Simulated annealing and genetic algorithms for the two-machine scheduling problem with a single server

Hasani, Keramat; Kravchenko, Svetlana A.; Werner, Frank

doi:10.1080/00207543.2013.874607

Cited by 39 publications

(16 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work by Huang et al [32] addressed a parallel dedicated machine scheduling problem with sequence-dependent setup times and a single server. Several papers have examined two-parallel machine scheduling problems with a server and proposed efficient heuristic algorithms [33][34][35]. The study by Cheng et al [36] considered a common server and job preemption in parallel machine scheduling with the makespan measure and provided a pseudo-polynomial time algorithm for two machine cases and analyzed the performance ratios of some natural heuristic algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources

Lee

Jang

2019

Sustainability

View full text Add to dashboard Cite

We examine a uniform parallel machine scheduling problem with dedicated machines, job splitting, and limited setup resources for makespan minimization. In this problem, machines have different processing speeds, and each job can only be processed at several designated machines. A job can be split into multiple sections and those sections can be processed on multiple machines simultaneously. Sequence-independent setup times are assumed, and setup operations between jobs require setup operators that are limited. For the problem, we first develop a mathematical optimization model and for large-sized problems a constructive heuristic algorithm is proposed. Finally, we show that the algorithm developed is efficient and provides good solutions by experiments with various scenarios.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources

Lee

Jang

2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…The data were obtained from Hasani et al. (), and the same runtimes were used, where the runtime limits are (300/8) n seconds for the instances with

n \in {8, 20}

and 3600 seconds for the larger instances of

n \in {100, 250, 500, 1000}

. The data itself were generated as follows: (a) each problem size was tested under different load values L that impacts the setup times, with

L \in {0.5, 1, 2}

; (b) setup times are uniformly distributed in the interval (0, 100 L ) and processing times are uniformly distributed in (0, 100); (c) for small problems (

n \in {8, 20}

), 10 instances were generated for each problem size and L value (total of 60 instances), and for larger problems (

n \in {100, 250, 500, 1000}

), five instances were generated for size and L value (total of 60 instances), that is, a total of 120 problem instances were solved by each algorithm.…”

Section: Computational Testsmentioning

confidence: 99%

“…As mentioned earlier, ACO was compared to GA, SA, and B&B. The GA and SA results were obtained from Hasani et al (2014b). The MIP presented in Section 2 was solved using B&B, the latter's solver used was Lingo 11.0 from Lindo Systems.…”

Section: Computational Testsmentioning

confidence: 99%

“…Hasani et al (2014a) suggested several MIP formulations, based on blocks and setups, which turned out to be superior to the algorithms presented in Abdekhodaee et al (2006) and Gan et al (2012). Hasani et al (2014b) developed a simulated annealing (SA) and GA, and the obtained results showed superiority to all previous algorithms and models proposed in the recent literature.…”

Section: Introductionmentioning

confidence: 96%

“…Hasani et al. () developed a simulated annealing (SA) and GA, and the obtained results showed superiority to all previous algorithms and models proposed in the recent literature.…”

Section: Introductionmentioning

confidence: 97%

See 2 more Smart Citations

Heuristics for the two‐machine scheduling problem with a single server

Arnaout

2016

Int Trans Operational Res

View full text Add to dashboard Cite

In this paper, the NP‐hard two‐machine scheduling problem with a single server is addressed. The problem consists of a given set of jobs to be scheduled on two identical parallel machines, where each job must be processed on one of the machines, and prior to processing, the job is set up on its machine using one server; the latter is shared between the two machines. An ant colony optimization (ACO) algorithm is introduced for the problem and its performance was assessed by comparing with an exact solution (branch and bound [B&B]), a genetic algorithm (GA), and simulated annealing (SA). The computational results reflected the superiority of “ACO” in large problems, with a performance similar to SA and GA in smaller problems, while solving the tested problems within a reasonable computational time.

show abstract

Variable Neighborhood Search for Identical Parallel Machine Scheduling Problem with a Single Server

Elidrissi

Benbrahim

Benmansour

et al. 2020

Variable Neighborhood Search

View full text Add to dashboard Cite

Simulated annealing and genetic algorithms for the two-machine scheduling problem with a single server

Cited by 39 publications

References 14 publications

Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources

Uniform Parallel Machine Scheduling with Dedicated Machines, Job Splitting and Setup Resources

Heuristics for the two‐machine scheduling problem with a single server

Variable Neighborhood Search for Identical Parallel Machine Scheduling Problem with a Single Server

Contact Info

Product

Resources

About