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.

Modeling and Optimization Using Taguchi Based Grey Relation and Genetic Algorithm: A Comparative Case Study

2018 ◽  
Vol 10 (3) ◽  
pp. 500-504
Author(s):  
C. N. Wani ◽  
C. V. Chavan ◽  
R. R. Shimpi
Keyword(s):  
Genetic Algorithm ◽  
Comparative Case Study ◽  
Modeling And Optimization ◽  
Grey Relation

Case study analysis and genetic algorithm adaptation for job process planning and scheduling in batch production

2014 ◽  
Vol 12 (1/2) ◽  
pp. 52
Author(s):  
Aleš Slak ◽  
Jože Tavčar ◽  
Jože Duhovnik
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Study Analysis ◽  
Batch Production ◽  
Planning And Scheduling ◽  
Case Study Analysis

scholarly journals A Hybrid Genetic Algorithm with a Knowledge-Based Operator for Solving the Job Shop Scheduling Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Hamed Piroozfard ◽  
Kuan Yew Wong ◽  
Adnan Hassan
Keyword(s):  
Genetic Algorithm ◽  
Production Management ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Function Evaluation ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Knowledge Based

Scheduling is considered as an important topic in production management and combinatorial optimization in which it ubiquitously exists in most of the real-world applications. The attempts of finding optimal or near optimal solutions for the job shop scheduling problems are deemed important, because they are characterized as highly complex andNP-hard problems. This paper describes the development of a hybrid genetic algorithm for solving the nonpreemptive job shop scheduling problems with the objective of minimizing makespan. In order to solve the presented problem more effectively, an operation-based representation was used to enable the construction of feasible schedules. In addition, a new knowledge-based operator was designed based on the problem’s characteristics in order to use machines’ idle times to improve the solution quality, and it was developed in the context of function evaluation. A machine based precedence preserving order-based crossover was proposed to generate the offspring. Furthermore, a simulated annealing based neighborhood search technique was used to improve the local exploitation ability of the algorithm and to increase its population diversity. In order to prove the efficiency and effectiveness of the proposed algorithm, numerous benchmarked instances were collected from the Operations Research Library. Computational results of the proposed hybrid genetic algorithm demonstrate its effectiveness.

scholarly journals A Hybrid Genetic Algorithm for Satellite Image Downlink Scheduling Problem

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Bingyu Song ◽  
Feng Yao ◽  
Yuning Chen ◽  
Yingguo Chen ◽  
Yingwu Chen
Keyword(s):  
Genetic Algorithm ◽  
Time Window ◽  
Satellite Image ◽  
Hybrid Genetic Algorithm ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Satellite Mission ◽  
Downlink Scheduling ◽  
The Given

The satellite image downlink scheduling problem (SIDSP) is included in satellite mission planning as an important part. A customer demand is finished only if the corresponding images are eventually downloaded. Due to the growing customer demands and the limited ground resources, SIDSP is an oversubscribed scheduling problem. In this paper, we investigate SIDSP with the case study of China’s commercial remote sensing satellite constellation (SuperView-1) and exploit the serial scheduling scheme for solving it. The idea is first determining a permutation of the downlink requests and then producing a schedule from the given ordered requests. A schedule generation algorithm (SGA) is proposed to assign the downlink time window for each scheduled request according to a given request permutation. A hybrid genetic algorithm (HGA) combined with neighborhood search is proposed to optimize the downlink request permutation with the purpose of maximizing the utility function. Experimental results on six groups of instances with different density demonstrate the effectiveness of the proposed approach.

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.

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