Integrated Scheduling of Machines and Agvs in FMS by Using Dispatching Rules

Nageswara, Medikondu; Narayana, R. Lakshmi; Janardhana, Ranga

doi:10.24867/jpe-2017-01-075

Cited by 19 publications

(4 citation statements)

References 22 publications

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“…Comparing with SPT 35 problems has given the better results (Nageswararao et al 2017). 33 problems have given the better results when compared with LPT.…”

Section: Discussionmentioning

confidence: 94%

Simultaneous Scheduling Through Heuristic Algorithms

Rao¹,

Lokesh²,

Harish³

et al. 2018

IJET

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Flexible Manufacturing System (FMS) is a compli-cated system because of work environments, recu-peration frameworks, mechanized putting away, and material dealing with gadgets like robots and AGVs. In this paper, an endeavor is made by con-sidering both the machine and vehicle planning angles in FMS for minimization of the make trav-erse. Game plan is ensnared with the arrangement of incomplete assets to assignments in finished time. It is like Information-gathering process. It is related with the cost, operations, time and several objectives of the industry. In this work, RAPID ACCESS (RA) heuristic algorithm is adopted to solve the scheduling problems in FMS. Eighty, two problems and their existing solutions with different approaches are examined. The RA heuristic algo-rithm provides better solutions with less computa-tional time.

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“…Comparing with SPT 35 problems has given the better results (Nageswararao et al 2017). 33 problems have given the better results when compared with LPT.…”

Section: Discussionmentioning

confidence: 94%

Simultaneous Scheduling Through Heuristic Algorithms

Rao¹,

Lokesh²,

Harish³

et al. 2018

IJET

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“…From Table 4 it can be observed that, out of 40 problems, 6 problems give better results using fuzzy heuristic algorithm when compared with all other four algorithms and it can also be observed from Table 5 that out of 42 problems, 7 problems give better results using fuzzy when compared with all other four algorithms. When comparing the results of fuzzy algorithm with FCFS by Nageswararao et al (2017), it is observed that Fuzzy heuristic gives better results for 14 problems out of 40 problems in the case of ti/pi>0.25 and gives better results for 23 problems out of 42 problems in the case of ti/pi<0.25. Similarly when comparing the results with SPT, LPT and NEH by Prakashbabu et al 2018, Fuzzy heuristic gives better results for 22,12,7 problems respectively out of 40 problems in the case of ti/pi>0.25 and gives better results for 23,22 and 7 problems respectively out of 42 problems in the case of ti/pi<0.25.…”

Section: Resultsmentioning

confidence: 99%

Fuzzy heuristic algorithm for simultaneous scheduling problems in flexible manufacturing system

Kanakavalli¹,

Vommi²,

Medikondu³

2018

10.5267/j.msl

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This paper addresses the flexible manufacturing system (FMS) problems considering both machines and automated guided vehicles (AGVs), simultaneously. A new fuzzy heuristic (FH) is proposed for solving the simultaneous scheduling problem, in order to minimize the makespan by considering the available resources. For increasing the performance of FMS one of the important factors is scheduling AGV with an integral part of the machine scheduling activity. The algorithm is tested based on 82 benchmark problems and the achieved makespan are compared with other algorithms.

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“…Productivity (P): Among the Productivity (P) factors, Setup time (P7), Cycle time (P12), Throughput time (P9), and Unit labor cost (P2) have the highest performing effects on FMS. Setup time (P7) has a global weight of 0.0215 and refers to the time required to prepare the machines, equipment, tools, and materials for a specific production run [60]. Furthermore, Cycle time (P12) has a final weight of 0.0208, and this is the time required to finish a part from beginning to completion [55].…”

Section: Findings and Discussionmentioning

confidence: 99%

“…Productivity (P) Machine utilization (P1) [48] Unit labor cost (P2) [4,49,50] Unit manufacturing cost (P3) [4,43] Production rate (P4) [45] Manufacturing lead time (P5) [43,51] Work-in-progress (WIP) inventory (P6) [4] Setup time (P7) [45,48] OEE (Overall Equipment Effectiveness) (P8) [52,53] Throughput time (P9) [4,43] Labor productivity (P10) [54] Setup cost (P11) [4,48] Cycle time (P12) [55] Flexibility (F) Changeover time (F1) [56] Equipment utilization (F2) [4,57] Volume flexibility (F3) [28] Routing flexibility (F4) [48] Product mix (F5) [4] Use of automated material handling device (F6) [25,58] Reduced work in process inventory (F7) [4,43] Redundancy (F8) [58] Use of reconfigurable machine tool (F9) [38] Flexible fixturing (F10) [59][60][61] Machine reconfiguration time (F11) [53] Automation (Q2) * in this study refers to the level of automation in the manufacturing process itself, not the automation of quality inspection.…”

Section: Major Factor Sub-factor Referencesmentioning

confidence: 99%

Evaluating the Ranking of Performance Variables in Flexible Manufacturing System through the Best-Worst Method

Bagherian,

Chauhan,

Srivastav

et al. 2024

Designs

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Flexible Manufacturing Systems (FMSs) provide a competitive edge in the ever-evolving manufacturing landscape, offering the agility to swiftly adapt to changing customer demands and product lifecycles. Nevertheless, the complex and interconnected nature of FMSs presents a distinct challenge: the evaluation and prioritization of performance variables. This study clarifies a conspicuous research gap by introducing a pioneering approach to evaluating and ranking FMS performance variables. The Best-Worst Method (BWM), a multicriteria decision-making (MCDM) approach, is employed to tackle this challenge. Notably, the BWM excels at resolving intricate issues with limited pairwise comparisons, making it an innovative tool in this context. To implement the BWM, a comprehensive survey of FMS experts from the German manufacturing industry was conducted. The survey, which contained 34 key performance variables identified through an exhaustive literature review and bibliometric analysis, invited experts to assess the variables by comparing the best and worst in terms of their significance to overall FMS performance. The outcomes of the BWM analysis not only offer insights into the factors affecting FMS performance but, more importantly, convey a nuanced ranking of these factors. The findings reveal a distinct hierarchy: the “Quality (Q)” factor emerges as the most critical, followed by “Productivity (P)” and “Flexibility (F)”. In terms of contributions, this study pioneers a novel and comprehensive approach to evaluating and ranking FMS performance variables. It bridges an evident research gap and contributes to the existing literature by offering practical insights that can guide manufacturing companies in identifying and prioritizing the most crucial performance variables for enhancing their FMS competitiveness. Our research acknowledges the potential introduction of biases through expert opinion, delineating the need for further exploration and comparative analyses in diverse industrial contexts. The outcomes of this study bear the potential for cross-industry applicability, laying the groundwork for future investigations in the domain of performance evaluation in manufacturing systems.

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Integrated Scheduling of Machines and Agvs in FMS by Using Dispatching Rules

Cited by 19 publications

References 22 publications

Simultaneous Scheduling Through Heuristic Algorithms

Simultaneous Scheduling Through Heuristic Algorithms

Fuzzy heuristic algorithm for simultaneous scheduling problems in flexible manufacturing system

Evaluating the Ranking of Performance Variables in Flexible Manufacturing System through the Best-Worst Method

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