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.

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.

Robust optimization approach to production system with failure in rework and breakdown under uncertainty: evolutionary methods

2015 ◽  
Vol 35 (1) ◽  
pp. 81-93 ◽  
Author(s):  
Masoud Rabbani ◽  
Neda Manavizadeh ◽  
Niloofar Sadat Hosseini Aghozi
Keyword(s):  
Mathematical Model ◽  
Robust Optimization ◽  
Production Planning ◽  
Production System ◽  
Demand Uncertainty ◽  
Optimization Techniques ◽  
Planning Problem ◽  
Optimization Approach ◽  
Content Type ◽  
Evolutionary Methods

Purpose – This paper aims to consider a multi-site production planning problem with failure in rework and breakdown subject to demand uncertainty. Design/methodology/approach – In this new mathematical model, at first, a feasible range for production time is found, and then the model is rewritten considering the demand uncertainty and robust optimization techniques. Here, three evolutionary methods are presented: robust particle swarm optimization, robust genetic algorithm (RGA) and robust simulated annealing with the ability of handling uncertainties. Firstly, the proposed mathematical model is validated by solving a problem in the LINGO environment. Afterwards, to compare and find the efficiency of the proposed evolutionary methods, some large-size test problems are solved. Findings – The results show that the proposed models can prepare a promising approach to fulfill an efficient production planning in multi-site production planning. Results obtained by comparing the three proposed algorithms demonstrate that the presented RGA has better and more efficient solutions. Originality/value – Considering the robust optimization approach to production system with failure in rework and breakdown under uncertainty.

The Research of Auto-Composing Test Paper Technology Based on Genetic Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 1188-1192
Author(s):  
Hai Feng Song ◽  
Wei Wei Yang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Key Words ◽  
Fitness Function ◽  
Test System ◽  
Test Paper ◽  
Improved Genetic Algorithm ◽  
Coding Strategy ◽  
Paper Technology ◽  
Network Test System

In order to meet the demand of test paper in the network test system, the paper establishes test paper mathematical model, uses the integer coding strategy, and applies the improved genetic algorithm to the auto-generating test paper module. The results of the test show that the algorithm can complete the intelligent test paper well, and improve it effectively. Key words: auto-generating test paper; genetic algorithm; fitness function

Improved Genetic Algorithms for Software Testing Cases Generation

2013 ◽  
Vol 380-384 ◽  
pp. 1464-1468
Author(s):  
Shun Kun Yang ◽  
Fu Ping Zeng
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Fitness Function ◽  
Automatic Generation ◽  
Improved Genetic Algorithm ◽  
Dynamic Adjustment ◽  
Genetic Operators ◽  
Adaptive Genetic Algorithms ◽  
Crossover And Mutation ◽  
Testing Coverage

In order to realize the adaptive Genetic Algorithms to balance the contradiction between algorithm convergence rate and algorithm accuracy for automatic generation of software testing cases, improved Genetic Algorithms is proposed for different aspects. Orthogonal method and Equivalence partitioning are employed together to make the initial testing population more effective with more reasonable coverage; Genetic operators of Crossover and Mutation is defined adaptively by the dynamic adjustment according to multi-objective Fitness function, which can guide the testing process more properly and realize the biggest testing coverage to find more defects as far as possible. Finally, the improved Genetic Algorithm are compared and analyzed by testing one benchmark program to verify its feasibility and effectiveness.

scholarly journals Multiobjective Real-Time Scheduling of Tasks in Cloud Manufacturing with Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Gilseung Ahn ◽  
Sun Hur
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Real Time ◽  
Fitness Function ◽  
Cloud Manufacturing ◽  
Genetic Operators ◽  
Real Time Scheduling ◽  
Chromosome Representation ◽  
Time Scheduling ◽  
The Mathematical Model

In cloud manufacturing, customers register customized requirements, and manufacturers provide appropriate services to complete the task. A cloud manufacturing manager establishes manufacturing schedules that determine the service provision time in a real-time manner as the requirements are registered in real time. In addition, customer satisfaction is affected by various measures such as cost, quality, tardiness, and reliability. Thus, multiobjective and real-time scheduling of tasks is important to operate cloud manufacturing effectively. In this paper, we establish a mathematical model to minimize tardiness, cost, quality, and reliability. Additionally, we propose an approach to solve the mathematical model in a real-time manner using a multiobjective genetic algorithm that includes chromosome representation, fitness function, and genetic operators. From the experimental results, we verify whether the proposed approach is effective and efficient.

Sign in / Sign up

Export Citation Format

Share Document