A Hybrid Genetic Algorithm for Production Capacity Allocation Problem with Multiple Regional Demands in Supply Chain

2014 ◽  
Vol 945-949 ◽  
pp. 3107-3111
Author(s):  
Zhen Wang ◽  
Lei Huang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Supply Chain ◽  
Hybrid Genetic Algorithm ◽  
Production Capacity ◽  
Experimental Tests ◽  
Capacity Allocation ◽  
Allocation Problem ◽  
Proposed Model

Concentrating on the supplier with limited production capacity in supply chain, this paper established a mathematical model for production capacity allocation problem with consideration of multiple regional demands. The genetic algorithm is employed as solution mainframe in which a heuristics rule is developed to initiate the population and an elite pool is adopted to store those solutions with outstanding fitness values. The experimental tests show that the proposed model and algorithm are feasible and effective.

scholarly journals A Hybrid Genetic Algorithm for Nurse Scheduling Problem considering the Fatigue Factor

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Atefeh Amindoust ◽  
Milad Asadpour ◽  
Samineh Shirmohammadi
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Hybrid Genetic Algorithm ◽  
Real Case ◽  
Scheduling Problem ◽  
Nurse Scheduling ◽  
Proposed Model ◽  
The World ◽  
New Situation

Nowadays and due to the pandemic of COVID-19, nurses are working under the highest pressure benevolently all over the world. This urgent situation can cause more fatigue for nurses who are responsible for taking care of COVID-19 patients 24 hours a day. Therefore, nurse scheduling should be modified with respect to this new situation. The purpose of the present research is to propose a new mathematical model for Nurse Scheduling Problem (NSP) considering the fatigue factor. To solve the proposed model, a hybrid Genetic Algorithm (GA) has been developed to provide a nurse schedule for all three shifts of a day. To validate the proposed approach, a randomly generated problem has been solved. In addition, to show the applicability of the proposed approach in real situations, the model has been solved for a real case study, a department in one of the hospitals in Esfahan, Iran, where COVID-19 patients are hospitalized. Consequently, a nurse schedule for May has been provided applying the proposed model, and the results approve its superiority in comparison with the manual schedule that is currently used in the department. To the best of our knowledge, it is the first study in which the proposed model takes the fatigue of nurses into account and provides a schedule based on it.

Solving a Two-stage Supply Chain Network Design Problem with Fixed Costs by a Hybrid Genetic Algorithm

2021 ◽  
Author(s):  
Ovidiu Cosma ◽  
Petrică C Pop ◽  
Cosmin Sabo
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Network Design ◽  
Design Problem ◽  
Optimization Problem ◽  
Hybrid Genetic Algorithm ◽  
Network Design Problem ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Fixed Costs

Abstract In this paper we investigate a particular two-stage supply chain network design problem with fixed costs. In order to solve this complex optimization problem, we propose an efficient hybrid algorithm, which was obtained by incorporating a linear programming optimization problem within the framework of a genetic algorithm. In addition, we integrated within our proposed algorithm a powerful local search procedure able to perform a fine tuning of the global search. We evaluate our proposed solution approach on a set of large size instances. The achieved computational results prove the efficiency of our hybrid genetic algorithm in providing high-quality solutions within reasonable running-times and its superiority against other existing methods from the literature.

Supply Chain Design of Manufacturing Processes with Blending Technologies

2017 ◽  
Vol 261 ◽  
pp. 509-515 ◽  
Author(s):  
Ágota Bányainé Tóth ◽  
Béla Illés ◽  
Fabian Schenk
Keyword(s):  
Mathematical Model ◽  
Linear Programming ◽  
Supply Chain ◽  
Literature Review ◽  
Linear Programming Problem ◽  
Integrated Approach ◽  
Integrated Model ◽  
Supply Chain Design ◽  
Manufacturing Processes ◽  
Proposed Model

Blending technologies play an important role in manufacturing. The design and operation of manufacturing processes using blending technologies represent a special range of manufacturing related logistics because the integrated approach of technological and logistic parameters is very significant. This research proposes an integrated model of supply of manufacturing processes using blending technologies. After a careful literature review, this paper introduces a mathematical model to formulate the problem of supply chain design for blending technologies. The integrated model includes the optimal purchasing strategy depending on the characteristics of components to be mixed in the desired proportion and the costs of supply. The integrated model will be described as a linear programming problem. Numerical results with different datasets demonstrate how the proposed model takes technological and logistic aspects into consideration.

Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

2014 ◽  
Vol 951 ◽  
pp. 274-277 ◽  
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Design ◽  
Rbf Neural Network ◽  
Hybrid Genetic Algorithm ◽  
Proposed Model ◽  
Crossover Probability ◽  
Ga Algorithm ◽  
The Hierarchical Structure ◽  
Adaptive Crossover

To improve the optimization design of Radial Basis Function (RBF) neural network, a RBF neural network based on a hybrid Genetic Algorithm (GA) is proposed. First the hierarchical structure and adaptive crossover probability is introduced into the traditional GA algorithm for the improvement, and then the hybrid GA algorithm is used to optimize the structure and parameters of the network. The simulation indicates that the proposed model has a good modeling performance.

Multiobjective Optimization of Hybrid Electrical Vehicle Powertrain Mounting System Using Hybrid Genetic Algorithm

2011 ◽  
Vol 87 ◽  
pp. 30-37 ◽  
Author(s):  
Jian Feng He ◽  
Xiao Xiong Jin
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Multiobjective Optimization ◽  
Energy Distribution ◽  
Reaction Force ◽  
Hybrid Genetic Algorithm ◽  
Electrical Vehicle ◽  
Distribution Matrix ◽  
Vehicle Powertrain ◽  
Hybrid Electrical Vehicle

Powertrain mounting system of a Hybrid Electrical Vehicle (HEV) is analyzed and researched, the expression of energy distribution matrix and that of mounting reaction force are derived, and mathematical model of the system is established in Matlab. Correctness of the model established is tested and verified through model establishing for simulation and calculation in ADAMS. Features of Hybrid Genetic Algorithm (HGA) for multiobjective optimization are analyzed and researched, model for calculation of multiobjective optimization using Hybrid Genetic Algorithm is established, targets for optimization of the system are determined, and optimization is executed based on the mounting stiffness parameters. The result that the system is optimized apparently by Hybrid Genetic Algorithm is revealed through contrast of the energy distribution matrix and mounting reaction force of pre and post-optimization.

An analytic approach to production capacity allocation and supply chain design

2002 ◽  
Vol 78 (2) ◽  
pp. 187-195 ◽  
Author(s):  
Jukka Korpela ◽  
Kalevi Kyläheiko ◽  
Antti Lehmusvaara ◽  
Markku Tuominen
Keyword(s):  
Supply Chain ◽  
Production Capacity ◽  
Capacity Allocation ◽  
Supply Chain Design ◽  
Analytic Approach
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