Mathematical model formulation and hybrid metaheuristic optimization approach for near-optimal blood assignment in a blood bank system

2019 ◽  
Vol 137 ◽  
pp. 74-99 ◽  
Author(s):  
Absalom E. Ezugwu ◽  
Micheal O. Olusanya ◽  
Prinolan Govender
Keyword(s):  
Mathematical Model ◽  
Blood Bank ◽  
Optimization Approach ◽  
Model Formulation ◽  
Hybrid Metaheuristic ◽  
Metaheuristic Optimization ◽  
Bank System

Infeasibility and Non-Optimality Tests for Solution Space Reduction in Discrete Optimal Design

1992 ◽  
Author(s):  
Leonard P. Pomrehn ◽  
Panos Y. Papalambros
Keyword(s):  
Mathematical Model ◽  
Solution Space ◽  
Global Optimum ◽  
Gear Train ◽  
Discrete Variables ◽  
Multistage Process ◽  
Model Formulation ◽  
Space Reduction ◽  
Nonlinear Design ◽  
The Mathematical Model

Abstract Techniques to be employed for nonlinear design optimization with discrete variables are studied in the context of a particular problem arising from the design of a gear train. The mathematical model formulation was presented in an earlier article. In this sequel, a solution derivation is described, patterned as a multistage process. After certain reformulation and relaxation, a variety of infeasibility and non-optimality tests are performed, greatly reducing the size of the space containing the global optimum. Methods used to investigate the remaining space do not guarantee a global optimum, but could be replaced by more costly methods that do provide such guarantees. A global infimum is generated, bounding any improvements on the best known solution.

scholarly journals Population Based Metaheuristic Algorithm Approach for Analysis of Multi-Item Multi-Period Procurement Lot Sizing Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Prasanna Kumar ◽  
Mervin Herbert ◽  
Srikanth Rao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Inventory Management ◽  
Lot Sizing ◽  
Population Based ◽  
Optimization Approach ◽  
Time Data ◽  
Significant Difference ◽  
Lot Sizing Problem ◽  
Order Quantities

This research study focuses on the optimization of multi-item multi-period procurement lot sizing problem for inventory management. Mathematical model is developed which considers different practical constraints like storage space and budget. The aim is to find optimum order quantities of the product so that total cost of inventory is minimized. The NP-hard mathematical model is solved by adopting a novel ant colony optimization approach. Due to lack of benchmark method specified in the literature to assess the performance of the above approach, another metaheuristic based program of genetic algorithm is also employed to solve the problem. The parameters of genetic algorithm model are calibrated using Taguchi method of experiments. The performance of both algorithms is compared using ANOVA analysis with the real time data collected from a valve manufacturing company. It is verified that two methods have not shown any significant difference as far as objective function value is considered. But genetic algorithm is far better than the ACO method when compared on the basis of CPU execution time.

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.

Sign in / Sign up

Export Citation Format

Share Document