A new meta-heuristics for optimum design of loop layout in flexible manufacturing system with integrated scheduling

Mallikarjuna, K.; Veeranna, Vikas; Reddy, K. Hemachandra

doi:10.1007/s00170-015-7715-9

Cited by 14 publications

(6 citation statements)

References 14 publications

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“…In the proposed MEIFM problem, different from the previous literature [11,13,19], this paper considers flexible processing routes of jobs and unequal-area machines concurrently, which embodies a strong interaction between FJSSP and MRWLP. In addition, both FJSSP and MRWLP have independent objectives.…”

Section: Literature Review On Solution Strategiesmentioning

confidence: 99%

“…Ripon et al [11,12] developed a multi-objective mathematical model for integrated job shop scheduling and the facility layout planning problem that considers makespan, mean flow time, material handling cost, and closeness rating score simultaneously. Mallikarjuna [13] proposed a flexible batch scheduling problem which was integrated with loop layout pattern design, and applied the genetic algorithm (GA) and simulated annealing algorithm (SA) to solve the problem for minimizing makespan and transportation cost. Liu et al [14] studied an integrated optimization problem of workshop layout and scheduling considering energy consumption to minimize makespan and carbon emission simultaneously, in which they proposed a multi-objective fruit fly optimization algorithm to solve the problem.…”

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confidence: 99%

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Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout

Zhang

Pan

et al. 2018

Algorithms

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The flexible job shop scheduling problem (FJSSP) and multi-row workshop layout problem (MRWLP) are two major focuses in sustainable manufacturing processes. There is a close interaction between them since the FJSSP provides the material handling information to guide the optimization of the MRWLP, and the layout scheme affects the effect of the scheduling scheme by the transportation time of jobs. However, in traditional methods, they are regarded as separate tasks performed sequentially, which ignores the interaction. Therefore, developing effective methods to deal with the multi-objective energy-aware integration of the FJSSP and MRWLP (MEIFM) problem in a sustainable manufacturing system is becoming more and more important. Based on the interaction between FJSSP and MRWLP, the MEIFM problem can be formulated as a multi-objective bi-level programming (MOBLP) model. The upper-level model for FJSSP is employed to minimize the makespan and total energy consumption, while the lower-level model for MRWLP is used to minimize the material handling quantity. Because the MEIFM problem is denoted as a mixed integer non-liner programming model, it is difficult to solve it using traditional methods. Thus, this paper proposes an improved multi-objective hierarchical genetic algorithm (IMHGA) to solve this model. Finally, the effectiveness of the method is verified through comparative experiments.

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Section: Literature Review On Solution Strategiesmentioning

confidence: 99%

mentioning

confidence: 99%

Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout

Zhang

Pan

et al. 2018

Algorithms

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“…Ripon and Torresen (2014) established a multi-objective mathematical model for integrated job shop scheduling and the facility layout planning problem that considers makespan, mean flow time, total material handling cost and closeness rating scores. Mallikarjuna et al (2015) proposed a flexible batch scheduling problem which was integrated with loop layout pattern design, and the optimization objectives were completion time and transportation cost.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective integrated scheduling optimization of semi-combined marine crankshaft structure production workshop for green manufacturing

Duan

Zhang

2020

Transactions of the Institute of Measurement and Control

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In order to realize green manufacturing in the production process of semi-combined marine crankshaft structural parts, good job scheduling and reasonable workshop layout are the key. In traditional method, flexible job shop scheduling problem (FJSP) and the multi-row workshop layout problem (MRWLP) are regarded as separate tasks. However, the separate optimization method ignores the interaction between FJSP and MRWLP. Because the process sequencing of FJSP affects the layout results of processing machines, while the layout scheme of MRWLP affects the scheduling completion time through the transportation between processes. Therefore, it is very important to establish an integrated mathematical model for optimization of both layout and scheduling simultaneously to explore the common influence of the two resource constraints on scheduling results. At the same time, the transportation task is also a manufacturing process that cannot be ignored, which affects the completion time and energy consumption of the workshop, especially the heavy industrial manufacturing workshop with crane as transportation equipment. According to the established model, a five-segment coding including transportation information, layout information and processing information is designed, and two heuristic selection strategies are integrated into non-dominated sorting genetic algorithm II (NSGA-II) to optimize the iterative results twice. Finally, the effectiveness of the integrated mathematical model is verified by an example, which provides guidance for green manufacturing in the shipbuilding industry.

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“…Various methods have been used to optimize the production performance, and many algorithms, such as genetic algorithms (GAs), artificial bee colony algorithms, and simulated annealing, have been developed to optimize workshop performance [4][5][6][7]. However, Zhang et al [3] pointed out that it was difficult for these algorithms to address more realistic dynamic problems with uncertain parameters.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Production Configuration Optimization for an Assembly Shop

SL¹,

ZG²,

Jy³

2019

Int. j. simul. model.

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Flexible production is able to respond to a changing production system. To efficiently optimize a changed production layout and configuration, a modelling and optimization method based on discrete event simulation (DES) was proposed. A mathematical optimization model of the workshop layout was formulated and solved using a genetic algorithm (GA). In addition, a general modelling method for workshop logistics processes using DES was proposed to efficiently develop a simulation model of the complex production processes. Moreover, the simulation model of the entire production process was constructed using the Plant Simulation software package, and more production factors were considered and optimized. After the optimization process, the logistics volume decreased by 63.5 %, and the throughput increased by 42.0 %. Additionally, the production process was optimized, and the optimal equipment allocation, worker allocation, buffer allocation, and logistics vehicle allocation were determined. Our work can provide decision guidance and simulation validation for workshop planning.

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A new meta-heuristics for optimum design of loop layout in flexible manufacturing system with integrated scheduling

Cited by 14 publications

References 14 publications

Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout

Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout

Multi-objective integrated scheduling optimization of semi-combined marine crankshaft structure production workshop for green manufacturing

Modelling and Production Configuration Optimization for an Assembly Shop

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