Multi-objective invasive weeds optimisation algorithm for solving simultaneous scheduling of machines and multi-mode automated guided vehicles

Nabovati, Hojat; Haleh, Hassan; Vahdani, Behnam

doi:10.1504/ejie.2020.10027216

Cited by 2 publications

(2 citation statements)

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“…Through the analysis of the references in Table I, it can be found that: Due to the fixed product process flow and relatively low transportation flexibility in the manufacturing environment of the flow-shop, the research on joint scheduling problems in the flow-shop mainly focuses on the analysis of complexity of the problem [11,12], and less on the control in the scheduling process [13]. As the important carriers of the flexible manufacturing workshop, the product process complexity in the manufacturing environment of the job-shop [14][15][16][17][18][19][20][21][22]28,29] and the flexible job-shop [23][24][25][26][27][30][31][32][33][34] with higher process complexity, which also puts forward higher requirements for the control in the joint scheduling process and get many research results.…”

Section: Minimize Production Costsmentioning

confidence: 99%

“…For joint scheduling problems, researchers have not limited themselves to single-objective optimization environments and have also conducted research on multi-objective optimization environments: Mahalakshmi [28] proposed a minimum delivery deviation scheme for solving joint scheduling problems by Artificial Immune System algorithm. Nabovati [29] established a new joint scheduling model for the job shop joint scheduling, and a multi-objective invasive weeds optimization algorithm is proposed to solve the problem.…”

Section: Minimize Production Costsmentioning

confidence: 99%

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Research on Multi-Objective Scheduling Algorithm of Job Shop Considering Limited Storage and Transportation Capacity

Peng

Zhong

et al. 2023

IEEE Access

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As with the continuous improvement of the workshop automation rate and the importance in energy consumption, more and more enterprises not only need to make scheduling decision on production equipment, but also need to consider whether the scheduling of transportation equipment supports scheduling decisions on workshop production. At the same time, because both workshop production scheduling decision and transportation scheduling decision are NP-hard problems, it is necessary to design an efficient algorithm to improve productivity of the workshop. In order to solve this problem, firstly, based on the analysis of the problem structure, production environment and optimization objectives, a "manufacturing-transportation" multi-objective joint scheduling optimization mathematical model is established. By converting the energy consumption into the total transportation time objective of the transportation equipment, both total transportation time and makespan are taken as the optimization objectives. Secondly, based on the design idea of memetic algorithm (MA), non-dominated sorting genetic algorithm-Ⅱ(NSGA-II) is employed as the basis framework of our new developed algorithm. An effective discrete encoding scheme of MO-MA, a new initialization method for initial population and a neighborhood search mechanism based on critical path are incorporated into our new proposed algorithm. Then the parameter design of the algorithm is completed through variance analysis. Finally, the proposed algorithm is compared and analyzed with other algorithms in the dimension of hypervolume and Set Coverage (SC), and advantages of the algorithm in solving this problem are verified.

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Section: Minimize Production Costsmentioning

confidence: 99%

Section: Minimize Production Costsmentioning

confidence: 99%

Research on Multi-Objective Scheduling Algorithm of Job Shop Considering Limited Storage and Transportation Capacity

Peng

Zhong

et al. 2023

IEEE Access

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A multi-objective vibration damping optimization algorithm for solving a cellular manufacturing system with manpower and tool allocation

et al. 2020

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In this paper, a novel bi-objectivemathematical model is proposed to designa fourdimensional (i.e., part, machine, operator, and tool) cellular manufacturing system (CMS) in a dynamic environment. The main objectives of this model are to 1) minimize total costs including tools processing cost, costs of transporting cells between various cells, machine setup cost, and operators' educational costs, and 2) maximizing skill level of operators. The developed model is strictly NP-hard and exact algorithms cannot find globally optimal solutions in reasonably computational time. So, a multi-objective vibration damping optimization algorithm (MOVDO) with a new solution structure that satisfies all the constraints and generates feasible solutions is proposed to find near-optimal solutions in reasonablycomputational time. Since there is no benchmark available in the literature, three other meta-heuristic algorithms (i.e., non-dominated sorting genetic algorithm (NSGA-II), multi-objective particle swarm optimization (MOPSO) and multi-objective invasive weeds optimization (MOIWO)) with the similar solution structure are developed to validate the performance of the proposedMOVDO algorithm for solving various instances of the developed model. The result of comparing theirperformances based on statistical tests and different measuring metrics reveals that the proposed MOVDO algorithm outperforms remarkably better than other meta-heuristics used in this paper.

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Multi-objective invasive weeds optimisation algorithm for solving simultaneous scheduling of machines and multi-mode automated guided vehicles

Cited by 2 publications

References 0 publications

Research on Multi-Objective Scheduling Algorithm of Job Shop Considering Limited Storage and Transportation Capacity

Research on Multi-Objective Scheduling Algorithm of Job Shop Considering Limited Storage and Transportation Capacity

A multi-objective vibration damping optimization algorithm for solving a cellular manufacturing system with manpower and tool allocation

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