Precast production scheduling using multi-objective genetic algorithms

► This research develops a multi-objective precast production scheduling model (MOPPSM). ► In the model, production resources and buffer size between stations are considered. ► A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness p...

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Veröffentlicht in:	Expert systems with applications Jg. 38; H. 7; S. 8293 - 8302
Hauptverfasser:	Ko, Chien-Ho, Wang, Shu-Fan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.07.2011
Schlagworte:	Buffer Buffers Delivery scheduling Mathematical models Multi-objective genetic algorithms Optimization Precast Precast production Production scheduling Schedules Scheduling Searching Stations Buffer Multi-objective genetic algorithms Scheduling Precast production
ISSN:	0957-4174, 1873-6793
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Abstract	► This research develops a multi-objective precast production scheduling model (MOPPSM). ► In the model, production resources and buffer size between stations are considered. ► A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. ► The performance of the proposed model is validated by using five case studies. ► The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. ► Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules. The goal of production scheduling is to achieve a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources. However, current practices in precast production scheduling are fairly basic, depending heavily on experience, thereby resulting in inefficient resource utilization and late delivery. Moreover, previous methods ignoring buffer size between stations typically induce unfeasible schedules. Certain computational techniques have been proven effective in scheduling. To enhance precast production scheduling, this research develops a multi-objective precast production scheduling model (MOPPSM). In the model, production resources and buffer size between stations are considered. A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. The performance of the proposed model is validated by using five case studies. The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules.
AbstractList	The goal of production scheduling is to achieve a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources. However, current practices in precast production scheduling are fairly basic, depending heavily on experience, thereby resulting in inefficient resource utilization and late delivery. Moreover, previous methods ignoring buffer size between stations typically induce unfeasible schedules. Certain computational techniques have been proven effective in scheduling. To enhance precast production scheduling, this research develops a multi-objective precast production scheduling model (MOPPSM). In the model, production resources and buffer size between stations are considered. A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. The performance of the proposed model is validated by using five case studies. The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules. ► This research develops a multi-objective precast production scheduling model (MOPPSM). ► In the model, production resources and buffer size between stations are considered. ► A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. ► The performance of the proposed model is validated by using five case studies. ► The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. ► Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules. The goal of production scheduling is to achieve a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources. However, current practices in precast production scheduling are fairly basic, depending heavily on experience, thereby resulting in inefficient resource utilization and late delivery. Moreover, previous methods ignoring buffer size between stations typically induce unfeasible schedules. Certain computational techniques have been proven effective in scheduling. To enhance precast production scheduling, this research develops a multi-objective precast production scheduling model (MOPPSM). In the model, production resources and buffer size between stations are considered. A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. The performance of the proposed model is validated by using five case studies. The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules.
Author	Ko, Chien-Ho Wang, Shu-Fan
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Snippet	► This research develops a multi-objective precast production scheduling model (MOPPSM). ► In the model, production resources and buffer size between stations... The goal of production scheduling is to achieve a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources....
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SubjectTerms	Buffer Buffers Delivery scheduling Mathematical models Multi-objective genetic algorithms Optimization Precast Precast production Production scheduling Schedules Scheduling Searching Stations
Title	Precast production scheduling using multi-objective genetic algorithms
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