Parallel machine scheduling with dynamic resource allocation via a master–slave genetic algorithm

Abstract

Scheduling problems with resource allocation have become of increasing interest in the academic and industrial areas in recent years. This study investigates parallel machine scheduling with dynamic resource allocation under the objective of minimizing the makespan, where the processing of jobs needs an additional resource that can be assigned and reassigned among all jobs, and the processing time of jobs is a function of the amount of allocated resources. Four decisions must be made in a schedule, namely job assignment, job sequence, resource allocation and jobs' starting time. In order to solve it efficiently, a master-slave genetic algorithm is proposed to determine the decisions of job assignment, job sequence and resource allocation, and a greedy heuristic rule is designed to determine the jobs' starting time. To examine the performance of the proposed algorithm, simulation experiments are carried out on a set of instances, and similar existing algorithms and the standard genetic algorithm are chosen as for comparison. The experimental results show that the proposed algorithm is a promising optimizer in solving the investigated problem.

Application Research of Chaotic Carrier Frequency Modulation Technology in Two‐Stage Matrix Converter

“…However, due to the fixed carrier frequency, the harmonics which appear at integer multiples of the carrier frequency of TSMC line to line voltage possess discrete and high values and produce powerful EMI. In order to meet the demands of electromagnetic compatibility (EMC), output filter should be adopted, which not only increases the volume and weight but also produces additional harmonics [7][8][9][10][11]. CCMFT is one of the most effective solutions of Carrier Frequency Modulation Technique (CFMT).…”

Production, maintenance and resource scheduling: A review

“…The objective function F is a combination of the total load, total completion time and total absolute deviation of job completion time. Fu, Tian, Li, & Wang (2018) study the problem of a single resource type with integer resource processing time. The authors propose a Master-Slave GA, where the master chromosome represents the resource allocations and the accompanying slave chromosomes represent job assignments and sequences.…”

Optimization of the Real-Time Response to Roadside Incidents through Heuristic and Linear Programming

“…One of the main differences regarding our work is that job resource requirements are known beforehand, while their running times are not. Similarly, in a more recent paper, Fu et al [23] defined a master-slave genetic algorithm to determine the job assignments, job sequence, and resource allocation. Unlike our work, there is a dynamic resource allocation for jobs that need additional resources, which allows assignments and reassignments.…”