Skip to Main content Skip to Navigation
Conference papers

Dual Resource Constrained Scheduling Considering Operator Working Modes and Moving in Identical Parallel Machines Using a Permutation-Based Genetic Algorithm

Abstract : This paper proposes a novel dual resource constrained (DRC) scheduling problem under identical parallel machine environment that consider operator working modes and moving activity between machines with regards to the makespan minimization objective. We define the working modes as all operator activities when the operators interact with the machines such as loading, setup, controlling, and unloading. Firstly, we provide the mathematical model of the problem using Mixed Integer Linear Programming (MILP). We add unloading activity beside setup to be included in the model. Also, we consider the moving activity that is usually neglected in DRC scheduling problem. Moreover, we propose a permutation-based genetic algorithm (PGA) to tackle the computational burden of the bigger size problem. Then, we run a full factorial experiment with replication to compare the solution quality and computational time of our PGA to the solver and random search method. The results show that our proposed PGA could solve the problem in a reasonable time that is faster than the solver with a good quality solution that is better than random search.
Document type :
Conference papers
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download

https://hal.inria.fr/hal-02164859
Contributor : Hal Ifip <>
Submitted on : Tuesday, June 25, 2019 - 2:25:01 PM
Last modification on : Tuesday, June 25, 2019 - 2:34:35 PM

File

472850_1_En_57_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Muhammad Akbar, Takashi Irohara. Dual Resource Constrained Scheduling Considering Operator Working Modes and Moving in Identical Parallel Machines Using a Permutation-Based Genetic Algorithm. IFIP International Conference on Advances in Production Management Systems (APMS), Aug 2018, Seoul, South Korea. pp.464-472, ⟨10.1007/978-3-319-99704-9_57⟩. ⟨hal-02164859⟩

Share

Metrics

Record views

58

Files downloads

26