Manufacturing Technology Today

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Hybrid Tabu Search and Genetic Algorithm for a Production Plan Optimization Problem


Affiliations
1 Dept. of Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore-641006, India
2 Dept. of Production Engineering, National Institute of Technology, Tiruchirappalli, India
     

   Subscribe/Renew Journal


Aggregate Production Plan (APP), a managerial statement of time phased production rates, workforce levels and inventory investment, considers the customer's needs and capacity limitations. It should balance various objectives viz. maximizing customer service, minimizing inventory investment, maintenance of stability in workforce, production cost minimization and profit maximization. In this paper, an APP problem Is solved for optimality using a hybrid algorithm, that combines the procedures of two efficient meta heuristics Genetic Algorithms and Tabu Search.
User
Subscription Login to verify subscription
Notifications
Font Size

Abstract Views: 254

PDF Views: 0




  • A Hybrid Tabu Search and Genetic Algorithm for a Production Plan Optimization Problem

Abstract Views: 254  |  PDF Views: 0

Authors

G. Mohan Kumar
Dept. of Mechatronics Engineering, Kumaraguru College of Technology, Coimbatore-641006, India
A. Noorul Haq
Dept. of Production Engineering, National Institute of Technology, Tiruchirappalli, India

Abstract


Aggregate Production Plan (APP), a managerial statement of time phased production rates, workforce levels and inventory investment, considers the customer's needs and capacity limitations. It should balance various objectives viz. maximizing customer service, minimizing inventory investment, maintenance of stability in workforce, production cost minimization and profit maximization. In this paper, an APP problem Is solved for optimality using a hybrid algorithm, that combines the procedures of two efficient meta heuristics Genetic Algorithms and Tabu Search.