Operations Research Perspectives最新文献

The educational system determines one of the significant strengths of an advanced society. A country with a lack of culture is less competitive due to the inequality suffered by its people. Institutions and organizations are putting their efforts into tackling that problem. Nevertheless, it is not an easy task to ascertain why their students have failed or what are the conditions that affect such situations. In this work, an intelligent system is proposed to predict academic failure by using student information stored by the Industrial University of Santander (Colombia). The prediction model is powered by the XGBoost algorithm, where a TOPSIS-based feature extraction and ADASYN oversampling have been conducted. Hyperparameters of the classifier were tuned by a cross-validated grid-search algorithm. We have compared our results with other decision-tree classifiers and displayed the feature importance of our intelligent system as an explainability phase. In conclusion, our intelligent system has shown a superior performance of our prediction model and has indicated to us that economic, health and social factors are decisive for the academic performance of the students.

We present a two-stage scheduling approach including proactive and reactive scheduling to solve the ground resource scheduling problem with uncertain arrival time. In the first stage, an integer programming model is constructed to minimize the delay and transfer costs. After solving this model, we obtain a baseline scheduling plan that considers the service arrival time uncertainty. In the second stage, the feasibility of the subsequent benchmark plan is evaluated based on the current state of the services and resources. The reactive scheduling model is enabled when trigger conditions are met. Moreover, an improved adaptive large neighborhood search is designed to solve the proactive scheduling model effectively. Real data from an international airport in South China is used as a test case to compare different scheduling strategies. The results show that it is difficult to handle the uncertainty of the problem with the benchmark plan that simply considered buffer time. Compared with rolling time-domain scheduling, the average transfer cost of the scheduling strategy proposed in this paper increased slightly, but the average service delay cost can be reduced significantly. Algorithm-wise, instances of different scales are designed to verify the effectiveness of the improved adaptive large neighborhood search algorithm. The efficiency of the algorithm scheme is better than that of the Gurobi solver scheme in medium to large-scale problems. Therefore, the forward and reactive strategies can better handle the uncertainty of airport ground protection services as they can simultaneously guide the allocation and utilization of airport ground protection resources.

Regulators’ increasingly stringent carbon rules to protect the environment are encouraging practitioners to modify their operational activities that are accountable for releasing emissions into the atmosphere. Thereby, practitioners dealing with product inventory planning are seeking proper management strategies not only to increase profits but also to reduce released carbons from operations. In addition, increasing uncertainty in supply operations has motivated suppliers to impose prepayment mechanisms in recent decades. This study examines the best prepayment installment policy for a practitioner for the first time, where the consumption behavior of consumers changes as a result of the combined effects of unit selling price and storage time. Moreover, to make the present inventory planning more realistic, the unit holding cost function is adopted as a power function of the inventory unit's storage period. The goal of this study is to provide the best combined installment for advance payment, price, and replenishment strategies for a practitioner under cap-and-price, cap-and-trade, and carbon tax environmental guidelines by ensuring maximum profit. For this purpose, an algorithm is created by combining all derived theoretical results from the analytical study, whereas the efficacy of the algorithm is assessed through the examination of five illustrative numerical instances. A plethora of noteworthy management insights for the practitioner are obtained by investigating the dynamic shifts in optimal strategies resulting from fluctuations in system parameters. The results reveal that if the demand is low in the nascent phases of the business cycle, then the prudent approach for the practitioner entails procuring a comparatively smaller lot-size using a modest number of payment frequencies and then setting a relatively small unit selling price to increase profits.

This paper is concerned with prescriptive price optimization, which integrates machine learning models into price optimization to maximize future revenues or profits of multiple items. The prescriptive price optimization requires accurate demand forecasting models because the prediction accuracy of these models has a direct impact on price optimization aimed at increasing revenues and profits. The goal of this paper is to establish a novel framework of prescriptive price optimization using optimal regression trees, which can achieve high prediction accuracy without losing interpretability by means of mixed-integer optimization (MIO) techniques. We use the optimal regression trees for demand forecasting and then formulate the associated price optimization problem as a mixed-integer linear optimization (MILO) problem. We also develop a scalable heuristic algorithm based on the randomized coordinate ascent for efficient price optimization. Simulation results demonstrate the effectiveness of our method for price optimization and the computational efficiency of the heuristic algorithm.

This study investigates a green supply chain consisting of a capital-constrained developer who sells a product via a platform. The parties interact via an agency contract, in which the platform charges a fixed proportion of the revenue gained from each sold unit and the developer receives the remaining sum. Since the development process is relatively protracted, at the early stages of this process, the commission rate to be charged by the platform is random from the developer’s perspective. Upon receiving information about the amount of capital the developer has committed to investing in greenness from his own resources, an external investor offers the developer a loan at a certain interest rate (to further enhance the developer’s investment in greenness), based on which the developer sets the product’s greenness level and selling price. The study provides a game-theoretic analysis of this model and compares its equilibrium solution with the optimal solution of a fully self-financing developer. The innovative feature of the study lies in its comparison between the case of a developer who might not be able to repay the loan, because his revenue from selling the product might be lower than the amount he is required to repay the investor (the loan plus interest), and the case in which it is certain that the developer will be able to repay any debt to the investor. Our study shows that, in the case where the investor takes on the financing risk, the customers benefit from a higher greenness level (albeit at a higher price), resulting in greater demand for the product.