scholarly journals Production Planning and Scheduling Using Machine Learning and Data Science Processes

2020 ◽  
Author(s):  
Paulo Henrique De Modesti ◽  
Ederson Carvalhar Fernandes ◽  
Milton Borsato
Keyword(s):  
Machine Learning ◽  
Production Scheduling ◽  
Manufacturing Industry ◽  
Data Science ◽  
Industrial Revolution ◽  
Data Availability ◽  
Planning And Scheduling ◽  
Systemic Analysis ◽  
Production Planning And Scheduling ◽  
Research Problems

Increasing manufacturing efficiency has been a constant challenge since the First Industrial Revolution. What started as mechanization and turned into electricity-driven operations has experienced the power of digitalization. Currently, the manufacturing industry is experiencing an exponential increase in data availability, but it is essential to deal with the complexity and dynamics involved to improve manufacturing indicators. The aim of this study is to identify and allow an understanding of the unfilled gaps and the opportunities regarding production scheduling using machine learning and data science processes. In order to accomplish these goals, the current study was based on the Knowledge Development Process – Constructivist (ProKnow-C) methodology. Firstly, selecting 30 articles from 3608 published articles across five databases between 2015 and 2019 created a bibliographic portfolio. Secondly, a bibliometric analysis, which generated comparative charts of the journals’ relevance regarding its impact factor, scientific recognition of the articles, publishing year, highlighted authors and keywords was carried out. Thirdly, the selected articles were read thoroughly through a systemic analysis in order to identify research problems, proposed solutions, and unfilled gaps. Then, research opportunities identified were: (i) Big data and associated analytics; (ii) Collaboration between different disciplines; (iii) Solution Customization; and (iv) Digital twin development.

scholarly journals Carbon-Efficient Production Scheduling of a Bioethanol Plant Considering Diversified Feedstock Pelletization Density: A Case Study

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1189
Author(s):  
Xinchao Li ◽  
Xin Jin ◽  
Shan Lu ◽  
Zhe Li ◽  
Yue Wang ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Optimization Model ◽  
Production Scheduling ◽  
Raw Materials ◽  
Future Research ◽  
Efficient Production ◽  
Emission Model ◽  
Planning And Scheduling ◽  
Production Planning And Scheduling ◽  
Total Profit

This paper presents a dual-objective optimization model for production scheduling of bioethanol plant with carbon-efficient strategies. The model is developed throughout the bioethanol production process. Firstly, the production planning and scheduling of the bioethanol plant’s transportation, storage, pretreatment, and ethanol manufacturing are fully considered. Secondly, the carbon emissions in the ethanol manufacturing process are integrated into the model to form a dual-objective optimization model that simultaneously optimizes the production plan and carbon emissions. The effects of different biomass raw materials with optional pelletization density and pretreatment methods on production scheduling are analyzed. The influence of demand and pretreatment cost on selecting a pretreatment method and total profit is considered. A membership weighted method is developed to solve the dual-objective model. The carbon emission model and economic model are integrated into one model for analysis. An example is given to verify the effectiveness of the optimization model. At the end of the paper, the limitation of this study is discussed to provide directions for future research.

An agent-based architecture for production scheduling in dynamic job-shop manufacturing system

2018 ◽  
Vol 66 (6) ◽  
pp. 492-502 ◽  
Author(s):  
Om Ji Shukla ◽  
Gunjan Soni ◽  
Rajesh Kumar ◽  
Sujil A
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Manufacturing System ◽  
Job Shop ◽  
Shop Floor ◽  
Efficient Production ◽  
Planning And Scheduling ◽  
Agent Based ◽  
Production Planning And Scheduling ◽  
Key Issues

Abstract In a highly competitive environment, effective production is one of the key issues which can be addressed by efficient production planning and scheduling in the manufacturing system. This paper develops an agent-based architecture which enables integration of production planning and scheduling. In addition, this architecture will facilitate real time production scheduling as well as provide a multi-agent system (MAS) platform on which multiple agents will interact to each other. A case study of job-shop manufacturing system (JMS) has been considered in this paper for implementing the concept of MAS. The modeling of JMS has been created in SimEvents which integrates an agent-based architecture developed by Stateflow to transform into dynamic JMS. Finally, the agent-based architecture is evaluated using utilization of each machine in the shop floor with respect to time.

PRODUCTION MANAGEMENT — SIMPLE OR COMPLEX

2004 ◽  
Vol 01 (04) ◽  
pp. 359-371 ◽  
Author(s):  
GIDEON HALEVI
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Production Management ◽  
Planning Method ◽  
Complex Task ◽  
Simple Task ◽  
Planning And Scheduling ◽  
Production Planning And Scheduling ◽  
Planning Methods ◽  
Delivery Dates

Theoretical production planning and scheduling is actually very simple task: The plant gets orders which defines the product, the quantity and delivery dates. The resources of the plants are known, the product bill of material is known and the task of production scheduling is to make sure that the orders will be ready on time, that's all. It seems strange that in order to meet this simple task, over 100 complex production planning methods were proposed. Some of the outstanding ones are: PICS; MRP; ERP; GT; TOC; FMS; IMS; CIM; CE; JIT; Kanaban; TQM; Agent…, AGILE etc. Yet the search for "THE" method is carried on. In this paper an attempt to analyze why production planning is regarded as a complex task, and why the search for "THE" production planning method is still an open topic for researchers. Furthermore, to demonstrate how introduction of flexibility will restore the simplicity of production planning.

A Production Planning and Scheduling Process Study Based on Dynamic Information Feedback

2014 ◽  
Vol 989-994 ◽  
pp. 3447-3451
Author(s):  
Qing Yang ◽  
Yan Hua Ni
Keyword(s):  
Production Scheduling ◽  
Information Feedback ◽  
Production Environment ◽  
Dynamic Information ◽  
Planning And Scheduling ◽  
Daily Work ◽  
Production Planning And Scheduling ◽  
Process Study ◽  
Fixed Parameter ◽  
Scheduling Plan

Many factories usually can’t use the production scheduling plan generated by some software directly, such as ERP and MES. Two main reasons are founded, one is the fixed parameter and data used in the planning and scheduling process; the other one is lack of Ability to respond to the dynamic production environment. This paper tries to improve the plan by using the dynamic information feedback and experience from workers, and a test states it is helpful in the daily work.

A Production Planning and Scheduling Architecture for Networked-Manufacturing System Based on Available-to-Promise

2011 ◽  
Vol 268-270 ◽  
pp. 292-296 ◽  
Author(s):  
Wen Hao Wang ◽  
Qiong Zhu ◽  
Jie Zhang
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Manufacturing System ◽  
Resource Planning ◽  
Networked Manufacturing ◽  
Planning And Scheduling ◽  
Production Planning And Scheduling ◽  
Order Management ◽  
Production Activity ◽  
Decision Making System

In the practical application of push-pull based production planning and scheduling architecture, the manufacturing system was found lacking of collaborative mechanism, especially for a networked-manufacturing environment, which requires each individual manufacturer interact and cooperate with each other for a collaborative manufacturing. This paper presents a production planning and scheduling architecture for networked-manufacturing system based on available-to-promise, which can effectively merge forecast-driven production activity with order-driven production activity, thus ensures the steady and prompt supply of material, and also cooperation and mutual benefit of individual manufacturer. This architecture consists of 1) an ATP-based order management and decision-making system, 2) a push-pull based multi-plant master production schedule collaboration model, 3) a pre-reactive collaborative replenishment model, 4) a production scheduling model of unrelated parallel machine and 5) the corresponding production planning and scheduling methods for each model. By combining the concept of ATP, this architecture can not only provide resource planning for networked-manufacturing system, but also offer quick response and promise to customer requests.

scholarly journals PERENCANAAN JADWAL INDUK PRODUKSI DENGAN MENGGUNAKAN LINIER PROGRAMMING PADA INDUSTRI MANUFAKTUR PT ”X”

Heuristic ◽  
2016 ◽  
Vol 12 (02) ◽  
Author(s):  
Yusuf Eko Nurcahyo
Keyword(s):  
Production Scheduling ◽  
Manufacturing Industry ◽  
Production Schedule ◽  
Planning Period ◽  
Planning And Scheduling ◽  
Inventory Costs ◽  
Advantages And Disadvantages ◽  
Master Production Scheduling ◽  
Master Production Schedule ◽  
The Cost

The fluctuated demand is one of constrain in planning and scheduling process because it affects the cost of production and inventory costs incurred. PT "X" is a sandal maker manufacturing industry that produces sandals and slippers men women. In the manufacture of slippers companies often do not correspond to the amount of consumer demand, so common advantages and disadvantages of production quantities. Master production scheduling (MPS) is a solution to overcome the problem of making sandals to avoid excess and shortage of production of slippers. To create a Master Production Schedule first made a forecast for the next planning period is 5 months of January, February, March, April, May and June.Keyword: Master Production Schedule, linier programming

Research on production planning and scheduling based on improved collaborative optimization

2019 ◽  
Vol 27 (2) ◽  
pp. 99-111 ◽  
Author(s):  
Song Zheng ◽  
Jiaxin Gao ◽  
Jian Xu
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Production Efficiency ◽  
Collaborative Optimization ◽  
Production Plan ◽  
The Novel ◽  
Integration Algorithm ◽  
Planning And Scheduling ◽  
Integrated Optimization ◽  
Production Planning And Scheduling

The production planning is aimed at the formulation and distribution of the overall production plan, while the production scheduling focuses on the implementation of the specific production plan. It is very important to coordinate each other in order to promote the production efficiency of enterprises, but the integrated optimization of production planning and scheduling has great challenges. This article proposes the novel integrated optimization method of planning and scheduling based on improved collaborative optimization. An integrated model of planning and scheduling with collaborative optimization structure is established, and the detailed solution strategy of the novel integrated optimization algorithm is presented. At last, the simulation results show that the proposed integration algorithm of planning and scheduling is competitive in global optimization and practicality.

scholarly journals Sustainable Production Scheduling in Open Innovation Perspective under the Fourth Industrial Revolution

2018 ◽  
Vol 4 (4) ◽  
pp. 42 ◽  
Author(s):  
Sang-Oh Shim ◽  
KyungBae Park ◽  
SungYong Choi
Keyword(s):  
Production Scheduling ◽  
Open Innovation ◽  
Industrial Revolution ◽  
Scheduling Algorithm ◽  
Point Of View ◽  
Scheduling Problem ◽  
Planning And Scheduling ◽  
Real Time Scheduling ◽  
Fourth Industrial Revolution ◽  
Operation Scheduling

This research addresses a specific issue in the field of operation scheduling. Even though there are lots of researches on the field of planning and scheduling, a specific scheduling problem is introduced here. We focus on the operation scheduling requirements that the Fourth Industrial Revolution has brought currently. From the point of view of open innovation, operation scheduling is known as the one that is using the Internet of Things, Cloud Computing, Big Data, and Mobile technology. To build proper operation systems under the Fourth Industrial Revolution, it is very essential to devise effective and efficient scheduling methodology to improve product quality, customer delivery, manufacturing flexibility, cost saving, and market competence. A scheduling problem on designated parallel equipments, where some equipments are grouped according to the recipe of lots, is considered. This implies that a lot associated with a specific recipe is preferred to be processed on an equipment among predetermined (designated) ones regardless of parallel ones. A setup operation occurs between different recipes of lots. In order to minimize completion time of the last lot, a scheduling algorithm is proposed. We conducted a simulation study with randomly generated problems, and the proposed algorithm has shown desirable and better performance that can be applied in real-time scheduling.

scholarly journals Machine learning and optimization for production rescheduling in Industry 4.0

2020 ◽  
Vol 110 (9-10) ◽  
pp. 2445-2463 ◽  
Author(s):  
Yuanyuan Li ◽  
Stefano Carabelli ◽  
Edoardo Fadda ◽  
Daniele Manerba ◽  
Roberto Tadei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Production Management ◽  
Data Science ◽  
Industrial Revolution ◽  
Ad Hoc ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Classification Model ◽  
Scheduling Problem ◽  
Solution Methods

Abstract Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

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