• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.121-135
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• 2007

Constraint directed scheduling techniques, representing problem constraints explicitly and constructing schedules by constrained heuristic search, have been successfully applied to real world scheduling problems that require satisfying a wide variety of constraints. However, there has been little basic research on the representation and optimization of the objective value of a schedule in the constraint directed scheduling literature. In particular, the cost objective is very crucial for enterprise decision making to analyze the effects of alternative business plans not only from operational shop floor scheduling but also through strategic resource planning. This paper aims to explicitly represent and optimize the total cost of a schedule including the tardiness and inventory costs while satisfying non-relaxable constraints such as resource capacity and temporal constraints. Within the cost based scheduling framework, a cost propagation algorithm is presented to update cost information throughout temporal constraints within the same job.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2A
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• pp.121-127
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• 2010

In this paper, we propose a computationally efficient scheduling algorithm that can arbitrarily control the throughput-fairness tradeoff in a multiuser wireless communication environment. As a new scheduling criterion, we combine linearly two well-known scheduling criteria such as one of achieving the maximum sum throughput and the other of achieving the maximum fairness, so as to control the relative proportion of the throughput and the fairness according to a control factor. For linear combining two different criteria, their optimization directivenesses and the units should be unified first. To meet these requirements, we choose an instantaneous channel capacity as a scheduling criterion for maximizing the sum throughput and the average serving throughput for maximizing the fairness. Through a unified linear combining of two optimization objectives with the control factor, it can provide various throughput-fairness tradeoffs according to the control factors. For further simplification, we exploit a high signal-to-noise ratio (SNR) approximation of the instantaneous channel capacity. Through computer simulations, we evaluate the throughput and fairness performances of the proposed algorithm according to the control factors, assuming an independent Rayleigh fading multiuser channel. We also evaluate the proposed algorithm employing the high SNR approximation. From simulation results, we could see that the proposed algorithm can control arbitrarily the throughput-fairness performance between the performance of the scheduler aiming to the maximum sum throughput and that of the scheduler aiming to the maximum fairness, finally, we see that the high SNR approximation can give a satisfactory performance in this situation.

• Korean Chemical Engineering Research
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• v.44 no.2
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• pp.149-159
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• 2006

This research is a development of useful S/W program for real industry about optimal product scheduling in real plant for manufacturing polymer products. For this, we used a fine model with total amount of losses in weight(ton) as an objective for optimal scheduling and a genetic algorithm for optimization in this factory they manufacture three different products. Major products are A and B but the product which can be process in the period of products change over. They also sells them as a chap product in market. The major products have several types of packing process-bulk, pack for domestic market, pack for export. The demands of product with each packing type are increased, and frequently they failed keep the deadline for sail. Based on realistic production situation, we composed a fine modeling for optimal scheduling. And we also develop a S/W program for optimal scheduling which can be used by non-specialist in scheduling problem. We used a modified genetic algorithm and it gave us a better solution in process. We can have a result of reducing the total amount of losses in weight by half compared with the losses when existing production schedule.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.389-398
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• 2010

The Vehicle Routing and Scheduling Problem with Time Windows(VRSPTW) is to establish a delivery route of minimum cost satisfying the time constraints and capacity demands of many customers. The VRSPTW takes a long time to generate a solution because this is a NP-hard problem. To generate the nearest optimal solution within a reasonable time, we propose the heuristic by using an ACO(Ant Colony Optimization) with multi-cost functions. The multi-cost functions can generate a feasible initial-route by applying various weight values, such as distance, demand, angle and time window, to the cost factors when each ant evaluates the cost to move to the next customer node. Our experimental results show that our heuristic can generate the nearest optimal solution more efficiently than Solomon I1 heuristic or Hybrid heuristic applied by the opportunity time.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.211-219
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• 2020

One of the major advancements of the Fourth Industrial Revolution is the use of Internet of Drones (IoD), which combines the Internet of Things (IoT) and drone technology. IoD technology is especially important for efficiently and economically operating C4ISR operations in actual battlefields supporting various combat situations. The purpose of this study is to solve the problems of limited battery capacity of drones and lack of budgeting criteria for military drone transcription, introduction, and operation. If the mission area is defined and corresponding multi-drone hovering check points and mission completion time limits are set, then an energy and time co-optimized scheduling and operation control scheme is needed. Because such a scheme does not exist, in this paper, a Drone Force Deployment Optimization (DFDO) scheme is proposed to help schedule multi-drone operation scheduling and networked based remote multi-drone control.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.4
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• pp.58-68
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• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.4
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• pp.33-51
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• 1998

The industrial operation is one of the three basic modes of shipping operation with liner and Tramp operations. Industrial operators usually control vessels of their own or on a time charter to minimize the cost of shipping their cargoes. Such operations abound in shipping of bulk commodities, such as oil, chemicals and ores. This work is concerned with an operational optimization analysis of the fleet owned by a major oil company. a typical industrial operator. The operational optimization problem of the fleet of a major oil company is divided Into two phase problem. The front end corresponds to the optimization problem of the transportation of crude oil. product mix. and the distribution of product oil to comply with the demand of the market. The back end tackles the scheduling optimization problem of the fleet to meet the seaborne transportation demand derived from the front end. A case study reflecting the practices of an international major oil company is demonstrated to make clear the underlying ideas.

• Journal of the Korean Mathematical Society
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• v.47 no.1
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• pp.17-28
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• 2010

In this paper we deal with linear chance-constrained optimization problems, a class of problems which naturally arise in practical applications in finance, engineering, transportation and scheduling, where decisions are made in presence of uncertainty. After giving the deterministic equivalent formulation of a linear chance-constrained optimization problem we construct a conjugate dual problem to it. Then we provide for this primal-dual pair weak sufficient conditions which ensure strong duality. In this way we generalize some results recently given in the literature. We also apply the general duality scheme to a portfolio optimization problem, a fact that allows us to derive necessary and sufficient optimality conditions for it.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.17-22
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• 2012

Lagrangian relaxation is the most widely adopted method for solving unit commitment (UC) problems. It consists of two steps: dual optimization and primal feasible solution construction. The dual optimization step is crucial in determining the overall performance of the solution. This paper intends to evaluate two dual optimization methods - one based on subgradient (SG) and the other based on the cutting plane. Large-scale UC problems with hundreds of thousands of variables and constraints have been generated for evaluation purposes. It is found that the evaluated SG method yields very promising results.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.3
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• pp.152-157
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• 2005

An objected-oriented programming(OOP) technique is introduced to dispatch schedules for TWBP. Some dispatch schedules such as constrained (pre)dispatch, unconstrained (pre)dispatch, and nominal self-dispatch schedule need to be peformed to make power market work. These dispatch schedules are similar but have some differences in required constraints, needed data, and scheduling time. Therefore, it makes the scheduling program simple to introduce the OOP technique to this problem: to have each instance of the OOP perform its own dispatch scheduling. The developed program adopts linear programming(LP) as an optimization tool and could consider some crucial constraints such as power balance, generation power limits, generation ramp-rates, power limitations of transmission lines, and power system security.