Annals of Operations Research最新文献

Sensibly planning the subjects to study during a university degree is one of the most crucial tasks that impact the future professional life of a student. Nonetheless, to the best of our knowledge, no automated solution is available for students who want to plan their desired degree path and maximize the skills required by desired or target job(s). In this paper, we consider the Degree Planning Problem (DPP), which aims at computing degree plans composed of university subjects for students during the completion of an undergraduate degree. Specifically, we aim to obtain the best set of skills matching the requirements of students’ preferred job(s). To achieve this objective, we propose a flexible and scalable approach that solves the DPP in real-time by means of a non-trivial formalization as an optimization problem that can be solved with standard solvers. Finally, we employ real data from our University’s Bachelor in Information and Communications Technology to show, through several use cases, that our approach can be a valuable decision-support tool for students and curriculum designers.

Evidence of the impact of climate change has increased in recent years, forcing supply chain management (SCM) entities to promote sustainability in their operations. Meanwhile, global nations promote their sustainable development goals (SDGs) as one of the most important indicators to achieve sustainability regardless of application. SCM is a complex phenomenon of decision-making, which becomes even more complex when SDGs are adopted in SCM. Despite the central importance of the topic, only a small number of studies have reported on decision-making methods to improve the adoption of SDGs in SCM. To explore the state-of-the-art knowledge of operations research methods (ORMs) and models, we present this important Annals of Operations Research (ANOR) special issue. To introduce the special issue, we review the existing literature on models and methods to promote sustainability in SCM effectively and establish some associated future research directions. The findings of this editorial paper, as well as the articles in the special issue, can be used by both researchers and practitioners to consolidate recent advances and practices for establishing ORMs and models in SCM for effective decision-making to adopt SDGs. Additionally, this special issue paves the way for a solid foundation of advanced methods.

Bunkering costs constitute the largest portion of operational expenses in the shipping industry, directly influencing both economic efficiency and environmental impact. In line with Sustainable Development Goals (SDGs) 12 and 13, this study develops a decision model that jointly optimizes bunkering and sailing speed in liner shipping, with the aim of minimizing fuel costs and reducing carbon emissions. The model explicitly incorporates two often-overlooked aspects of the bunkering process: the requirement for empty tanks before refueling and the fuel inspection process. Due to the presence of infinite-dimensional and nonlinear terms, solving the model is computationally challenging. To address this complexity, we employ approximation algorithms and linearization techniques to transform the model into a mixed-integer linear programming (MILP) formulation. Additionally, we implement a Branch-and-Cut algorithm to enhance computational efficiency. Numerical experiments are conducted to evaluate the model’s performance, along with sensitivity analyses to assess the impact of key parameters. The results demonstrate that both the empty tank requirement and fuel inspection significantly influence bunkering decisions and sailing strategies, with the latter having a more pronounced effect. Moreover, our findings highlight the potential for sustainable fuel management practices to contribute to carbon reduction in maritime transportation. This study provides valuable insights for policymakers and industry stakeholders seeking to balance cost efficiency and environmental sustainability in shipping operations.

This paper investigates the impacts of trading behaviors on price discovery in futures markets. A dynamical model with difference equations is proposed to depict the interactions of heterogenous investors and the spot-futures coevolution. The system equilibrium and its stability conditions are mathematically analyzed. In the equilibrium, the futures price and the spot price converge to the fundamental value simultaneously. Stability conditions are necessary for the convergence process as well as the price discovery function. To ensure stability conditions, factors such as investor bounded rationality, risk appetites and market liquidity need to satisfy specific relationships. As the findings show, the arbitrage is not always beneficial to market stability and price discovery. It may increase price fluctuations in some cases. If investors have high degree of rationality, they tend to switch trading strategies with high intensity, which may destabilize the market. The simulations suggest the occurrence of complicated dynamics when stability conditions are violated. It provides theoretical insights into complicated phenomena in futures markets.

This paper proposes a game-theoretic model to investigate store brand (SB) introduction and information sharing decisions within a co-opetitive supply chain between a national brand (NB) manufacturer and a retailer, where the retailer chooses whether to introduce a store brand and share demand information with the NB manufacturer. In the main model, we consider that the retailer’s store brand introduction decision precedes the information sharing decision. We fully explore four scenarios involving the retailer’s strategic decision characteristics: no store brand introduction and no information sharing (NN), no store brand introduction and information sharing (NS), store brand introduction and no information sharing (IN), and store brand introduction and information sharing (IS). Our analysis draws the following interesting conclusions: Firstly, our research reveals that the NB manufacturer will benefit from SB introduction under certain conditions. Secondly, we demonstrate that the retailer’s store brand introduction and information sharing reinforce each other. Finally, our study also makes a contribution to how the degree of SB spillover and the fixed introduction cost affect the equilibrium strategy outcome.

Several recent works on non-atomic routing games suggest that the performance degradation of selfish routing with respect to optimal routing is overall low and far from worst-case scenarios. In this work, we study the performance degradation induced by the lack of coordination in an atomic routing game over parallel links in which there are two types of links. The latency function of "cheap" links is of the form (c_1 phi (x)), whereas the latency function of "expensive" links is of the form (c_2 phi (x)), where (c_2>c_1). We obtain an explicit characterization of the optimal and equilibrium flow configurations, and establish sufficient conditions on the latency function (phi (x)) under which the worst traffic conditions occur when all users have the same traffic demand and the total traffic demand is such that "expensive" link are marginally used by selfish routing. We also obtain some partial results on the worst network configuration for the inefficiency of selfish routing. All in all, our results suggest that the worst-case scenario for the inefficiency of selfish routing corresponds to very specific traffic conditions and to highly asymmetric network configurations, and thus that the Price of Anarchy is probably an overly pessimistic performance measure for non-cooperative routing games, as advocated in the above-mentioned works.

The performance level of a random weighted r-out-of-n system is measured by its total capacity. However, this measure is not meaningful for an arbitrary coherent structure as it does not involve the structure of the system. To overcome this drawback, we introduce here a new notion of performance measure (namely, the structural capacity) and then define three different notions of random weighted coherent systems, namely, Type-I, Type-II and Type-III systems. We then derive explicit formulas for computing the reliabilities of these systems. We further give a signature-based reliability representation for these systems. Further, we derive the Birnbaum marginal and joint reliability importance measures for the components of these systems and subsequently provide an algorithm for computing the same. Then, we study several ordering results based on these importance measures. For the Type-III random weighted coherent system, we introduce a new structure-based weighted importance measure and provide an algorithm for its evaluation. The developed results are illustrated through several numerical examples. Finally, we carry out the reliability estimation for a random weighted coherent system using two different simulated data sets.

In this paper, we focus on the certification cost for green bonds to understand the decision rationale of an issuer in regard to applying for a certification. We find that if this cost is null, requesting a certification is always optimal for the issuer, regardless of the outcome of the process. However, with a strictly positive cost, we show the existence of a threshold above which the certification cost becomes too high and applying for a certification is no longer optimal. This threshold depends on the distribution of the prior belief of potential buyers about the issuer’s commitment to its “green” claim before the issuance announcement and the certification process. We find that the more positive this prior belief is, the lower the threshold is.

Society currently experiences a growing interest in renting as an alternative to exclusive ownership (Fota et al., 2019; Yeganeh, 2021). This paper supports rental companies in this transition by providing a method to evaluate rental system occupancy. The method considers time-dependent systems in which customers rent substitutes during stockouts. Specifically, the paper provides a queueing model based on a continuous-time Markov chain for systems where rentals occur according to Poisson processes and rental times follow phase-type distributions. Additionally, the paper provides a heuristic optimization algorithm to minimize inventory capacity while maintaining an upper stockout probability limit. The paper evaluates these methods through numerical experiments, including a company case. The results indicate that the queueing model adequately reflects the occupancy distributions of the investigated items, and that the optimization algorithm minimizes the inventory capacity to near-optimality. In the company case, these methods reduce the inventory capacity while simultaneously improving the maximum stockout probability.

In this study, we propose a novel machine-learning-based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross-sectional regression analysis. The findings confirm that the proposed method effectively captures stock price crash risk, with the model demonstrating strong performance in terms of both statistical significance and economic relevance. Furthermore, leveraging a newly developed firm-specific investor sentiment index, the analysis identifies a positive correlation between stock price crash risk and firm-specific investor sentiment. Specifically, higher levels of sentiment are associated with an increased likelihood of stock price crash risk. This relationship remains robust across different firm sizes and when using the detoned version of the firm-specific investor sentiment index, further validating the reliability of the proposed approach.