue chip) stocks, which reflects investors' cognitive biases quite accurately. However, investors who express confidence in sentiment on the MFD platform have a different cognitive pattern, which is explained by the characterisation of users on the MFD platform. These users include older individuals with more trading experience who are more reticent to social media optimism and are more likely to perceive optimism as a signal to sell stocks. Based on the findings, this paper enhances the understanding of the role of retail investor liquidity in emerging financial markets. The lack of analysis of private investors' sentiment and explanation of market trends may not cover a significant gap in knowledge about the psychology of market participants in liquidity classes. Our results provide a foundation for future research that should be useful for investors, regulators, and developers of online social platforms.

We study capacity coordination in a dynamic duopoly with perfect Cournot complements where firms face investment adjustment costs when choosing their production capacities. We show that optimal capacities must become identical from a certain date on. We perform a numerical analysis of the optimal investment and capital paths and the optimal date at which capacities become equal. We find that eliminating overcapacity does not always mean decreasing the highest initial capacity (this is especially true when the firm with the lowest initial capacity has the highest depreciation rate). Moreover, it is possible that the investment of the firm with the lowest initial capacity is non-monotonic.

Workforce planning deals with determining the number of employees and associated skills necessary to meet the future operational needs of an organization. A workforce system consists of six elements: recruitment, attrition, promotion, training, retention, and scheduling. Historically, several workforce modeling and analysis methodologies have been developed to capture these elements. This paper reviews the results of workforce and manpower models published within peer-reviewed literature between 1959 and 2021 to provide an in-depth analysis of current models. The focus of this review is on analytical, simulation, and empirical models found in literature that were collected based on a citation requirement and keyword search criteria. Results demonstrate the trends in workforce modeling research and discuss the common uses of each model type and the advantages/disadvantages related to each model. Based on the common attributes of workforce systems, the discussion focuses on the most frequently used model type for each element and the best use for each model. Finally, recommendations are made for the development of workforce models that allow the most comprehensive view of the workforce systems of the future.

This study investigates the application of Multiple Criteria Decision Analysis (MCDA) methods for portfolio selection in the Vietnamese stock market using daily stock price data from the VN100 index spanning January 2015 to November 2023. Creating 150 criteria based on stock returns, volatility, and correlation, we employ five popular MCDA methods and four weighting methods to compare the performance of up to 500 portfolios. While MCDA methods may not effectively differentiate between stocks with higher and lower future returns, they consistently excel in selecting portfolios with superior risk-adjusted returns and lower drawdown compared to both the benchmark and the traditional Mean-Variance (MV) method. The PROMETHEE II (Preference Ranking Organization Method for Enrichment of Evaluations) method stands out as a robust performer, and the CRITIC (Criteria Importance Through Intercriteria Correlation) weighting method emerges as a reliable choice for constructing portfolios with favorable risk-adjusted returns. Moreover, the MCDA methods demonstrate potential computational efficiency over the tested MV implementation, enhancing their practicality. These findings highlight the practical utility of MCDA, particularly PROMETHEE II with CRITIC weighting, for navigating the complexities of portfolio optimization in dynamic emerging markets like Vietnam, offering a compelling alternative to traditional approaches.

When a disaster occurs, Humanitarian Logistics and Supply Chain Management (HLSCM) works to ensure the timely and appropriate distribution of relief goods. One is food distribution, one of the most needed essential supplies after a disaster. However, delays in the delivery of food logistics often occur, resulting in fatal consequences for disaster victims. In HLSCM, delays are referred to as risks, while the causes of delays are risk agents that must be identified. Identifying risk agents in distribution delays is crucial because it dramatically affects the entire HLSCM flow. This study proposes a hybrid literature search strategy combined with expert judgment to identify risk agents that cause food delivery delays in the HLSCM process. The results of this study indicate that there are 17 risk agents affecting food logistics delivery delays. Based on the mathematical aggregation of expert judgment results, the highest average values ​​are shown by three risk agents: “inventory quantity,” “road conditions,” and “communication problems.” This investigation fills the gap in the existing HLSCM literature by explicitly identifying and validating these risk factors. The hybrid literature search strategy combines a systematic literature review with expert judgment and provides a strong framework for future research. These findings offer practical insights for stakeholders to improve logistics efficiency and ensure timely aid delivery. While focusing on risk identification, this study lays the groundwork for future research to prioritize risk mitigation strategies and further refine HLSCM practices.

With the rapid development of e-commerce and platform economy, an increasing number of products exhibit network effects. Facing the network effects as well as system service congestion, the propensities of customers for interacting with other users are heterogeneous. In this paper, we investigate pricing in a two-stage sales model in the presence of network effects and customer heterogeneity, modeled as a single-server queueing model with congestion in each stage. In the two-stage sales system, initial sales are usually targeted at establishing the brand, while later sales are aimed at maximizing profit. In contrast to traditional sales, we find that the initial pricing decision under the influence of network effects can be optimized on either a subsidy or charging strategy. Specifically, our results indicate that the subsidy strategy is more advantageous under situations where either the second-stage price is relatively high or the strength of network effects is relatively low; otherwise, the charging strategy is better off. Further, it is revealed that strategic behavior of customers may drive prices up in the first stage, which in turn damages the firm’s revenue. Our results provide valuable insights that ignoring the impacts of network effects and strategic customer behavior may result in significant revenue loss to the firm.