Loo Hay Lee

In recent years, advancements in battery technology have led to increased adoption of electric automated guided vehicles in container terminals. Given how critical these vehicles are to terminal operations, this trend requires efficient recharging scheduling for automated guided vehicles, and the main challenges arise from limited charging station capacity and tight vehicle schedules. Motivated by the dynamic nature of the problem, the recharging scheduling problem for an entire vehicle fleet given capacitated stations is formulated as a Markov decision process model. Then, it is solved using a multiagent Q-learning (MAQL) approach to produce a recharging schedule that minimizes the delay of jobs. Numerical experiments show that under a stochastic environment in terms of vehicle travel time, MAQL enables the exploration of better scheduling by coordinating across the entire vehicle fleet and charging facilities and outperforms various benchmark approaches, with an additional improvement of 18.8% on average over the best rule-based heuristic and 5.4% over the predetermined approach. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72101203], the Shaanxi Provincial Key R&D Program, China [Grant 2022KW-02], and the Singapore Maritime Institute [Grant SMI-2017-SP-002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0113 .

This study considers the multi-period dynamic integrated optimization problem in automated transshipment hubs, which includes berth allocation, quay crane assignment, and yard assignment problems. The planner needs to determine the berth and storage schedule in real-time. In the previous literature, the scholars always assume that all of the required information, especially the arrival time and operation time of vessels, is accessible prior to making any design decisions, and formulate the problem into an integrated model. However, the information of vessels is updated dynamically and changed frequently. Therefore, we consider a problem in a dynamic setting that decisions are made in each period and the scenario at each period depends on the decisions in previous periods. We formulate the problem into a multi-objective model, which aims to maximize total revenue, saved time deviation, saved transportation distance, and service quality. An efficient adaptive dynamic scheduling policy is developed, which adaptively balances the trade-offs between multiple objectives. Through numerical experiments, we demonstrate that our approach presents solutions with a delicate balance between multiple objectives and brings value to the automated container terminals compared to benchmark policies. Note to Practitioners —Automated technologies have brought great opportunities for container terminals. While the construction of an automated container terminal requires enormous investment. Utilizing the resources of container terminals reasonably and efficiently can improve the operation efficiency and economic performance of automated container terminals. In practice, the scheduling of resources in container terminals always encounters uncertain factors. Therefore, this study proposes a dynamic integrated optimization policy for resources in automated container terminals to balance the trade-offs between multiple objectives given any pre-determined target in a dynamic environment.

The growth of e-commerce has created increasing complexity in logistics services. To remain competitive, logistics and e-commerce companies are exploring new modes as supplements to traditional home delivery, one of which is the self-service parcel locker. This paper studies a parcel locker location problem where a company plans to introduce the locker service by locating locker facilities to attract customers. The objective is to maximize the profit, accounting for the revenue and the cost of facilities. To estimate the revenue, we use the threshold Luce model (TLM) to predict customers' likelihood of using the locker service. We then propose a combinatorial optimization model and develop exact solution methodologies that are practically implementable according to our extensive computational experiments. In effect, our modeling framework generalizes the traditional facility location problems based on the binomial logit model (BNL) and the multinomial logit model (MNL), both of which impose strong and strict assumptions on the customer's choice sets. That is, they assume that the choice sets will either contain only one facility or all facilities. In our numerical experiment, we demonstrate that using the BNL and the MNL in the locker location problem could lead to, respectively, pessimistic and optimistic revenue estimation. Consequently, the suggested location decisions will be either conservative or aggressive. Our proposed model, by contrast, can effectively relax these assumptions. Our results also reveal that the aggressive decision due to the use of the MNL will incur an unnecessarily high facility cost that cannot be compensated by the additional revenue, leading to profit loss that can be significant in various scenarios. Finally, we conduct sensitivity analysis on the input parameters and draw additional implications.

Container terminal capacity, as an important indicator of container terminal competitiveness, has become a primary concern of port managers, whereas container flows, which are supported by quay cranes, yard cranes, and automated guided vehicles, have a substantial influence on container terminal capacity. Container flows are analyzed in this article to estimate the performance of an automated container terminal considering traffic congestion, unbalanced task assignment, container batch arrival, and different berth and yard layouts. The operating system is formulated as a closed-queuing network with four stations. Theoretical analysis and the solution method are adopted to determine system capacity. The resource allocation and system design, specifically the number of vehicles, berth and yard layouts, and task assignment strategy, are optimized. Numerical experiments are conducted to provide management insights into port capacity planning, resource optimization, and layout design. Note to Practitioners —The construction of an automated container terminal (ACT) requires a sizable investment. Analyzing the performance of the container terminal with different configurations in the planning stage, which is before the construction of ports, is very important. Thus, planners can select better resource configurations. Generally, simulation is an intuitive way to estimate performance. However, formulating and running the simulation model with different configurations is time consuming. An analytic model for estimating the performance of an ACT is developed to reduce computational time in this article.

This paper studies joint decisions on pricing and empty container repositioning in two-depot shipping services with stochastic shipping demand. We formulate the problem as a stochastic dynamic programming model. The exact dynamic program may have a high-dimensional state space because of the in-transit containers. To cope with the curse of dimensionality, we develop an approximate model where the number of in-transit containers on each vessel is approximated with a fixed container flow predetermined by solving a static version of the problem. Moreover, we show that the approximate value function is [ ]-concave, thereby characterizing the structure of the optimal control policy for the approximate model. With the upper bound obtained by solving the information relaxation–based dual of the exact dynamic program, we numerically show that the control policies generated from our approximate model are close to optimal when transit times span multiple periods.

Recent advances in sensing, data analytics, and manufacturing technologies (e.g., 3-D printing, soft robotics, nanotechnologies, etc.) provide the potential to produce highly customized products by allowing flexible system design, endless device configurations, and unprecedented information flows. These opportunities also increase the complexity of controlling such systems optimally, which typically requires fast exploration of an increasingly large number of alternative operation strategies. Simulation and stochastic models have been particularly successful to support control and optimization of production systems, and methods have been developed to exploit them separately. Herein, we argue that the simultaneous use of these models can allow for better control and optimization by balancing the simulation accuracy, and related high computational costs, with the computational efficiency and lower accuracy of stochastic models. In this article, we assume that high fidelity models have higher accuracy and computational costs, and we present a novel multifidelity approach, which utilizes several models at different levels of fidelity to efficiently and effectively estimate and optimize the performance of asynchronous serial production lines with machines suffering from multiple failure types. Experimental results show that the multifidelity approach leads to better estimations, requiring less computational effort for optimization compared with the use of only high fidelity simulations.

One innovative solution to the last-mile delivery problem is the self-service locker system. Motivated by a real case in Singapore, we consider a POP-Locker Alliance who operates a set of POP-stations and wishes to improve the last-mile delivery by opening new locker facilities. We propose a quantitative approach to determine the optimal locker location with the objective to maximize the overall service provided by the alliance. Customer's choices regarding the use of facilities are explicitly considered. They are predicted by a multinomial logit model. We then formulate the location problem as a multi-ratio linear-fractional 0-1 program and provide two solution approaches. The first one is to reformulate the original problem as a mixed-integer linear program, which is further strengthened using conditional McCormick inequalities. This approach is an exact method, developed for small-scale problems. For large-scale problems, we propose a Suggest-and-Improve framework with two embedded algorithms. Numerical studies indicated that our framework is an efficient approach that yields high-quality solutions. Finally, we conducted a case study. The results highlighted the importance of considering the customers' choices. Under different parameter values of the multinomial logit model, the decisions could be completely different. Therefore, the parameter value should be carefully estimated in advance.

In this study, we consider the budget allocation problem for binary classification with noisy labels. The classification accuracy can be improved by reducing the label noises which can be achieved by observing multiple independent observations of the labels. Hence, an efficient budget allocation strategy is needed to reduce the label noise and meanwhile guarantees a promising classification accuracy. Two problem settings are investigated in this work. One assumes that we do not know the underlying classification structures and labels can only be determined by comparing the sample average of its Bernoulli success probability with a given threshold. The other case assumes that data points with different labels can be separated by a hyperplane. For both cases, the closed-form optimal budget allocation strategies are developed. A simulation analytics example is used to demonstrate how the budget is allocated to different scenarios to further improve the learning of optimal decision functions.

This paper studies a multi-attribute reverse auction in which one manufacturer/buyer purchases multi-unit identical components/goods from a group of capacity-constrained suppliers considering the procurement cost and delivery time. The unknown bidding preference and the discrete cost structure are particularly investigated. Constructing a bi-level distributed decision-making model, a novel iterative multi-attribute reverse auction mechanism embedding negotiation is proposed to improve the procurement efficiency under the decision-making framework. Specifically, in the upper level, the buyer determines the optimal allocation by solving the winner determination problem. To induce suppliers to adjust their delivery times, three guiding strategies are proposed, i.e., the guiding strategy based on the deviation of delivery time (GDD), the guiding strategy based on the deviation of objective function value (GDF), and the guiding strategy randomly based on the deviation of objective function value (GRDF). In the lower level, suppliers adopt the concession strategies for determining the bid price and delivery time in response to the buyer’s feedback. The numerical experiments illustrate the effectiveness and applicability of the proposed mechanism by comparing it with the centralized model. When the buyer places higher importance on the procurement cost than on the delivery time, the GRDF achieves the best negotiation outcome; otherwise, the GDF is the buyer’s best option. Also, the proposed mechanism is robust to the variance of suppliers’ decision parameters and could be a useful procurement tool for the buyer.