• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.130-137
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• 2013

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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.309-312
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• 2007

We consider a proportionate flow shop problem with controllable processing times. The objective is to minimize the sum of total completion time and total compression cost, in which the cost function of compressing the processing times is non-decreasing concave. We show that the problem can be solved in polynomial time by reducing it to a polynomially solvable 0-1 unconstrained quadratic program.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.343-352
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• 1993

In this study, a modified algorithm for the exchange heuristic is developed and applied to a resource-constrained scheduling problem. The problem involves multiple projects and multiple resource categories and allows flexible resource allocation to each activity. The objective is to minimize the maximum completion time. The exchange heuristkc is a multiple pass algorithm which makes improvements upon a given initial feasible schedule. Four different modified algorithms are proposed. The original algorithm and the new algorithms were compared through an experimental investigation. All the proposed algorithms reduce the maximum completion time much more effectively than the original algorithm. Especially, one of four proposed algorithms obviously outperforms the other three algorithms. The algorithm of the best performance produces significantly shorter schedules than the original algorithm, though it requires up to three times more computation time. However, in most situations, a reduction in schedule length means a significant reduction in the total cost.

• Journal of Veterinary Clinics
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• v.40 no.6
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• pp.423-428
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• 2023

The mouse kidney transplantation model serves as an invaluable tool for exploring various aspects of the transplant process, including acute rejection, cellular and humoral rejection, ischemia-reperfusion injury, and the evaluation of novel therapeutic strategies. However, conducting venous anastomosis in this model poses a significant challenge due to the thin and pliable characteristics of the renal vein, which often obstruct clear visualization of the resected vein's edge. This study proposes the adoption of a two stay suture technique to enhance the visualization of the renal vein's edge, thereby facilitating efficient and successful venous anastomosis. A total of 22 mice served as kidney donors in this study. The conventional anchoring suture technique was employed for venous anastomosis in 11 of these mice, while the remaining 11 underwent the two stay suture technique. The anastomosis duration and completion rates were then compared between these two groups. The conventional anchoring suture technique yielded an average anastomosis time of 29 minutes and a completion rate of 64%. In contrast, the two stay suture technique demonstrated a substantial improvement, with an average anastomosis time of 14 minutes and a completion rate of 100%. The two stay suture technique offers a promising solution to enhance visualization during venous anastomosis in murine kidney transplantation. This technique may particularly benefit novices by enabling them to perform venous anastomosis more easily, swiftly, and successfully.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.6-15
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• 1993

A generalization of one machine maximum lateness minimization problem is considered. There are one achine with controllable speed and n weighting jobs $J_{j}$, j=1, 2, ..., n with ambiguous duedates. Introducing fuzzy formulation, a membership function of the duedate associated with each job $J_{j}$, which describes the satisfaction level with respect to completion time of $J_{j}$. Thus the duedates are not constants as in conventional scheduling problems but decision variables reflecting the fuzzy circumstance of the job completing. We develop the polynomial time algorithm to find an optimal schedule and jobwise machine speeds, and to minimize the total sum of costs associated with jobwise machine speeds and dissatisfaction with respect to completion times of weighting jobs. jobs.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.1
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• pp.76-85
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• 2007

A reliability acceptance sampling plan (RASP) consists of a set of life test procedures and rules for eitheraccepting or rejecting a collection of items based on the sampled lifetime data. Most of the existing RASPs areconcerned with the case where test items are available at the same time. However, as in the early stage ofproduct development, it may be difficult to secure test items at the same time. In such a case, it is inevitable toconduct a life test using sequentially supplied samples.In this paper, it is assumed that test items are sequentially supplied, the lifetimes of test items follow anexponential disthbution, failures are monitored continuously, arrival times of test items are known, and thenumber of test items at each arrival time is given. Under these assumptions, RASPs are developed by deter-mining the test completion time and the critical value for the maximum likelihood estimator of the mean lifetimesuch that the producer and consumer risks are satisfied. Then, the developed plans are compared to thetraditional Type-I censored RASPs in terms of the test completion time. Computational results indicate that thetest completion time of the developed RASP is shorter than that of the traditional Type-I censored plan in mostcases considered. It is also found that the superiority of the developed RASP becomes more prominent as theinter-arrival times of test items increase and/or the total number of test items gets larger.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.2
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• pp.81-87
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• 1999

This paper discusses a bicriterion approach to sequencing with time/cost trade-offs. The first problem is to minimize the total flow time and the maximum tardiness. And second is to the maximum tardiness and resource allocation costs. This approach, which produces an efficient flintier of possible schedules, has the advantage that it does not require the sequencing criteria to be measurable in the same units as the m allocation cost. The basic single machine model is used to treat a class of problems in which the sequencing objective is to minimize the maximum completion penalty. It is further assumed that resource allocation costs can be represented by linear time/cost function.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.419-432
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• 2005

In this paper we study the no-wait or no-idle permutation flowshop scheduling problem with an increasing and decreasing series of dominating machines. The objective is to minimize one of the five regular performance criteria, namely, total weighted completion time, maximum lateness, maximum tardiness, number of tardy jobs and makespan. We establish that these five cases are solvable by presenting a polynomial-time solution algorithm for each case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1802-1807
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• 2015

The cloud computing service provides physical IT resources to VM instances to users using virtual technique and the users pay cost of VM instances to service provider. The auction model based on cloud computing provides available resources of service provider to users through auction mechanism. The users bid spot instances to process their a job until its deadline time. If the bidding price of users is higher than the spot price, the user will be provided the spot instances by service provider. In this paper, we propose a new bidding strategy to minimize the total cost for job completion. Typically, the users propose bidding price as high as possible to get the spot instances and the spot price get high. we lower the spot price using proposed strategy and minimize the total cost for job completion. To evaluate the performance of our strategy, we compare the spot price and the total cost for job completion with real workload data.

• Management Science and Financial Engineering
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• v.20 no.2
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• pp.33-37
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• 2014

A flow shop scheduling problem involves scheduling jobs on multiple machines in series in order to optimize a given criterion. The flow time of a job is the amount of time the job spent before its completion and the stretch of the job is the ratio of its flow time to its processing time. In this paper, a hybrid genetic algorithm (HGA) approach is proposed for minimizing the total stretch in flow shop scheduling. HGA adopts the idea of seed selection and development in order to reduce the chance of premature convergence that may cause the loss of search power. The performance of HGA is compared with that of genetic algorithms (GAs).