A New Interactive Method to Solve Multiobjective Linear Programming Problems

Sadrabadi, Mahmood Rezaei; Sadjadi, Seyed Jafar

doi:10.4236/jsea.2009.24031

Cited by 10 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the present era of technology, the infusion of machine learning algorithms into different machine tools has emerged as an innovative way to minimize the human involvement for different machining operations and also to improve accuracy and productivity in various machining operations. The use of machine learning algorithms into the machine tools promotes the development of artificial intelligence into the different machines by experienced-based learning (Sadjadi & Makui, 2002;Sadrabadi & Sadjadi, 2009;Moghaddam et al, 2012;Angra et al, 2018;Balic et al, 2006;Chanda et al, 2018;Deb et al, 2006;Chawla et al, 2017;Chawla et al, 2018aChawla et al, , 2018bChawla et al, , 2018cChawla et al, , 2018dChawla et al, , 2019aChawla et al, , 2019bChawla et al, , 2019cChawla et al, , 2019d. Commonly the machine learning algorithm is provided with some data sets or machining programs which are used for training of the algorithm (Warwick, 2013;Russell & Norvig, 2016).…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning algorithms for the automatic programming of computer numerical control machine

Sharma¹,

Chawla²,

Ram³

2020

10.5267/j.ijdns

View full text Add to dashboard Cite

The computer numerical control (CNC) machines are chiefly used for the production of jobs with high accuracy and high speed. The CNC machining centers perform the machining operations according to the given program instructions which are commonly programmed by a CNC programmer. In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed. The machine learning algorithms namely support vector machine (SVM) and restricted boltzmann machine algorithm (RBM) with deep belief network (DBN) are used for the automatic development of CNC machining programs of different types of holes. Initially, the position and other parameters of machining operations are identified and thereafter the CNC machining program is developed by using the MATLAB application. The automatically developed CNC programs are tested on a CNC simulator. It is found that the application of RBM machine learning algorithm with DBN outperforms the SVM machine learning algorithm for the development of automatic CNC machining program for the machining of blind holes, through holes, counterbores and countersink operations.

show abstract

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning algorithms for the automatic programming of computer numerical control machine

Sharma¹,

Chawla²,

Ram³

2020

10.5267/j.ijdns

View full text Add to dashboard Cite

show abstract

“…A new reference value for the unsatisfactory objective is reached after the solution of an auxiliary problem. Sadrabadi and Sadjadi [15] developed a new algorithm based on a new approach to solve the MOP by starting from utopian point, which is usually infeasible, and moving towards the feasible region via stepwise movement and a simple continuous interaction with DM. De and Yadav [5] proposed an algorithm for solving multiobjective assignment problem through interactive fuzzy programming approach.…”

Section: Introductionmentioning

confidence: 99%

Solving Multi-Objective Optimization Problems through Unified Approach

Khalifa¹,

AL-harbi²

2017

APAM

View full text Add to dashboard Cite

Abstract. In this paper, unified approach for solving multi-objective optimization problem is introduced. The approach is based on the Reference Direction (RD) method introduced by Narula et al. [14], and the Attainable Reference Point (ARP) method introduced by Wang et al. [19]. This approach improves the performance of the ARP method by using the initial weak efficient solution of the RD method that is to improve the weights in the Lexicographic weighted Techebycheff program. The weights in the unified approach are constructed through the ARP and the weak efficient solution. A numerical example is given in the sake of the paper to clarify the obtained results.

show abstract

“…In order to address the production planning problems statically and dynamically, Sadjadi and Makui (2002) introduced a novel method. Sadrabadi and Sadjadi (2009) introduced an interactive algorithm and solved multi-objective problems. The algorithm solved nonlinear utility effectively and also developed solutions towards the feasible area.…”

Section: Literature Reviewmentioning

confidence: 99%

The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm

Chawla¹,

Chanda²,

Angra³

2019

10.5267/j.jpm

View full text Add to dashboard Cite

The real-time scheduling of automatic guided vehicles (AGVs) in flexible manufacturing system (FMS) is observed to be highly critical and complex due to the dynamic variations of production requirements such as an imbalance of AGVs loading, the high travel time of AGVs, variation in jobs, and AGV routes to name a few. The output from FMS considerably depends on the efficient scheduling of AGVs in the FMS. The multi-objective scheduling decisions for AGVs by nature inspired algorithms yield a considerable reduction throughput time in the FMS. In this paper, investigations are carried out for the multi-objective scheduling of AGVs to simultaneously balance the workload of AGVs and to minimize the travel time of AGVs in the FMS. The multi-objective scheduling is carried out by the application of nature-inspired grey wolf optimization algorithm (GWO) to yield a balanced work-load for AGVs and also to minimize the travel time of AGVs simultaneously in the FMS. The output yield of the GWO algorithm is compared with the results of benchmark problems from the literature. The resulting yield of the proposed algorithm for the multi-objective scheduling of AGVs is observed to outperform the existing algorithms for scheduling of AGVs.

show abstract

A New Interactive Method to Solve Multiobjective Linear Programming Problems

Cited by 10 publications

References 27 publications

Comparison of machine learning algorithms for the automatic programming of computer numerical control machine

Comparison of machine learning algorithms for the automatic programming of computer numerical control machine

Solving Multi-Objective Optimization Problems through Unified Approach

The scheduling of automatic guided vehicles for the workload balancing and travel time minimi-zation in the flexible manufacturing system by the nature-inspired algorithm

Contact Info

Product

Resources

About