• Management Science and Financial Engineering
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• v.20 no.1
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• pp.1-9
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• 2014

In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.9-11
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• 2010

Multiple-input multiple-output (MIMO) systems assisted by multi-relays with single antenna are considered. Signal transmission consists of two hops. In the first hop, the source node broadcasts the vector symbols to all relays, then all relays forward the received signals multiplied by each power gain to the destination simultaneously. Unlike the case of full cooperation between relays such as single relay with multiple antennas, in our case there is no closed form solution for optimal relay power gain with respect to minimum mean square error (MMSE). Thus we propose an alternative approach in which we use an approximation of the cost function based on rank-one matrix decomposition. As a cost function, we choose the trace of MSE matrix. We give several simulation results to validate that our proposed method obtains a negligible performance loss compared to optimal solution obtained by exhaustive search.

• Advances in Computational Design
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• v.2 no.3
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• pp.241-256
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• 2017

This paper introduces the process for Multi-objective Optimization Framework (MOF) which mediates multiple conflicting design targets. Even though the extensive researches have shown the benefits of optimization in engineering and design disciplines, most optimizations have been limited to the performance-related targets or the single-objective optimization which seek optimum solution within one design parameter. In design practice, however, designers should consider the multiple parameters whose resultant purposes are conflicting. The MOF is a BIM-integrated and simulation-based parametric workflow capable of optimizing the configuration of building components by using performance and non-performance driven measure to satisfy requirements including build programs, climate-based daylighting, occupant's experience, construction cost and etc. The MOF will generate, evaluate all different possible configurations within the predefined each parameter, present the most optimized set of solution, and then feed BIM environment to minimize data loss across software platform. This paper illustrates how Multi-objective optimization methodology can be utilized in design practice by integrating advanced simulation, optimization algorithm and BIM.

• Bulletin of the Korean Mathematical Society
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• v.51 no.5
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• pp.1527-1538
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• 2014

The purpose of this paper is to address the multiple split common fixed point problem. We present two different methods to approximate a solution of the problem. One is cyclic iteration method; the other is simultaneous iteration method. Under appropriate assumptions on the operators and iterative parameters, we prove both the proposed algorithms converge to the solution of the multiple split common fixed point problem. Our results generalize and improve some known results in the literatures.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.87-95
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• 1991

The exact solution of the closed queueing networks(CQN) is known only for the product form (BCMP) queueing networks. Various computational algorithms are available to derive system throughput(the rate at which a system completes units of computational work) of the networks. However, the computational expense of an exact solution is often excessive when there are multiple classes of cutomers. Instead of computing the exact values, it may be sufficient to derive bounds on the performance measures. Techniques for obtaining bounds on BCMP queueing networks have appeared in the past years. This paper also presents bounds on throughput in CQN models with multiple classes of customers.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.2
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• pp.1-8
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• 2010

A vendor selection problem consists of two different kinds of decision making. First one is to choose the best suppliers among all possible suppliers and the next is to allocate the optimal quantities of orders among the selected vendors. In this study, an integration of the technique for order preference by similarity to ideal solution (TOPSIS) and a multi-objective mixed integer programming (MOMIP) is developed to account for all qualitative and quantitative factors which are used to evaluate and choose the best group of vendors and to decide the optimal order quantity for each vendor. A solution methodology for the vendor selection model of multiple-vendor, multiple-item with multiple decision criteria and in respect to finite vendor capacity is presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.17-24
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• 2013

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

• Proceedings of the KIEE Conference
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• 2007.11b
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• pp.115-117
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• 2007

This paper presents a new systematic approach to find a global solution to economic dispatch (ED) with multiple fuel units using a function merger (FM). Currently, no systematic approach has been developed to find a global solution to economic dispatch with multiple fuel units. Various heuristic methods have been proposed, however it is almost impossible to guarantee a global solution by those methods yet. The proposed method uses the FM and $\lambda$-P functions. A FM merges several fuel cost functions into one that satisfies the optimal conditions of an ED. The FM procedures are described in detail with illustrative examples.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.4
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• pp.767-779
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• 2017

Automated mission planning for unmanned aerial vehicles (UAVs) is difficult because of the propagation of several sources of error into the solution, as for any large scale autonomous system. To ensure reliable system performance, we quantify all sources of error and their propagation through a mission planner for operation of UAVs in an obstacle rich environment we developed in prior work. In this sequel to that work, we show that the mission planner developed before can be made robust to errors arising from the mapping, sensing, actuation, and environmental disturbances through creating systematic buffers around obstacles using the calculations of uncertainty propagation. This robustness makes the mission planner truly autonomous and scalable to many UAVs without human intervention. We illustrate with simulation results for trajectory generation of multiple UAVs in a surveillance problem in an urban environment while optimizing for either maximal flight time or minimal fuel consumption. Our solution methods are suitable for any well-mapped region, and the final collision free paths are obtained through offline sub-optimal solution of an mTSP (multiple traveling salesman problem).

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.4
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• pp.185-194
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• 2000

The learning automaton is an automaton to update systematically the strategy for enhancing the performance in response to the output results, and several schemes of learning automata have been presented. In this paper, S-model learning automata are applied in order to achieve the best compromise solution between an optimal solution for economic operation and an optimal solution for stable operation of the power system under the circumstance that the loads vary randomly. It is shown that learning automata are applied satisfactorily to the multiobjective optimization problem for obtaining the best tradeoff among the conflicting economy and stability objectives of power systems.