• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.947-951
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• 2005

The concept of group technology has been successfully applied to many production systems including flexible manufacturing systems. In this paper we apply group technology principles to the economic lot scheduling problem which has been intensively studied over 40 years. We obtain a production schedule of several family products on a single facility where setup times and costs can be reduced by using the concept of group technology. We develop a heuristic algorithm and a hybrid genetic algorithm for the group technology economic lot scheduling problem (GT-ELSP). Numerical example shows that the developed heuristic and the hybrid genetic algorithm outperform the existing heuristics.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.47-53
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• 1995

The Generalized Assignment (GAP) determines the minimum assignment of n tasks to m workstations such that each task is assigned to exactly one workstation, subject to the capacity of a workstation. In this paper, we presented a new heuristic search algorithm for GAPs. Then we tested it on 4 different benchmark sample sets of random problems generated according to uniform distribution on a microcomputer.

• Journal of Information Processing Systems
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• v.10 no.2
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• pp.193-214
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• 2014

Load balancing is the major benefit of any distributed system. To facilitate this advantage, task duplication and migration methodologies are employed. As this paper deals with dependent tasks (DAG), we used duplication. Task duplication reduces the overall schedule length of DAG along-with load balancing. This paper proposes a new task duplication algorithm at the time of tasks assignment on various processors. With the intention of conducting proposed algorithm performance computation; simulation has been done on the Netbeans IDE. The mesh topology of a distributed system is simulated at this juncture. For task duplication, overall schedule length of DAG is the main parameter that decides the performance of a proposed duplication algorithm. After obtaining the results we compared our performance with arbitrary task assignment, CAWF and HEFT-TD algorithms. Additionally, we also compared the complexity of the proposed algorithm with the Duplication Based Bottom Up scheduling (DBUS) and Heterogeneous Earliest Finish Time with Task Duplication (HEFT-TD).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.9-18
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• 2000

This paper considers the problem of two-machine, M-parallel flow shop scheduling to minimize makespan, and proposes a series of heuristic algorithms and a branch and bound algorithm. Two processing times of each job at two machines on each line are identical on any line. Since each flow-shop line consists of two machines, Johnson's sequence is optimal for each flow-shop line. Heuristic algorithms are developed in this paper by combining a "list scheduling" method and a "local search with global evaluation" method. Numerical experiments show that the proposed heuristics can efficiently give optimal or near-optimal schedules with high accuracy. with high accuracy.

• Journal of Korean Institute of Industrial Engineers
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• v.36 no.2
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• pp.125-137
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• 2010

This study focuses on the problem of scheduling wafer lots of several product families in the deposition workstation in a semiconductor wafer fabrication facility. There are multiple identical parallel machines in the deposition workstation, and two types of setups, record-dependent setup and family setup, may be required at the deposition machines. A record-dependent setup is needed to find optimal operational conditions for a wafer lot on a machine, and a family setup is needed between processings of different families. We suggest two-phase heuristic algorithms in which a priority-rule-based scheduling algorithm is used to generate an initial schedule in the first phase and the schedule is improved in the second phase. Results of computational tests on randomly generated test problems show that the suggested algorithms outperform a scheduling method used in a real manufacturing system in terms of the sum of weighted flowtimes of the wafer lots.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.5
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• pp.687-692
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• 1998

Test pattern generation is one of most difficult problems encountered in automating the design of logic circuits. The goal is to obtain the highest fault coverage with the minimum number of test patterns for a given circuit and fault set. although there have been many deterministic algorithms and heuristics the problem is still highly complex and time-consuming. Therefore new approach-es are needed to augment the existing techniques. This paper considers the problem of test pattern improvement for combinational circuits as a restricted subproblem of the test pattern generation. The problem is to maximize the fault coverage with a fixed number of test patterns for a given cir-cuit and fault set. We propose a new approach by use of a genetic algorithm. In this approach the genetic algorithm evolves test patterns to improve their fault coverage. A fault simulation is used to compute the fault coverage of the test patterns Experimental results show that the genetic algorithm based approach can achieve higher fault coverages than traditional techniques for most combinational circuits. Another advantage of the approach is that the genetic algorithm needs no detailed knowledge of faulty circuits under test.

• Journal of Intelligence and Information Systems
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• v.14 no.1
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• pp.117-129
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• 2008

The cost-based scheduling work has been done in both the Operations Research (OR) and Artificial Intelligence (AI) literature. To deal with more realistic problems, AI-based heuristic scheduling approach with non-regular performance measures has been studied. However, there has been little research effort to develop a full inter-job cost propagation algorithm (CPA) for different jobs having multiple downstream and upstream activities. Without such a CPA, decision-making in scheduling heuristics relies upon local, incomplete cost information, resulting in poor schedule performance from the overall cost minimizing objective. For such a purpose, we need two types of CPAs : intra-job CPA and inter-job CPA. Whenever there is a change in cost information of an activity in a job in the process of scheduling, the intra-job CPA updates cost curves of other activities connected through temporal constraints within the same job. The inter-job CPA extends cost propagation into other jobs connected through precedence relationships. By utilizing the cost information provided by CPAs, we propose cost-based scheduling heuristics that attempt to minimize the total schedule cost. This paper develops inter-job CPAs that create and update cost curves of each activity in each search state, and propagate cost information throughout a whole network of temporal constraints. Also we propose various cost-based scheduling heuristics that attempt to minimize the total schedule cost by utilizing the cost propagation algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.133-140
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• 2014

An efficient heuristic for two-vehicle-one-depot problems is developed in this research. Vehicle moving speeds are various along hour based time intervals due to traffic jams of rush hours. Two different heuristics are examined. One is that the delivery area assignment is made using Sweep algorithm for two vehicles by splitting the whole area in half to equally divide all delivery points. The other is using common area by leaving unassigned area between the assigned for two vehicles. The common area is reassigned by two stages to balance the completion time of two vehicle's delivery. The heuristic with common area performed better than the other due to various vehicle moving speeds and traffic jams.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.8-20
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• 2019

The vehicle routing problem is one of the vibrant research problems for half a century. Many studies have extensively studied the vehicle routing problem in order to deal with practical decision-making issues in logistics. However, developments of new logistics strategies have inevitably required investigations on solution methods for solving the problem because of computational complexity and inherent constraints in the problem. For this reason, this paper suggests a simulated annealing (SA) algorithm for a variant of vehicle routing problem introduced by a previous study. The vehicle routing problem is a multi-depot and multi-trip vehicle routing problem with multiple heterogeneous vehicles restricted by the maximum permitted weight and the number of compartments. The SA algorithm generates an initial solution through a greedy-type algorithm and improves it using an enhanced SA procedure with three local search methods. A series of computational experiments are performed to evaluate the performance of the heuristic and several managerial findings are further discussed through scenario analyses. Experiment results show that the proposed SA algorithm can obtain good solutions within a reasonable computation time and scenario analyses show that a transportation system visiting non-dedicated factories shows better performance in truck management in terms of the numbers of vehicles used and trips for serving customer orders than another system visiting only dedicated factories.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1442-1462
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• 2014

Packet classification is a key technology of the Internet for routers to classify the arriving packets into different flows according to the predefined rulesets. Previous packet classification algorithms have mainly focused on search speed and memory usage, while overlooking update performance. In this paper, we propose PreCuts, which can drastically improve the update speed. According to the characteristics of IP field, we implement three heuristics to build a 3-layer decision tree. In the first layer, we group the rules with the same highest byte of source and destination IP addresses. For the second layer, we cluster the rules which share the same IP prefix length. Finally, we use the heuristic of information entropy-based bit partition to choose some specific bits of IP prefix to split the ruleset into subsets. The heuristics of PreCuts will not introduce rule duplication and incremental update will not reduce the time and space performance. Using ClassBench, it is shown that compared with BRPS and EffiCuts, the proposed algorithm not only improves the time and space performance, but also greatly increases the update speed.