A multi-objective optimisation model to integrating flexible process planning and scheduling based on hybrid multi-objective simulated annealing

Mohammadi, Ghorbanali; Karampourhaghghi, Ali; Samaei, Farshid

doi:10.1080/00207543.2011.631602

Cited by 48 publications

(29 citation statements)

References 27 publications

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“…Table 4 shows the experimental results obtained from the different algorithms, and the scheduling results are shown in the Gantt chart in Figure 6. The experimental results show that the best makespan is 14 and is obtained with the proposed approach, which is superior to those of Moon et al 33 and Mohammadi et al 26 The optimal process plan is 2-2-9-1-3, and the corresponding processing time is shown in bold in Table 3.…”

Section: Performance Evaluation Of the Algorithmsupporting

confidence: 53%

“…The alternative process plans and processing time are shown in Table 1. The proposed algorithm is compared with several different algorithms, including the genetic multi-objective SAA of Mohammadi et al, 26 the immune GA of Dong and Sun, 32 and the particle optimisation algorithm of Zhu et al 34 The experimental results are shown in Table 2, and one of the best schedules is illustrated with the Gantt chart, as shown in Figure 5.…”

Section: Performance Evaluation Of the Algorithmmentioning

confidence: 99%

“…The work of Moon et al 33 and Mohammadi et al 26 is given as the second example to demonstrate the performance of the proposed modified GA. The problem consists of five jobs and five machines, and the makespan is set as the objective function; each job has multiple alternative process plans, and each operation of jobs can be processed on different machines.…”

Section: Performance Evaluation Of the Algorithmmentioning

confidence: 99%

“…Moon et al 33 Mohammadi et al 26 Proposed algorithm Makespan 16 15 14 single-objective optimisation problem are compared in Figure 8. In the bi-objective optimisation problem, the optimal makespan value is 27, while the corresponding minimum energy consumption value is 785.96.…”

Section: Job Numbermentioning

confidence: 99%

“…The research objectives for IPPS mostly focus on traditional production performance indicators, such as the makespan, the job tardiness, the balanced level of machine utilisation and the manufacturing cost. [21][22][23][24][25][26] However, most of these process planning and scheduling methods have paid little attention to the energy efficiency of manufacturing processes. This study proposes an energy-aware mathematical model for IPPS to achieve energy-saving design and manufacturing of a product based on a non-linear process planning method.…”

Section: Integrated Process Planning and Schedulingmentioning

confidence: 99%

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Energy-aware integrated process planning and scheduling for job shops

Dai

Tang

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part B:

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Process planning that is based on environmental consciousness and energy-efficient scheduling currently plays a critical role in sustainable manufacturing processes. Despite their interrelationship, these two topics have often been considered to be independent of each other. It therefore would be beneficial to integrate process planning and scheduling for an integrated energy-efficient optimisation of product design and manufacturing in a sustainable manufacturing system. This article proposes an energy-aware mathematical model for job shops that integrates process planning and scheduling. First, a mixed integrated programming model with performance indicators such as energy consumption and scheduling makespan is established to describe a multi-objective optimisation problem. Because the problem is strongly nondeterministic polynomial-time hard (NP-hard), a modified genetic algorithm is adopted to explore the optimal solution (Pareto solution) between energy consumption and makespan. Finally, case studies of energy-aware integrated process planning and scheduling are performed, and the proposed algorithm is compared with other methods. The approach is shown to generate interesting results and can be used to improve the energy efficiency of sustainable manufacturing processes at the process planning and scheduling levels.

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Section: Performance Evaluation Of the Algorithmsupporting

confidence: 53%

Section: Performance Evaluation Of the Algorithmmentioning

confidence: 99%