@Article{10798587.2016.1261956,
AUTHOR = {S. M. Mousavi, M. Zandieh},
TITLE = {An Efficient Hybrid Algorithm for a Bi-objectives Hybrid Flow Shop Scheduling},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {1},
PAGES = {9--16},
URL = {http://www.techscience.com/iasc/v24n1/39720},
ISSN = {2326-005X},
ABSTRACT = {This paper considers the problem of scheduling n independent jobs in g-stage hybrid flow shop 
environment. To address the realistic assumptions of the proposed problem, two additional traits 
were added to the scheduling problem. These include setup times, and the consideration of maximum 
completion time together with total tardiness as objective function. The problem is to determine 
a schedule that minimizes a convex combination of objectives. A procedure based on hybrid the 
simulated annealing; genetic algorithm and local search so-called HSA-GA-LS are proposed to handle 
this problem approximately. The performance of the proposed algorithm is compared with a genetic 
algorithm proposed in the literature on a set of test problems. Several performance measures are 
applied to evaluate the effectiveness and efficiency of the proposed algorithm in finding a good quality 
schedule. From the results obtained, it can be seen that the proposed method is efficient and effective.},
DOI = {10.1080/10798587.2016.1261956}
}