A hybrid cuckoo search algorithm in parallel batch processing machines with unequal job ready times. (October 2018)

• Record Type: Journal Article
• Authors: Majumder, Arindam; Laha, Dipak; Suganthan, P.N.
• Is Part Of: Computers & industrial engineering. Volume 124(2018)
• Page Start: 65
• Page End: 76
• Publication Date: 2018-10
• Subjects: Scheduling -- Parallel batch processing machines -- Makespan -- Cuckoo search algorithm -- Variable neighborhood search -- Optimization; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.07.001
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We consider the parallel batch processing machine scheduling problem. We propose an improved cuckoo search algorithm to solve this problem. For the first time, we use this optimization technique for this problem. We show that the proposed algorithm is superior to existing algorithms. We suggest some directions for fruitful future research. Abstract: This paper deals with the problem of scheduling identical parallel batch processing machines. In this scheduling system, each machine processes a set of jobs in a batch simultaneously and each job in the batch is characterized by its processing time, ready time and job size. We propose a hybrid discrete cuckoo search (HDCS) algorithm to minimize makespan for this scheduling problem. The HDCS is constructed, based on a modified variable neighborhood search and cuckoo search algorithm. In the proposed algorithm, we present a modified Lévy flight in the cuckoo search to transform a continuous position in the HDCS into a discrete schedule for generating a new solution. The process parameters of the proposed HDCS are tuned by implementing the desirability-based Taguchi method to optimize both solution quality and run time. The results of exhaustive computational experimentation on a large number of randomly generated sparse as well as non-sparse problem instances show that the proposed algorithm is more effective and efficient than the state-of-the-art algorithms.