• Journal of Korean Society of Industrial and Systems Engineering
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• 제38권3호
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• pp.169-180
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• 2015

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.

• Journal of the Korea Society of Computer and Information
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• 제13권3호
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• pp.85-90
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• 2008

In this paper, we propose a new robot cleaning algorithm, which we call SetClean. The new algorithm cleans from the most less complex area. Sometimes, when the cleaning completion time can be longer or can not be estimated, cleaning larger area first is better than optimizing the whole time for cleaning. To do this, SetClean algorithm divides the whole area into cleanable sub-areas using Set Cover algorithm and cleans the area in the order of high efficiency that maximize the cleanable area per unit time. SetClean algorithm decides the navigation flow by considering not only the size of the area but also the distance from the current robot location to the area to be cleaned and the delay time caused by the number of turns within the area. The experimental results show the mechanism and performance of the SetClean algorithm.

• Korean Management Science Review
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• 제25권3호
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• pp.45-57
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• 2008

This paper considers a genetic algorithm for sequencing activities and allocating resources to reduce the over all completion time of workflow in the presence resource constraints. The algorithm provides an integrated solution for two sub-problems. The first is to decide the priority for the activities which require the same resource. The other problem is to select one among available resources for each activity by considering the incurred setup time and the performance factor of each resource. We evaluate the algorithm performance for three different kinds of workflows including parallel structures. Computational results show that the proposed algorithm is more effective than a previous work.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 대한산업공학회/한국경영과학회 1993년도 춘계공동학술대회 발표논문 및 초록집; 계명대학교, 대구; 30 Apr.-1 May 1993
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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 Society of Industrial and Systems Engineering
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• 제20권44호
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• pp.1-12
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• 1997

In this paper a new heuristic algorithm has been developed and presented for minimizing the total completion time of a scheduling problem in a multi stage automated machining and assembly systems. The proposed Higher Ratio First(HRF) algorithm is based on the idea that jobs with higher processing time ratio should be a higher priority. The heuristic algorithm is implemented on the various problem cases by number of jobs and machines. The proposed algorithm provided smaller makespan than the makespan applied by three previously documented heuristics. The results obtained show a superior solution by the new heuristic over previous heuristics on all problem sizes.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.327-332
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• 2007

An engineering design problem involving Multi-Disciplinary Analysis(MDA) generally requires a large amounts of CPU time for the entire design process, and therefore Multiple Processing System (MPS) are essential to reduce the completion time. However, when applying conventional parallel processing techniques, all of the CAE S/W required for the MDA should be installed on all the servers making up NIPS because of characteristic of MDA and it would be a great expense in CAE S/W licenses. To solve this problem, we propose a Weight-based Multiqueue Load Balancing algorithm for a heterogeneous MPS where performance of servers and CAE S/W installed on each server are different of each other. To validate the performance, a computational experiments comparing the First Come First Serve algorithm and our proposed algorithm was accomplished.

• Journal of the Korea Safety Management & Science
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• 제13권4호
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• pp.125-137
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• 2011

Customers are generally requiring a variety of products, earlier due date, and lower price. A manufacturing process needs the efficient scheduling to meet those customer's requirements. This study proposes the novel algorithm named MJA(Minimum Job completion time and AGV time) that increases the performance of machines and AGV(Automated Guided Vehicles) in many kinds of job types. MJA optimizes the bottleneck of machines and efficiency of AGV with considering two types of dispatching at the same time. Suggested algorithm was compared with existing heuristic methods by several simulations, it performed better for reducing the time of tardiness.

• International conference on construction engineering and project management
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.243-251
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• 2013

Cash management is a major concern for all contractors in the construction industry. It is arguable that cash is the most critical resource of all. A contractor needs to secure sufficient funds to navigate the project to the end, while keeping an eye on maximizing profits along the way. Past research attempted to address such topic via developing models to tackle the time-cost tradeoff problem, cash flow forecasting, and cash flow management. Yet, little was done to integrate the three aspects of cash management together. This paper, as such, presents a comprehensive model that integrates the time-cost tradeoff problem, cash flow management, and cash flow forecasting. First, the model determines the project optimal completion time by considering the different alternative construction methods available for executing project activities. Second, it investigates different funding alternatives and proposes a project-level cash management plan. Two funding alternatives are considered; they are borrowing and company own financing. The model was built as a combinatorial optimization model that utilizes ant colony search capabilities. The model also utilizes Microsoft Project software and spreadsheets to maintain an environment that incorporates activities, their durations, and other project data, in order to estimate project completion time and cost. Ant Colony Optimization algorithm was coded as a Macro program using VBA. Finally, an example project was used to test the developed model, where it acted reliably in maximizing the contractor's profit in the test project.

• The KIPS Transactions:PartA
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• 제16A권1호
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• pp.35-42
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• 2009

This paper proposes a new coverage algorithm for intelligent robot. Many algorithms for improving the performance of coverage have been focused on minimizing the total coverage completion time. However, if one does not have enough time to finish the whole coverage, the optimal path could be different. To tackle this problem, we propose a new coverage algorithm, which we call MaxCoverage algorithm, for covering maximal area within the deadline. The MaxCoverage algorithm decides the navigation flow by greedy algorithm for Set Covering Problem. The experimental results show that the MaxCoverage algorithm performs better than other algorithms for random deadlines.

• Journal of the Korea Society for Simulation
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• 제24권4호
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• pp.77-88
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• 2015

In this paper, we consider a two-agent single-machine scheduling problem with exponential job-dependent position-based learning effects. The objective is to minimize the total weighted completion time of one agent with the restriction that the makespan of the other agent cannot exceed an upper bound. First, we propose a branch-and-bound algorithm by developing some dominance /feasibility properties and a lower bound to find an optimal solution. Second, we design an efficient simulated annealing (SA) algorithm to search a near optimal solution by considering six different SAs to generate initial solutions. We show the performance superiority of the suggested SA using a numerical experiment. Specifically, we verify that there is no significant difference in the performance of %errors between different considered SAs using the paired t-test. Furthermore, we testify that random generation method is better than the others for agent A, whereas the initial solution method for agent B did not affect the performance of %errors.