I want to learn advanced methods in Operations Research and Management Science (OR/MS), which may be used to solve problems in manufacturing and logistics fields, or to develop some new theoretical concepts. Please let me know which OR/MS methods, which are widely being used in good journals. Some supporting references will be very appreciated. Thank you.
Dear Ivan
There are many OR methods such as Linear Programming, Non-linear programming, Stocks theory, Critical Path Method, (CPM), PERT, Queuing Theory, Transportation, Assignment, some job shop methods, etc
For manufacturing I would recommend, Linear Programming and Stocks theory, which is also good for Logistics. Monte Carlo method is also good for both
There are literally hundreds of publications and you can see them if you enter in Internet
Manufacturing - as a logistics field - is (to a high degree) a case of applied combinatorial optimization, as are logistics sub-fields as vehicle routing, and goods transport.
So you need books on optimization modelling, linear and network optimisation, as well as integer and combinatorial optimisation.
There are plenty of books in those fields. Depending on your background, you may need plenty of books, or none but modern-day articles. As I do not know your background, I assume no special knowledge, and therefore recommend the following books:
Read on integer and combinatorial optimization in the basic textbook "Integer Programming", by Laurence Wolsey (published by Wiley). Wolsey has a more advanced book also, together with G. L. Nemhauser called "Integer and Combinatorial Optimization" (also Wiley).
If you have very little expertise in the modelling of optimization problems in the first place, you can gain a lot of knowledge from the book "Model Building in Mathematical Programming" by H.P. Williams.
If these books are unsuitable, you need to describe in more detail what you lack in terms of background knowledge.
Good luck in any case - the field is interesting, and needed more and more as the world gets more complex day by day.
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