This paper proposes an algorithm for the Unrelated Parallel Machine Scheduling Problem(UPMSP) without setup times, aiming to minimize total tardiness. As an NP-hard problem, the UPMSP is hard to get an optimal solution. Consequently, practical scenarios are solved by relying on operator's experiences or simple heuristic approaches. The proposed algorithm has adapted two methods: a policy network method, based on Transformer to compute the correlation between individual jobs and machines, and another method to train the network with a reinforcement learning algorithm based on the REINFORCE with Baseline algorithm. The proposed algorithm was evaluated on randomly generated problems and the results were compared with those obtained using CPLEX, as well as three scheduling algorithms. This paper confirms that the proposed algorithm outperforms the comparison algorithms, as evidenced by the test results.

In the realm of dental prosthesis fabrication, obtaining accurate impressions has historically been a challenging and inefficient process, often hindered by hygiene concerns and patient discomfort. Addressing these limitations, Company D recently introduced a cutting-edge solution by harnessing the potential of intraoral scan images to create 3D dental models. However, the complexity of these scan images, encompassing not only teeth and gums but also the palate, tongue, and other structures, posed a new set of challenges. In response, we propose a sophisticated real-time image segmentation algorithm that selectively extracts pertinent data, specifically focusing on teeth and gums, from oral scan images obtained through Company D's oral scanner for 3D model generation. A key challenge we tackled was the detection of the intricate molar regions, common in dental imaging, which we effectively addressed through intelligent data augmentation for enhanced training. By placing significant emphasis on both accuracy and speed, critical factors for real-time intraoral scanning, our proposed algorithm demonstrated exceptional performance, boasting an impressive accuracy rate of 0.91 and an unrivaled FPS of 92.4. Compared to existing algorithms, our solution exhibited superior outcomes when integrated into Company D's oral scanner. This algorithm is scheduled for deployment and commercialization within Company D's intraoral scanner.

This study focuses on a job-shop scheduling problem with the objective of minimizing total tardiness for the job orders that have different due dates and different process flows. We suggest the dispatching rule based scheduling algorithm to generate fast and efficient schedule. First, we show the delay schedule can be optimal for total tardiness measure in some cases. Based on this observation, we expand search space for selecting the job operation to explore the delay schedules. That means, not only all job operations waiting for process but also job operations not arrived at the machine yet are considered to be scheduled when a machine is available and it is need decision for the next operation to be processed. Assuming each job operation is assigned to the available machine, the expected total tardiness is estimated, and the job operation with the minimum expected total tardiness is selected to be processed in the machine. If this job is being processed in the other machine, then machine should wait until the job arrives at the machine. Simulation experiments are carried out to test the suggested algorithm and compare with the results of other well-known dispatching rules such as EDD, ATC and COVERT, etc. Results show that the proposed algorithm, MET, works better in terms of total tardiness of orders than existing rules without increasing the number of tardy jobs.

With the advent of the digital age, production and distribution of web pages has been exploding. Internet users frequently need to extract specific information they want from these vast web pages. However, it takes lots of time and effort for users to find a specific information in many web pages. While search engines that are commonly used provide users with web pages containing the information they are looking for on the Internet, additional time and efforts are required to find the specific information among extensive search results. Therefore, it is necessary to develop algorithms that can automatically extract specific information in web pages. Every year, thousands of international conference are held all over the world. Each international conference has a website and provides general information for the conference such as the date of the event, the venue, greeting, the abstract submission deadline for a paper, the date of the registration, etc. It is not easy for researchers to catch the abstract submission deadline quickly because it is displayed in various formats from conference to conference and frequently updated. This study focuses on the issue of extracting abstract submission deadlines from International conference websites. In this study, we use three machine learning models such as SVM, decision trees, and artificial neural network to develop algorithms to extract an abstract submission deadline in an international conference website. Performances of the suggested algorithms are evaluated using 2,200 conference websites.

Maritime transport is now regarded as one of the main contributors to global climate change by virtue of its CO2 emissions. Meanwhile, slow steaming, i.e., slower ship speed, has become a common practice in the maritime industry so as to lower CO2 emissions and reduce bunker fuel consumption. The practice raised various operational decision issues in terms of shipping companies: how much ship speed is, how much to bunker the fuel, and at which port to bunker. In this context, this study addresses an operation problem in a shipping companies, which is the problem of determining the ship speed, bunkering ports, and bunkering amount at the ports over a given ship route to minimize the bunker fuel and ship time costs as well as the carbon tax which is a regulatory measure aiming at reducing CO2 emissions. The ship time cost is included in the problem because slow steaming increases transit times, which implies increased in-transit inventory costs in terms of shippers. We formulate the problem as a nonlinear lot-sizing model and suggest a Lagrangian heuristic to solve the problem. The performance of the heuristic algorithm is evaluated using the data obtained from reliable sources. Although the problem is an operational problem, the heuristic algorithm is used to address various strategic issues facing shipping companies, including the effects of bunker prices, carbon taxes, and ship time costs on the ship speed, bunkering amount and number of bunkering ports. For this, we conduct sensitivity analyses of these factors and finally discuss study findings.

This study focuses on the formation of input release lots in a semiconductor wafer fabrication facility. After the order-lot pegging process assigns lots in the fab to orders and calculates the required quantity of wafers for each product type to meet customers’ orders, the decisions on the formation of input release lots should be made to minimize the production costs of the release lots. Since the number of lots being processed in the wafer fab directly is related to the productivity of the wafer fab, the input lot formation is crucial process to reduce the production costs as well as to improve the efficiency of the wafer fab. Here, the input lot formation occurs before every shift begins in the semiconductor wafer fab. When input quantities (of wafers) for product types are given from results of the order-lot pegging process, lots to be released into the wafer fab should be formed satisfying the lot size requirements. Here, the production cost of a homogeneous lot of the same type of product is less than that of a heterogeneous lot that will be split into the number of lots according to their product types after passing the branch point during the wafer fabrication process. Also, more production cost occurs if a lot becomes more heterogeneous. We developed a multi-dimensional dynamic programming algorithm for the input lot formation problem and showed how to apply the algorithm to solve the problem optimally with an example problem instance. It is necessary to reduce the number of states at each stage in the DP algorithm for practical use. Also, we can apply the proposed DP algorithm together with lot release rules such as CONWIP and UNIFORM.

Korea ocean research stations manage the weather and environmental data collected from coastal and ocean areas to provide short-term and long-term ocean forecasts. The purpose of this paper is to analyze and quantify economic benefits of the ocean research stations with sensors to observe physical, chemical, and biological data. The construction and operation of an integrated ocean observation station is expected to reduce uncertainty about ocean and coastal areas and to improve the quality of ocean forecasts. The economic benefits are mainly come from improved search and rescue operations, ocean pollution management, yellow dust management, and improved productivity in ocean-related industries. In addition, an input-output analysis is performed to evaluate the economic impacts of ocean research stations nationwide. The analysis shows that the system can contribute to industries such as fishing, maritime and air cargo, medical and health care.

Max-plus algebra is a nonlinear system made of two operations, maximization (max) and additions (plus), that are corresponding to the addition and multiplication in the traditional algebra, in respect. This algebraic method can be applied to many types of discrete event systems showing the state transition with the maximization and addition operations. Robotic cell with predetermined cyclic schedule is one of such systems. We consider a robotic cell that consists of a single robot and several isolated processing modules. We discuss steady state analysis of such robot cell when all the parts have the processing order as a flow shop. To model timing behavior of a robotic cell, we develop a max-plus algebraic system. Given a particular robot transfer sequence, we prove the unique existence of the stationary cyclic behavior and identify the schedule. Furthermore we suggest max-plus algebraic way to develop a robust schedule which endures the outside processing variability. Finally, a monitoring and control system for such robotic cell based on the max-plus algebraic model is also proposed.

Lot-order assignment is the process of assigning items in lots being processed in a production facility to orders to meet due-dates of the orders. In this study, we consider the lot-order assignment problem (LOAP) with the objective of minimizing total tardiness of the orders with distinct due dates. We address similarity relationships between the LOAP and the single machine total tardiness scheduling problem (SMTTSP) and suggest priority rules for the LOAP based on those for the SMTTSP. Performances of the priority rules are compared with each other and with that of the commercial optimization software package in computational experiments.

몽골은 석탄 매장량이 매우 풍부하고 석유연료를 전적으로 수입에 의존하기 때문에, 석탄액화플랜트 건설에 필요한 충분한 여건을 갖추고 있다. 본 연구에서는 몽골에 하루 10,000배럴의 석유연료를 생산할 수 있는 석탄액화플랜트를 건설할 경우에 대한 경제성분석을 수행한다. 먼저 기존에 있는 산업계의 석탄액화플랜트 프로젝트 데이터와 학계의 연구결과를 토대로 몽골 석탄액화플랜트 건설에 필요한 비용과 기대수명, 그리고 예상 수명기간 동안 운영했을 때 발생하는 운영

지속적으로 강화되고 있는 국제 환경규제로, 해운사들은 선박의 온실가스 배출량을 줄이기 위한 노력을 해야 하는 상황에 직면해 있다. 본 연구에서는 총 온실가스 배출량에 대한 제약이 있는 상황에서 복수개의 항로를 운영하는 해운사에서 일 평균 선박 운영비용의 총합을 최소화하는 항로별 최적 선박대수와 운항속도를 결정하는 문제를 다룬다. 이 문제를 풀기위해 라그랑지안 휴리스틱 알고리즘을 개발하고 라그랑지안 쌍대문제를 풀어 최적해에 대한 하한값을 구한다. 제시한

황철석의 산화에 의한 토양 및 지표수의 산성화는 심각한 환경문제를 야기하여 왔다. 특이산 성토의 이차광물과 화학적 특징은 풍화과정을 반영하고 있다. 택지 및 골프장 조성과정에서 지표에 노출된 11.8% 황철석을 함유한 열수변질 안산암의 풍화에 따른 광물학적 변화를 X-선 회절, 전자현미경 (SEM, TEM), 배수의 화학분석을 통하여 연구하였다 수용성 염, ferrihydrite, jarosite가 풍화과정의 이차 광물로서 관찰되었다. 전자현미경하에서, ferrihydrite는 미세입자들의 입단, jarosite는 판상, 수용성 염은 기둥모양을 나타내었다. 안산암 내에 존재하는 황철석은 입자의 크기가 증가할수록 정육면체 형태/육각기둥 형태의 비가 증가하였다. 배수는 강산성 (pH 3.5) 이었으며 ferrihydrite, jarosite와 화학적 평형을 이루고 있었다.