Browsing by Author "Low, Chinyao"

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Coordination of production scheduling and delivery problems with heterogeneous fleet
(2014-07-01)
Low, Chinyao
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Chang, Chien-Min
;
Li, Rong-Kwei
;
Huang, Chia-Ling
;
工業工程與管理學系
In a production scheduling with delivery problem, there are different types of products processed by a distribution center and then delivered to retailers. Each retailer order might be consisted of different products. The resolution of this problem is to determine the production sequence, retailers\' needs to heterogeneous fleet of vehicles and the visiting sequence of each vehicle for delivery goods within time windows. In this article, a nonlinear mathematical model is proposed with minimizing the total cost which includes transportation cost, vehicle arrangement cost and penalty costs, subjected to satisfy all demands of each retailer. Following, two adaptive genetic algorithms (AGAs) are designed and tested in variety of production and delivery scenarios. The computational experiments show that the total cost gradually decreases as the vehicle type employed in the delivery stage increasing. In addition, more kinds of vehicle types provided in the delivery stage could reduce fixed vehicle cost and variable routing cost. (C) 2014 Elsevier B.V. All rights reserved.
D Minimizing Total Earliness and Tardiness for Common Due Date Single-Machine Scheduling with an Unavailability Interval
(2016-01-01)
Low, Chinyao
;
Li, Rong-Kwei
;
Wu, Guan-He
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
This paper addresses the problem of scheduling.. independent jobs on a single machine with a fixed unavailability interval, where the aim is to minimize the total earliness and tardiness (TET) about a common due date. Two exact methods are proposed for solving the problem: mixed integer linear programming formulations and a dynamic programming based method. These methods are coded and tested intensively on a large data set and the results are analytically compared. Computational experiments show that the dynamic programming method is efficient in obtaining the optimal solutions and no problems due to memory requirement.
Integrated scheduling of production and delivery with time windows
(2013-02-01)
Low, Chinyao
;
Li, Rong-Kwei
;
Chang, Chien-Min
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
This paper deals with an integrated scheduling problem in which orders have been processed by a distribution centre and then delivered to retailers within time windows. We propose a nonlinear mathematical model to minimise the time required to complete producing the product, delivering it to retailers and returning to the distribution centre. The optimal schedule and vehicle routes can be determined simultaneously in the model. In addition, two kinds of genetic-algorithm-based heuristics are designed to solve the large-scale problems. The conventional genetic algorithm provides the search with a high transition probability in the beginning of the search and with a low probability toward the end of the search. The adaptive genetic algorithm provides an adaptive operation rate control scheme that changes rate based on the fitness of the parents. The experimental results have shown that the solution quality of these two algorithms is not significant but that the adaptive genetic algorithm can save more time in finding the best parameter values of the genetic algorithm.
Minimizing Total Earliness and Tardiness for Common Due Date Single-Machine Scheduling with an Unavailability Interval
(2016-01-01)
Low, Chinyao
;
Li, Rong-Kwei
;
Wu, Guan-He
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
This paper addresses the problem of scheduling.. independent jobs on a single machine with a fixed unavailability interval, where the aim is to minimize the total earliness and tardiness (TET) about a common due date. Two exact methods are proposed for solving the problem: mixed integer linear programming formulations and a dynamic programming based method. These methods are coded and tested intensively on a large data set and the results are analytically compared. Computational experiments show that the dynamic programming method is efficient in obtaining the optimal solutions and no problems due to memory requirement.