Parallel-machine scheduling in shared manufacturing

Ji, Min; Ye, Xinna; Qian, Fangyao; Cheng, T. C. E.; Jiang, Yiwei

doi:10.3934/jimo.2020174

Cited by 11 publications

(7 citation statements)

References 44 publications

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“…Wei et al consider two-machine hybrid flow-shop scheduling problems in shared manufacturing [18]. Ji et al consider parallel-machine scheduling problems in shared manufacturing [19].…”

Section: Shared Manufacturingmentioning

confidence: 99%

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Liu¹,

Liu²

2023

Intelligent Automation &Amp; Soft Computing

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Shared manufacturing is recognized as a new point-to-point manufacturing mode in the digital era. Shared manufacturing is referred to as a new manufacturing mode to realize the dynamic allocation of manufacturing tasks and resources. Compared with the traditional mode, shared manufacturing offers more abundant manufacturing resources and flexible configuration options. This paper proposes a model based on the description of the dynamic allocation of tasks and resources in the shared manufacturing environment, and the characteristics of shared manufacturing resource allocation. The execution of manufacturing tasks, in which candidate manufacturing resources enter or exit at various time nodes, enables the dynamic allocation of manufacturing tasks and resources. Then non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) algorithms are designed to solve the model. The optimal parameter settings for the NSGA-II and MOPSO algorithms have been obtained according to the experiments with various population sizes and iteration numbers. In addition, the proposed model's efficiency, which considers the entries and exits of manufacturing resources in the shared manufacturing environment, is further demonstrated by the overlap between the outputs of the NSGA-II and MOPSO algorithms for optimal resource allocation.

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“…Wei et al consider two-machine hybrid flow-shop scheduling problems in shared manufacturing [18]. Ji et al consider parallel-machine scheduling problems in shared manufacturing [19].…”

Section: Shared Manufacturingmentioning

confidence: 99%

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Liu¹,

Liu²

2023

Intelligent Automation &Amp; Soft Computing

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“…Zhang et al [10] proposed a monitoring and maintenance method for idle resources in shared manufacturing using sensor perception and proved its effectiveness. Ji et al [11] established a resource scheduling model that includes sustainable manufacturing in a shared manufacturing environment. Wei and Wu [12] studied the problem of two two-machine hybrid assembly workshops with fixed processing sequences to solve the scheduling problem of a series of selected processing sequences on a shared manufacturing platform.…”

Section: Literature Reviewmentioning

confidence: 99%

Differential Game Analysis of Shared Manufacturing Platform Pricing Considering Cooperative Advertising Under Government Subsidies

Liu,

2023

IEEE Access

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Shared manufacturing is a new sustainable manufacturing model formed under the rapid development of the sharing economy. This paper studies the shared manufacturing model composed of one manufacturer and one shared manufacturing platform under the condition of government subsidies based on differential game. The pricing of the shared manufacturing platform, the effectiveness and profitability of collaboration advertising with the manufacturer are investigated. The best decision-making for the centralized, the decentralized, and the bilateral cost-sharing contract model are discussed and the equilibrium outcomes of differential game are analyzed. The bilateral cost-sharing contract is improved and analyzed that the improved profit level reaches the concentration level. The numerical examples by using Matlab are analyzed, and management implications are provided. According to the findings, the centralized decision-making leads to lower prices for shared manufacturing platform than the decentralized decision-making but higher advertising effort and profit levels. Although the amount of the decentralized decision-making, advertising effort, and profit have increased due to the introduction of the bilateral cost-sharing contract, the overall profits have yet to reach the concentration level. This paper provides reference for government subsidy policies and shared manufacturing cooperation models.

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“…Industrial manufacturing Sustainable manufacturing Bank (Hwang et al, 2004) Credit card (Hwang et al, 2004) Online antivirus (Tseng et al, 2017) Flour mill (Glass and Kellerer, 2007) Loading/unloading cranes (Ou et al, 2008) Painting processing (Mateo et al, 2018) Sugarcane unloading systems (Kusoncum et al, 2021) Tempered glass processing (Liu et al, 2020) Shared manufacturing (Ji et al, 2022) The research on GoS constraints started with the scheduling problem in the service industry. The service industry provides different services according to customer membership levels (Hwang et al, 2004).…”

Section: Table 1 the Practical Cases Of Gos Constraints Service Industrymentioning

confidence: 99%

“…The GoS constraint is used to meet the environment-oriented objective. Ji et al (2022) investigated the problem of shared manufacturing schedules with the concept of sustainable manufacturing. Each job can be processed in a set of machine sets, and such a new constraint is called a processing set.…”

Section: Table 1 the Practical Cases Of Gos Constraints Service Industrymentioning

confidence: 99%

Heuristics for parallel machine scheduling with GoS eligibility constraints

Tseng,

Zhang

2023

Int. J. Appl. Sci. Eng.

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The parallel machine scheduling problem with the consideration of GoS (grade of service) constraints originates from the service provision of different membership levels in the service industry. This scheduling problem has been widely found in industrial manufacturing and sustainable manufacturing practices. For example, in a sustainable manufacturing production line, multiple machines are set up as parallel machines to support each other to achieve shared manufacturing. However, the machines still have processing constraints with different service level constraints and do not support all jobs. This study investigates this parallel machine scheduling problem and uses the total completion time as the objective criterion. This criterion has not been studied in the literature for this scheduling problem. Several heuristics are proposed to solve the twomachine and multi-machine scheduling problems. The structure of these heuristics is mainly divided into two stages: the SPT rule and the insertion method. In addition, a fast method for calculating the total completion time is developed from the insertion method. The experimental results prove that the proposed heuristics are effective in solving this scheduling problem. For both small-and large-size problems, the proposed heuristics can obtain good solutions quickly and are potentially suitable for practical use.

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Parallel-machine scheduling in shared manufacturing

Cited by 11 publications

References 44 publications

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Dynamic Allocation of Manufacturing Tasks and Resources in Shared Manufacturing

Differential Game Analysis of Shared Manufacturing Platform Pricing Considering Cooperative Advertising Under Government Subsidies

Heuristics for parallel machine scheduling with GoS eligibility constraints

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