Heuristics to minimize the completion time variance of jobs on a single machine and on identical parallel machines

Rajkanth, Raju; Rajendran, Chandrasekharan; Ziegler, Holger

doi:10.1007/s00170-016-8879-7

“…One major exception is the goal of minimizing completion time variance (CTV), which was first discussed by [10] and has received much attention. For instance, [11] seeks to minimize the CTV of items in both a single-machine and parallel machine system. The authors use a genetic algorithm to establish an upper bound, and then analyze the performance of various heuristics.…”

Section: Literaturementioning

confidence: 99%

Protecting a Sensitive Queue from Arrival Variability

Vandenberghe

¹

,

Vuyst

²

,

Aghezzaf

³

et al. 2018

2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

0

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Minimizing item waiting time between stages is a general focus of operations research, and of particular concern for certain industries. We propose a two-stage production system where, to minimize the waiting time before stage 2, we focus on spreading the completion times of the stage 1 machines across the available interval. We contrast this objective with a similar problem defined in a healthcare context, but that has an assumption of fixed assignment. We obtain insights in the added value that free assignment can provide, by comparing the solutions of a local search method for assignment, with those of a reference case where assignment is fixed. Computational results show that this added value is highest in cases where task means differ insufficiently to be ordered effectively, and where task distributions have low variance. For the discussed instances, significant reductions in item waiting times can be achieved while making minimal concessions on expected makespan.

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“…It is based on packaging and list sequencing. Rajkanth et al (2017) case studied on job allocation phase and job scheduling phase on parallel machines in automobile auxiliary unit. Villa et al (2018) have used the heuristic method to solve the unrelated parallel machine problem with an additional resource to minimise the maximum completion time of the jobs.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of identical parallel machine scheduling problem

Kamaraj¹,

Saravanan

²

2019

IJRAPIDM

3

0

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Scheduling is allocating the resources optimally over a period of time. The productivity and customer goodwill of the company increases by proper scheduling. This work focuses on scheduling of identical parallel machines (IPM) with an objective to makespan minimisation by using Grey Wolf Optimiser (GWO) algorithm. Makespan is the maximum completion time of all the jobs. In this work, the different jobs in different identical parallel machines are classified as experiments E1, E2, E3 and E4. These experiments were computationally solved by new metaheuristic GWO algorithm. The experimental computational results of GWO were compared with GA to obtain near optimal solution in all experiments.

show abstract

“…It is based on packaging and list sequencing. Rajkanth et al (2017) case studied on job allocation phase and job scheduling phase on parallel machines in automobile auxiliary unit. Villa et al (2018) have used the heuristic method to solve the unrelated parallel machine problem with an additional resource to minimise the maximum completion time of the jobs.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of identical parallel machine scheduling problem

Kamaraj

¹

,

Saravanan²

2019

IJRAPIDM

0

View full text Add to dashboard Cite

Scheduling is allocating the resources optimally over a period of time. The productivity and customer goodwill of the company increases by proper scheduling. This work focuses on scheduling of identical parallel machines (IPM) with an objective to makespan minimisation by using Grey Wolf Optimiser (GWO) algorithm. Makespan is the maximum completion time of all the jobs. In this work, the different jobs in different identical parallel machines are classified as experiments E1, E2, E3 and E4. These experiments were computationally solved by new metaheuristic GWO algorithm. The experimental computational results of GWO were compared with GA to obtain near optimal solution in all experiments.

show abstract

Heuristics to minimize the completion time variance of jobs on a single machine and on identical parallel machines

Cited by 10 publications

References 24 publications

Protecting a Sensitive Queue from Arrival Variability

Protecting a Sensitive Queue from Arrival Variability

Optimisation of identical parallel machine scheduling problem

Optimisation of identical parallel machine scheduling problem

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