An Improved Tabu Search Algorithm for a Type of Single Machine Sequencing Problem

2013 ◽  
Vol 756-759 ◽  
pp. 3997-4001
Author(s):  
Meng Lan Wang ◽  
Wen Bin Liu
Keyword(s):  
Tabu Search ◽  
Single Machine ◽  
Search Algorithm ◽  
Machine Scheduling ◽  
Tabu Search Algorithm ◽  
Scheduling Problem ◽  
Hard Problem ◽  
Sequencing Problem ◽  
Np Hard Problem ◽  
One Machine

Machine scheduling is a central task in production planning. In general it means the problem of scheduling job operations on a given number of available machines. In this paper we consider a machine scheduling problem with one machine, or the Single Machine Total Tardiness Problem. To solve this NP-hard problem, we develop an improved Tabu Search Algorithm, which is tested to have the ability to find good results by an example.

scholarly journals Parallel Algorithm with Blocks for a Single-Machine Total Weighted Tardiness Scheduling Problem

2021 ◽  
Vol 11 (5) ◽  
pp. 2069
Author(s):  
Mariusz Uchroński
Keyword(s):  
Tabu Search ◽  
Parallel Algorithm ◽  
Single Machine ◽  
Message Passing Interface ◽  
Search Algorithm ◽  
Solution Space ◽  
Total Weighted Tardiness ◽  
Tabu Search Algorithm ◽  
Scheduling Problem ◽  
Weighted Tardiness

In this paper, the weighted tardiness single-machine scheduling problem is considered. To solve it an approximate (tabu search) algorithm, which works by improving the current solution by searching the neighborhood, is used. Methods of eliminating bad solutions from the neighborhood (the so-called block elimination properties) were also presented and implemented in the algorithm. Blocks allow a significant shortening of the process of searching the neighborhood generated by insert type moves. The designed parallel tabu search algorithm was implemented using the MPI (Message Passing Interface) library. The obtained speedups are very large (over 60,000×) and superlinear. This may be a sign that the parallel algorithm is superior to the sequential one as the sequential algorithm is not able to effectively search the solution space for the problem under consideration. Only the introduction of diversification process through parallelization can provide an adequate coverage of the entire search process. The current methods of parallelization of metaheuristics give a speedup which strongly depends on the problem’s instances, rarely greater than number of used parallel processors. The method proposed here allows the obtaining of huge speedup values (over 60,000×), but only when so-called blocks are used. The above-mentioned speedup values can be obtained on high performance computing infrastructures such as clusters with the use of MPI library.

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