Time, cost, and quality trade-offs in material requirements planning using fuzzy multi-objective programming

The main purpose of this paper is the allocation of orders to suppliers in an agile and flexible manner suitable to the automobile industry. In this paper, parts supplied by a single source were eliminated from the set of parts. Using mathematical modeling and through the interval-valued fuzzy-rough numbers best worst method (IVFRN-BWM), we try to achieve the results that can meet the proposed model's needs and provide the ideal results by introducing new modes. This paper addressed some new aspects of the subject and achieved robust results by considering five objective functions. These five functions are as follows: minimization of production line disruptions due to the performance of suppliers, minimization of the complaints of production line about supplied parts, minimization of defective parts received from suppliers (PPM), maximization of on-time delivery services, and minimization of overall costs of supplied parts. Reviewing the literature, the originality of this study are as follows: 1) identifying the structure of a supply chain (SC) in general and particularly in an automobile industry SC; 2) investigating the modeling techniques of the existing SC models for coordinating all the members of a product SC; 3) building a hybrid model of IVFRN-BWM and a robust goal programming agile and flexible supply chain in an uncertain situation; and 4) identifying the suitable scenarios/cases for testing the proposed models to validate the models. This paper can help decision makers and managers to opt for the best suppliers and also allocate the right numbers of parts to those supplier(s) based on a real situation of each firm. INDEX TERMS Robust optimizations, IVFRN-BWM, allocation, agile and flexible SC, automobile industry.

show abstract

“…Furthermore, constraints presented in equations (23) to (27) are rewritten based on equation 28as equations (29) to (39).…”

Section: A: Goal Programming Modelmentioning

confidence: 99%

Hybrid Model of IVFRN-BWM and Robust Goal Programming in Agile and Flexible Supply Chain, a Case Study: Automobile Industry

et al. 2019

View full text Add to dashboard Cite

show abstract

“…In a multi-objective model, several objectives have to be simultaneously optimized considering different membership functions [25], as model (3):…”

Section: Fuzzy Multi-objective Linear Programmingmentioning

confidence: 99%

“…Using the approaches given by [25] and [26], the above mentioned model can be transformed to a constrained single objective model (4). (5) - (7) (using Eq.…”

Section: Fuzzy Multi-objective Linear Programmingmentioning

confidence: 99%

Manufacturing Performance Measurement Using Fuzzy Multi-Attribute Utility Theory and Z-Number

Feylizadeh

Bagherpour

2018

TFAMENA

View full text Add to dashboard Cite

SummaryThe Earned Value Management (EVM) has been extensively employed in the literature for analysing the schedule and cost performance indexes. However, the effects of risk factors on the project success have been previously ignored in the project management conventional context. In this paper, a well-organized project control and monitoring system is developed by incorporating the EVM basic principles, risk analysis, and utility theory for improving the performance of manufacturing systems. Weight values corresponding to the schedule performance index (SPI), the cost performance index (CPI), and the risk performance index (RPI) are calculated based on expert judgments using Z-number and Analytic Hierarchy Process (Z-AHP). Finally, a Multi-Attribute Utility Theory (MAUT) and Multi-Objective Linear Programming (MOLP) under fuzzy condition are utilized to demonstrate the applicability of the proposed approach. Sensitivity analysis indicated the risk performance is the most sensitive when compared with the schedule and the cost index. The approach given in this paper can be further employed by both academicians and managers in heavy intensive manufacturing systems. Key words: Manufacturing systems, Multi-Objective Linear Programming (MOLP), Earned Value Management (EVM), Risk analysis, Z-Number IntroductionThe Earned Value Management (EVM) is a famous tool assisting project managers in checking the project schedule and budget to see whether they are on track. The EVM provides an excellent insight into the current trend in the project as well as a study on the planning of the project budget and the time required for the project completion. Traditional EVM-based control systems are used to analyse and detect the current schedule and cost performance indexes. In addition, there are some other models tending to provide a good insight into the cost required for the completion of the project. Thus, the schedule performance index SPI(t) has been presented as a new metric in the EVM context where the schedule performance index has been calculated based on the earned schedule and not based on the conventional earned value.The EVM has rarely been merged with other fields under investigation. The EVM has also been incorporated within financial issues and a new index, entitled financial performance

show abstract

“…Many researchers have employed the fuzzy programming technique to deal with the multi-objective problem by considering the preferences of decision makers in several production planning fields. [25][26][27][28] A number of studies have reported on the progress based on fuzzy programming for the specific applications of production planning. Selim et al 29 and Jamalnia and Soukhakian 30 studied the fuzzy goal programming approach to solve the multi-objective problem in the distribution planning of supply chain and aggregate production planning, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

A hybrid approach for multi-weapon production planning with large-dimensional multi-objective in defense manufacturing

Jiang

Yang

Yue-jin

2013

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Multi-weapon production planning contains multi-objective combinatorial optimization and decision-making problems with the NP-hard and large-dimensional natures, which are difficult to be attacked by one single technique successfully. A four-stage hybrid approach is proposed to solve this problem. In the first stage, the multi-weapon production planning problem is formulated with 2 N ( N > 5) objectives based on operational capability requirements and expected downside risk measure. In the second stage, the formulation addressed is converted into a bi-objective optimization model using goal programming. In the third stage, an algorithm DENS based on differential evolution and nondominated sorting genetic algorithm–II is developed to obtain the Pareto set. Finally, the multiple attribute decision-making method technique for order preference by similarity to ideal solution is employed to acquire the compromise solution from the Pareto set. A case study is given to demonstrate the effectiveness of the proposed approach. The concrete advantages of goal programming, DENS, and technique for order preference by similarity to ideal solution are also validated in this case. This approach can support the weapon production planning in defense manufacturing and is also applicable to solve the multi-level and multi-objective problem in other manufacturing fields.

show abstract

Time, cost, and quality trade-offs in material requirements planning using fuzzy multi-objective programming

Cited by 12 publications

References 9 publications

Hybrid Model of IVFRN-BWM and Robust Goal Programming in Agile and Flexible Supply Chain, a Case Study: Automobile Industry

Hybrid Model of IVFRN-BWM and Robust Goal Programming in Agile and Flexible Supply Chain, a Case Study: Automobile Industry

Manufacturing Performance Measurement Using Fuzzy Multi-Attribute Utility Theory and Z-Number

A hybrid approach for multi-weapon production planning with large-dimensional multi-objective in defense manufacturing

Contact Info

Product

Resources

About