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Maritime transportation plays a pivotal role in global trade and international supply chains. However, the sector is also a significant source of emissions. One of the most promising technologies for reducing these emissions is air lubrication, which involves installing bubbles along the hull of a ship. Despite its potential, the design of cost-effective bubble-installation plans for ship fleets over the planning horizon remains unexplored in the literature. This paper addresses this gap by proposing a mathematical programming model designed to optimize the installation of bubble-based systems. We present several propositions concerning the model's properties, supported by rigorous proofs. To validate the model's effectiveness, we conduct a series of computational experiments. The findings demonstrate that our optimization model enables shipping companies to devise bubble-installation plans that are cost-effective. This contribution not only extends the current understanding of emission reduction technologies in maritime transportation, but also offers practical insights for their implementation. [ABSTRACT FROM AUTHOR]

Stochastic optimization models always assume known probability distributions about uncertain parameters. However, it is unrealistic to know the true distributions. In the era of big data, with the knowledge of informative features related to uncertain parameters, this study aims to estimate the conditional distributions of uncertain parameters directly and solve the resulting contextual stochastic optimization problem by using a set of realizations drawn from estimated distributions, which is called the contextual distribution estimation method. We use an energy scheduling problem as the case study and conduct numerical experiments with real-world data. The results demonstrate that the proposed contextual distribution estimation method offers specific benefits in particular scenarios, resulting in improved decisions. This study contributes to the literature on contextual stochastic optimization problems by introducing the contextual distribution estimation method, which holds practical significance for addressing data-driven uncertain decision problems. [ABSTRACT FROM AUTHOR]

This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making. [ABSTRACT FROM AUTHOR]

Decision making under uncertainty is pivotal in real-world scenarios, such as selecting the shortest transportation route amidst variable traffic conditions or choosing the best investment portfolio during market fluctuations. In today's big data age, while the predict-then-optimize framework has become a standard method for tackling uncertain optimization challenges using machine learning tools, many prediction models overlook data intricacies such as outliers and heteroskedasticity. These oversights can degrade decision-making quality. To enhance predictive accuracy and consequent decision-making quality, we introduce a data transformation technique into the predict-then-optimize framework. Our approach transforms target values in linear regression, decision tree, and random forest models using a power function, aiming to boost their predictive prowess and, in turn, drive better decisions. Empirical validation on several datasets reveals marked improvements in decision tree and random forest models. In contrast, the benefits of linear regression are nuanced. Thus, while data transformation can bolster the predict-then-optimize framework, its efficacy is model-dependent. This research underscores the potential of tailoring transformation techniques for specific models to foster reliable and robust decision-making under uncertainty. [ABSTRACT FROM AUTHOR]

Maritime transportation safety is pivotal in international trade, with port state control (PSC) inspections being crucial to vessel safety. However, port authorities need to identify substandard vessels effectively because of resource constraints and high costs. Therefore, we propose robust predictive models and optimization strategies for vessel selection, using the random forest (RF) algorithm. We first use a traditional RF model serving as a benchmark, denoted as model M0. Then, we construct model M1 by refining the RF algorithm with a batch-processing method, thereby providing a better measure of the relative relationship between the predicted deficiency counts within a batch of ships. Then, we propose model M2, incorporating a decision-focused learning (DFL) framework into the tree construction process, enhancing the decision performance of the algorithm. In addition, we propose a variant model of M2, denoted as M2-0, considering the worst-case scenario when designing the decision loss function. By conducting experiments with data from the port of Hong Kong, we demonstrate that models M1 and M2 offer superior decision-making performance compared to model M0, and model M2 outperforms model M2-0 in both decision performance and stability. We further verify the robustness of these models by testing them under various instance scales. Overall, our study enhances the PSC inspection efficiency, ultimately bolstering maritime transportation safety. [ABSTRACT FROM AUTHOR]

In traffic flow, the relationship between speed and density exhibits decreasing monotonicity and continuity, which is characterized by various models such as the Greenshields and Greenberg models. However, some existing models, i.e., the Underwood and Northwestern models, introduce bias by incorrectly utilizing linear regression for parameter calibration. Furthermore, the lower bound of the fitting errors for all these models remains unknown. To address above issues, this study first proves the bias associated with using linear regression in handling the Underwood and Northwestern models and corrects it, resulting in a significantly lower mean squared error (MSE). Second, a quadratic programming model is developed to obtain the lower bound of the MSE for these existing models. The relative gaps between the MSEs of existing models and the lower bound indicate that the existing models still have a lot of potential for improvement. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the decision quality of the predict-then-optimize (PO) framework, an extensively used prescriptive analytics framework in uncertain optimization problems. Our primary aim is to investigate whether an increase in data size invariably leads to better decisions within the PO framework. We focus our analysis on two contextual stochastic optimization problems—one with a non-linear objective function and the other with a linear objective function—under the PO framework. The novelty of our work lies in uncovering a previously unknown relationship: the decision quality can deteriorate with increasing data size in the non-linear case and exhibit non-monotonic behavior in the linear case. These findings highlight a potential pitfall of the PO framework and constitute our main contribution to the field, offering invaluable insights for both researchers and practitioners. [ABSTRACT FROM AUTHOR]

• Develop a smart predict-then-optimize framework for targeted and cost-effective ship maintenance planning. • Determine the risk cost of a ship's deficiency code by code feature importance generated from the random forest model for ship detention prediction. • The smart predict-then-predict ship maintenance scheme reduces the total operating expenses of a ship by approximately 1% over the predict-then-optimize scheme and by at least 3% over unintelligent schemes. In maritime transportation, port state control (PSC) is the last line of defense against substandard ships. During a PSC inspection, PSC officers (PSCOs) identify ship deficiencies that lead to a ship's detention, which can cause severe economic and reputational losses to the ship operator. Therefore, this study innovatively uses PSC inspection data to design ship maintenance plans for ship operators to minimize overall operational costs. We identify three types of operational costs associated with each deficiency code: inspection cost, repair cost, and risk cost in the ship operators' decision-making process. The risk cost of a deficiency code is strongly related to the detention contribution of the deficiency items under a deficiency code, as indicated by the feature importance of that code in the random forest (RF) model used to predict detention outcomes. To design ship maintenance plans, the sequential predict-then-optimize (PO) method typically solves the optimization problem using input parameters, including the predicted probabilities of having deficiency items under each code and the three types of operational costs. However, the loss function in this two-stage framework does not consider the effect of predictions on the downstream decisions. Hence, we use a smart predict-then-optimize (SPO) method using an ensemble of SPO trees (SPOTs). Each SPOT uses an SPO loss function that measures the sub-optimality of the decisions derived from the predicted parameters. By exploiting the structural properties of the optimization problem analyzed in this study, we demonstrate that training an SPOT for this problem can be simplified tremendously by using the relative class frequency of true labels within a leaf node to yield a minimum SPO loss. Computational experiments show that the SPO-based ship maintenance scheme is superior to other schemes and can reduce a ship's total operating expenses by approximately 1% over the PO-based scheme and by at least 3% over schemes that do not use machine learning methods. In the long run, SPO-based ship maintenance plans also improve the efficiency of port logistics by reducing the resources needed for formal PSC inspections and alleviating port congestion. [ABSTRACT FROM AUTHOR]

The development of the Internet of things (IoT) and online platforms enables companies and governments to collect data from a much broader spatial and temporal area in the logistics industry. The huge amount of data provides new opportunities to handle uncertainty in optimization problems within the logistics system. Accordingly, various prescriptive analytics frameworks have been developed to predict different parts of uncertain optimization problems, including the uncertain parameter, the combined coefficient consisting of the uncertain parameter, the objective function, and the optimal solution. This tutorial serves as the pioneer to introduce existing literature on state-of-the-art prescriptive analytics methods, such as the predict-then-optimize framework, the smart predict-then-optimize framework, the weighted sample average approximation framework, the empirical risk minimization framework, and the kernel optimization framework. Based on these frameworks, this tutorial further proposes possible improvements and practical tips to be considered when we use these methods. We hope that this tutorial will serve as a reference for future prescriptive analytics research on the logistics system in the era of big data. [ABSTRACT FROM AUTHOR]

Port state control (PSC) is the last line of defense for substandard ships. During a PSC inspection, ship detention is the most severe result if the inspected ship is identified with critical deficiencies. Regarding the development of ship detention prediction models, this paper identifies two challenges: learning from imbalanced data and learning from unlabeled data. The first challenge, imbalanced data, arises from the fact that a minority of inspected ships were detained. The second challenge, unlabeled data, arises from the fact that in practice not all foreign visiting ships receive a formal PSC inspection, leading to a missing data problem. To address these two challenges, this paper adopts two machine learning paradigms: cost-sensitive learning and semi-supervised learning. Accordingly, we expand the traditional logistic regression (LR) model by introducing a cost parameter to consider the different misclassification costs of unbalanced classes and incorporating a graph regularization term to consider unlabeled data. Finally, we conduct extensive computational experiments to verify the superiority of the developed cost-sensitive semi-supervised learning framework in this paper. Computational results show that introducing a cost parameter into LR can improve the classification rate for substandard ships by almost 10%. In addition, the results show that considering unlabeled data in classification models can increase the classification rate for minority and majority classes by 1.33% and 5.93%, respectively. [ABSTRACT FROM AUTHOR]

Researchers and industrial practitioners are now interested in combining machine learning (ML) and operations research and management science to develop prescriptive analytics frameworks. By and large, a single value or a discrete distribution with a finite number of scenarios is predicted using an ML model with an unknown parameter; the value or distribution is then fed into an optimization model with the unknown parameter to prescribe an optimal decision. In this paper, we prove a deficiency of prescriptive analytics, i.e., that no perfect predicted value or perfect predicted distribution exists in some cases. To illustrate this phenomenon, we consider three different frameworks of prescriptive analytics, namely, the predict-then-optimize framework, smart predict-then-optimize framework and weighted sample average approximation (w-SAA) framework. For these three frameworks, we use examples to show that prescriptive analytics may not be able to prescribe a full-information optimal decision, i.e., the optimal decision under the assumption that the distribution of the unknown parameter is given. Based on this finding, for practical prescriptive analytics problems, we suggest comparing the prescribed results among different frameworks to determine the most appropriate one. [ABSTRACT FROM AUTHOR]

The maritime industry is shifting toward sustainability to help combat climate change, given the sector's significant contribution to global greenhouse gas emissions. This study focuses on the strategic integration of hydrogen fuel—a zero-emission alternative—into inland waterway transport systems. We recognize the operational characteristics of inland shipping networks and the complexities of two-stage financial planning. Consequently, we propose a two-stage stochastic framework for the deployment of hydrogen refueling hubs, taking into account financial uncertainties in the future. This approach contrasts with a myopic strategy and a two-stage deterministic framework, addressing both immediate financial constraints and future uncertainties. We focus on the Yangtze River's shipping network as a case study, and our analysis validates the superior performance of our proposed two-stage stochastic strategy. This suggests its potential to accommodate volatile future financial uncertainties while ensuring the economic and operational viability of hydrogen integration. [ABSTRACT FROM AUTHOR]

• Container slot allocation and pricing problem with uncertain shipping demand. • Two chance-constrained programming models under two slot allocation strategies. • A tailored SAA-RLT solution algorithm to solve the models. This paper studies the container slot allocation problem for time-sensitive cargoes with consideration of dynamic pricing and port congestion under uncertain demand. Time-sensitive cargoes call for express delivery. However, port congestion is a non-negligible factor to affect the delivery time. Hence, a new freight rate function with respect to the delivery time considering port congestion is proposed for time-sensitive cargoes, and two different slot allocation strategies are proposed: Loyal Strategy and Expansive Strategy. Accordingly, we formulate the problem as a one-stage container slot allocation model and a two-stage container slot allocation model, respectively. Both of the two models are stochastic mixed-integer quadratic programming models with chance constraints due to the involvement of dynamic pricing, port congestion, and uncertain demand. To solve the proposed models, a tailored algorithm that combines the Sample Average Approximation approach and the Reformulation Linearization Technique (SAA-RLT) is developed in this paper. Finally, numerical experiments are carried out to verify the applicability and effectiveness of the proposed models and the solution algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we study a dynamic container slot allocation problem (DCSAP) for a liner container shipping company that aims to make an acceptance or rejection decision to each dynamically arriving container slot booking request. To capture the dynamic arrival feature and real-time acceptance/rejection decision of the booking request, we formulate the DCSAP as a dynamic programming (DP) model with the objective of maximizing the total revenues generated by accepted container booking requests over the entire booking horizon. Due to the well-known curse of dimensionality of solving a DP model, we develop a series of models to transform the intractable DP model into a solvable approximate linear programming (ALP) model. We further propose a spatiotemporal-heterogeneity-based (STH-based) decomposition preprocessor by identifying the spatiotemporal property of the DCSAP before solving the ALP model. Extensive numerical experiments are conducted to assess the applicability of the developed research methodology. [ABSTRACT FROM AUTHOR]

Green scheduling is an important means to achieve sustainable industrial development and enhance the green efficiency of enterprises. Given the characteristics of a modern crude oil supply system, the shuttle tanker fleet green scheduling problem (FGSP) is discussed considering a carbon tax and variable tanker speed factor. To minimize the green operating cost (i.e., sum of general operating cost and carbon tax) of the tanker fleet, an integer programming model for shuttle tanker fleet green scheduling (FGSM) is established. The FGSM optimizes the number and sizes of tankers, the number and positions of floating production storage and offloading units (FPSO) at which to berth and the scheduling plan (i.e., berthing order and sailing speed) of each tanker in the fleet. Based on the column generation algorithm, a shuttle tanker fleet green scheduling algorithm is designed to solve the above model accurately. The experimental results show that considering the speed factor, the green operating cost of an example fleet decreases. For different carbon tax rates, speed optimization is an effective way to reduce the green operating cost of the fleet. The above results also show that the FGSM and algorithm can effectively solve the FGSP, improve the operation level and efficiency, and reduce the green operating cost of oil companies. • A model is established to optimize fleet design and scheduling plan. • The model aims to minimize the green operating cost of the fleet. • The model considers the impact of carbon tax and vessel speed factor. • A column generation algorithm is designed to solve the model accurately. [ABSTRACT FROM AUTHOR]