• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.823-835
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• 2019

Purpose: This study deals with a production planning and scheduling problem to minimize the total weighted tardiness on hybrid flow shop with sets of non-identical parallel machines on stages, where parallel machines in the set are dedicated to perform specific subsets of jobs and sequence-dependent setup times are also considered. Methods: A two-stage approach, that applies MILP model in the 1st stage and dispatching rules in the 2nd stage, is proposed in this paper. The MILP model is used to assign jobs to a specific machine in order to equalize the workload of the machines at each stage, while new dispatching rules are proposed and applied to sequence jobs in the queue at each stage. Results: The proposed two-stage approach was implemented by using a commercial MILP solver and a commercial simulation software and a case study was developed based on the spark plug manufacturing process, which is an automotive component, and verified using the company's actual production history. The computational experiment shows that it can reduce the tardiness when used in conjunction with the dispatching rule. Conclusion: This proposed two-stage approach can be used for HFS systems with dedicated machines, which can be evaluated in terms of tardiness and makespan. The method is expected to be used for the aggregated production planning or shop floor-level production scheduling.

• 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.

• The Journal of Bigdata
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• v.7 no.1
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• pp.75-87
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• 2022

This study investigates a dynamic flexible flow shop scheduling problem under uncertain rework operations for an automobile pipe production line. We propose a weighted dispatching rule (WDR) based on the multiple dispatching rules to minimize the weighted sum of average flowtime and tardiness. The set of weights in WDR should be carefully determined because it significantly affects the performance measures. We build a discrete-event simulation model and propose a genetic algorithm to optimize the set of weights considering complex and variant operations. The simulation experiments demonstrate that WDR outperforms the baseline dispatching rules in average flowtime and tardiness.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.2
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• pp.107-119
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• 2004

A study on the following parallel machine problem is addressed in this research. An order is completed only when a given number of processes (cycle) are repeated. Anew cycle is possible only upon the completion of the previous cycle. Orders are classified into job group according to product feature. For a machine to switch to a different job group from the currently processing one a major setup is required while a minor setup time is inserted in between two jobs of the same job group. The objective of the study is to find a schedule that minimizes total weighted tardiness. An initial solution is obtained by the RATCS(Restricted Apparent Tardiness Cost with Setup) rule, and a Tabu search is applied to improve the solution. Numerical examples are also presented.

• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.621-633
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• 1997

This paper is concerned with the multiobjective vehicle scheduling problem with time and area-dependent travel speeds(MVSPTD), in which two conflicting objectives are explicitly treated and the travel speed between two locations depends on the passing area and time of day. The two objectives are the minimization of total vehicle travel time and the minimization of total weighted tardiness. First, I construct a mixed integer linear programming formulation of the MVSPTD, and present o heuristic algorithm that builds the vehicle schedules based on the savings computed. The results of computational experiments showed that the heuristic performs very well. Finally, I propose an expert system for vehicle scheduling in the MVSPTD. Its whole process is executed under VP-Expert expert system environment.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.121-134
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• 1999

In this paper, I propose a hybrid genetic algorithm(HGAM) incorporating a greedy interchange local optimization procedure for the multiobjective vehicle scheduling problems with service due times where three conflicting objectives of the minimization of total vehicle travel time, total weighted tardiness, and fleet size are explicitly treated. The vehicle is allowed to visit a node exceeding its due time with a penalty, but within the latest allowable time. The HGAM applies a mixed farming and migration strategy in the evolution process. The strategy splits the population into sub-populations, all of them evolving independently, and applys a local optimization procedure periodically to some best entities in sub-populations which are then substituted by the newly improved solutions. A solution of the HCAM is represented by a diploid structure. The HGAM uses a molified PMX operator for crossover and new types of mutation operator. The performance of the HGAM is extensively evaluated using the Solomons test problems. The results show that the HGAM attains better solutions than the BC-saving algorithm, but with a much longer computation time.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.704-708
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• 2002

Production scheduling has been one of the most critical issues in a manufacturing environment Job-shop scheduling problems(JSP) are well know from the standpoint of production planning and operations control in this research scheduling against due date is a measure of performance and the objective is minimizing total weighted tardiness This paper presents an idea of decomposition of the problem and shows robustmess of the schedule under various disturbances along with exact and approximation methods. The proposed method can indeed handle shop disturbances more effectively when compared with traditional and dynamic scheduling methods.

• IE interfaces
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• v.19 no.2
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• pp.124-132
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• 2006

The system under study is a single item, multi-echelon distribution system with a capacitated production facility. All the nodes at the downstream ends are demand-sites, i.e., ordered items are delivered to the customers from the node. Also any transshipment depots in the midstream can be demand-sites as well. For a given planning period, at each of demand-site, demand is forecasted and known. Our objective is to minimize the average system cost per period which is the sum of holding and backorder costs in the entire network. Due to the capacity restrictions, it is difficult to establish efficient distribution planning. To overcome such a difficulty and obtain a reasonable and better solution, we convert this problem into a single machine earliness and weighted tardiness scheduling. We propose a simple but cost-effective heuristic for this problem. The experimental results showed that the proposed heuristic obtained much better solutions compared with another approach.

• Industrial Engineering and Management Systems
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• v.4 no.1
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• pp.23-35
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• 2005

This paper aims to develop a practical finite capacity MRP (FCMRP) system based on the needs of an automotive parts manufacturing company in Thailand. The approach includes a linear goal programming model to determine the optimal start time of each operation to minimize the sum of penalty points incurred by exceeding the goals of total earliness, total tardiness, and average flow-time considering the finite capacity of all work centers and precedence of operations. Important factors of the proposed FCMRP system are penalty weights and dispatching rules. Effects of these factors on the performance measures are statistically analyzed based on a real situation of an auto-part factory. Statistical results show that the dispatching rules and penalty weights have significant effects on the performance measures. The proposed FCMRP system offers a good tradeoff between conflicting performance measures and results in the best weighted average performance measures when compared to conventional forward and forward-backward finite capacity scheduling systems.