• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.3
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• pp.254-262
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• 2014

Radar task scheduling deals with the assignment of task to efficiently enhance the radar performance on the limited resource environment. In this paper, total weighted tardiness is adopted as the objective function of task scheduling in operation of multiple multi-function radars. To take into account real-time implementability, heuristic index-based methods are presented and investigated. Numerical simulations for generic search and track scenarios are performed to evaluate the proposed methods, in particular investigating the effectiveness of multi-radar operation concepts.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.4
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• pp.367-381
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• 2011

Reducing waste for the efficiency of production is becoming more important because of the rapidly changing market circumstances and the rising material and oil prices. The dispatching also has to consider the characteristic of production circumstance for the efficiency. The production circumstance has the non-identical parallel machines with rework rate since machines have different capabilities and deterioration levels in the real manufacturing field. This paper proposes a dispatching method, FTLR (Flow Time Loss Index with Rework Rate) for production efficiency. The goal of FTLR is to minimize flow time based on such production environments. FTLR predicts the flow time with rework rate. After assessing dominant position of expected flow time per each machine, FTLR performs dispatching to minimize flow time. Experiments compare various dispatch methods for evaluating FTLR with mean flow time, mean tardiness and max tardiness in queue.

• Management Science and Financial Engineering
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• v.18 no.1
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• pp.1-12
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• 2012

In this paper, we report a case study on the common due-date assignment and scheduling problem in a paper remanufacturing system that produces corrugated cardboards using collected waste papers for a given set of orders under the make-to-order (MTO) environment. Since the system produces corrugated cardboards in an integrated process and has sequence-dependent setups, the problem considered here can be regarded as common due-date assignment and sequencing on a single machine with sequence-dependent setup times. The objective is to minimize the sum of the penalties associated with due-date assignment, earliness, and tardiness. In the study, the earliness and tardiness penalties were obtained from inventory holding and backorder costs, respectively. To solve the problem, we adopted two types of algorithms: (a) branch and bound algorithm that gives the optimal solutions; and (b) heuristic algorithms. Computational experiments were done on the data generated from the case and the results show that both types of algorithms work well for the case data. In particular, the branch and bound algorithm gave the optimal solutions quickly. However, it is recommended to use the heuristic algorithms for large-sized instances, especially when the solution time is very critical.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1838-1855
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• 2008

The manufacturing industry seeks for improvements in efficiency at the manufacturing process. This paper presents a method for effective real time dispatching for parallel machines with multi product that minimizes mean tardiness and maximizes the quality of the product. What is shown in this paper is that using the Rolling Horizon Tabu search method in the real time dispatching process, mean tardiness can be reduced to the minimum. The effectiveness of the method presented in this paper has been examined in the simulation and compared with other dispatching methods. In fact, using this method manufacturing companies can increase profits and improve customer satisfaction as well.

• Industrial Engineering and Management Systems
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• v.12 no.2
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• pp.151-160
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• 2013

In the past decades, several algorithms based on evolutionary approaches have been proposed for solving job shop scheduling problems (JSP), which is well-known as one of the most difficult combinatorial optimization problems. Most of them have concentrated on finding optimal solutions of a single objective, i.e., makespan, or total weighted tardiness. However, real-world scheduling problems generally involve multiple objectives which must be considered simultaneously. This paper proposes an efficient particle swarm optimization based approach to find a Pareto front for multi-objective JSP. The objective is to simultaneously minimize makespan and total tardiness of jobs. The proposed algorithm employs an Elite group to store the updated non-dominated solutions found by the whole swarm and utilizes those solutions as the guidance for particle movement. A single swarm with a mixture of four groups of particles with different movement strategies is adopted to search for Pareto solutions. The performance of the proposed method is evaluated on a set of benchmark problems and compared with the results from the existing algorithms. The experimental results demonstrate that the proposed algorithm is capable of providing a set of diverse and high-quality non-dominated solutions.

• Advances in aircraft and spacecraft science
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• v.9 no.4
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• pp.335-347
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• 2022

Airport operations are well-known as a bottleneck in the air traffic system, putting growing pressure on the world's busiest airports to schedule arrivals and departures as efficiently as possible. Effective planning and control are essential for increasing airport efficiency and reducing aircraft delays. Many algorithms for controlling the arrival/departure queuing area are handled, considering it as first in first out queues, where any available aircraft can take off regardless of its relative sequence with other aircraft. In the suggested system, this problem was compared to the problem of scheduling n tasks (plane takeoffs and landings) on a multiple machine (runways). The proposed technique decreases delays (via efficient runway allocation or allowing aircraft to be expedited to reach a scheduled time) to enhance runway capacity and decrease delays. The aircraft scheduling problem entails arranging aircraft on available runways and scheduling their landings and departures while considering any operational constraints. The topic of this work is the scheduling of aircraft landings and takeoffs on multiple runways. Each aircraft's takeoff and landing schedules have time windows, as well as minimum separation intervals between landings and takeoffs. We present and evaluate a variety of comprehensive concepts and solutions for scheduling aircraft arrival and departure times, intending to reduce delays relative to scheduled times. When compared to First Come First Serve scheduling algorithm, the suggested strategy is usually successful in reducing the average waiting time and average tardiness while optimizing runway use.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.293-307
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• 2004

We consider the problem of scheduling tasks of a precedence constrained task graph, where each task has its execution time and deadline, onto a set of identical processors in a way that simultaneously minimizes the number of processors required and the total tardiness of tasks. Most existing approaches tend to focus on the minimization of the total tardiness of tasks. In another methods, solutions to this problem are usually computed by combining the two objectives into a simple criterion to be optimized. In this paper, the minimization is carried out using a multiobjective genetic algorithm (GA) that independently considers both criteria by using a vector-valued cost function. We present various GA components that are well suited to the problem of task scheduling, such as a non-trivial encoding strategy. a domination-based selection operator, and a heuristic crossover operator We also provide three local improvement heuristics that facilitate the fast convergence of GA's. The experimental results showed that when compared to five methods used previously, such as list-scheduling algorithms and a specific genetic algorithm, the Performance of our algorithm was comparable or better for 178 out of 180 randomly generated task graphs.

• Journal of applied mathematics & informatics
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• v.17 no.1_2_3
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• pp.419-432
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• 2005

In this paper we study the no-wait or no-idle permutation flowshop scheduling problem with an increasing and decreasing series of dominating machines. The objective is to minimize one of the five regular performance criteria, namely, total weighted completion time, maximum lateness, maximum tardiness, number of tardy jobs and makespan. We establish that these five cases are solvable by presenting a polynomial-time solution algorithm for each case.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.203-208
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• 2002

In case of the problems with multiple plants, alternative operation sequence, alternative machine, setup time, and transportation time between plants, we need a robust methodology for the integration of process planning and scheduling in supply chain. The objective of this model is to minimize the tardiness and to maximize the resource utilization. So, we propose a multi-objective model with limited-capacity constraint. To solve this model, we develope an efficient and flexible model using adaptive genetic algorithm(AGA), compared to traditional genetic algorithm(TGA)

• Journal of Korean Institute of Industrial Engineers
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• v.19 no.3
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• pp.11-23
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• 1993

A basic problem of sequencing a set of independent tasks at a single facility with the objective of minimizing total tardiness is considered. A variation of beam search, called knowledge-based beam search, has been studied which uses domain knowledge to reduce the problem size with an evaluation function to generate nodes probable to include the optimal solution. Its performance behavior is compared with some existing algorithms.