• Proceedings of the Korea Society for Industrial Systems Conference
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• 1997.03a
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• pp.209-222
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• 1997

생산시스템에 있어서 스케쥴링 문제에 관한 이론적 연구는 종래부터 많이 연구되어 왔지만, 그 대부분은 작업의 가공시간이 확정적(deterministic)인 경우의 평가척도(criterion)를 고려한 것이었다. 그러나 실제 노동집약형 산업이라든가 고장율이 높은 자동화 공장등에 있어서는 가공시간 또는 제조 리드타임이 확률적인 변동을 갖는 경우가 많다. 이러한 가공시간의 변동은 각 작업의 가공완료시각의 변동으로 되어 후공정의 작업계획에 중대한 영향을 미치게 된다. 따라서 가공시간이 확률적(probabilistic)인 경우의 생산 시스템을 대상으로 한 스케쥴링 문제에 관한 연구는 최근에 와서 연구되고 있는 실정이다. 본 연구에서는 종래의 단일 공정의 생산 시스템을 대상으로 n개의 작업이 확정적인 가공시간을 갖는 경우의 총체류시간(total flow time)과 납기(due date)의 평가척도에 관한 스케쥴링 문제를 가공시간이 확률적인 분포를 갖는 경우로 확장시킨 스케쥴링 문제를 제안한다. 그리고 납기에 있어서 평균 납기지연확률(mean probability of tardiness)을 최소화하는 스케쥴링 문제의 휴리스틱 해법을 제안하여 그 실용성을 검토하였다.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.45-54
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• 2022

In this study, a multi-objective robust job-shop scheduling (JSS) model was developed. The model considered multi-jobs and multi-machines. The model also considered uncertain processing times for all tasks. Each job was assigned a specific due date and a tardiness penalty to be paid if the job was not delivered on time. If any job was completed early, holding expenses would be assigned. In addition, the model added idling penalties to accommodate the idling of machines while waiting for jobs. The problem assigned was to determine the optimal start times for each task that would minimize the expected penalties. A numerical problem was solved to minimize both the makespan and the total penalties, and a comparison was made between the results. Analysis of the results produced a prescription for optimizing penalties that is important to be accounted for in conjunction with uncertainties in the job-shop scheduling problem (JSSP).

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.85-92
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• 2018

We consider a project scheduling problem in which the jobs can be compressed by using additional resource to meet the corresponding due dates, referred to as a time-cost tradeoff problem. The project consists of two independent subprojects of which precedence graph is a chain. The due dates of jobs constituting the project can be interpreted as the multiple assessments in the life of project. The penalty cost occurs from the tardiness of the job, while it may be avoided through the compression of some jobs which requires an additional cost. The objective is to find the amount of compression that minimizes the total tardy penalty and compression costs. Firstly, we show that the problem can be decomposed into several subproblems whose number is bounded by the polynomial function in n, where n is the total number of jobs. Then, we prove that the problem can be solved in polynomial time by developing the efficient approach to obtain an optimal schedule for each subproblem.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.299-311
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• 2015

Nowadays, the distribution centres aim to reduce costs by reducing inventory and timely shipment. Cross docking is a logistics strategy in which products delivered to a distribution centre by inbound trucks are directly unloaded and transferred to outbound trucks with minimum warehouse storage. Moreover, on-time delivery in a distribution network becomes very crucial especially when several distribution centres and customers are involved. Therefore, an efficient truck scheduling is needed to synchronize the delivery throughout the network in order to satisfy all stake-holders. This paper presents a mathematical model of a mixed integer programming for door assignment and truck scheduling in a multiple inbound and outbound doors cross docking problem according to Just-In-Time concept. The objective is to find the schedule of transhipment operations to simultaneously minimize the total earliness and total tardiness of trucks. Then, a multi-objective differential evolution (MODE) is proposed with an encoding scheme and four decoding strategies, called ITSH, ITDD, OTSH and OTDD, to find a Pareto frontier for the multi-door cross docking problems. The performances of MODE are evaluated using 15 generated instances. The numerical experiments demonstrate that the proposed algorithm is capable of finding a set of diverse and high quality non-dominated solutions.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.23-35
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• 2005

This paper aims to develop a practical finite capacity MRP (FCMRP) system based on the needs of an automotive parts manufacturing company in Thailand. The approach includes a linear goal programming model to determine the optimal start time of each operation to minimize the sum of penalty points incurred by exceeding the goals of total earliness, total tardiness, and average flow-time considering the finite capacity of all work centers and precedence of operations. Important factors of the proposed FCMRP system are penalty weights and dispatching rules. Effects of these factors on the performance measures are statistically analyzed based on a real situation of an auto-part factory. Statistical results show that the dispatching rules and penalty weights have significant effects on the performance measures. The proposed FCMRP system offers a good tradeoff between conflicting performance measures and results in the best weighted average performance measures when compared to conventional forward and forward-backward finite capacity scheduling systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.187-190
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• 2003

Many manufacturing companies consider the integrated and concurrent scheduling because they need the global optimization technology that could manufacture various products more responsive to customer needs. In this paper, we propose an advanced scheduling model to generate the schedules considering resource constraints and precedence constraints in make-to-order (MTO) manufacturing environments. Precedence of work- in-process(WIP) and resources constraints have recently emerged as one of the main constraints in advanced scheduling problems. The advanced scheduling problems is formulated as a multiobjective mathematical model for generating operation schedules which are obeyed resources constraints, alternative workstations of operations and the precedence constraints of WIP in MTO manufacturing. For effectively solving the advanced scheduling problem, the multi-objective hybrid genetic algorithm (m-hGA) is proposed in this paper. The m-hGA is to minimize the makespan, total flow time of order, and maximum tardiness for each order, simultaneously. The m-hGA approach with local search-based mutation through swap mutation is developed to solve the advanced scheduling problem. Numerical example is tested and presented for advanced scheduling problems with various orders to describe the performance of the proposed m-hGA.

• IE interfaces
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• v.23 no.2
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• pp.164-175
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• 2010

In logistics e-marketplaces, brokerage agents intermediate empty vehicles and freights registered by car owners and shippers. In the previous research, we proposed constant periodic/fixed matching algorithms for the logistics brokerage agents with the objective of minimizing the total transportation lead time and the transportation due date tardiness of freights(Jeong, 2004; Jeong, 2007). However, the constant type algorithms cannot consider changes in the balance status of an e-marketplace, i.e. the difference between the numbers of freights and vehicles to wait for matching, because they use non-changing matching periods and amounts. In this paper, we propose variable type algorithms for the logistics brokerage agent, in which the matching periods and amounts are changed continuously by considering the balance status between the freights and vehicles. In order to compare performance of the variable type algorithms to the previous constant type algorithms, we carried out computational experiments on various problem instances. The results show that the variable type algorithms give better performance than the constant type algorithms. We can expect that the logistics brokerage agents can improve their performance by using the proposed variable periodic/fixed matching algorithms.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.116-123
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• 2024

In the manufacturing industry, dispatching systems play a crucial role in enhancing production efficiency and optimizing production volume. However, in dynamic production environments, conventional static dispatching methods struggle to adapt to various environmental conditions and constraints, leading to problems such as reduced production volume, delays, and resource wastage. Therefore, there is a need for dynamic dispatching methods that can quickly adapt to changes in the environment. In this study, we aim to develop an agent-based model that considers dynamic situations through interaction between agents. Additionally, we intend to utilize the Q-learning algorithm, which possesses the characteristics of temporal difference (TD) learning, to automatically update and adapt to dynamic situations. This means that Q-learning can effectively consider dynamic environments by sensitively responding to changes in the state space and selecting optimal dispatching rules accordingly. The state space includes information such as inventory and work-in-process levels, order fulfilment status, and machine status, which are used to select the optimal dispatching rules. Furthermore, we aim to minimize total tardiness and the number of setup changes using reinforcement learning. Finally, we will develop a dynamic dispatching system using Q-learning and compare its performance with conventional static dispatching methods.

• The Journal of Bigdata
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• v.9 no.1
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• pp.141-152
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• 2024

In the biopharmaceutical contract manufacturing organization (CMO) business, establishing a production schedule that satisfies the due date for various customer orders is crucial for competitiveness. In a CMO process, each order consists of multiple batches that can be allocated to multiple production lines in small batch units for parallel production. This study proposes a meta-heuristic algorithm to establish a scheduling plan that minimizes the total delivery delay of orders in a CMO process with identical parallel machine. Inspired by biological evolution, the proposed algorithm generates random data structures similar to chromosomes to solve specific problems and effectively explores various solutions through operations such as crossover and mutation. Based on real-world data provided by a domestic CMO company, computer experiments were conducted to verify that the proposed algorithm produces superior scheduling plans compared to expert algorithms used by the company and commercial optimization packages, within a reasonable computation time.

• Journal of the Korean Society of School Health
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• v.4 no.1
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• pp.88-99
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• 1991

The purpose of this study was attempted to analyze the relationship between drug abuse and the other problem behavior Patterns among high school students in Cheju. In order to achieve these set-goals, questionaires were finally supplied to the total 379 case of 9 high school from October to December, 1990. The collected data were processed using the SPSS-X computer program and statistically analyzed by the Chi-square method and. percentage. Results of the study were as follows: Among the 370 adolescents, 32.4% of students experienced cigarette smoking in their life, adolescents who experienced alcohol drinking were 46.8%, 0.5% of the students ever used marihuana; cocaine 0.3%, stimulant 3.2%, hallucinogen and inhalants 0.5%, tranquilizer 1.4%, analgesics 31:6%, antitussives 6.5%, antihistamines 1.9%. And all students never experienced the amphetamines and narcotics. The rates of drug use except stimulant and antitussive were higher in the male than in the female students. For the analysis of personal identifying datum, the rates of experienced smokers increased among groups of buddhist and the rates of experienced alconoi drinking increased among groups of no religion. Drug abuser increased among the group lower socio-economic status student, the adolescents whose parents have traditional education point of view. And it was also higher in those who were living only one than in those who were living together. Most students tended to use drugs after 17 or 18 years old. Drug users were more inclined to commit other problem behaviors when compared non-drug users. In the conclusion of the above results, it will be necessary to investigate the drug problem of adolescent. Drug abuse of students must be seen in an environmental context including family, school, peer group and society and not solely as the characteristics of an individual adolescent. And their parents and teachers must be on the alert for the behavior changes of their children such as changes of school performance, neglecting homework, tardiness or truancy from school, runaway from home, and mingled with bad companions, etc. We must recognize that drug abuse is frequently symptomatic of problems in the adolescent's environment.