Multi-objective invasive weeds optimisation algorithm for solving simultaneous scheduling of machines and multi-mode automated guided vehicles
Hassan Haleh and
European Journal of Industrial Engineering, 2020, vol. 14, issue 2, 165-188
In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem, considering the transferring of jobs between different machines using a limited number of multi-mode automated guided vehicles. Since the model is strictly NP-hard, and because objectives contradict each other, a meta-heuristic algorithm called 'multi-objective invasive weeds optimisation algorithm' with a new chromosome structure which guarantees the feasibility of solutions is developed to solve the proposed problem. Two other meta-heuristic algorithms namely 'non-dominated sorting genetic algorithm' and 'multi-objective particle swarm optimisation algorithm' are applied to validate the solutions obtained by the developed multi-objective invasive weeds optimisation algorithm. A certain method was applied to select the algorithm with the best performance. The result of ranking the algorithms indicated that the developed multi-objective invasive weeds optimisation algorithm had the best performance in terms of solving the mentioned problems. [Received: 7 January 2017; Revised: 30 December 2017; Revised: 17 August 2018; Revised: 22 January 2019; Accepted: 26 July 2019]
Keywords: MOIWO; AGV; scheduling; machines scheduling; job shop scheduling; simultaneous scheduling; invasive weeds optimisation; industrial engineering. (search for similar items in EconPapers)
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ids:eujine:v:14:y:2020:i:2:p:165-188
Access Statistics for this article
More articles in European Journal of Industrial Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().