Abstract
Selection and scheduling are an important topic in production systems. To tackle the order acceptance and scheduling problem on a single machine with release dates, tardiness penalty, and sequence-dependent setup times, in this paper a diversity controlling genetic algorithm (DCGA) is proposed, in which a diversified population is maintained during the whole search process through survival selection considering both the fitness and the diversity of individuals. To measure the similarity between individuals, a modified Hamming distance without considering the unaccepted orders in the chromosome is adopted. The proposed DCGA was validated on 1500 benchmark instances with up to 100 orders. Compared with the state-of-the-art algorithms, the experimental results show that DCGA improves the solution quality obtained significantly, in terms of the deviation from upper bound.
Highlights
In make-to-order production systems with limited capacity and tight delivery requirements, the order acceptance and scheduling problem has been considered as an important topic
In our previous work [14], we proposed an improved genetic algorithm with local search (IGAL) for order acceptance and scheduling problem
The upper bound for each instance was generated by Oguz et al [6]
Summary
In make-to-order production systems with limited capacity and tight delivery requirements, the order acceptance and scheduling problem has been considered as an important topic. Hall and Magazine [2] incorporated Lagrangian relaxation into a dynamic programming without considering sequence-dependent setup times and tardiness penalty. Charnsirisakskul et al [4, 5] put forward a mixed-integer programming method for single machine production system where setup times were negligible. Oguz et al [6] gave a mixed-integer linear programming formulation of order acceptance and scheduling problem, which can be solved optimally for problems with up to 15 orders. Slotnick and Morton [8] developed an optimal branchand-bound procedure using a linear(integer) relaxation for bounding and performed job acceptance and sequencing jointly. Due to the strongly NP-hard complexity of the problem [6], only small or moderate size order acceptance and scheduling problems can be optimally solved using exact algorithms. It is crucial to apply advanced approximate algorithms to solve problems in practical applications with large numbers of orders
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