Showing papers by "Shuaian Wang published in 2023"

Tutorial on prescriptive analytics for logistics: What to predict and how to predict

Abstract: 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.

An extended smart “predict, and optimize” (SPO) framework based on similar sets for ship inspection planning

Abstract: This study aims to address one critical issue in ship inspection planning optimization, where the first step is to accurately predict ship risk, and the second step is to assign scarce port inspection resources, aiming to identify as much non-compliance from the inspected ships as possible. A traditional decision tree is first developed as the benchmark. Then, to go from a good prediction to a good decision, the structure and performance of the following optimization problem are integrated in the prediction model, which we denote by integrated decision trees. Three modes are proposed to develop integrated decision trees with different combination ways and degrees. Especially, we innovatively propose the concept of “similar set” in data sets, and use the similar sets to select the hyperparameter tuple leading to the best decision optimization problem in mode 1. Then, the structure of the decision problem is considered into the decision tree construction facilitated by similar sets in mode 2. Finally, similar sets are used to integrate the performance of the following decision optimization problem directly into the decision tree construction process in mode 3. Numerical experiments show that mode 3 can achieve the best performance in the decision optimization model. Conservative estimations show that the proposed models can save at least millions to tens of millions inspection cost in Hong Kong dollars for the Hong Kong port each year, and up to 837 million inspection cost in Hong Kong dollars all over the world per year.

LNG Bunkering Station Deployment Problem—A Case Study of a Chinese Container Shipping Network

Abstract: Liquefied natural gas (LNG) is a promising measure to reduce shipping emissions and alleviate air pollution problem, especially in coastal areas. Currently, the lack of a complete infrastructure system is preventing the extensive application of dual-fueled ships that are mainly LNG-powered. Given that groups of LNG bunkering stations are under establishment in various countries and areas, the construction plan becomes critical. In this paper, we focus on the LNG bunkering station deployment problem, which identifies the locations of the stations to be built. A large-scale case study of China’s container shipping network was conducted. The problem scale of this case paper exceeds those in previous academic studies. Thus, this study better validates the model and solution method proposed than numerical experiments that are randomly generated. Sensitive analyses on the LNG price, bunkering station construction costs, and total budget were carried out. The results yielded provide practical suggestions and managerial insights for the competent department. In addition to building a complete bunkering system, subsidies to ship operators for consuming LNG and higher production efficiency in bunkering station construction also help promote the application of LNG as marine fuel.

Routing Optimization with Vehicle-Customer Coordination

Abstract: In several transportation systems, vehicles can choose where to meet customers rather than stopping in fixed locations. This added flexibility, however, requires coordination between vehicles and customers that adds complexity to routing operations. This paper develops scalable algorithms to optimize these operations. First, we solve the one-stop subproblem in the [Formula: see text] space and the [Formula: see text] space by leveraging the geometric structure of operations. Second, to solve a multistop problem, we embed the single-stop optimization into a tailored coordinate descent scheme, which we prove converges to a global optimum. Third, we develop a new algorithm for dial-a-ride problems based on a subpath-based time–space network optimization combining set partitioning and time–space principles. Finally, we propose an online routing algorithm to support real-world ride-sharing operations with vehicle–customer coordination. Computational results show that our algorithm outperforms state-of-the-art benchmarks, yielding far superior solutions in shorter computational times and can support real-time operations in very large-scale systems. From a practical standpoint, most of the benefits of vehicle–customer coordination stem from comprehensively reoptimizing “upstream” operations as opposed to merely adjusting “downstream” stopping locations. Ultimately, vehicle–customer coordination provides win–win–win outcomes: higher profits, better customer service, and smaller environmental footprint. This paper was accepted by Chung Piaw Teo, optimization. Funding: This research was supported by the National Natural Science Foundation of China [Grants 72288101, 52221005 and 52220105001]. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4739 .

Development of Connected and Automated Vehicle Platoons With Combined Spacing Policy

Abstract: Vehicle platoon has the potential to significantly improve traffic throughput and reduce fuel consumption and emissions and thus has attracted extensive attention recently. In this study, we propose a vehicle platoon of connected and automated vehicles (CAVs) with a combined spacing policy to enhance traffic performance. First, a combined spacing policy composed of the constant time gap (CTG) and constant spacing (CS) is formulated for the proposed vehicle platoon, where the leader adopts the CTG and the followers use the CS policy. Based on the $h_{2}$ -norm string stability criteria, the notion of exogenous-head-to-tail string stability is newly introduced, and the sufficient conditions of the local stability and string stability in the frequency domain are derived using the Routh-Hurwitz criterion and Laplace transform respectively. Numerical experiments are conducted to validate the string stability. The effectiveness of the proposed vehicle platoons is verified by theoretical analysis and numerical experiments using two typical scenarios and several measurements of effectiveness (MOE) in various performance aspects, including efficiency, safety, energy, and emission. The results show that the proposed vehicle platoon performs better than the CS-based vehicle platoon in all aspects except for efficiency. It also indicates that the proposed vehicle platoon has obvious advantages over the CTG-based vehicle platoon in efficiency and safety aspects. The findings have demonstrated the merits of the combined application of CTG and CS policies for the vehicle platoon in enhancing stability and traffic performance.

How to operate ship fleets under uncertainty

Abstract: Ships operated by a liner company are scattered around the world to transport goods. A liner company needs to adjust its shipping network every few months by repositioning its ships to respond to uncertain container shipping demand. Few studies investigate a liner company's multi-period heterogeneous fleet deployment problem under uncertainty, considering fleet repositioning, ship chartering, demand fulfillment, cargo allocation, and adaptive fleet sizes. To this end, this study formulates a mixed integer linear programming model that captures all of these elements. This study also designs a Benders-based branch-and-cut algorithm for this NP-hard problem. Two types of acceleration strategies, including approximate upper bound tightening inequalities and Pareto-optimal cuts, are applied to improve the performance of the algorithm. Extensive numerical experiments show that the proposed algorithm significantly outperforms CPLEX and its Benders decomposition framework in solving the model. We conduct an intensive analysis and find that multistage stochastic programming can lead to better solutions than two-stage stochastic programming. We also find that 10% of the benefit provided by the multistage model over the two-stage model is due to better fleet deployment decisions and that 90% of the benefit is due to better demand fulfillment and allocation decisions. By exploring three practical questions regarding driver analysis of liner company profitability, benefits analysis of adaptive fleet sizes, and the influence of the COVID-19 pandemic on liner shipping, we show how liner companies can benefit from managerial insights obtained in this study. This article is protected by copyright. All rights reserved

A Gaussian-Process-Based Data-Driven Traffic Flow Model and Its Application in Road Capacity Analysis

Abstract: To estimate the accurate fundamental relationship in traffic flow, this paper proposes a novel framework that extends classical fundamental diagram (FD) models to incorporate more dimensions of traffic state variables and allow for the impact of the supply-side factors of roads. The proposed framework is suitable for real-time traffic management, especially in urban areas, due to its reliance on minimal assumptions, its flexibility in adapting to various data sources, and its scalability to higher-dimensional data. The Gaussian process (GP) model is adopted as the base model for learning the optimal mapping from these input features to traffic volume. To enhance the GP model, an in-depth analysis of the properties of its kernel and likelihood function is provided. To cope with the hyperparameter optimisation of the GP, a modified Newton method for GP-based traffic flow model is also designed, which can jump over regions with small gradients. Experiments based on simulation data demonstrate the ability of the proposed framework to capture complex relationships between traffic state variables and supply-side factors, and show its value for estimating dynamic road capacity.

Optimized Scenario Reduction: Solving Large-Scale Stochastic Programs with Quality Guarantees

Abstract: Stochastic programming involves large-scale optimization with exponentially many scenarios. This paper proposes an optimization-based scenario reduction approach to generate high-quality solutions and tight lower bounds by only solving small-scale instances, with a limited number of scenarios. First, we formulate a scenario subset selection model that optimizes the recourse approximation over a pool of solutions. We provide a theoretical justification of our formulation, and a tailored heuristic to solve it. Second, we propose a scenario assortment optimization approach to compute a lower bound—hence, an optimality gap—by relaxing nonanticipativity constraints across scenario “bundles.” To solve it, we design a new column-evaluation-and-generation algorithm, which provides a generalizable method for optimization problems featuring many decision variables and hard-to-estimate objective parameters. We test our approach on stochastic programs with continuous and mixed-integer recourse. Results show that (i) our scenario reduction method dominates scenario reduction benchmarks, (ii) our scenario assortment optimization, combined with column-evaluation-and-generation, yields tight lower bounds, and (iii) our overall approach results in stronger solutions, tighter lower bounds, and faster computational times than state-of-the-art stochastic programming algorithms. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms–Discrete. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2023.1295 .

Column generation for the multi-port berth allocation problem with port cooperation stability

Abstract: This paper proposes a multi-port berth allocation problem (MPBAP) under a cooperative environment, which aims to determine berthing times and berthing positions for all considered vessels arriving at multiple neighboring ports. The previous studies on the MPBAP (or the BAP with multiple ports) consider that multiple ports have established stable cooperation, while the port cooperation stability problem (PCSP) has not been addressed. This paper investigates the PCSP with the MPBAP, where our MPBAP further integrates the vessel diverting issue that vessels with excessive waiting times can be diverted to neighboring ports. For the PCSP, we investigate how to group multiple neighboring ports into different stable port groups, and then determine optimal port groups. For all possible port groups, we propose a mixed integer programming model for the MPBAP, and a column generation approach is devised to solve it. Based on optimal solutions of the MPBAP for various port groups, cooperative game theory is utilized to obtain stable port groups, and then the PCSP can be formulated as a binary programming model for determining optimal port groups. Numerical experiments are carried out to account for the efficiency and effectiveness of the proposed models and solution method.

Comparison of deterministic and ensemble weather forecasts on ship sailing speed optimization

Abstract: Ship sailing speed optimization models are constructed based on prediction of ship fuel consumption, whose accuracy is highly influenced by the quality of sea and weather information. In this study, we develop two fusion methods for combining external meteorological data with ship noon report data, including the rhumb line based fusion method and the direct fusion method, and compare them in terms of accuracy in providing meteorological data. Next, we propose a framework based on the better data fusion strategy for comparing the impacts of deterministic and ensemble weather forecasts on ship speed optimization performance, enabling the evaluation of ship fuel consumptions under different speed plans based on weather forecast data available before departure. Results show that speed optimization based on ensemble weather forecasts has greater potential than that based on deterministic weather forecasts to diminish ship fuel consumption and thus to reduce greenhouse gas emissions.

A fair evaluation of the potential of machine learning in maritime transportation

Abstract: Machine learning (ML) techniques are extensively applied to practical maritime transportation issues. Due to the difficulty and high cost of collecting large volumes of data in the maritime industry, in many maritime studies, ML models are trained with small training datasets. The relative predictive performances of these trained ML models are then compared with each other and with the conventional model using the same test set. The ML model that performs the best out of the ML models and better than the conventional model on the test set is regarded as the most effective in terms of this prediction task. However, in scenarios with small datasets, this common process may lead to an unfair comparison between the ML and the conventional model. Therefore, we propose a novel process to fairly compare multiple ML models and the conventional model. We first select the best ML model in terms of predictive performance for the validation set. Then, we combine the training and the validation sets to retrain the best ML model and compare it with the conventional model on the same test set. Based on historical port state control (PSC) inspection data, we examine both the common process and the novel process in terms of their ability to fairly compare ML models and the conventional model. The results show that the novel process is more effective at fairly comparing the ML models with the conventional model on different test sets. Therefore, the novel process enables a fair assessment of ML models' ability to predict key performance indicators in the context of limited data availability in the maritime industry, such as predicting the ship fuel consumption and port traffic volume, thereby enhancing their reliability for real-world applications.

Joint Optimization of Bus Scheduling and Targeted Bus Exterior Advertising

Abstract: Bus exterior advertising provides a powerful way to establish brand awareness because it can reach a mass of audiences with a high frequency. For a certain advertisement category, advertising effectiveness is largely dependent upon its exposure times to the target audience who takes interest in advertisement, which is termed targeted advertising. Given that the distribution of target audiences over a city varies among different advertisement categories, a practical way of enhancing overall advertising effectiveness is to deploy a bus with certain advertisement category to the bus line that best fits its target area. This gives rise to a decision-making problem of targeted bus exterior advertising and bus scheduling. In this paper, the problem is formulated as a biobjective optimization model with objectives of maximizing the quantified advertising effectiveness and minimizing the number of bus fleet size to cover all trips. The advertising effectiveness is quantified using audience demographic data. The deadheading of buses is also enabled in the scheduling process to facilitate both objectives. The Non-dominated Sorting Genetic Algorithm-II-Large Neighborhood Search (NSGA-II-LNS) algorithm is developed to solve the biobjective problem with the incorporation of large neighborhood search operators into the framework of the NSGA-II to improve solution quality. Various experiments were set up to verify the proposed model and solution algorithm.

Vehicle routing for customized on-demand bus services

Abstract: This study investigates a variant of the Vehicle Routing Problem (VRP) for customized on-demand bus service platforms. In this problem, the platform plans customized bus routes upon receiving a batch of orders released by passengers and informs the passengers of the planned pick-up and drop-off locations. The related decision process takes into account some passenger-side time window-related requirements, walking limits, the availability and capacities of various types of buses. A mixed-integer linear programming model of this new VRP variant with floating targets (passengers) is formulated. To solve the model efficiently, a solution method is developed that combines the branch-and-bound and column generation algorithms and also includes embedded acceleration techniques such as the multi-labeling algorithm. Experiments based on real data from Dalian, China are conducted to validate the effectiveness of the proposed model and efficiency of the algorithm; the small-scale experimental results demonstrate our algorithm can obtain optimal results in the majority of instances. Additionally, sensitivity analysis is conducted, and model extensions are investigated, to provide customized bus service platform operators with potentially useful managerial insights; for example, a platform need not establish as many candidate stops as possible, a wide range of walking distance may not bring early arrival at destinations for customers, more mini-buses should be deployed than large buses in our real-world case. Moreover, the rolling horizon-based context and zoning strategies are also investigated by extending our proposed methodology.

Mathematical Optimization of Carbon Storage and Transport Problem for Carbon Capture, Use, and Storage Chain

Abstract: The greenhouse effect caused by carbon dioxide (CO2) emissions has forced the shipping industry to actively reduce the amount of CO2 emissions emitted directly into the atmosphere over the past few years. Carbon capture, utilization, and storage (CCUS) is one of the main technological methods for reducing the amount of CO2 emissions emitted directly into the atmosphere. CO2 transport, i.e., shipping CO2 to permanent or temporary storage sites, is a critical intermediate step in the CCUS chain. This study formulates a mixed-integer programming model for a carbon storage and transport problem in the CCUS chain to optimally determine ship allocation, ship departure scheduling, and CO2 storage and transport. Taking advantage of the structure of the problem, we transform the mixed-integer programming model into a simpler model that can be computed efficiently. To evaluate the performance of the simpler model, numerous computational experiments are conducted. The results show that all small-scale instances (each with 10 power plants) and medium-scale instances (each with 30 power plants) can be solved optimality by Gurobi within 14.33 s. For large-scale instances with 60 and 65 power plants, feasible solutions with average gap values of 0.06% and 6.93% can be obtained by Gurobi within one hour, which indicates that the proposed methodology can be efficiently applied to practical problems. In addition, important parameters, including the unit fuel price, the time-charter cost, and the ship sailing speed, are examined in sensitivity analyses to investigate the impacts of these factors on operations decisions. In summary, a lower fuel price, a lower charter cost, or a higher ship sailing speed can increase the profit of the CCUS chain.

An optimization model for express delivery with high-speed railway

Abstract: With the expansion of the high-speed railway (HSR) network in China, high-speed rail express delivery (HSReD) is being used to satisfy the increasing demand for express cargo. The decisions on transportation resources arrangement and freight flow allocation are two of the key issues for practical implementation of HSReD. In this study, we examine the above key issues by developing a two-stage stochastic integer linear programming model to maximize the expected net operation profit of HSReD. A meta-heuristic solution approach introduced some tailored tactics is proposed to speed up the process of solving the above model in the large-scale instances. Numerical experiments based on different sizes and practical investigation on China Railway Nanchang Group are conducted to validate the effectiveness of the proposed model and solution approach. Some managerial implications are also obtained based on the sensitivity analysis, which may be potentially useful for optimizing the daily operation management of HSReD.

New challenges in fleet deployment considering EU oil sanctions

Abstract: Due to European Union (EU) oil sanctions, tanker shipping companies need to redeploy their tankers by moving tankers between ship routes with the consideration of flag states of tankers, but the literature lacks quantitative methods for this problem. To fill this research gap, this paper studies an integrated problem of fleet deployment, fleet repositioning, round trip completion, and speed optimization with the consideration of flag states of tankers. The problem is formulated as a nonlinear integer programming model to minimize the total cost, including the fleet repositioning cost, the mismatch cost, and the fuel cost, during the planning period while satisfying the total crude oil transportation demand of each voyage and the minimum shipping frequency. Some linearization methods are used to transform the nonlinear model to a linear one which can be directly solved by Gurobi. The average solving time required for 17 computational instances is 4.5 minutes, which validates the effectiveness of the proposed model. Sensitivity analyses, including the influences of the unit fuel price, the total crude oil transportation demand, the mismatch cost of completing a round trip by a deployed tanker, and the repositioning cost for each deployed tanker, on operations decisions, are conducted to obtain managerial insights.

Sustainable Operations in Maritime Industry

Abstract: Maritime transportation serves as the backbone of international trade and the global economy [...]

Evaluation and prediction of punctuality of vessel arrival at port: a case study of Hong Kong

Abstract: The punctuality of vessel arrival at port is a crucial issue in contemporary port operations. Uncertainties in vessel arrival can lead to port handling inefficiency and result in economic losses. Although vessels typically report their estimated time of arrival (ETA) en-route to the destination port, their actual time of arrival (ATA) often differs from the reported ETA due to various factors. To address this issue and enhance terminal operational efficiency, we first quantitatively evaluate vessel arrival uncertainty in different time slides prior to arrival at the port using 2021 vessel arrival data for Hong Kong port (HKP). Our results confirm that the overall vessel arrival uncertainty decreases as vessels approach the HKP. Then, we implement a random forest (RF) approach to predict vessel arrival time. Our model reduces the error in ship ATA data prediction by approximately 40% (from 25.5 h to 15.5 h) using the root mean squared error metric and 20% (from 13.8 h to 11.0 h) using the mean absolute error metric compared with the reported ETA data. The proposed vessel arrival time evaluation and prediction models are applicable to port management and operation, laying the foundation for future research on port daily operations.