A Production Planning Model for Make-To-Order Companies with Capacity Constraint

2011 ◽  
Vol 201-203 ◽  
pp. 1066-1069 ◽  
Author(s):  
Hua Li Gao ◽  
Bin Dan ◽  
You Guo Jing
Keyword(s):  
Decision Making ◽  
Production Planning ◽  
Production Systems ◽  
Optimal Solution ◽  
Capacity Constraint ◽  
Planning Model ◽  
Numerical Example ◽  
Make To Order ◽  
Decision Making Model

This paper proposes a decision-making model of the planning quantity put into production for Make-To-Order (MTO) companies with capacity constraint. The low repeatability and the uncertain products eligibility-rate of the MTO production systems are fully taken into account, and an optimal solution is presented. Finally, a numerical example is given to illustrate the validity of the model.

scholarly journals A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xixing Li ◽  
Shunsheng Guo ◽  
Yi Liu ◽  
Baigang Du ◽  
Lei Wang
Keyword(s):  
Mathematical Model ◽  
Production Planning ◽  
Flow Shop ◽  
Fitness Function ◽  
Capacity Constraint ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Make To Order ◽  
Delay Cost

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.

scholarly journals Smart Production Planning and Control: Concept, Use-Cases and Sustainability Implications

2020 ◽  
Vol 12 (9) ◽  
pp. 3791 ◽  
Author(s):  
Olumide Emmanuel Oluyisola ◽  
Fabio Sgarbossa ◽  
Jan Ola Strandhagen
Keyword(s):  
Production Planning ◽  
Production Management ◽  
Production Systems ◽  
Production Planning And Control ◽  
Market Uncertainty ◽  
Planning And Control ◽  
Make To Order ◽  
Smart Technologies ◽  
And Control ◽  
Process Strategy

Many companies are struggling to manage their production systems due to increasing market uncertainty. While emerging ‘smart’ technologies such as the internet of things, machine learning, and cloud computing have been touted as having the potential to transform production management, the realities of their adoption and use have been much more challenging than anticipated. In this paper, we explore these challenges and present a conceptual model, a use-case matrix and a product–process framework for a smart production planning and control (smart PPC) system and illustrate the use of these artefacts through four case companies. The presented model adopts an incremental approach that companies with limited resources could employ in improving their PPC process in the context of industry 4.0 and sustainability. The results reveal that while make-to-order companies are more likely to derive greater benefits from a smart product strategy, make-to-stock companies are more likely to derive the most benefit from pursuing a smart process strategy, and consequently a smart PPC solution.

scholarly journals Investigation of the Potential to Use Real-Time Data in Production Planning and Control of Make to Order (MTO) Manufacturing Companies

2021 ◽  
pp. 165-185
Author(s):  
Manuel Woschank ◽  
Patrick Dallasega ◽  
Johannes A. Kapeller
Keyword(s):  
Real Time ◽  
Production Planning ◽  
Production Systems ◽  
Lead Times ◽  
Production Planning And Control ◽  
Time Data ◽  
Planning And Control ◽  
Make To Order ◽  
Real Time Data ◽  
And Control

AbstractThe integrated planning and control of logistics processes can be seen as one of the basic prerequisites for the successful implementation of smart production systems and smart and lean supply chains, as well. Therefore, modern Industry 4.0 approaches are mainly focusing on (1) the principles of decentralization and (2) the usage of real-time data to improve the overall logistics performance in terms of promised delivery dates, work in progress, capacity utilization, and lead-times. In this context, this chapter systematically evaluates the application of decentralized production planning and control strategies, e.g., KANBAN and CONWIP, in comparison with traditional approaches, like MRP. Moreover, the impact of real-time data usage in production planning and control systems on lead-times and work in progress is investigated using a discrete event simulation based on primary data from a make to order manufacturer. The results of this industrial case study research confirm the significant potential that lies in smart production systems and smart and lean supply chains and, therefore, in the introduction of Industry 4.0 technologies and technological concepts in production and logistics systems.

Decision Making Model Based on the Z-Information

2020 ◽  
Vol 26 (11) ◽  
pp. 625-630
Author(s):  
O. M. Poleshchuk ◽  
Keyword(s):  
Decision Making ◽  
Multicriteria Decision Making ◽  
Decision Models ◽  
Probabilistic Assessment ◽  
Multicriteria Decision ◽  
Numerical Example ◽  
Model Based ◽  
Definition Of ◽  
Decision Making Model ◽  
The Ideal

A model of multicriteria decision making is developed taking into account the reliability of the data obtained. To formalize the information containing the data and assess their reliability, Z-numbers are used, the definition of which was given by Lotfie Zadeh in 2011. Most of the well-known decision models based on Z-numbers are limited by the assumption of a probabilistic assessment of the reliability of the data, which significantly narrows the scope of these models. This article partially removes the restrictive requirements when working with Z-numbers. For components of Z-numbers, aggregate indicators are calculated using a-cuts, based on which the similarity indicator between Z-numbers is determined. Choosing the best alternative is based on the minimum indicator of similarity with the ideal alternative. A numerical example is presented that shows the operation of the model and its effectiveness under conditions of multi-criteria selection.

An integrated decision-making model for multi-attributes decision-making (MADM) problems in additive manufacturing process planning

2014 ◽  
Vol 20 (5) ◽  
pp. 377-389 ◽  
Author(s):  
Yicha Zhang ◽  
Alain Bernard
Keyword(s):  
Decision Making ◽  
Additive Manufacturing ◽  
Process Planning ◽  
Integrated Model ◽  
Content Type ◽  
Numerical Example ◽  
Research Areas ◽  
Proposed Model ◽  
Integrated Decision Making ◽  
Decision Making Model

Purpose – The purpose of this paper is to propose an integrated decision-making model for multi-attributes decision-making (MADM) problems in additive manufacturing (AM) process planning and for related MADM problems in other research areas. Design/methodology/approach – This research analyzed the drawbacks of former methods and then proposed two sub-decision-making models, “deviation model” and “similarity model”. The former sub-model aimed to measure the deviation extent of each alternative to the aspired goal based on analyzing Euclidean distance between them, whereas the latter sub-model applying grey incidence analysis was used to measure the similarity between alternatives and the expected goal by investigating the curve shape of each alternative. Afterwards, an integrated model based on the aggregation of the two sub-models was proposed and verified by a numerical example and simple case studies. Findings – The calculating results of the cited numerical example and the comparison to former related research showed that this proposed model is more practical and reasonable than former methods applied in MADM problems of AM. In addition, the proposed model can be applied in other fields where MADM problems exist. Originality/value – This proposed integrated model not only considered the deviation extent of alternatives to the aspired goal but also investigated the similarity between alternatives and the expected goal. The similarity analysis compensates the drawbacks of traditional “distance-based” models or methods that cannot distinguish alternatives which have the same distance-based index value.

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