Recent trends in distributed production network scheduling problem

Rad, N. Bagheri; Behnamian, J.

doi:10.1007/s10462-021-10081-5

Cited by 15 publications

(4 citation statements)

References 114 publications

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“…In recent years, distributed systems have become increasingly important, with their application to a wide range of sciences (from multimedia to production control) and many efforts to use them in other areas (Bagheri and Behnamian 2022). In the field of production, industrial managers in recent years have tended to use distributed systems due to changes in the production environment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to Bagheri and Behnamian (2022), as well as the reviewed papers in this study, it can be said that although researchers approach many problems of distributed scheduling with different objective functions and constraints that bring the problems closer to the real world, but the vacancy of this type of problem can be seen in environments with the unrelated parallel machines. Therefore, the present study examines the problem of distributed scheduling with unrelated parallel machines to fill part of the existing research gap.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The continued growth of factories has necessitated the decision to establish a production network consisting of multi-factories. On the other hand, changes in manufacturing factories and the interest of smaller companies in entering the global market, have created a new challenge in structuring efficient operations management in geographically distributed production networks (Bagheri and Behnamian 2022). As a result, the small organizations are interested in merging to overcome the following problems:…”

Section: Introductionmentioning

confidence: 99%

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A hyper-heuristic for distributed parallel machine scheduling with machine-dependent processing and sequence-dependent setup times

Behnamian

Asgari

2022

RAIRO-Oper. Res.

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Today, because the market is scattered around the world, manufacturing activities are not limited to a single location and have spread globally. As a result, the discussion of scheduling the factory has changed from a classic single to a network scheduling as a need in the real world. In this regard, this study considers the scheduling of multiple factories by taking into account the job transportation time between factories. The main problem here is that each job would be assigned to which factory and machine. In this research, unrelated parallel machines are considered in which the processing time of jobs depends on the machine and setup time. To minimize the makespan, first, a mixed-integer linear model was proposed in which two types of modeling have been combined. Then, a hyper-heuristic algorithm (HHA) was designed to solve the problem in a reasonable time by choosing the best method among four low-level heuristic methods that are precisely designed according to the properties of the problem. Finally, the efficiency of the proposed algorithm has been compared with the imperialist competitive algorithm (ICA) by conducting experiments. The results show that the proposed algorithm performs very well compared to the ICA and, in more than 75% of the test problems, the proposed algorithm was superior. Also, based on the analysis, in comparing the proposed algorithm with the ICA, it can be concluded that there is a significant difference between the results, and in all cases, the HHA was remarkably better. Considering the challenges and rapid changes of today's market that traditional centralized production planning does not have enough flexibility to respond to them, the results of this research are expected to be useful and attractive for planners in this field.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hyper-heuristic for distributed parallel machine scheduling with machine-dependent processing and sequence-dependent setup times

Behnamian

Asgari

2022

RAIRO-Oper. Res.

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“…Such conventional working mode is not beneficial to realizing effective management towards the complete production manufacturing process. Based on the realistic circumstances of production manufacturing process in tobacco industry, this paper explores applications of the new generation of intelligent production control method into tobacco industry [23], [24]. In detail, this work manages to design a centralized information integration platform for tobacco industry, so as to realize efficient optimal scheduling towards all the components of the whole production manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry

Liu

Wang

et al. 2023

IEEE Access

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The whole process of tobacco production is composed of many components, in which their operation and administration are currently independent. It is required to deploy smart manufacturing workflow for the whole production process, in order to realize centralized effective global scheduling. This requires an advanced administration control platform that has strong abilities of multisource data integration and automatic decision support. To bridge such research gap, this paper designs an optimal scheduling control system for smart manufacturing processes of tobacco industry. First of all, this work discusses major characteristics of future-generation production control patterns in intelligent tobacco factories (ITF). Then, a five-layer architecture for optimal scheduling control of ITF is proposed, which contains Internetof-Things layer, centralized control layer, model layer, platform layer and operation layer. In addition, a production scheduling optimization strategy is also developed for the proposed system to serve as the software algorithm that drives the running of whole smart manufacturing processes. Finally, this paper presents a comparative analysis of the proposed system's transformation in a cigarette factory. Naturally, the effectiveness of the proposed production optimization scheduling strategy is verified through simulation.

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A data-driven multi-channel supply chain multi-factory collaborative production planning problem

Wang,

Yang,

Liu

2024

Soft Comput

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Recent trends in distributed production network scheduling problem

Cited by 15 publications

References 114 publications

A hyper-heuristic for distributed parallel machine scheduling with machine-dependent processing and sequence-dependent setup times

A hyper-heuristic for distributed parallel machine scheduling with machine-dependent processing and sequence-dependent setup times

Design of an Optimal Scheduling Control System for Smart Manufacturing Processes in Tobacco Industry

A data-driven multi-channel supply chain multi-factory collaborative production planning problem

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