In many manufacturing or service industries, there exists maximum allowable tardiness for orders, according to purchase contracts between the customers and suppliers. Customers may cancel their orders and request compensation for damages, for breach of contract, when the delivery time is expected to exceed maximum allowable tardiness, whereas they may accept the delayed delivery of orders with a reasonable discount of price within maximum allowable tardiness. Although many research works have been produced on the job shop scheduling problem relating to minimizing total tardiness, none of them have yet considered problems with maximum allowable tardiness. In this study, we solve a job shop scheduling problem under maximum allowable tardiness, with the objective of minimizing tardiness penalty costs. Two kinds of penalty costs are considered, i.e., one for tardy jobs, and the other for canceled jobs. To deal with this problem within a reasonable time at actual production facilities, we propose several dispatching rules by extending well-known dispatching rules for the job shop scheduling problem, in cooperation with a probabilistic conception of those rules. To evaluate the proposed rules, computational experiments were carried out on 300 test instances. The test results show that the suggested probabilistic dispatching rules work better than the existing rules and the optimization solver CPLEX, with a time limit.

In this study, we propose a model to forecast container throughput for the Singapore port, one of the busiest ports globally. Accurate forecasting of container throughput is critical for efficient port operations, strategic planning, and maintaining a competitive advantage. Using monthly container throughput data of the Singapore port from 2010 to 2021, we develop a Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model. For the exogenous variables included in the SARIMAX model, we consider the West Texas Intermediate (WTI) crude oil price and China’s export volume, alongside the impact of the COVID-19 pandemic measured through global confirmed cases. The predictive performance of the SARIMAX model was evaluated against a diverse set of benchmark methods, including the Holt–Winters method, linear regression, LASSO regression, Ridge regression, ECM (Error Correction Mechanism), Support Vector Regressor (SVR), Random Forest, XGBoost, LightGBM, Long Short-Term Memory (LSTM) networks, and Prophet. This comparative analysis was conducted by forecasting container throughput for the year 2022. Results indicated that the SARIMAX model, particularly when incorporating WTI prices and China’s export volume, outperformed other models in terms of forecasting accuracy, such as Mean Absolute Percentage Error (MAPE).

This research considers a two-stage assembly-type flowshop scheduling problem with the objective of minimizing the total tardiness. The first stage consists of two independent machines, and the second stage consists of a single machine. Two types of components are fabricated in the first stage, and then they are assembled in the second stage. Dominance properties and lower bounds are developed, and a branch and bound algorithm is presented that uses these properties and lower bounds as well as an upper bound obtained from a heuristic algorithm. The algorithm performance is evaluated using a series of computational experiments on randomly generated instances and the results are reported.

In this paper, we address a two-stage hybrid flowshop scheduling problem with identical parallel machines in each stage. The problem assumes that the queue (Q)-time for each job, which represents the waiting time to be processed in the current stage, must be limited to a predetermined threshold due to quality concerns for the final product. This problem is motivated by one that occurs in the real field, especially in the diffusion workstation of a semiconductor fabrication. Our objective is to minimize the makespan of the jobs while considering product quality. To achieve this goal, we formulated mathematical programming, developed two dominance properties for this problem, and proposed three heuristics with the suggested dominance properties to solve the considered problem. We conducted simulation experiments to evaluate the performance of the proposed approaches using randomly generated problem instances that are created to closely resemble real production scenarios, and the results demonstrate their superiority over existing methods. Furthermore, we applied the proposed methods in a real-world setting within the semiconductor fabrication industry, where they have exhibited better performance compared to the dispatching rules commonly used in practical applications. These findings validate the effectiveness and applicability of our proposed methodologies in real-world scenarios.

In this study, we conducted preliminary research with the objective of leveraging artificial intelligence to optimize the efficiency and safety of the entire Ambient Air Vaporizer (AAV) system for LNG (Liquid Natural Gas). By analyzing a year-long dataset of real operational data, we identified key variables that significantly influence the outlet temperature of Natural Gas (NG). Based on these insights, a Deep Neural Network (DNN) prediction model was developed to forecast the NG outlet temperature. The endeavor to create an effective prediction model faced specific challenges, primarily due to the narrow operational range of fan speeds and safety-focused guidelines. To surmount these obstacles, various learning algorithms were evaluated under multiple conditions. Ultimately, a DNN model exhibiting lower values of both absolute mean error (MAE) and mean square error (MSE) was successfully established.