This paper considers the problem of minimizing the number of tardy jobs with release time on a single machine. Given that the problem has been classified as strongly NP-Hard, three heuristics (EOO, HR2, and HR3) are proposed for this problem. They are compared with a heuristic by Dauzere-Perez (selected from the literature). Randomly-generated problems ranging from 3 to 500 jobs are solved. Experimental results show that one of the proposed heuristics (EOO) outperforms other heuristics, both in terms of quality of solution (effectiveness) and speed of execution (efficiency).
OPSOMMINGDie navorsing behandel die ministering van voltooiingstyd van die aantal draaltake by 'n enkele werktuig. As aanvaar word dat die problem geklassifiseer word as hoofsaaklike NP-hard, word voorgestel dat die vraagstuk bestudeer word deur gebruik te maak van drie heuristiese metodes (EOO, HR2, HR3). Die metodes word vergelyk ten opsigte van vertoning met die Dauzere-Perez-metode. Toevalsgegenereerde probleme wat strek vanaf 3 tot 500 draaltake word behandel. Die eksperimentele werk lewer bewys dat die EOO-metode ander metodes die loef afsteek ten opsigte van oplossingsgoedheid en -snelheid.
This paper consider the bicriteria scheduling problem of simultaneously minimizing the total completion time (Ctot) and number of tardy jobs (NT) with release dates on a single machine. A heuristic (called HR7) was proposed for solving this problem and was compared with a heuristic (called HR6) selected from the literature and a branch and bound (BB) method. The proposed heuristic makes use of the idea of truncation and composition of schedules. The three solution methods were tested on 1100 randomly generated problems ranging from 3 to 500 jobs. Performance evaluation, based on both effectiveness and efficiency of the solution methods, were carried out. Experimental results are provided.
This paper focuses on the problem of scheduling n jobs with release dates on a single machine in order to minimize the total completion time. Since the problem has been characterized as strongly NP-hard, two heuristics (HR1 and AEO) were proposed for solving the problem in polynomial time. The heuristics were compared with the best approximation algorithm for this problem to date (Best-alpha). Experimental results show that AEO performed better than the Bestalpha algorithm (selected from the literature) when the number of jobs (n) exceeds 5. This observation should prove useful in the operational dispatch of jobs in industrial production settings as well as the service sector.
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