By Feng Jin, Shiji Song, Cheng Wu (auth.), Uday K. Chakraborty (eds.)
For over fifty years now, the well-known challenge of movement store and activity store scheduling has been receiving the eye of researchers in operations study, engineering, and laptop technology. during the last a number of years, there was a spurt of curiosity in computational intelligence heuristics and metaheuristics for fixing this challenge. This e-book seeks to provide a research of the state-of-the-art during this box and likewise instructions for destiny research.
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Extra info for Computational Intelligence in Flow Shop and Job Shop Scheduling
We will also test the following metaheuristics. These are stochastic and do not provide the same result after each run. Most of them also have a stopping criterion that will be discussed later. 1. HDPSO from Pan and Wang (2008a). 2. DDELS from Pan and Wang (2008b). We will simply refer to this method as “DDE”. 3. FRB5 from Rad et al. (2009). utzle (2007). 4. IGLS from Ruiz and St¨ It has to be reminded that the local search step in HDPSO and DDE is actually a few applications of the IGLS method.
1. HDPSO from Pan and Wang (2008a). 2. DDELS from Pan and Wang (2008b). We will simply refer to this method as “DDE”. 3. FRB5 from Rad et al. (2009). utzle (2007). 4. IGLS from Ruiz and St¨ It has to be reminded that the local search step in HDPSO and DDE is actually a few applications of the IGLS method. In turn, IGLS and FRB5 share the same local search step which was detailed in Figure 3 before. All methods have been coded in Delphi 2007. All algorithms share most code and especially the critical functions that evaluate the no-idle Cmax as well as accelerations.
As a matter of fact, for instances with 500 jobs, FRB5 is actually slower than most methods. 75. A striking outcome are the results of DDE (DDELS as named in Pan and Wang, 2008b). 65 as well. We checked our implementation carefully and found no errors. Dr. Quan-Ke Pan did send us his full source code. However, we decided to implement this DDE method following the details given in Pan and Wang, 2008b to the letter. In any case, DDE shares about 90% of the code with the HDPSO method by the same authors, since both use the NEH for initialization, PTL crossover, insertion mutation and IG for local search.
Computational Intelligence in Flow Shop and Job Shop Scheduling by Feng Jin, Shiji Song, Cheng Wu (auth.), Uday K. Chakraborty (eds.)