We examine a single machine scheduling problem with step-improving jobs in which job processing times decrease step-wisely over time according to their starting times. The objective is to minimize total completion time which is defined as the sum of completion times of jobs. The total completion time is frequently considered as an objective because it is highly related to the total time spent by jobs in the system as well as work-in-progress. Many applications of this problem can be observed in the real world such as data gathering networks, system upgrades or technological shock, and production lines operated with part-time workers in each shift. Our goal is to develop a scheduling algorithm that can provide an optimal solution. For this, we present an efficient branch and bound algorithm with an assignment-based node design and tight lower bounds that can prune branch and bound nodes at early stages and accordingly reduce the computation time. In numerical experiments well designed to consider various scenarios, it is shown that the proposed algorithm outperforms the existing method and can solve practical problems within reasonable computation time.

The printing process can have to print various colors with a limited capacity of printing facility such as ink containers that are needed cleaning to change color. In each container, cleaning time exists to assign corresponding inks, and it is considered as the setup cost required to reduce the increasing productivity. The existing manual method, which is based on the worker’s experience or intuition, is difficult to respond to the diversification of color requirements, mathematical modeling and algorithms are suggested for efficient scheduling. In this study, we propose a new type of scheduling problem for the printing process. First, we suggest a mathematical model that optimizes the color assignment and scheduling. Although the suggested model guarantees global optimality, it needs a lot of computational time to solve. Thus, we decompose the original problem into sequencing orders and allocating ink problems. An approximate function is used to compute the job scheduling, and local search heuristic based on 2-opt algorithm is suggested for reducing computational time. In order to verify the effectiveness of our method, we compared the algorithms' performance. The results show that the suggested decomposition structure can find acceptable solutions within a reasonable time. Also, we present schematized results for field application.

In this paper, we introduce a pilot's scheduling model which is able to maintain and balance their capabilities for each relevant skill level in military helicopter squadron. Flight scheduler has to consider many factors related pilot's flight information and spends a lot of times and efforts for flight planning without scientific process depending on his/her own capability and experience. This model reflected overall characteristics that include pilot's progression by basis monthly and cumulative flight hours, operational recent flight data and quickly find out a pinpoint areas of concern with respect to their mission subjects etc. There also include essential several constraints, such as personnel qualifications, and Army helicopter training policy’s constraints such as regulations and guidelines. We presented binary Integer Programming (IP) mathematical formulation for optimization and demonstrated its effectiveness by comparisons of real schedule versus model's solution to several cases experimental scenarios and greedy random simulation model. The model made the schedule in less than 30 minutes, including the data preprocessing process, and the results of the allocation were more equal than the actual one. This makes it possible to reduce the workload of the scheduler and effectively manages the pilot's skills. We expect to set up and improve better flight planning and combat readiness in Korea Army aviation.

Many small and medium-sized manufacturing companies process various product types to respond different customer orders in a single production line. To improve their productivity, they often apply batch processing while considering various product types, constraints on batch sizes and setups, and due date of each order. This study introduces a batch scheduling heuristic for a production line with multiple product types and different due dates of each order. As the process times vary due to the different batch sizes and product types, a recursive equation is developed based on a flow line model to obtain the upper bound on the completion times with less computational complexity than full computation. The batch scheduling algorithm combines and schedules the orders with same product types into a batch to improve productivity, but within the constraints to match the due dates of the orders. The algorithm incorporates simple and intuitive principles for the purpose of being applied to small and medium companies. To test the algorithm, two case studies are introduced; a high pressure coolant (HPC) manufacturing line and a press process at a plate-type heat exchanger manufacturer. From the case studies, the developed algorithm provides significant improvements in setup frequency and thus convenience of workers and productivity, without violating due dates of each order.

This paper considers the problem of scheduling loading and unloading operations of a crane in a railway terminal motivated from rail-road container transshipment operations at Uiwang Inland Container Depot (ICD). Unlike previous studies only considering the total handling time of containers, this paper considers a bi-criteria objective of minimizing the weighted sum of the total handling time and tenant service time. The tenant service time is an important criterion in terms of terminal tenants who are private logistics companies in charge of moving containers from/to the terminal using their trucks. In the rail-road container shipment yard, the tenant service time of a tenant can be defined by a time difference between beginning and finishing loading and unloading operations of a crane. Thus, finding a set of sequences and time of the crane operations becomes a crucial decision issue in the problem. The problem is formulated as a nonlinear program which is improved by linearizing a nonlinear constraint in the model. This paper develops a genetic algorithm to solve the problem and performs a case study on the Uiwang ICD terminal. Computational experiment results show that the genetic algorithm shows better performance than commercial optimization solvers. Operational implications in terms of tenants are drawn through sensitivity analyses.

This paper will present a simulation-optimization model for the scheduling of multi-projects. The objectives of this research include the minimization of value added projects execution cost, project completion time, project tardiness, and underutilization of contracted or outsourced resources. It is the three-phase research. In first phase, a mathematical and simulation models will be developed for multi-objectives. In second phase simulation model will be coupled with genetic algorithm to form a simulation-optimization model. The efficiency of genetic algorithm (GA) will be improved simultaneously with fine-tuning and hybridizing with other algorithms. The third phase will involve the presentation of a numerical example for the real time application of proposed research. Solution of numerical obtained with fine-tuned and hybridized simulation integrated GA will be compared with already available methods of simulation-optimization. This research will be useful for the scheduling of projects to achieve the befits of high profit, effective resource utilization, and customer satisfaction with on time delivery of projects.

The up-to-date business environment for Korean manufacturers is very complex and rapidly changing. Especially, the companies have faced with various changes derived from small quantity batch production, diversification of customer demands, and short life cycles of products. Consequently, the Korean manufacturing companies are in need of more efficient production planning and scheduling techniques. In this paper, the research trend of scheduling techniques is investigated to provide relevant information to researchers in this field. Furthermore, some implications for future researches are presented regarding literatures published in Korea over the last 10 years. This paper presents an entire investigation into Korean research works on scheduling (2,569 papers) that are published from 2007 to 2016. Especially, detailed analysis was carried out in the following three industry : 1) semiconductor, 2) shipbuilding and 3) automobile. In this paper, approaches to scheduling presented in the literature are categorized into the following three categories : 1) application, 2) algorithm, and 3) simulation modeling. First, in the semiconductor industry, scheduling techniques related to semiconductor cleaning processes, photolithography processes, chemical processes, transport and transport equipment have been found to be dominant. Second, the shipbuilding industry is focused on assembly processes, transporter, crane and various existing production management system. On the other hand, the scheduling research of the automobile industry is mainly focused on the vehicle movement routing and procurement supply-chain planning algorithm in terms of logistics. The conclusion of this study are expected to provide many implications for various types of academic and practical follow-up studies related to scheduling in consideration of main characteristics of semiconductor, shipbuilding and automobile industries.

In the painting process of automotive factory, color changeover cost is incurred every time the color of vehicle is changed. To solve this problem, automotive company usually uses storage space such as Selectivity Banks(SB) or Car Rescheduling Storage and carries out sequence planning so that vehicles of the same color are consecutive, which is called Car Resequencing Problem (CRP). So far, research works for CRP has focused on algorithms finding optimal or approximated optimal solutions under the condition that the number of vehicles is fixed in SB. However, these results cannot be directly applied to the actual automotive paint shops since they have continuous flows of cars into SB to be handled in a day. Therefore, in this paper, we propose an efficient cyclic scheduling method that starts the painting process using the result of Accelerated Dynamic Programming (ADP) and then reapplies the ADP to the vehicles in SB for renewing the painting schedule whenever a certain number of vehicles is painted, represented as a threshold. To show the effectiveness of the proposed method, we performed a numerical experiment by designing system configurations, based onthe actual vehicle painting process, and proposed a good threshold that can reduce overall color changeover cost.

Semiconductor processes are mainly divided into FAB process, package and test. The FAB process is promoting smart factories over a long period of time. The situation of the package and test process is manual, but there is a wafer testing process that prepares the latest topic "SmartFactory". The purpose of this study is to study the key variables for the completion of the material allocation scheduling system which will be the base environment of the smart factory in the wafer test process and to build the system based on the designed research model.

Semiconductor manufacturing has suffered from the complex process behavior of the technology oriented control in the production line. While the technological processes are in charge of the quality and the yield of the product, the operational management is also critical for the productivity of the manufacturing line. The fabrication line in the semiconductor manufacturing is considered as the most complex part because of various kinds of the equipment, re-entrant process routing and various product devices. The efficiency and the productivity of the fabrication line may give a significant impact on the subsequent processes such as the probe line, the assembly line and final test line. In the management of the re-entrant process such as semiconductor fabrication, it is important to keep balanced fabrication line. The Performance measures in the fabrication line are throughput, cycle time, inventory, shortage, etc. In the fabrication, throughput and cycle time are the conflicting performance measures. It is very difficult to achieve two conflicting goal simultaneously in the manufacturing line. The capacity of equipment is important factor in the production planning and scheduling. The production planning consideration of capacity can make the scheduling more realistic. In this paper, an input and scheduling rule are to achieve the balanced operation in semiconductor fabrication line through equipment capacity and workload are proposed and evaluated. New backward projection and scheduling rule consideration of facility capacity are suggested. Scheduling wafers on the appropriate facilities are controlled by available capacity, which are determined by the workload in terms of the meet the production target.

Recently, scheduling problems with position-dependent processing times have received considerable attention in the literature, where the processing times of jobs are dependent on the processing sequences. However, they did not consider cases in which each processed job has different learning or aging ratios. This means that the actual processing time for a job can be determined not only by the processing sequence, but also by the learning/aging ratio, which can reflect the degree of processing difficulties in subsequent jobs. Motivated by these remarks, in this paper, we consider a two-agent single-machine scheduling problem with linear job-dependent position-based learning effects, where two agents compete to use a common single machine and each job has a different learning ratio. Specifically, we take into account two different objective functions for two agents: one agent minimizes the total weighted completion time, and the other restricts the makespan to less than an upper bound. After formally defining the problem by developing a mixed integer non-linear programming formulation, we devise a branch-and-bound (B&B) algorithm to give optimal solutions by developing four dominance properties based on a pairwise interchange comparison and four properties regarding the feasibility of a considered sequence. We suggest a lower bound to speed up the search procedure in the B&B algorithm by fathoming any non-prominent nodes. As this problem is at least NP-hard, we suggest efficient genetic algorithms using different methods to generate the initial population and two crossover operations. Computational results show that the proposed algorithms are efficient to obtain near-optimal solutions.

구조물의 풍진동 제어에 사용되는 능동질량감쇠기(Active Mass Damper, AMD)는 구조물의 가속도, 속도, 변위 응답을 계측하고 제어알고리즘에 따라 제어력을 산정한 후, 질량체에 연결된 모터 구동를 통해 제어력을 발생시키는 장치로, 핵심 설계기술은 이동 질량체의 질량, 모터용량, 이송거리를 최소화하면서 제어성능을 확보하는 데 있다. 하지만 이동질량을 최소화하는 경우 제어성능을 증가시키기 위해 AMD에 요구되는 가속도가 증가하게 되고, 이에 따라 이송거리가 증가하는 문제점이 있다. 본 연구에서는 AMD의 제어성능은 유지하면서, 질량 및 이송거리를 최소할 수 있는 방안으로 제어력 게인 스케줄링을 위한 가중함수와 등속도 원점보정을 위한 속도입력 함수를 제시하였다. 또한, 구조물 응답에서 제어 대상 신호만을 추출하기 위한 입력필터 설계 방안을 제시하여, 이에 대한 효용성 검증을 위한 해석을 수행한 후, 39층 구조물에 설치한 56ton 용량의 AMD에 적용하여 제어실험을 수행하였다. 실험 결과, 게인 스케줄링 가중함수와 등속도 원점보정 속도입력 함수에 의해 이동질량체의 이송거리를 최소화하면서도, 원점근처에서의 안정적인 거동이 가능함을 확인하였고, 입력 필터를 통해 제어 모드 이외의 신호를 제거함으로써, 목표 제어성능을 만족시킬 수 있음을 확인하였다.

In this paper, we consider a two-agent scheduling with sequence-dependent exponential learning effects consideration, where two agents A and B have to share a single machine for processing their jobs. The objective function for agent A is to minimize the total completion time of jobs for agent A subject to a given upper bound on the objective function of agent B, representing the makespan of jobs for agent B. By assuming that the learning ratios for all jobs are the same, we suggest an enumeration-based backward allocation scheduling for finding an optimal solution and exemplify it by using a small numerical example. This problem has various applications in production systems as well as in operations management.

This study develops a dynamic scheduling model for parallel machine scheduling problem based on genetic algorithm (GA). GA combined with discrete event simulation to minimize the makespan and verifies the effectiveness of the developed model. This research consists of two stages. In the first stage, work sequence will be generated using GA, and the second stage developed work schedule applied to a real work area to verify that it could be executed in real work environment and remove the overlapping work, which causes bottleneck and long lead time. If not, go back to the first stage and develop another schedule until satisfied. Small size problem was experimented and suggested a reasonable schedule within limited resources. As a result of this research, work efficiency is increased, cycle time is decreased, and due date is satisfied within existed resources.