Auction-based cooperation mechanism to parts scheduling for flexible job shop with inter-cells

Tang, Jiafu; Zeng, Chengkuan; Zhang, Pan

doi:10.1016/j.asoc.2016.08.046

Cited by 24 publications

(14 citation statements)

References 31 publications

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“…There are some constraints existing in FJSP, which must be well solved in real-life environment [4]. Many researchers have studied various issues in FJSP, e.g., machine breakdowns [5], [6], fuzzy processing time [7]- [9], resource and energy constraints [10], [11], transportation time [12] and new job insertion [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

Gao

Yang

et al. 2020

IEEE Access

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Machine recovery is met from time to time in real-life production. Rescheduling is often a necessary procedure to cope with it. Its instability gauges the number of changes to the existing scheduling solutions. It is a key criterion to measure a rescheduling solution's quality. This work aims at solving a flexible job shop problem with machine recovery, which arises from the scheduling and rescheduling of pump remanufacturing systems. In their scheduling phase, the objective is to minimize makespan. In their rescheduling phase, two objectives are to minimize both instability and makespan. By introducing two novel local search operators into the original Jaya algorithm, this work proposes an improved Jaya algorithm to solve it. It performs experiments on ten different-scale cases of real-life remanufacturing environment. The results show that the improved Jaya is effective and efficient for solving a flexible job shop scheduling and rescheduling problems. It can effectively balance instability and makespan in a rescheduling phase. INDEX TERMS Jaya algorithm, flexible job shop scheduling, machine recovery, remanufacturing, scheduling and rescheduling.

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Section: Introductionmentioning

confidence: 99%

Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

Gao

Yang

et al. 2020

IEEE Access

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“…If the required coverage rate is not yet achieved (line 2), this random TPC solution will be corrected (lines [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. The idea of repair is to iteratively cover the uncovered GPs using the potential APs (whose current transmit power is below the maximal level) while maintaining the increase in transmit power as slight as possible (to comply with interference minimization).…”

Section: Algorithm 1 Generation Of a Random Tpc Solution (Rtpc)mentioning

confidence: 99%

“…(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14), and named the interference minimization based TPC model (IM-TPC). According to the definition of large-scale problems [24], the scale of a TPC model is influenced by (1) the size of a target industrial indoor environment (which is linked to the number of APs), (2) the grid size (gs), (3) the number of dominant obstacles, and (4) .…”

Section: Interferencementioning

confidence: 99%

“…These obstacles easily shadow radio propagation and cause coverage hole for a WLAN [1,3]. Secondly, an industrial indoor layout may occasionally be altered with the prevalence of flexible manufacturing [4]. These dynamic shadowing effects make it increasingly difficult to maintain the expected wireless coverage in a target industrial environment.…”

Section: Introductionmentioning

confidence: 99%

“…Fourthly, strong interaction exists between variables. Concerning a TPC problem, the factors that may make it large scale are illustrated as (1) the environment size, (2) the number of APs, (3) the number of coverage layers (or k-coverage), (4) the complexity of coverage calculation which is fundamentally based on a path loss model. To solve largescale optimization problems, metaheuristics are extensively recognized as effective approaches [25], among which a genetic algorithm (GA) is an important method [26,27].…”

Section: Introductionmentioning

confidence: 99%

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An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

Gong

David

Tanghe

et al. 2018

Applied Soft Computing

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The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), it becomes an unavoidable challenge to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To tackle this challenge, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small-and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC according to varying target coverage percentage as well as number and placement direction of dominant obstacles.

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Coordination mechanisms with mathematical programming models for decentralized decision-making: a literature review

Rius-Sorolla

Maheut

Miguel

et al. 2018

Cent Eur J Oper Res

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The increase in the complexity of supply chains requires greater efforts to align the activities of all its members in order to improve the creation of value of their products or services offered to customers. In general, the information is asymmetric; each member has its own objective and limitations that may be in conflict with other members. Operations managements face the challenge of coordinating activities in such a way that the supply chain as a whole remains competitive, while each member improves by cooperating. This document aims to offer a systematic review of the collaborative planning in the last decade on the mechanisms of coordination in mathematical programming models that allow us to position existing concepts and identify areas where more research is needed.

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Auction-based cooperation mechanism to parts scheduling for flexible job shop with inter-cells

Cited by 24 publications

References 31 publications

Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

Improved Jaya Algorithm for Flexible Job Shop Rescheduling Problem

An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

Coordination mechanisms with mathematical programming models for decentralized decision-making: a literature review

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