Design of Cellular Manufacturing Systems Considering Dynamic Production Planning and Worker Assignments

Aljuneidi, Tariq; Bulgak, Akif Asil

doi:10.17265/2159-5291/2016.01.001

Cited by 4 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, it should be noted that, the variance (V) is defined as the difference between the initial value (IV) and the optimum value (OV) of the parameter and the variance ratio (V %) is defined as the percentage ratio between the variance of the parameter (V) and its initial value (IV). In general the results presented in this section agree fairly well with the results of the sensitivity analysis performed by Tariq [13] and Aljuneidi [14].…”

Section: Sensitivity Analysis On the Optimization Model For Cms Resultssupporting

confidence: 89%

Optimization for Hierarchical Production Planning of Industrial Processes

Mahmoud

Afefy

Abdel-Karim

2014

The International Conference on Applied Mechanics and Mechanica

View full text Add to dashboard Cite

In this paper, a generalized mathematical model formulation for Cellular Manufacturing System (CMS) using Hierarchical Production Planning (HPP) approach, which is a methodology to introduce the planning process by a series of mathematical models, is proposed. Particularly, the main objective of the CMS is to obtain the optimum method and this optimum method helps to solve the problem of minimizing the variable production costs (production cost, cells setup cost, inventory holding cost and regular capacity cost). Subsequently, the model is applied to two different real case studies and is solved by using operation research optimization software (Lingo-12.0 program). The model is divided into three main steps as follows: data collection, mathematical model formula, and results. The proposed mathematical model of the optimization can solve the problems of the system under utilizing the limited resources in a production plan. To prove the applicability of the proposed model, two real case studies are introduced. The results show generally that the proposed mathematical model can be used to minimize manufacturing total costs of products for similar cases. Specifically, for the first case (Electric Water Heater with capacity 50 liter (EWH1)); the results show that the total cost decreases by 8.46 % for the optimum conditions. In addition, for the second case, (Electric Water Heater with capacity 80 liter (EWH2)); the results indicate that the total cost decreases by 3.7% for the optimum conditions.

show abstract

Section: Sensitivity Analysis On the Optimization Model For Cms Resultssupporting

confidence: 89%

Optimization for Hierarchical Production Planning of Industrial Processes

Mahmoud

Afefy

Abdel-Karim

2014

The International Conference on Applied Mechanics and Mechanica

View full text Add to dashboard Cite

show abstract

“…Sakhaii et al (2016) developed a robust optimization approach for a new integrated mixed integer linear programming model with unreliable machines. Aljuneidi and Bulgak (2016) presented a comprehensive mathematical model for the design and analysis of dynamic CMS that considers several manufacturing attributes such as the available time for workers, worker assignments, and machine procurement. Deep and Singh (2016) proposed an integrated mathematical model of multi-period CF and part operation tradeoff in a dynamic CMS with emphasize on the production flexibility.…”

Section: Literature Reviewmentioning

confidence: 99%

A three-phase heuristic approach to solve an integrated cell formation and production planning problem

Kheirkhah¹,

Ghajari²

2018

10.5267/j.uscm

View full text Add to dashboard Cite

In today's dynamic business environment, the product mix and demand may vary from period to period. Therefore, the same configuration of cellular systems will not be optimal in different periods. In this paper, a new mixed integer non-linear mathematical programming model is presented to design dynamic cellular manufacturing systems and combines several design features including multi-period production planning, alternate routings, system reconfiguration, duplicate machines, lot splitting, workload balance among machines and cells, cell size limits, and material flow between machines. The required capacity for parts production is modeled based on flow shop perspective and the aim of mixed integer model is to find optimal independent cells, the quantity of machine types in each cell, and production quantity of the parts during each period of the time horizon. Since this problem belongs to NPhard class, a three-phase approach is developed to solve the model for practical purposes. Phase 1 finds a feasible solution, phase 2 finds the neighbor solutions, and phase 3 improves a feasible solution. To analyze the computational efficiency, eight test problems with different sizes are considered and the optimal and near-optimal solutions are compared. The efficiency of the algorithm in terms of the objective function values and computational times is shown by the obtained results.Growing Science Ltd. All rights reserved. 8

show abstract

“…Total costs include maintenance costs, outsourcing costs, intracellular material shipping costs, maintenance and overhead costs, car relocation costs. The proposed model addresses CMS workforce management issues such as payroll [7].…”

Section: Introductionmentioning

confidence: 99%

Optimization of manpower allocation by considering customer relationship management criteria and uncertainty conditions in car dealerships

Bathaee

2021

IJIMES

View full text Add to dashboard Cite

Purpose A mathematical mixed integer model was used in this research in order to optimize manpower allocation in car industry. The objective function of proposed model subjected to minimization of the maximum waiting time for customers in service queue and limitations included manpower allocation and time calculation for each service in each center. Methodology: Therefore, mathematical optimization methods were employed in this research. To solve the problem at small dimensions, BARON solver was used through GAMS software. Metaheuristic algorithms were used to solve the large dimensions of problem due to NP-hard nature of allocation problem. However, these algorithms have been designed based on the natural elements; hence, a stochastic procedure is applied to generate initial responses and to improve the process to obtained the final response. Therefore, proper comparisons should be done to make sure of accurate performance of such procedure. To this end, three metaheuristic algorithms of Genetic, Harmony Search and Gray Wolf were used to solve the final problem. Findings: According to the obtained computational results, gray wolf algorithm had the highest performance efficiency compared to other algorithms so it is more practical in solving the real numerical samples. Originality/Value: The objective function of proposed model subjected to minimization of the maximum waiting time for customers in service queue and limitations included manpower allocation and time calculation for each service in each center. We used three metaheuristic algorithms, Genetic, Harmony Search and Gray Wolf, to solve the final problem.

show abstract

Design of Cellular Manufacturing Systems Considering Dynamic Production Planning and Worker Assignments

Cited by 4 publications

References 20 publications

Optimization for Hierarchical Production Planning of Industrial Processes

Optimization for Hierarchical Production Planning of Industrial Processes

A three-phase heuristic approach to solve an integrated cell formation and production planning problem

Optimization of manpower allocation by considering customer relationship management criteria and uncertainty conditions in car dealerships

Contact Info

Product

Resources

About