• 한국산업정보학회논문지
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• 제15권3호
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• pp.59-65
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• 2010

This paper proposes a modified heuristic mixed model assembly line (MMAL) balancing algorithm that provides consistent station assignments on a model by model basis as well as on a station by station. Basically, some of single model line balancing techniques are modified and incorporated to be fit into the MMAL. The proposed algorithm is based on N.T. Thomopoulos' [8] method and supplemented with several well proven single model line balancing techniques proposed in the literature until recently. Hoffman's precedence matrix [2] is used to indicate the ordering relations among tasks. Arcus' Rule IX [1] is applied to generate rapidly a fairly large number of feasible solutions. Consequently, this proposed algorithm reduces the fluctuations in operation times among the models as well as the stations and the balance delays. A numerical example shows that the proposed algorithm can provide a good feasible solution in a relatively short time and generate relatively better solutions comparing to other three existing methods.

• 대한산업공학회지
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• 제23권2호
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• pp.433-450
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• 1997

In this study, a heuristic algorithm is developed to solve 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 algorithm takes advantage of the basic structure of a heuristic algorithm, called the exchange heuristic, but employs different strategies on some critical steps of the original algorithm which have significant effects on the algorithm performance. The original algorithm and the modified algorithm were compared through an experimental investigation. The modified algorithm produces significantly shorter schedules than the original algorithm, though it requires up to three times more computation time.

• 대한산업공학회지
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• 제11권2호
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• pp.155-163
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• 1985

This paper presents a heuristic algorithm for a multidepot aircraft scheduling and crew scheduling with deal-head flights. This algorithm is extended from a Greedy heuristic algorithm for a multi-depot multi-salesman traveling salesman problem. We first transform a given flight schedule into a multi-depot multi-traveling salesman problem, considering aircraft flight policies and crew management constraints. Then we solve this problem by applying a modified Greedy heuristic algorithm.

• Structural Engineering and Mechanics
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• 제55권3호
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• pp.535-554
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• 2015

Dolphin echolocation (DE) optimization algorithm is a recently developed meta-heuristic in which echolocation behavior of Dolphins is utilized for seeking a design space. The computational performance of meta-heuristic algorithms is highly dependent to its internal parameters. But the computational time of adjusting these parameters is usually extensive. The DE is an efficient optimization algorithm as it includes few internal parameters compared with other meta-heuristics. In the present paper a modified Dolphin echolocation (MDE) algorithm is proposed for optimization of steel frame structures. In the MDE the step locations are determined using one-dimensional chaotic maps and this improves the convergence behavior of the algorithm. The effectiveness of the proposed MDE algorithm is illustrated in three benchmark steel frame optimization test examples. Results demonstrate the efficiency of the proposed MDE algorithm in finding better solutions compared to standard DE and other existing algorithms.

• 한국경영과학회지
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• 제6권2호
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• pp.13-19
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• 1981

This paper developed 3 flow-shop sequencing heuristic methods: modified RA method, modified RACS method and modified RAES method. These methods modified RA method, RACS method and RAES method developed by D. G. Dannenbring. These methods can easily determine desirable sequence of orders and can improve nx3 flow-shop's productivity and efficiency. The maximum flow-time criterion is selected as the evaluation criterion of flow-shop's efficiency, We evaluated these 6 heuristic methods’ performance. By the evaluation of the result, we can see that the modified methods produce a shorter maximum flow-time than the original methods.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1993년도 춘계공동학술대회 발표논문 및 초록집; 계명대학교, 대구; 30 Apr.-1 May 1993
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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.

• 산업공학
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• 제15권4호
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• pp.338-348
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• 2002

In this paper, we present a restricted tabu search(RTS) algorithm that schedules jobs on identical parallel machines in order to minimize the maximum lateness of jobs. Jobs have release times and due dates. Also, sequence-dependent setup times exist between jobs. The RTS algorithm consists of two main parts. The first part is the MATCS(Modified Apparent Tardiness Cost with Setups) rule that provides an efficient initial schedule for the RTS. The second part is a search heuristic that employs a restricted neighborhood generation scheme with the elimination of non-efficient job moves in finding the best neighborhood schedule. The search heuristic reduces the tabu search effort greatly while obtaining the final schedules of good quality. The experimental results show that the proposed algorithm gives better solutions quickly than the existing heuristic algorithms such as the RHP(Rolling Horizon Procedure) heuristic, the basic tabu search, and simulated annealing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권12호
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• pp.5780-5802
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• 2017

Cloud computing system consists of distributed resources in a dynamic and decentralized environment. Therefore, using cloud computing resources efficiently and getting the maximum profits are still challenging problems to the cloud service providers and cloud service users. It is important to provide the efficient scheduling. To schedule cloud resources, numerous heuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search (CS) algorithms have been adopted. The paper proposes a Modified Particle Swarm Optimization (MPSO) algorithm to solve the above mentioned issues. We first formulate an optimization problem and propose a Modified PSO optimization technique. The performance of MPSO was evaluated against PSO, and GA. Our experimental results show that the proposed MPSO minimizes the task execution time, and maximizes the resource utilization rate.

• 산업경영시스템학회지
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• 제21권45호
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• pp.165-180
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• 1998

To make a scheduling for minimizing the tardiness of delivery, we present an efficient heuristic algorithm for job-shop scheduling problems with due dates. The SDME(Slack Degree Modified Exchange) algorithm, which is the proposed heuristic algorithm, carries out the operation in two phases. In the phase 1, a dispatching rule is employed to generate an active or non-delay initial schedule. In the phase 2, tasks selected from a predetermined set of promising target operations in the initial schedule are tested to ascertain whether, by left-shifting their start times and rearranging some subset of the remaining operations, one can reduce the total tardiness. In the numerical example, the results indicate that the proposed SDME algorithm is capable of yielding notable reductions in the total tardiness for practical size problems.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2004년도 학술대회지
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• pp.62-67
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• 2004

Many ships in voyage experience weight and buoyancy distribution change by various reasons such as change of sea water density and waves, weather condition, and consumption of fuel, provisions, etc . The weight and buoyancy distribution change can bring the ships out of allowable trim, heeling angle. In these case, the ships should adjust trim and heeling angle by shifting of liquid cargo or ballasting, deballasting of ballast tanks for recovery of initial state or for a stable voyage. But, if the adjustment is performed incorrectly, ship's safety such as longitudinal strength, intact stability, propeller immersion, wide visibility, minimum forward draft cannot be secured correctly. So it is required that the adjustment of trim and heeling angle should be planned not by human operators but by optimization computer algorithm. To make an optimized plan to adjust trim and heeling angle guaranteeing the ship's safety and quickness of process, Uk! combined mechanical analysis and optimization algorithm. The candidate algorithms for the study were heuristic algorithm, meta-heuristic algorithm and uninformed searching algorithm. These are widely used in various kinds of optimization problems. Among them, heuristic algorithm $A^\ast$ was chosen for its optimality. The $A^\ast$ algorithm is then applied for the study. Three core elements of $A^\ast$ Algorithm consists of node, operator, evaluation function were modified and redefined. And we analyzed the $A^\ast$ algorithm by considering cooperation with loading instrument installed in most ships. Finally, the algorithm has been applied to tanker ship's various conditions such as Normal Ballast Condition, Homo Design Condition, Alternate Loading Condition, Also the test results are compared and discussed to confirm the efficiency and the usefulness of the methodology developed the system.