scholarly journals Genetic Algorithm for Scheduling Optimization Considering Heterogeneous Containers: A Real-World Case Study

Axioms ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 27
Author(s):  
Gilberto Rivera ◽  
Luis Cisneros ◽  
Patricia Sánchez-Solís ◽  
Nelson Rangel-Valdez ◽  
Jorge Rodas-Osollo
Keyword(s):  
Genetic Algorithm ◽  
Public Hospital ◽  
Real Data ◽  
Operating Rooms ◽  
Surgery Scheduling ◽  
Scheduling Problems ◽  
Scheduling Optimization ◽  
Local Hospital ◽  
Operational Time

In this paper, we develop and apply a genetic algorithm to solve surgery scheduling cases in a Mexican Public Hospital. Here, one of the most challenging issues is to process containers with heterogeneous capacity. Many scheduling problems do not share this restriction; because of this reason, we developed and implemented a strategy for the processing of heterogeneous containers in the genetic algorithm. The final product was named “genetic algorithm for scheduling optimization” (GAfSO). The results of GAfSO were tested with real data of a local hospital. Said hospital assigns different operational time to the operating rooms throughout the week. Also, the computational complexity of GAfSO is analyzed. Results show that GAfSO can assign the corresponding capacity to the operating rooms while optimizing their use.

scholarly journals A new model for logistic and transportation of fashion goods in the presence of stochastic market demands considering restricted retailers capacity

2019 ◽  
Author(s):  
Aidin Delgoshaei ◽  
Hengameh Norozi ◽  
Abolfazl Mirzazadeh ◽  
Maryam Farhadi ◽  
Golnaz Hooshmand Pakdel ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Case Studies ◽  
Programming Model ◽  
Real Data ◽  
Objective Functions ◽  
Mathematical Programming Model ◽  
Multi Objective ◽  
Scheduling Method ◽  
Stochastic Market

In today’s world, using fashion goods is a vital of human. In this research, we focused on developing a scheduling method for distributing and selling fashion goods in a multi-market/multi-retailer supply chain while the product demands in markets are stochastic. For this purpose, a new multi-objective mathematical programming model is developed where maximizing the profit of selling fashion goods and minimizing delivering time and customer’s dissatisfaction are considered as objective functions. In continue due to the complexity of the problem, a number of metaheuristics are compared and a hybrid of Non-dominated Sorting Genetic Algorithm II (NSGAII) and simulated annealing is selected for solving the case studies. Then, in order to find the best values for input parameters of the algorithm, a Taguchi method is applied. In continue, a number of case studies are selected from literature review and solved by the algorithm. The outcomes are analyzed and it is found that using multi-objective models can find more realistic solutions. Then, the model is applied for a case study with real data from industry and outcomes showed that the proposed algorithm can be successfully applied in practice.

scholarly journals An Improved Compact Genetic Algorithm for Scheduling Problems in a Flexible Flow Shop with a Multi-Queue Buffer

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 302 ◽  
Author(s):  
Zhonghua Han ◽  
Quan Zhang ◽  
Haibo Shi ◽  
Jingyuan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Production Efficiency ◽  
Flow Shop ◽  
Probability Model ◽  
Real Data ◽  
Flow Shop Scheduling ◽  
Dispatching Rules ◽  
Scheduling Problems ◽  
Flexible Flow Shop ◽  
Compact Genetic Algorithm

Flow shop scheduling optimization is one important topic of applying artificial intelligence to modern bus manufacture. The scheduling method is essential for the production efficiency and thus the economic profit. In this paper, we investigate the scheduling problems in a flexible flow shop with setup times. Particularly, the practical constraints of the multi-queue limited buffer are considered in the proposed model. To solve the complex optimization problem, we propose an improved compact genetic algorithm (ICGA) with local dispatching rules. The global optimization adopts the ICGA, and the capability of the algorithm evaluation is improved by mapping the probability model of the compact genetic algorithm to a new one through the probability density function of the Gaussian distribution. In addition, multiple heuristic rules are used to guide the assignment process. Specifically, the rules include max queue buffer capacity remaining (MQBCR) and shortest setup time (SST), which can improve the local dispatching process for the multi-queue limited buffer. We evaluate our method through the real data from a bus manufacture production line. The results show that the proposed ICGA with local dispatching rules and is very efficient and outperforms other existing methods.

Tool requirement and pre-scheduling optimization model of the tool flow system of a digital workshop

2017 ◽  
Vol 231 (5) ◽  
pp. 838-849
Author(s):  
Qi Lei ◽  
Li Zeng ◽  
Yuchuan Song
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Method ◽  
Double Layer ◽  
Heuristic Algorithm ◽  
Optimization Model ◽  
Optimization Problems ◽  
Flow System ◽  
Scheduling Optimization ◽  
Purchase Cost

A new mathematical method and an optimization model are proposed in this study to solve the tool requirement and pre-scheduling optimization problems involved in the tool flow system of digital workshops. This model aims to minimize the system makespan under the constraint of the tool purchase cost. A double-layer genetic algorithm based on the heuristic algorithm is then developed. This algorithm not only combines the advantages but also avoids the weaknesses of the two algorithms. Finally, a case study is conducted to validate the effectiveness and superiority of the proposed algorithm and the tool-machine dual-resource pre-scheduling optimization model.

scholarly journals Modeling and optimization of batch production based on layout and cutting problems under uncertainty

2020 ◽  
Author(s):  
Mohammadhossein Saeedi ◽  
Ramyar Feizi
Keyword(s):  
Genetic Algorithm ◽  
Classical Method ◽  
Scenario Planning ◽  
Neighborhood Search ◽  
Scheduling Problems ◽  
Batch Production ◽  
Modeling And Optimization ◽  
Production Modeling ◽  
Cutting Problems

This paper presents a modeling and optimization of batch production based on layout, cutting and project scheduling problems by considering scenario planning. In order to solve the model, a novel genetic algorithm with an improvement procedure based on variable neighborhood search (VNS) is presented. Initially, the model is solved in small sizes using Lingo software and the combined genetic algorithm; then, the results are compared. Afterwards, the model is solved in large sizes by utilizing the proposed algorithm and simple genetic algorithm. The main findings of this paper show: 1) To prove the validity of the proposed method, a case study has been solved by employing the classical method (employing Lingo 11) and the results were compared to the ones developed by the proposed algorithm. Since the results are the same in both cases, the suggested algorithm is valid and able to achieve optimal and near-optimal solutions. 2) The combined genetic algorithm is more effective in achieving optimal boundaries and closer solutions in all cases compared to classical genetic algorithm. In other words, the main finding of this paper is a combined genetic algorithm to optimize batch production modeling problems, which is more efficient than the methods provided in the literature.

scholarly journals Hybrid Seven-bar Press Mechanism

2018 ◽  
Vol 12 (3) ◽  
pp. 181-187
Author(s):  
M. Erkan Kütük ◽  
L. Canan Dülger
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Design Optimization ◽  
Hybrid System ◽  
Degrees Of Freedom ◽  
Dimensional Synthesis ◽  
Design Variables ◽  
Optimization Study ◽  
Metal Stamping

An optimization study with kinetostatic analysis is performed on hybrid seven-bar press mechanism. This study is based on previous studies performed on planar hybrid seven-bar linkage. Dimensional synthesis is performed, and optimum link lengths for the mechanism are found. Optimization study is performed by using genetic algorithm (GA). Genetic Algorithm Toolbox is used with Optimization Toolbox in MATLAB®. The design variables and the constraints are used during design optimization. The objective function is determined and eight precision points are used. A seven-bar linkage system with two degrees of freedom is chosen as an example. Metal stamping operation with a dwell is taken as the case study. Having completed optimization, the kinetostatic analysis is performed. All forces on the links and the crank torques are calculated on the hybrid system with the optimized link lengths

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