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We have first solved JSPs using an improved memetic algorithm and extended the algorithm to deal with the disruption situations, and then developed a simulation model to analyze the risk of using a job order and delivery scenario. This paper deals with job scheduling under ideal conditions and rescheduling under machine breakdown, and provides a risk analysis for a production business case. The extended algorithm provides better understanding and results than existing algorithms, the rescheduling shows a good way of recovering from disruptions, and the risk analysis shows an effective way of maximizing return under such situations.<\/jats:p>","DOI":"10.1017\/s0890060415000323","type":"journal-article","created":{"date-parts":[[2015,6,9]],"date-time":"2015-06-09T08:56:30Z","timestamp":1433840190000},"page":"289-299","source":"Crossref","is-referenced-by-count":3,"title":["Managing risk in production scheduling under uncertain disruption"],"prefix":"10.1017","volume":"30","author":[{"given":"Ruhul","family":"Sarker","sequence":"first","affiliation":[]},{"given":"Daryl","family":"Essam","sequence":"additional","affiliation":[]},{"given":"S.M. Kamrul","family":"Hasan","sequence":"additional","affiliation":[]},{"given":"A.N. 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