Material supply scheduling in a ubiquitous manufacturing system

Nielsen, Izabela; Dung, Ngoc Anh; Banaszak, Zbigniew; Janardhanan, Mukund Nilakantan

doi:10.1016/j.rcim.2016.08.009

Cited by 20 publications

(9 citation statements)

References 30 publications

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“…Zhang et al (2014) propose a dynamic scheduling method based on the multi-agent manufacturing workshop, and combines with the genetic algorithm to realize the dynamic scheduling decision of realtime data-driven manufacturing tasks in the joint workshop. Based on real-time monitoring of vehicle status, Nielsen et al (2017) propose a decision-making method to optimize the quantity and distribution order of materials. Ding et al (2018) used RFID to realize real-time monitoring and scheduling of production and transportation tasks to improved productivity and flexibility through realtime monitoring, simulation and prediction of manufacturing operations.…”

Section: Internet Of Thingsmentioning

confidence: 99%

A Collaborative Decision-Making System for Production Operation

Jiang¹

2020

AJIBM

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To meet a large number of personalized demands caused by the increasing living standards of people, the production model of manufacturing enterprises has gradually shifted to small-batch, multi-variety customized production models. For customized production, the limited resources of a single manufacturing company have been difficult to independently and quickly respond to the personalized needs of high-frequency changes. It has become an inevitable choice to integrate the resources of each link in the supply chain for collaborative operation. This paper takes the production and distribution links that are at the core of the supply chain as research objects. Based on the Internet of Things technology, collaborative decision-making to support production and distribution effectively meets individual needs. Finally, the collaborative operation of the production and distribution of an air-conditioning manufacturing company is taken as an example to verify the effectiveness of the IoT-driven collaborative decision-making system proposed in this paper.

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Section: Internet Of Thingsmentioning

confidence: 99%

A Collaborative Decision-Making System for Production Operation

Jiang¹

2020

AJIBM

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“…(2014) tackled the sequencing feeding problem of a single mobile robot by a genetic algorithm-based heuristics, which enabled the mobile robot to provide parts for the feeders of machines on production lines in time. Similarly, Nielsen et al. (2017) focused on the efficient material handling system and developed a methodology for scheduling the part feeding in a ubiquitous manufacturing environment.…”

Section: Introductionmentioning

confidence: 99%

“…From the environmental perspective of minimizing the total traveling time in the panning horizon, Dang et al (2014) tackled the sequencing feeding problem of a single mobile robot by a genetic algorithm-based heuristics, which enabled the mobile robot to provide parts for the feeders of machines on production lines in time. Similarly, Nielsen et al (2017) focused on the efficient material handling system and developed a methodology for scheduling the part feeding in a ubiquitous manufacturing environment. The problem was formulated as a nonlinear programming problem to find an optimal material supply schedule for a single mobile robot with minimum energy consumption in terms of moving distances.…”

Section: Introductionmentioning

confidence: 99%

Synergetic energy-conscious scheduling optimization of part feeding systems via a novel chaotic reference-guided policy

Zhou

et al. 2022

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PurposeThis paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks.Design/methodology/approachA chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance.FindingsThe involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries.Originality/valueThis research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

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“…Boudella et al 17 studied a hybrid kitting system consisting of a robot and an operation worker to optimize the stock keeping units and cycle time between robots and operators. Given the limitations of the robots' power consumption, Nielsen et al 18 aimed to develop a scheduling method of material supply for a single robot. Differing from the existing literature, the MMRSP proposed in this article not only incorporates the energy consumption into the traditional PFP but also requires multiple handling for several stations, which is more complicated and realistic in MMALs, so that it can meet both production and energy-saving needs.…”

Section: Introductionmentioning

confidence: 99%

Hybrid self-adaptive biobjective optimization of multiple robot scheduling problem for mixed-model assembly lines considering energy savings

Zhou

Fei

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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With the wide application of robots in the material distribution process on the assembly lines, single robot scheduling cannot meet the actual production needs. However, the high degree of mechanization also brings about environmental problems. Therefore, this article aims to develop a scheduling methodology to accomplish material supply tasks using multiple robots with energy consumption consideration. Meanwhile, a targeted mathematical model to minimize total weighted penalty costs and total energy consumption is developed. Due to the NP-hard nature of the problem, an adaptive hybrid mutation population extremal optimization multi-objective algorithm based on uniform distribution selection is proposed to solve multi-objective problems. Furthermore, a new coding method for initialization is designed to optimize the whole iterative process. The performance of the proposed algorithm is evaluated by comparing with three benchmark multi-objective algorithms. Computational experiments are represented to prove the validity and feasibility of the proposed algorithm.

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Material supply scheduling in a ubiquitous manufacturing system

Cited by 20 publications

References 30 publications

A Collaborative Decision-Making System for Production Operation

A Collaborative Decision-Making System for Production Operation

Synergetic energy-conscious scheduling optimization of part feeding systems via a novel chaotic reference-guided policy

Hybrid self-adaptive biobjective optimization of multiple robot scheduling problem for mixed-model assembly lines considering energy savings

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