Designing a scheduling decision support system for the skin pass line: A case study of the steel finishing line

2020 ◽  
Vol 234 (13) ◽  
pp. 1640-1655 ◽  
Author(s):  
Parinaz Vaez ◽  
Armin Jabbarzadeh ◽  
Nader Azad
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Heuristic Algorithm ◽  
Production Scheduling ◽  
Mixed Integer ◽  
Total Production ◽  
Steel Company ◽  
Manual Method ◽  
Skin Pass

In this paper, we investigate the scheduling policies in the iron and steel industry, and in particular, we formulate and propose a solution to a complicated problem called skin pass production scheduling in this industry. The solution is to generate multiple production turns for the skin pass coils and, at the same time, determine the sequence of these turns so that productivity and product quality are maximized, while the total production scheduling cost, including the costs of tardiness, flow of material, and the changeover cost between adjacent and non-adjacent coils, is minimized. This study has been prompted by a practical problem in an international steel company in Iran. In this study, we present a new mixed integer programming model and develop a heuristic algorithm, as the commercial solvers would have difficulty in solving the problem. In our heuristic algorithm, initial solutions are obtained by a greedy constraint satisfaction algorithm, and then a local search method is developed to improve the initial solution. The experimental results tested on the data collected from the steel company show the efficiency of the proposed heuristic algorithm by solving a large-sized instance in a reasonable computation time. The average deviation between the manual method and the heuristic algorithm is 30%. Also, in all the components of the objective function, the algorithm performs better compared to the manual method. The improved values are greater than 15. In addition, we develop a commercial decision support system for the implementation of the proposed algorithm in the steel company.

scholarly journals Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 912
Author(s):  
Balázs Kocsi ◽  
Michael Maiko Matonya ◽  
László Péter Pusztai ◽  
István Budai
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Production Scheduling ◽  
Industry 4.0 ◽  
Process Time ◽  
Total Production ◽  
Competitive Advantages ◽  
Low Volume

Numerous organizations are striving to maximize the profit of their businesses by the effective implementation of competitive advantages including cost reduction, quick delivery, and unique high-quality products. Effective production-scheduling techniques are methods that many firms use to attain these competitive advantages. Implementing scheduling techniques in high-mix low-volume (HMLV) manufacturing industries, especially in Industry 4.0 environments, remains a challenge, as the properties of both parts and processes are dynamically changing. As a reaction to these challenges in HMLV Industry 4.0 manufacturing, a newly advanced and effective real-time production-scheduling decision-support system model was developed. The developed model was implemented with the use of robotic process automation (RPA), and it comprises a hybrid of different advanced scheduling techniques obtained as the result of analytical-hierarchy-process (AHP) analysis. The aim of this research was to develop a method to minimize the total production process time (total make span) by considering the results of risk analysis of HMLV manufacturing in Industry 4.0 environments. The new method is the combination of multi-broker (MB) optimization and a genetics algorithm (GA) that uses general key process indicators (KPIs) that are easy to measure in any kind of production. The new MB–GA method is compatible with industry 4.0 environments, so it is easy to implement. Furthermore, MB–GA deals with potential risk during production, so it can provide more accurate results. On the basis of survey results, 16% of the asked companies could easily use the new scheduling method, and 43.2% of the companies could use it after a little modification of production.

A DECISION SUPPORT SYSTEM FOR THE ALLOCATION OF YARD CRANES AND BLOCKS IN CONTAINER TERMINALS

2011 ◽  
Vol 28 (06) ◽  
pp. 803-829 ◽  
Author(s):  
CANRONG ZHANG ◽  
ZHIHAI ZHANG ◽  
LI ZHENG ◽  
LIXIN MIAO
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Programming Model ◽  
Solution Procedure ◽  
Container Terminals ◽  
Mixed Integer ◽  
Scarce Resources ◽  
Iterative Decomposition ◽  
Gantry Cranes

This paper examines the allocation of yard cranes and blocks for yard activities in container terminals. In this paper, the yard cranes are confined to rail mounted gantry cranes (RMGC), which are characterized by the restricted traveling range on a pair of rails. Since RMGCs and yard blocks are tightly bound to each other, when allocating them, we should make sure that the RMGCs allocated for a yard activity are able to together cover the blocks allocated for the corresponding yard activity. In addition, considering that there are four basic activities occurring in the yard which compete with each other for the scarce resources and have different requirements and priorities in the allocation of blocks and yard cranes, we treat them in a single model rather than in multiple independent models as were generally done in literature. A mixed integer programming model is constructed, and an iterative decomposition solution procedure is proposed for the problem. Based on the solution procedure, a decision support system is developed and implemented for a terminal in Tianjin seaport. Using the actual data, the numerical experiments show the effectiveness and efficiency of the decision support system.

scholarly journals Decision Support System for Production Scheduling (DSSPS)

2016 ◽  
Vol 96 ◽  
pp. 315-323
Author(s):  
Watt Kwong Wai ◽  
Lin Ting ◽  
Liang Wei Pang ◽  
Hadianto Budihardjo ◽  
Gan Chiu Liang ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Production Scheduling

scholarly journals Application of SMART Methods (Simple Multi-Attribute Rating Technique) In the Decision Support System for the Provision of People's Business Loans At Bank Sumut (Case Study: KCP Pasar Martubung)

2019 ◽  
Vol 1 (2) ◽  
pp. 1-9
Author(s):  
Romindo Romindo ◽  
Siti Hardianti
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Monthly Income ◽  
Manual Method ◽  
High Interest ◽  
The Subject ◽  
To Receive ◽  
Ranking Technique

Bank SUMUT KCP Pasar Martubung in the provision of People's Business Credit (KUR) and the subject of this research is the People's Business Credit Account (KUR), which amounts to one person. The high interest of the community in obtaining KUR made it difficult for Bank SUMUT to determine who was entitled to receive KUR. In the process of determining the eligibility of previous KUR recipients, the method of comparison between monthly income and monthly needs is still using the manual method. This method is considered to be still simple, this is due to dependence on one indicator that causes bad credit. To overcome this problem, a decision support system is needed with the SMART Multi Attribute Ranking Technique which can assist in the provision of KUR to the community that meets the criteria in the provision of KUR

scholarly journals A Decision Support System for Sustainable Agriculture: The Case Study of Coconut Oil Extraction Process

Agronomy ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 177
Author(s):  
Gianfranco Gagliardi ◽  
Antonio Igor Maria Cosma ◽  
Francesco Marasco
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Decision Maker ◽  
Support System ◽  
Coconut Oil ◽  
Extraction Process ◽  
Extraction Methods ◽  
Oil Extraction ◽  
Mixed Integer

The high demand of information and communication technology (ICT) in agriculture applications has led to the introduction of the concept of smart farming. In this respect, moving from the main features of the Fourth Industrial Revolution (Industry 4.0) promoted by the European Community, new approaches have been suggested and adopted in agriculture, giving rise to the so-called Agriculture 4.0. Improvements in automation, advanced information systems and Internet technologies allow for farmers to increase the productivity and to allocate the resources reasonably. For these reasons, agricultural decision support systems (DSS) for Agriculture 4.0 have become a very interesting research topic. DSS are interactive tools that enable users to make informed decisions about unstructured problems, and can be either fully computerized, human or a combination of both. In general, a DSS analyzes and synthesizes large amounts of data to assist in decision making. This paper presents an innovative decision support system solution to address the issues faced by coconut oil producers in making strategic decisions, particularly in the comparison of different methods of oil extraction. In more detail, the adopted methodology describes how to address the problems of coconut oil extraction in order to minimize the processing time and processing cost and to obtain energy savings. To this end, the coconut oil extraction process of the Leão São Tomé and Principe Company is presented as a case study: a DSS instance that analyzes the problem of the optimal selection between two different oil coconut extraction methods (fermentation-based and standard extraction processes) is developed as a meta-heuristics with a mixed integer linear programming problem. The obtained results show that there is clearly a trade-off between the increase in cost and reliability that the decision-maker may be willing to evaluate. In this respect, the proposed model provides a tool to support the decision-maker in choosing the best combination between the two different coconut oil extraction methods. The proposed DSS has been tested in a real application context through an experimental campaign.

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