본 연구는 통합공정일정계획(Integrated Process Planning and Scheduling; IPPS)의 최적화를 위한 계산 효율성이 높은 탐욕적 휴리스틱과 유전알고리즘(Genetic Algorithm; GA)을 결합한 하이브리드형 유전 알고리즘을 제안한다. IPPS는 기존의 공정계획과 일정계획을 동시에 풀고자 하는 NP-Hard 문제이다. 특히, 본 연구에서 다루는 IPPS는 tool related constraints가 포함된 것으로서 전통적인 GA는 수행도중 infeasible schedule을 빈번히 발생시킨다. 제안하는 방법의 아이디어는 전체적인 schedule의 구조에 영향을 미치는 operation의 sequence와 machine의 결정은 GA의 procedure를 따르고, 목적함수의 부분계산이 가능한 tool과 Tool Access Direction(TAD)는 greedy heuristics을 통하여 infeasibility를 해소하자는 것이다. 이를 통하여 계산시간의 급격한 증가 없이 또는 기존에 비해 계산시간을 감소시키면서 좋은 품질의 해를 구할 수 있다. 본 연구에서 제안하는 알고리즘은 benchmark problems을 이용하여 성능을 평가한다.

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 satellite operation phase, a ground station should continuously monitor the status of the satellite and sends out a tasking order, and a satellite should transmit data acquired in the space to the Earth. Therefore, the communication between the satellites and the ground stations is essential. However, a satellite and a ground station located in a specific region on Earth can be connected for a limited time because the satellite is continuously orbiting the Earth, and the communication between satellites and ground stations is only possible on a one-to-one basis. That is, one satellite can not communicate with plural ground stations, and one ground station can communicate with plural satellites concurrently. For such reasons, the efficiency of the communication schedule directly affects the utilization of the satellites. Thus, in this research, considering aforementioned unique situations of spacial communication, the mixed integer programming (MIP) model for the optimal communication planning between multiple satellites and multiple ground stations (MS-MG) is proposed. Furthermore, some numerical experiments are performed to verify and validate the mathematical model. The practical example for them is constructed based on the information of existing satellites and ground stations. The communicable time slots between them were obtained by STK (System Tool Kit), which is a well known professional software for space flight simulation. In the MIP model for the MS-MG problems, the objective function is also considered the minimization of communication cost, and ILOG CPLEX software searches the optimal schedule. Furthermore, it is confirmed that this study can be applied to the location selection of the ground stations.

Tomato leaves were inoculated with 1x104 spores · mL-1 and placed in an acryl box at 10, 15, 20, 25, and 30oC for 24 h. Ten days after inoculation, the incidence of late blight appeared as a typical symptom in 6 hrs treatment of leaf wet duration when the temperature is between 15 and 20oC at that time. The incidence of disease was 26% and 41% at 10oC and 25oC treatment although the disease did not occur even after treatment at 30oC for 16 h, respectively. The most important factors in the incidence of Late blight were leaf wet duration and temperature. Optimum growth temperature of tomato is from 15 to 25oC, thus the management of leaf wet duration is better than control by temperature to prevent the incidence of Late blight. After inoculation, the symptoms of Late blight occurred in 5 days, therefore the latency period was estimated to be 5 days. The incidence rate of Late blight was the highest at 15 and 20oC. At the time of chemicals application, when Fluopicolide 5%+Propamocarb hydrochloride 25% was applied at 12 h of leaf wet duration, the control effect was the highest as 95% at 36 h but decreased by 70% when treated after 48 h. On the other hand Cymoxanil 12% + Famoxadone 9% was applied at 18 h of leaf wet duration, the control effect was the highest as 95% at 36 h but decreased by 70% after 48 h as similar as Fluopicolide 5% +Propamocarb hydrochloride 50% treatments. In the application of Dimethomorph 15% +Dithianon 30%, the control effect was more or less low as 80% at 20 h of leaf wet duration and was decreased to 60% at 48 h.

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.

This research focuses on a scheduling problem in the semiconductor probing facility. Probing facility is composed of identical parallel machines and the parallel machines form three workstations for the tests with different recipes. Each machine can be set to three different tests and sequence-dependent setup times are required between operations due to temperature and probe card loading/unloading. Precedence relationship exists between three tests of each wafer lot. The scheduling problem for the probing facility is a parallel machine scheduling problem with precedence relationship and sequence dependent setup time. We develop heuristic algorithm to minimize makespan for the scheduling problem and numerical experiments are conducted to evaluate the performance.

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.

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.

The systematic management of technology infrastructure increasingly becomes critical as it represents the driving power for creating competitive advantage of nations and enterprises. In this sense, public facility and equipment, which is one major asset of technology infrastructure, should be managed effectively, systematically and efficiently to increase its overall equipment effectiveness. However, the manual and off-line communication was the unique way to communicate between the suppliers who provide facilities in the public sector and the customers who utilize the facilities in the industrial sector. This communication way caused invisibility of available usage time and increase of idle time, thereby decreasing the equipment effectiveness. This paper presents an web-based scheduling system that enables real-time collaboration and information sharing for increasing the equipment effectiveness. The designed system performs scheduling, facility management, order management and customer relationship management in an on-line platform, and carries out the processing and storing of relevant information in an integrated database. The paper includes a development approach to identify business process models and standardized system models by using object-modeling technique. The deliverable of the present work will contribute to increasing the equipment effectiveness by enhancing visibility, transparency and predictability by means of the integration with a legacy Enterprise Resource Planning.

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13 ~33.34 seconds depending on the size of scenarios.

We consider a satellite mission scheduling problem, which is a promising problem in recent satellite industry. This problem has various considerations such as customer importance, due date, limited capacity of energy and memory, distance of the location of each mission, etc. Also we consider the objective of each satellite such as general purpose satellite, strategic mission and commercial satellite. And this problem can be modelled as a general knapsack problem, which is famous NP-hard problem, if the objective is defined as to maximize the total mission score performed. To solve this kind of problem, heuristic algorithm such as taboo and genetic algorithm are applied and their performance are acceptable in some extent. To propose more efficient algorithm than previous research, we applied a particle swarm optimization algorithm, which is the most promising method in optimization problem recently in this research. Owing to limitation of current study in obtaining real information and several assumptions, we generated 200 satellite missions with required information for each mission. Based on generated information, we compared the results by our approach algorithm with those of CPLEX. This comparison shows that our proposed approach give us almost accurate results as just less than 3% error rate, and computation time is just a little to be applied to real problem. Also this algorithm has enough scalability by innate characteristic of PSO. We also applied it to mission scheduling problem of various class of satellite. The results are quite reasonable enough to conclude that our proposed algorithm may work in satellite mission scheduling problem.

We focus on the fire scheduling problem (FSP), the problem of determining the sequence of targets to be fired at, for the objective of minimizing makespan to achieve tactical goals. In this paper, we assume that there are m available weapons to fire at n targets (> m) and the weapons are already allocated to targets. One weapon or multiple weapons can fire at one target and these fire operations should start simultaneously while the finish time of them may be different. We develop several dominance properties and a lower bound for the problem, and suggest a branch and bound algorithm implementing them. Also, In addition, heuristic algorithms that can be used for obtaining an initial upper bound in the B&B algorithm and for obtaining good solutions in a short time were developed. Computational experiments are performed on randomly generated test problems and results show that the suggested algorithm solves problems of a medium size in a reasonable amount of computation time. The proposed lower bound, the dominance properties, and the heuristics for upper bound are tested in B&B respectively, and the result showed that lower bound is effective to fathoming nodes and the dominance properties and heuristics also worked well. Also, it is showed that the CPU time required by this algorithm increases rapidly as the problem size increases. Therefore, the suggested B&B algorithm would be limited to solve large size problems. However, the employed heuristic algorithms can be effectively used in the B&B algorithm and can give good solutions for large problems within a few seconds.

Up-to-date business environment for manufacturers is very complex and rapidly changing. In other words, companies are facing a variety of changes, such as diversifying customer requirements, shortening product life cycles, and switching to small quantity batch production. In this situation, the companies are introducing the concept of JIT (just-in-time) to solve the problem of on-time production and on-time delivery for survival. Though many companies have introduced ERP (enterprise resource planning) systems and MRP (material requirement planning) systems, the performance of these systems seems to fall short of expectations. In this paper, the case study on introducing an APS (advanced planning and scheduling) system based on dispatching rules to a machining company and on finding a method to establish an efficient production schedule is presented. The case company has trouble creating an effective production plan and schedule, even though it is equipped with an MRP-based ERP system. The APS system is applied to CNC (computer numerical control) machines, which are key machines of the case company. The overall progress of this research is as follows. First, we collect and analyze the master data on individual products and processes of the case company in order to build a production scheduling model. Second, we perform a pre-allocation simulation based on dispatching rules in order to calculate the priority of each order. Third, we perform a set of production simulations applying the priority value in order to evaluate production lead time and tardiness of pre-defined dispatching rules. Finally, we select the optimal dispatching rule suitable for work situation of the case company. As a result, an improved production schedule leads to an increase in production and reduced production lead time.

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.

This paper considers a scheduling problem in a two-machine flowshop with outsourcing strategy incorporated. The jobs can be either processed in the first machine or outsourced to outside subcontractors. This paper wants to determine which jobs to be processed in-house and which jobs to be outsourced. If any job is decided to be outsourced, then an additional outsourcing cost is charged The objective of this paper is to minimize the sum of scheduling cost and outsourcing cost under a budget constraint. At first this paper characterizes some solution properties, and then it derives solution procedure including DP (Dynamic Programming) and B&B (Branch-and-Bound) algorithms and a greedy-type heuristic. Finally the performance of the algorithms are evaluated with some numerical tests.

In this paper, we deal with a single machine scheduling problems integrating with step deterioration effect and a rate-modifying activity (RMA). The scheduling problem assumes that the machine may have a single RMA and each job has the processing time of a job with deterioration is a step function of the gap between recent RMA and starting time of the job and a deteriorating date that is individual to all jobs. Based on the two scheduling phenomena, we simultaneously determine the schedule of step deteriorating jobs and the position of the RMA to minimize the makespan. To solve the problem, we propose a hybrid typed genetic algorithm compared with conventional GAs.

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.