• Management Science and Financial Engineering
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• v.13 no.2
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• pp.47-77
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• 2007

This paper considers a scheduling problem where a customer orders multiple products(jobs) from a production facility. The objective is to minimize the sum of the order(batch) completion times. While a machine can process only one job at a time, multiple machines can simultaneously process jobs in a batch. Although each job has a unique processing time, we consider the case where batch processing times are identical. This simplification allows us to develop heuristics with improved performance bounds. This problem was motivated by a real world problem encountered by foreign electronics manufacturers. We first establish the complexity of the problem. For the two parallel machine case, we introduce two simple but intuitive heuristics, and find their worst case relative error bounds. One bound is tight and the other bound goes to 1 as the number of orders goes to infinity. However, neither heuristic is superior for all instances. We extend one of the heuristics to an arbitrary number of parallel machines. For a fixed number of parallel machines, we find a worst case bound which goes to 1 as the number of orders goes to infinity. Then, a tighter bound is found for the three parallel machine case. Finally, the heuristics are empirically evaluated.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.254-264
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• 2007

This paper considers a scheduling problem to minimize the total weighted completion time in the two-stage assembly-type flowshop. The system is composed of multiple fabrication machines in the first stage and a final-assembly machine in the second stage. Each job consists of multiple components, each component is machined on the fabrication machine specified in advance. The manufactured components of each job are subsequently assembled into a final product on the final-assembly machine. The objective of this paper is to find the optimal schedule minimizing the total weighted completion time of jobs. Three lower bounds are derived and tested in a branch-and-bound (B&B) Procedure. Also, three heuristic algorithms are developed based on the greedy strategies. Computational results show that the proposed B&B procedure is more efficient than the previous work which has considered the same problem as this paper.

• Industrial Engineering and Management Systems
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• v.15 no.3
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• pp.278-288
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• 2016

Automation ensures accurate and well-organized container transportation in container terminals. This paper addresses operational issues such as equipment scheduling and the coordination between various pieces of equipment in a rail-based automated container terminal. Containers are relayed using multiple types of equipment from road trucks to a vessel and vice versa. Therefore, handshaking is required during a container transfer between different pieces of equipment. Synchronization between the schedules of all the equipment is important to reduce equipment waiting times and the time required for transporting containers, which results in a short turnaround time for a vessel. This paper proposes an integrated control system with the objective of synchronizing the operations of different types of equipment, provides a list of decisions to be made by the control module of each type of equipment, and shows all the required information transfers between control modules. A scheme for the integrated scheduling of multiple types of equipment is proposed. The decisions made by each control module in a real-time fashion are listed with detailed explanations, and the information transfer between managers in a real-time situation at the proposed terminal is described.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.4
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• pp.35-47
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• 2006

This paper considers an integrated scheduling problem for consecutive production and delivery stages in a two-stage supply chain. The Production is performed on a single facility and then the finished products are delivered to the customer by capacitated multiple vehicles. The objective of this paper is to obtain job sequencing and delivery batching minimizing the total cost of the associated WIP inventory, finished product inventory and delivery. The inventory cost is characterized by the sum of weighted flowtime. The delivery cost is proportional to the required number of delivery batches. Some polynomial-solvable cases are derived. For the general case, two efficient heuristic algorithms are suggested, and then the heuristics are tested through some numerical experiments.

• Korean Management Science Review
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• v.34 no.1
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• pp.47-56
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• 2017

We focus on the Real time Fire Scheduling Problem (RFSP), the problem of determining the sequence of targets to be fired at, for the objective of minimizing threatening probability to achieve tactical goals. In this paper, we assume that there are m available weapons to fire at n targets (> m) and the weapons are already allocated to targets. One weapon or multiple weapons can fire at one target and these fire operations should start simultaneously while the finish time of them may be different. We suggest mathematical modeling for RFSP and several heuristic algorithms. Computational experiments are performed on randomly generated test problems and results show that the suggested algorithms outperform the firing method which is generally adopted in the field artillery.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.2
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• pp.213-220
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• 2007

This paper considers a two-stage hybrid flow shop scheduling problem for the objective of minimizing the number of tardy jobs. Each job is processed through the two production stages in stages, each of which has multiple identical parallel machines. The problem is to determine the allocation and sequence of jobs at each stage. A branch and bound algorithm that gives the optimal solutions is suggested that incorporates the methods to obtain the lower and upper bounds. Dominance properties are also suggested to reduce the search space. To show the performance of the algorithm, computational experiments are done on randomly generated problems, and the results are reported.

• ETRI Journal
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• v.32 no.3
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• pp.422-429
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• 2010

We present guaranteed dynamic priority assignment schemes for multiple real-time tasks subject to (m, k)-firm deadlines. The proposed schemes have two scheduling objectives: providing a bounded probability of missing (m, k)-firm constraints and maximizing the probability of deadline satisfactions. The second scheduling objective is especially necessary in order to provide the best quality of service as well as to satisfy the minimum requirements expressed by (m, k)-firm deadlines. We analytically establish that the proposed schemes provide a guarantee on the bounded probability of missing (m, k)-firm constraints. Experimental studies validate our analytical results and confirm the effectiveness and superiority of the proposed schemes with regard to their scheduling objectives.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2509-2528
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• 2012

Time division multiple access (TDMA) is a widely used media access control (MAC) technique that can provide collision-free and reliable communications, save energy and bound the delay of packets. In TDMA, energy saving is usually achieved by switching the nodes' radio off when such nodes are not engaged. However, the frequent switching of the radio's state not only wastes energy, but also increases end-to-end delay. To achieve high energy efficiency and low delay, as well as to further minimize the number of time slots, a multi-objective TDMA scheduling problem for industrial wireless mesh networks is presented. A hybrid algorithm that combines genetic algorithm (GA) and simulated annealing (SA) algorithm is then proposed to solve the TDMA scheduling problem effectively. A number of critical techniques are also adopted to reduce energy consumption and to shorten end-to-end delay further. Simulation results with different kinds of networks demonstrate that the proposed algorithm outperforms traditional scheduling algorithms in terms of addressing the problems of energy consumption and end-to-end delay, thus satisfying the demands of industrial wireless mesh networks.

• Korean Management Science Review
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• v.32 no.4
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• pp.109-133
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• 2015

This paper considers the allocation and engagement scheduling of air defense missiles by using MIP (mixed integer programming). Specifically, it focuses on developing a realistic MIP model for a real battle situation where multiple enemy missiles are headed toward valuable defended assets and there exist multiple air defense missiles to counteract the threats. In addition to the conventional objective such as the minimization of surviving target value, the maximization of total intercept altitude is introduced as a new objective. The intercept altitude of incoming missiles is important in order to minimize damages from debris of the intercepted missiles and moreover it can be critical if the enemy warhead contains an atomic or chemical bomb. The concept of so called the time window is used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. Lastly, the model is extended to simulate the situation where the guidance radar, which guides a defense missile to its target, has the maximum guidance capacity. The initial mathematical model developed contains several non-linear constraints and a non-linear objective function. Hence, the linearization of those terms is performed before it is solved by a commercially available software. Then to thoroughly examine the MIP model, the model is empirically evaluated with several test problems. Specifically, the models with different objective functions are compared and several battle scenarios are generated to evaluate performance of the models including the extended one. The results indicate that the new model consistently presents better and more realistic results than the compared models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1164-1183
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• 2019

The rapid development of cloud computing and high requirements of operators requires strong support from the underlying Data Center Networks. Therefore, the effectiveness of using resources in the data center networks becomes a point of concern for operators and material for research. In this paper, we discuss the online virtual-cluster provision problem for multiple tenants with an aim to decide when and where the virtual cluster should be placed in a data center network. Our objective is maximizing the total revenue for the data center networks under the constraints. In order to solve this problem, this paper divides it into two parts: online multi-tenancy scheduling and virtual cluster placement. The first part aims to determine the scheduling orders for the multiple tenants, and the second part aims to determine the locations of virtual machines. We first approach the problem by using the variational inequality model and discuss the existence of the optimal solution. After that, we prove that provisioning virtual clusters for a multi-tenant data center network that maximizes revenue is NP-hard. Due to the complexity of this problem, an efficient heuristic algorithm OMS (Online Multi-tenancy Scheduling) is proposed to solve the online multi-tenancy scheduling problem. We further explore the virtual cluster placement problem based on the OMS and propose a novel algorithm during the virtual machine placement. We evaluate our algorithms through a series of simulations, and the simulations results demonstrate that OMS can significantly increase the efficiency and total revenue for the data centers.