• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.181-189
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• 2001

The software components of embedded control systems get extremely complex as they are designed into distributed systems get extremely complex as they are designed into distributed systems consisting of a large number of inexpensive microcontrollers interconnected by low-bandwidth real-time networks such as the controller area network (CAN). While recently emerging middleware technologies such as CORBA and DCOM address the complexity of distributed programming, they cannot be directly applied to distributed control system design due to their excessive resource demand and inadequate communication models. In this paper, we propose a CORBA-based middleware design for CAN-based distributed embedded control systems. Our design goal is to minimize its resource need and make it support group communication without losing the IDL (interface definition language) level compliance to the OMG standards. To achieve this, we develop a transport protocol on the CAN and a group communication scheme based on the well-known publisher/subscriber model. The protocol effectively realizes subject-based addressing and supports anonymous publisher/subscriber communication. We also customize the method invocation and message passing protocol, referred to as the general inter-ORB protocol (GIOP), of CORBA so that CORBA method invocations are efficiently serviced on a low-bandwidth network such as the CAN. This customization includes packed data encoding and variable-length integer encoding for compact representation of IDL data types. We have implemented our CORBA-based middleware on the mArx real-time operating system we have developed at Seoul National University. Our experiments clearly demonstrate that it is feasible to use CORBA in developing distributed embedded control systems possessing severe resource limitations. Our design clearly demonstrates that it is feasible to use a CORBA-based middleware in developing distributed embedded systems on real-time networks possessing severe resource limitations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.109-116
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• 2015

This paper is concerned with the single vendor single buyer integrated production inventory problem. To make this problem more practical, space restriction and lead time proportional to lot size are considered. Since the space for the inventory is limited in most practical inventory system, the space restriction for the inventory of a vendor and a buyer is considered. As product's quantity to be manufactured by the vendor is increased, the lead time for the order is usually increased. Therefore, lead time for the product is proportional to the order quantity by the buyer. Demand is assumed to be stochastic and the continuous review inventory policy is used by the buyer. If the buyer places an order, then the vendor will start to manufacture products and the products will be transferred to the buyer with equal shipments many times. The mathematical formulation with space restriction for the inventory of a vendor and a buyer is suggested in this paper. This problem is constrained nonlinear integer programming problem. Order quantity, reorder points for the buyer, and the number of shipments are required to be determined. A Lagrangian relaxation approach, a popular solution method for constrained problem, is developed to find lower bound of this problem. Since a Lagrangian relaxation approach cannot guarantee the feasible solution, the solution method based on the Lagrangian relaxation approach is proposed to provide with a good feasible solution. Total costs by the proposed method are pretty close to those by the Lagrangian relaxation approach. Sensitivity analysis for space restriction for the vendor and the buyer is done to figure out the relationships between parameters.

• Journal of Navigation and Port Research
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• v.45 no.3
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• pp.102-108
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• 2021

The purpose of this study is to develop a proactive methodology for disruption due to uncertainty in vessels' arrival time. As worldwide imports and exports increased rapidly, the importance of berth planning in container terminals has increased accordingly. Since the berth plan determines the capacity of the container terminal, it aims to maximize efficiency by minimizing the time and space gap between the vessels. In reality, several uncertainties disrupt the initial berth plan resulting in economic losses. In this study, we propose a robust berth plan for preventing disruption.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1278-1285
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• 2018

For saving electricity bill, energy storage system(ESS) is being installed in factories, public building and commercial building with a Time-of-Use(TOU) tariff which consists of demand charge(KRW/kW) and energy charge(KRW/kWh). However, both of peak reduction and ESS special tariff are not considered in an analysis of initial cost payback period(ICPP) on ESS. Since it is difficult to reflect base rate by an amount of uncertain peak demand reduction during mid-peak and on-peak periods in the future days. Therefore, the ICPP on ESS can be increased. Based on this background, this paper presents the advanced analysis method for the ICPP on ESS. In the proposed algorithm, the representative days of monthly electricity consumption pattern for the amount of peak reduction can be found by the k­means clustering algorithm. Moreover, the total expected energy costs of representative days are minimized by optimal daily ESS operation considering both peak reduction and the special tariff through a mixed-integer linear programming(MILP). And then, the amount of peak reduction becomes a value that the sum of the expected energy costs for 12 months is maximum. The annual benefit cost is decided by the amount of annual peak reduction. Two simulation cases are considered in this study, which one only considers the special tariff and another considers both of the special tariff and amount of peak reduction. The ICPP in the proposed method is shortened by 18 months compared to the conventional method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.14 no.1
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• pp.69-80
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• 1996

This Study aims at identifying optimum residential area of small and medium local cities to be relocated in the future. In the object of study, Chinju City was chosen and it consists of three stages of application to choose the priority of residential development us well as available residential locations. In the first stage, a digital image processing technique was applied to generate the existing urban land use information from the satellite image data. In the second stage, GIS technique was used to choose the avaliable residential area by evaluating the elements for residential site allocation such as road accessibility, topographic height, slope, aspect, legal limit, population density, and land price. In the third stage, some mathematical location models were applied to identify optimum candidates of residential areas chosen by earier stages. The results were evaluated by statistical methods and integer programming to identify the development priority. We expect this procedures and the results will be able to be used as a guide-line to support housing policies of Chinju City by allocating residential sites as well as a technique to apply a locational analysis for the future residential areas of small and medium local cities.

• Asia pacific journal of information systems
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• v.12 no.4
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• pp.193-213
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• 2002

The sales activity of most of small manufacturing companies is based on orders of buyers. The process of promotion, negotiation, receipt and selection of orders of the manufacturers is closely coupled with the load status of the production lines. The decision on whether to accept an order or not, or the selection of optimal order set among excessive orders is entirely dependent on the schedule of production lines. However, in the real world, since the production scheduling activity is mainly performed by human experts, most of small manufacturers are suffer from being unable to meet due dates, lack of rapid decision on the acceptance of new order. Recently, Internet based Electronic Commerce is recognized as one of the alternatives for strengthening sales power of small and medium companies. However, small and medium manufacturers can't adjust properly to the new environment because they are in short of money, personnel, and technology. To cope with this problem, this paper deals with development of part sales agent coupled with virtual manufacturing in Internet environment that consist of selection agent, advertisement agent, selection agent, negotiation agent, and virtual manufacturing system. This paper develops a time-bounded negotiation mechanism for small and medium manufacturers in agent-based automated negotiation between customers and negotiation agents. Furthermore, to select optimal order set maximized profit, we first formulate the order selection problem with mixed integer programming, but the computation time of IP is not acceptable for real world scale problem. To overcome this problem and dynamic nature of virtual manufacturing, we suggest a genetic algorithm approach, which shows a reasonable computation time for real world case and good incremental problem solving capability.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.1
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• pp.41-41
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• 1992

In the real world situations that some jobs need be processed only on certain limited machines frequently occur due to the capacity restrictions of machines such as tools fixtures or material handling equipment. In this paper we consider n-job non-preemptive and m parallel machines scheduling problem having two machines group. The objective function is to minimize the sum of earliness and tardiness with different release times and due dates. The problem is formulated as a mixed integer programming problem. The problem is proved to be Np-complete. Thus a heuristic is developed to solve this problem. To illustrate its suitability and efficiency a proposed heuristic is compared with a genetic algorithm and tabu search for a large number of randomly generated test problems in ship engine assembly shop. Through the experimental results it is showed that the proposed algorithm yields good solutions efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3876-3895
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• 2014

Recently, Heterogeneous Network (HetNet) with Coordinated Multi-Point (CoMP) scheme is introduced into Long Term Evolution-Advanced (LTE-A) systems to improve digital services for User Equipments (UEs), especially for cell-edge UEs. However, Radio Resource Management (RRM), including Resource Block (RB) scheduling and Power Allocation (PA), in this scenario becomes challenging, due to the intercell cooperation. In this paper, we investigate the RRM problem for downlink transmission of HetNet system with Joint Processing (JP) CoMP (both joint transmission and dynamic cell selection schemes), aiming at maximizing weighted sum data rate under the constraints of both transmission power and backhaul capacity. First, joint RB scheduling and PA problem is formulated as a constrained Mixed Integer Programming (MIP) which is NP-hard. To simplify the formulation problem, we decompose it into two problems of RB scheduling and PA. For RB scheduling, we propose an algorithm with less computational complexity to achieve a suboptimal solution. Then, according to the obtained scheduling results, we present an iterative Karush-Kuhn-Tucker (KKT) method to solve the PA problem. Extensive simulations are conducted to verify the effectiveness and efficiency of the proposed algorithms. Two kinds of JP CoMP schemes are compared with a non-CoMP greedy scheme (max capacity scheme). Simulation results prove that the CoMP schemes with the proposed RRM algorithms dramatically enhance data rate of cell-edge UEs, thereby improving UEs' fairness of data rate. Also, it is shown that the proposed PA algorithms can decrease power consumption of transmission antennas without loss of transmission performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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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 Electrical Engineering and Technology
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• v.9 no.2
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• pp.433-440
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• 2014

In this study, a method for the low-carbon active distribution system (ADS) planning is proposed. It takes into account the impacts of both network capacity and demand correlation to the renewable energy accommodation, and incorporates demand response (DR) as an available resource in the ADS planning. The problem is formulated as a mixed integer nonlinear programming model, whereby the optimal allocation of renewable energy sources and the design of DR contract (i.e. payment incentives and default penalties) are determined simultaneously, in order to achieve the minimization of total cost and $CO_2$ emissions subjected to the system constraints. The uncertainties that involved are also considered by using the scenario synthesis method with the improved Taguchi's orthogonal array testing for reducing information redundancy. A novel cuckoo search (CS) is applied for the planning optimization. The case study results confirm the effectiveness and superiority of the proposed method.