scholarly journals A Finite Capacity of Production Planning Approach for Industrial Manufacturing

2013 ◽  
Vol 17 (1) ◽  
pp. 84-88
Author(s):  
Aboubaker Altiaieb Moussttfa ◽  
Dušan Malinžák
Keyword(s):  
Production Planning ◽  
Finite Capacity ◽  
Industrial Manufacturing ◽  
Planning Approach

Rekonfiguration von Produktionsnetzwerken*/Planning of reconfigurable production networks

2018 ◽  
Vol 108 (03) ◽  
pp. 137-142
Author(s):  
J. Atug ◽  
S. Braunreuther ◽  
G. Reinhart
Keyword(s):  
Production Planning ◽  
Wird Eine ◽  
Production Networks ◽  
Short Term ◽  
Delivery Times ◽  
Future Production ◽  
Planning Approach ◽  
High Adherence ◽  
Demand Volatility ◽  
Increasing Demand

Starke Nachfrageschwankungen insbesondere in der auftragsbezogenen Produktion sowie die Forderung nach kürzeren Lieferzeiten und höherer Termintreue führen zu erhöhten Anforderungen an die zukünftige Produktion. Um diesen Anforderungen gerecht zu werden, bedarf es dynamischer Produktionsnetzwerke. In diesem Fachbeitrag wird eine Methode zur kurzfristigen Anpassung von Produktionsnetzwerken durch Rekonfiguration in der auftragsbezogenen Produktion vorgestellt.   Increasing demand volatility in order-based production as well as the need of shorter delivery times and high adherence to schedules leads to high requirements of the future production. To meet these essential requirements dynamic production networks are needed. In this technical contribution a production planning approach of short-term reconfiguration of production networks in the order-based production is given.

A New Modeling and Application of Hierarchical Production Planning Approach

2013 ◽  
pp. 113-144
Author(s):  
Reza Tanha Aminlouei
Keyword(s):  
Power Systems ◽  
Production Planning ◽  
Power Plants ◽  
Production Capacity ◽  
Total Load ◽  
Hierarchical Production Planning ◽  
Time Horizons ◽  
Planning Approach ◽  
Utility Cost ◽  
Equal Space

In real power systems, power plants are not in the equal space from the load center, and their fuel cost is different. With common utilization conditions, production capacity is more than total load demand and losses. Therefore, there are different criteria for active and inactive power planning in each power plant. The best selection is to choose a framework in which the utility cost is minimized. On the other hand, planning in power systems has different time horizons; thus, for effective planning in power systems, it is very important to find a suitable mathematical relationship between them. In this chapter, the authors propose a modeling by selecting a Fuzzy Hierarchical Production Planning (FHPP) technique with zone covering in the mid-term and long-term time horizons electricity supply modeling in the Iran global compact network.

Hybrid Evolutionary Optimization Algorithms

2015 ◽  
pp. 1072-1107
Author(s):  
Pandian Vasant
Keyword(s):  
Objective Function ◽  
Production Planning ◽  
Membership Function ◽  
Industrial Production ◽  
Manufacturing Sector ◽  
Evolutionary Optimization ◽  
Industrial Manufacturing ◽  
Non Linear ◽  
Evolutionary Optimization Algorithms ◽  
S Curve

The novel industrial manufacturing sector inevitably faces problems of uncertainty in various aspects such as raw material availability, human resource availability, processing capability and constraints and limitations imposed by the marketing department. These problems have to be solved by a methodology which takes care of such unexpected information. As the analyst faces this man made chaotic and due to natural disaster problems, the decision maker and the implementer have to work collaboratively with the analyst for taking up a decision on an innovative strategy for implementation. Such complex problems of vagueness and uncertainty can be handled by the hybrid evolutionary intelligence algorithms. In this chapter, a new hybrid evolutionary optimization based methodology using a specific non-linear membership function, named as modified S-curve membership function, is proposed. The modified S-curve membership function is first formulated and its flexibility in taking up vagueness in parameters is established by an analytical approach. This membership function is applied for its useful performance through industrial production problems by employing hybrid evolutionary optimization algorithms. The novelty and the originality of this non-linear S-curve membership function are further established using a real life industrial production planning of an industrial manufacturing sector. The unit produces 8 products using 8 raw materials, mixed in various proportions by 9 different processes under 29 constraints. This complex problem has a cubic non-linear objective function. Comprehensive solutions to a non-linear real world objective function are achieved thus establishing the usefulness of the realistic membership function for decision making in industrial production planning.

DEA based production planning considering technology heterogeneity with undesirable outputs

2020 ◽  
Vol 54 (2) ◽  
pp. 325-339
Author(s):  
Changyong Liang ◽  
Binyou Wang ◽  
Tao Ding ◽  
Yinchao Ma
Keyword(s):  
Data Envelopment Analysis ◽  
Production Planning ◽  
Industrial Development ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Planning Approach ◽  
Technology Heterogeneity ◽  
Using Data ◽  
Planning Problems ◽  
Decision Making Units

Many researchers have concentrated on production planning issues by using data envelopment analysis (DEA). However, the assumption made by existing approaches that all decision making units (DMUs) are equipped with the same level of production technology is not realistic. Additionally, with the development in the society, environmental factors have come to play important roles in the production process as well. Thus, undesirable outputs should be considered in production planning problems. Therefore, this paper considers the technology heterogeneity factors and undesirable outputs using the data envelopment analysis-based production planning approach. Two examples containing a numerical example that compare with other method and a real sample that concerns the industrial development of 30 provinces in China are used to validate the applicability of our approach.

Hybrid Evolutionary Optimization Algorithms

2014 ◽  
pp. 59-95 ◽  
Author(s):  
Pandian Vasant
Keyword(s):  
Objective Function ◽  
Production Planning ◽  
Membership Function ◽  
Industrial Production ◽  
Manufacturing Sector ◽  
Evolutionary Optimization ◽  
Industrial Manufacturing ◽  
Non Linear ◽  
Evolutionary Optimization Algorithms ◽  
S Curve

The novel industrial manufacturing sector inevitably faces problems of uncertainty in various aspects such as raw material availability, human resource availability, processing capability and constraints and limitations imposed by the marketing department. These problems have to be solved by a methodology which takes care of such unexpected information. As the analyst faces this man made chaotic and due to natural disaster problems, the decision maker and the implementer have to work collaboratively with the analyst for taking up a decision on an innovative strategy for implementation. Such complex problems of vagueness and uncertainty can be handled by the hybrid evolutionary intelligence algorithms. In this chapter, a new hybrid evolutionary optimization based methodology using a specific non-linear membership function, named as modified S-curve membership function, is proposed. The modified S-curve membership function is first formulated and its flexibility in taking up vagueness in parameters is established by an analytical approach. This membership function is applied for its useful performance through industrial production problems by employing hybrid evolutionary optimization algorithms. The novelty and the originality of this non-linear S-curve membership function are further established using a real life industrial production planning of an industrial manufacturing sector. The unit produces 8 products using 8 raw materials, mixed in various proportions by 9 different processes under 29 constraints. This complex problem has a cubic non-linear objective function. Comprehensive solutions to a non-linear real world objective function are achieved thus establishing the usefulness of the realistic membership function for decision making in industrial production planning.

A Production Planning Optimization Model for Maximizing Battery Manufacturing Profitability

2012 ◽  
pp. 343-351
Author(s):  
Hesham K. Alfares
Keyword(s):  
Production Planning ◽  
Formation Stage ◽  
Planning Optimization ◽  
Multi Stage ◽  
Planning Approach ◽  
Total Profit ◽  
Stage Process ◽  
Excel Solver ◽  
Time Limitations ◽  
Production Plans

This paper presents an integer programming (IP) model for production planning, which is used to maximize the profitability of battery manufacturing in a mid-size company. Battery production is a complicated multi-stage process. The formation stage, during which the batteries are filled with acid and charged with electricity, is considered to be the bottleneck of this process. The IP model maximizes the total profit of batteries produced in the formation stage, subject to limited manufacturing resources as well as time limitations and demand restrictions. The IP model is able to accommodate a large variety of battery models and sizes, and also different charging circuit capacities and speeds. The model is formulated and optimally solved using Microsoft Excel Solver. Compared to the current manual production planning approach, the optimum IP-generated production plans lead to an average increase of 12% in daily profits.

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