• Industrial Engineering and Management Systems
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• 제15권3호
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• pp.206-214
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• 2016

This paper deals with the no-wait flow shop scheduling problem in order to minimize the total time to complete the schedule or makespan. It is introduced a constructive heuristic which builds the production schedule from job partial sequences by using an appropriate mechanism of insertion. An extensive computational experiment has been performed to evaluate the performance of proposed heuristic. Experimental results have clearly shown that the presented heuristic provides better solutions than those from the best heuristics existing.

• Industrial Engineering and Management Systems
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• 제11권1호
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• pp.114-122
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• 2012

This paper considers a non-identical parallel machine scheduling problem with sequence and machine dependent setup times. The objective of this problem is to determine the allocation of jobs and the scheduling of each machine to minimize makespan. A mathematical model for optimal solution is derived. An in-depth analysis of the model shows that it is very complicated and difficult to obtain optimal solutions as the problem size becomes large. Therefore, two meta-heuristics, genetic algorithm (GA) and a new population-based evolutionary meta-heuristic called self-evolution algorithm (SEA), are proposed. The performances of the meta-heuristic algorithms are evaluated through compare with optimal solutions using randomly generated several examples.

• 품질경영학회지
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• 제22권3호
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• pp.161-178
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• 1994

FMS are being installed to improve productivity, manufacturing consistency and flexibility. However, FMS are quite expensive and efforts must be made to avoid the high investment risk. The objective of this paper is to enable the real-time rescheduling under dynamic changes in FMS environment. For this purpose, a KBSS (Knowledge-Based Scheduling System) in FMS environment is developed. This KBSS will meet various requirements of users, for example, to minimize makespan, average flow time, or to maximize machine utilization.

• 산업경영시스템학회지
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• 제27권4호
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• pp.42-48
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• 2004

This paper concerns on a multiprocessor task scheduling problem with precedence relation, in which each task requires several processors simultaneously. Meta-heuristic generally finds a good solution if it starts from a good solution. In this paper, a tabu search is presented to find a schedule of minimal time to complete all tasks. A modified tabu search is also presented which uses a new initial solution based on the best solution during the previous run as the new starting solution for the next iteration. Numerical results show that a tabu search and a modified tabu search yield a better performance than the previous studies.

• 산업경영시스템학회지
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• 제29권4호
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• pp.34-42
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• 2006

This paper considers an assembly-type flowshop scheduling problem in which each job is assembled with two types of components. One type of the components is outsourced with positive lead time but the other type is fabricated in-house at the first stage. The two types of the components should be prepared at the first stage before starting the assembly operation for each job at the second stage. The objective is to schedule the jobs so that the makespan is minimized. Some solution properties and lower bounds are derived and incorporated into a branch and bound algorithm. Also, an efficient heuristic is developed. The performances of the proposed branch and bound algorithm and heuristic are evaluated through computational experiments.

• International Journal of Computer Science & Network Security
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• 제22권8호
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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).

• Korean Chemical Engineering Research
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• 제58권3호
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• pp.346-355
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• 2020

의약 중간체 제조 공장을 포함한 정밀 화학 산업에서 생산 계획 및 전사적 자원 관리(enterprise resource planning) 관한 의사 결정은 기업 운영 효율성 최적화에 매우 주요한 연구 주제이다. 기존의 의사 결정 시스템은 공장 간의 자원관리와 공장내 생산 계획이 분리되어, 전사적 관점에서의 전역 해를 도출하는데 한계가 있다. 본 연구에서는 무선 식별 시스템(Radio Frequency Identification; RFID) 기반의 실시간 물류 관리 시스템을 이용하여 의약품 중간체 산업의 전사적 자원 관리와 최적 생산 계획 수립을 동시에 수행할 수 있는 의사결정 플랫폼을 개발하고, 실제 제조 공정에 적용하여 그 효과를 분석한다. 기존의 분리된 운영 체제와는 달린 본 연구에서 제안하는 통한 의사결정 플랫폼은 원료 주문 및 운송 시간에 관한 정보가 RFID 기술을 통해 제조 공정 생산 계획 수립의 핵심 데이터로 사용됨으로써, 별도의 재고 관리 시설 없이 공장간 물류 현황에 대응하여 최적 생산 계획이 수립된다. 이를 위하여 제품 생산 시간 최소화를 목적 함수로 설정하고, 마감 기한 및 주문량에 대한 제약조건 등을 포함한 혼합정수선형계획 모델을 개발하였다. 이후 개발된 최적화 모델을 실제 의약품 중간체 생산 문제로 적용함으로써, 제안한 통합 의사결정 플랫폼의 효용성을 입증하였다. 본 연구 결과로 생산 시간 최적화를 위해 단순한 공정 조합을 필요로 하는 생산물이 먼저 제조되고 복잡한 공정 구조를 가진 생산물이 순차적으로 생산되는 생산 일정이 최적임을 확인하였다.

• Industrial Engineering and Management Systems
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• 제12권4호
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• pp.306-316
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• 2013

The semiconductor industry has grown rapidly, and subsequently production planning problems have raised many important research issues. The reentrant flow-shop (RFS) scheduling problem with time windows constraint for harddisk devices (HDD) manufacturing is one such problem of the expanded semiconductor industry. The RFS scheduling problem with the objective of minimizing the makespan of jobs is considered. Meeting this objective is directly related to maximizing the system throughput which is the most important of HDD industry requirements. Moreover, most manufacturing systems have to handle the quality of semiconductor material. The time windows constraint in the manufacturing system must then be considered. In this paper, we propose a hybrid genetic algorithm (HGA) for improving chromosomes/offspring by checking and repairing time window constraint and improving offspring by left-shift routines as a local search algorithm to solve effectively the RFS scheduling problem with time windows constraint. Numerical experiments on several problems show that the proposed HGA approach has higher search capability to improve quality of solutions.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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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.

• Industrial Engineering and Management Systems
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• 제12권1호
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• pp.51-62
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• 2013

This paper considers a single machine rescheduling problem whose original (efficiency related) objective is minimizing makespan. We assume that disruptions such as order cancelations and newly arrived orders occur after the initial scheduling, and we reschedule this disrupted schedule with the objective of minimizing a disruption related objective while preserving the original objective. The disruption related objective measures the impact of the disruptions as difference of completion times in the remaining (uncanceled) jobs before and after the disruptions. The artificial due dates for the remaining jobs are set to completion times in the original schedule while newly arrived jobs do not have due dates. Then, the objective of the rescheduling is minimizing the maximum earliness without tardiness. In order to preserve the optimality of the original objective, we assume that no-idle time and no tardiness are allowed while rescheduling. We first define this new problem and prove that the general version of the problem is unary NP-complete. Then, we develop three simple but intuitive heuristics. For each of the three heuristics, we find a tight bound on the measure called modified z-approximation ratio. The best theoretical bound is found to be 0.5 - ${\varepsilon}$ for some ${\varepsilon}$ > 0, and it implies that the solution value of the best heuristic is at most around a half of the worst possible solution value. Finally, we empirically evaluate the heuristics and demonstrate that the two best heuristics perform much better than the other one.