Scheduling optimization of AGVs in workshop based on cost-efficiency-emission competition mechanism

Abstract

The untimely material transportation induced by the backward logistics scheduling method in the ship panel block construction workshop is a complex engineering issue with multi-parameter coupling. To overcome the problem, the cost-efficiency-carbon emission competition model is proposed to solve the scheduling optimization for the AGVs in ship panel block construction workshop. Firstly, the logistics path plan of the panel block construction workshop is obtained by establishing the path-planning node map of panel block construction workplace. Secondly, a scheduling model for AGVs is constructed based on the competition mechanism of the lowest transportation cost, maximum transportation efficiency, and lowest carbon emission. Then, the optimized schedule is achieved by a novel hybrid method of the genetic and population migration algorithm considering the competition mechanism. Finally, the case validation indicates that the solving ability of the proposed hybrid method is improved by 52.4% compared with Genetic Algorithm under competitive mechanism. Besides, the results demonstrate material transportation time can be reduced by 17.5%.

Keywords

Scheduling optimization automatic guided vehicle competition mechanism similarity ship panel block

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References

Tanchoco

. A review of research on AGVS vehicle management. Eng Costs Prod Econ 1991; 21(1): 35–42.

Roh

Lee

. Optimal scheduling of block lifting in consideration of the minimization of traveling distance while unloaded and wire and shackle replacement of a gantry crane. J Mar Sci Technol 2010; 15(2): 190–200.

Guo

Yang

, et al. Green scheduling optimization of ship plane block flow line considering carbon emission and noise. Comput Ind Eng 2020; 148: 106680.

Yang

Liu

Wang

, et al. An improved multiobjective PSO for the scheduling problem of panel block construction. Discrete Dyn Nat Soc 2016; 2016: 1–13.

Lee

Shin

Ryu

. Development of simulation-based production execution system in a shipyard: a case study for a panel block assembly shop. Prod Plan Control 2009; 20(8): 750–768.

Miyamoto

Inoue

. Local and random searches for dispatch and conflict-free routing problem of capacitated AGV systems. Comput Ind Eng 2016; 91: 1–9.

Fazlollahtabar

Hassanli

. Hybrid cost and time path planning for multiple autonomous guided vehicles. Appl Intell 2018; 48(2): 482–498.

Mousavi

Yap

Musa

, et al. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization. PLoS One 2017; 12(3): e0169817.

Yang

Zhong

Dessouky

, et al. An integrated scheduling method for AGV routing in automated container terminals. Comput Ind Eng 2018; 126: 482–493.

10.

Yoshitake

Kamoshida

Nagashima

. New automated guided vehicle system using real-time holonic scheduling for warehouse picking. IEEE Robot Autom Lett 2019; 4(2): 1045–1052.

11.

Lyu

Song

, et al. Approach to integrated scheduling problems considering optimal number of automated guided vehicles and conflict-free routing in flexible manufacturing systems. IEEE Access 2019; 7: 74909–74924.

12.

Wang

, et al. Research on multi-load AGV path planning of weaving workshop based on time priority. Math Biosci Eng 2019; 16(4): 2277–2292.

13.

Zhong

Yang

Dessouky

, et al. Multi-AGV scheduling for conflict-free path planning in automated container terminals. Comput Ind Eng 2020; 142(Apr): 106371.1–106371.11.

14.

Yuan

Zhang

, et al. Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm. Robot Comput Integr Manuf 2021; 71: 102141.

15.

Shi

Tang

Zou

. Multi-objective automated guided vehicle scheduling based on MapReduce framework. Adv Prod Eng Manag 2021; 16(1): 37–46.

16.

Zhang

Luan

, et al. Double-cycling AGV scheduling considering uncertain crane operational time at container terminals. Appl Sci 2022; 12(10): 4820.

17.

Lee

Kim

. Development of simulation-based ship production execution system(SPEXS) for a panel block assembly shop. J Korean Inst Inf Commun Eng 2011; 15(11): 2313–2320.

18.

Save

Kadu

. Gap analysis of the workshop of transportation vertical. Int J Innov Technol Explor Eng 2020; 9(7): 589–599.

19.

Hao

Sun

Ban

, et al. Vehicle index estimation for signalized intersections using sample travel times. Proc Soc Behav Sci 2013; 80: 473–490.

20.

Liao

Wang

. A novel collaborative optimization model for job shop production–delivery considering time window and carbon emission. Sustainability 2019; 11(10): 2781.

21.

Lobo

Lima

Michalewicz

. (Eds.) Parameter setting in evolutionary algorithms, vol. 54. Berlin: Springer Science & Business Media, 2007.