Abstract
The Job Shop Parallel Machine Scheduling (JSPMS) is a hybrid production system, and hence has received significant attention in the past few years. The JSPMS problem is a rationalization of the traditional job shop scheduling problem in computer science and operation research that permits to process operations on single machine out of a set of possible parallel machines. To maximize the job completion rate and minimize job completion time, a hybrid production system is necessary. With this objective, a novel meta-heuristic method is designed. This paper develops a scheduling method called, Ant Colonized and Taguchi Parallel Operation Scheduling (ACTPOS), for JSPMS, aimed to minimize job completion time and maximize job completion rate. The design of AC-TPOS method involves two different models, namely, Ant Colonized Parallel Machine Selection (ACPMS) model and Taguchi Parallel Operation Scheduling (TPOS) model. In ACPMS model, optimal selection of machine is done via operation being processed by parallel machines using local pheromone updating rule concentrating on the makespan time. In addition, the processing time and sequence-independent setup time are considered. Next, in TPOS model, optimal scheduling of operation is performed using Taguchi method concentrating on the makespan rate. Finally, the test results first show that our algorithm outperforms existing methods in terms of job completion rate, job completion time and computational complexity involved in scheduling operations.
Highlights
Scheduling problems exists in several economic domains
Three experiments based on the benchmark OR-Library, were performed to validate the proposed Ant Colonized and Taguchi Parallel Operation Scheduling (AC-TPOS) method and comparison was made with the two existing Teaching Learning Based Optimization (TLBO) [1] and Flexible Job Shop Scheduling Problem with Sequence Dependent Setup Times (FJSP-SDST) [2]
We present a new meta-heuristic method called, Ant Colonized and Taguchi Parallel Machine Scheduling (AC-TPMS), for the Job Shop Parallel Machine Scheduling (JSPMS) problem
Summary
Scheduling problems exists in several economic domains. Some of them are, airplane scheduling, tour scheduling, train scheduling, and in the manufacturing shop scheduling. A Teaching Learning Based Optimization (TLBO) was presented in [1] to address the issues related to Flexible Job Shop Scheduling Problem (FJSP) based on the integrated approach. Though the enhancement of solution quality with diversity maintenance was assured, less focus was done on job completion time To address this issue, Ant Colonized Parallel Machine Selection (ACPMS) model is designed in this work that introduces local pheromone updating rule ensuring minimum job completion time. Flexible Job Shop Scheduling Problem with Sequence Dependent Setup Times (FJSP-SDST) was presented in [2] to reduce makespan. We present how to solve the Job Shop Parallel Machine Scheduling (JSPMS) problem by hybridization of two meta-heuristic models.
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