An optimization approach for DAL assignments

2018 ◽  
Vol 90 (2) ◽  
pp. 328-335
Author(s):  
Xianan Li ◽  
Zhong Lu ◽  
Jingyi Wang
Keyword(s):  
Mathematical Model ◽  
Hybrid Algorithm ◽  
Optimization Problem ◽  
System Development ◽  
Optimization Method ◽  
Optimization Approach ◽  
Development Cost ◽  
Content Type ◽  
Aircraft Systems ◽  
Aircraft System

Purpose Development assurance level (DAL) is the measurement of the rigor of development assurance tasks performed to functions or items. The DAL assignment is an important process of aircraft system development that can make the reliability and safety of the system stay at acceptable levels. This paper aims to propose an optimization approach for the DAL assignments to minimize the development cost of aircraft systems. Design/methodology/approach The mathematical model for the DAL assignment optimization has been developed on the basis of the given expressions of objective function and constraints. In addition, a hybrid algorithm model synthesizing the advantages of genetic algorithm (GA) and Tabu search (TS) has been proposed to solve the optimization problem of the DAL assignment. Findings The results of the case study show that the proposed hybrid algorithm is more efficient and effective than the exhaustive method as well as the pure GA. Practical implications The proposed approach can be applied in the development of aircraft systems, and it has great significance in minimizing the development cost as well as keeping the system reliability and safety at an acceptable level. Originality/value The constrained optimization method has been applied in the DAL assignments, the corresponding mathematical model has been built and a hybrid evolutionary algorithm has been proposed to solve the optimization problem.

Chemotherapy scheduling template development using an optimization approach

2019 ◽  
Vol 32 (1) ◽  
pp. 59-70 ◽  
Author(s):  
Yu-Li Huang ◽  
Sarah M. Bach ◽  
Sherry A. Looker
Keyword(s):  
Resource Constraints ◽  
Optimization Method ◽  
Resource Constraint ◽  
Patient Treatment ◽  
Optimization Approach ◽  
Resource Assignment ◽  
Treatment Needs ◽  
Content Type ◽  
Research Areas ◽  
Patient Load

Purpose The purpose of this paper is to develop a chemotherapy scheduling template that accounts for nurse resource availability and patient treatment needs to alleviate the mid-day patient load and provide quality services for patients. Design/methodology/approach Owing to treatment complexity in chemotherapy administration, nurses are required at the beginning, end and during treatment. When nurses are not available to continue treatment, the service is compromised, and the resource constraint is violated, which leads to inevitable delay that risks service quality. Consequently, an optimization method is used to create a scheduling template that minimizes the violation between resource assignment and treatment requirements, while leveling patient load throughout a day. A case study from a typical clinic day is presented to understand current scheduling issues, describe nursing resource constraints, and develop a constraint-based optimization model and leveling algorithm for the final template. Findings The approach is expected to reduce the variation in the system by 24 percent and result in five fewer chemo chairs used during peak hours. Adjusting staffing levels could further reduce resource constraint violations and more savings on chair occupancy. The actual implementation results indicate a 33 percent reduction on resource constraint violations and positive feedback from nursing staff for workload. Research limitations/implications Other delays, including laboratory test, physician visit and treatment assignment, are potential research areas. Originality/value The study demonstrates significant improvement in mid-day patient load and meeting treatment needs using optimization with a unique objective.

ENHANCED INTERACTIVE SATISFYING OPTIMIZATION APPROACH TO MULTIPLE OBJECTIVE OPTIMIZATION WITH PREEMPTIVE PRIORITIES

2006 ◽  
Vol 05 (01) ◽  
pp. 47-63 ◽  
Author(s):  
CHAOFANG HU ◽  
SHAOYUAN LI
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Previous Method ◽  
Optimization Models ◽  
Multiple Objective ◽  
Multiple Objective Optimization ◽  
Optimization Approach ◽  
Fuzzy Relations ◽  
Objective Functions ◽  
Preemptive Priority

This paper proposes an enhanced interactive satisfying optimization method based on goal programming for the multiple objective optimization problem with preemptive priorities. Based on the previous method, the approach presented makes the higher priority achieve the higher satisfying degree. For three fuzzy relations of the objective functions, the corresponding optimization models are proposed. Not only can satisfying results for all the objectives be acquired, but the preemptive priority requirement can also be simultaneously actualized. The balance between optimization and priorities is realized. We demonstrate the power of this proposed method by illustrative examples.

Software warranty cost optimization under imperfect debugging

2019 ◽  
Vol 37 (9/10) ◽  
pp. 1233-1257
Author(s):  
Vibha Verma ◽  
Sameer Anand ◽  
Anu Gupta Aggarwal
Keyword(s):  
Software Development ◽  
Optimization Problem ◽  
Failure Process ◽  
Release Time ◽  
Development Cost ◽  
Data Set ◽  
Imperfect Debugging ◽  
Content Type ◽  
Software Release ◽  
The Impact

Purpose The purpose of this paper is to identify and quantify the key components of the overall cost of software development when warranty coverage is given by a developer. Also, the authors have studied the impact of imperfect debugging on the optimal release time, warranty policy and development cost which signifies that it is important for the developers to control the parameters that cause a sharp increase in cost. Design/methodology/approach An optimization problem is formulated to minimize software development cost by considering imperfect fault removal process, faults generation at a constant rate and an environmental factor to differentiate the operational phase from the testing phase. Another optimization problem under perfect debugging conditions, i.e. without error generation is constructed for comparison. These optimization models are solved in MATLAB, and their solutions provide insights to the degree of impact of imperfect debugging on the optimal policies with respect to software release time and warranty time. Findings A real-life fault data set of Radar System is used to study the impact of various cost factors via sensitivity analysis on release and warranty policy. If firms tend to provide warranty for a longer period of time, then they may have to bear losses due to increased debugging cost with more number of failures occurring during the warrantied time but if the warranty is not provided for sufficient time it may not act as sufficient hedge during field failures. Originality/value Every firm is fighting to remain in the competition and expand market share by offering the latest technology-based products, using innovative marketing strategies. Warranty is one such strategic tool to promote the product among masses and develop a sense of quality in the user’s mind. In this paper, the failures encountered during development and after software release are considered to model the failure process.

scholarly journals Multi-Objective Optimization of Gas Pipeline Networks

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5141
Author(s):  
Andrzej J. Osiadacz ◽  
Niccolo Isoli
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Nonlinear Constrained Optimization ◽  
Scalar Optimization ◽  
Gas Networks ◽  
Pipeline Networks ◽  
Gas Supply

The main goal of this paper is to prove that bi-objective optimization of high-pressure gas networks ensures grater system efficiency than scalar optimization. The proposed algorithm searches for a trade-off between minimization of the running costs of compressors and maximization of gas networks capacity (security of gas supply to customers). The bi-criteria algorithm was developed using a gradient projection method to solve the nonlinear constrained optimization problem, and a hierarchical vector optimization method. To prove the correctness of the algorithm, three existing networks have been solved. A comparison between the scalar optimization and bi-criteria optimization results confirmed the advantages of the bi-criteria optimization approach.

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.

Robust Optimization of an Accelerometer Considering Fabrication Errors

2009 ◽  
Vol 628-629 ◽  
pp. 353-356 ◽  
Author(s):  
Guang Jun Liu ◽  
Tao Jiang ◽  
An Lin Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Sensitivity Analysis ◽  
Robust Optimization ◽  
Optimization Problem ◽  
Design Space ◽  
Optimization Approach ◽  
Objective Functions ◽  
Micro Fabrication ◽  
Fabrication Errors

A robust optimization approach of an accelerometer is presented to minimize the effect of variations from micro fabrication. The sensitivity analysis technology is employed to reduce design space and to find the key parameters that have greatest influence on the accelerometer. And then, the constraint conditions and objective functions for robust optimization and the corresponding mathematical model are presented. The optimization problem is solved by the Multiple-island Genetic Algorithm and the results show that an accelerometer with better performance is obtained.

Robust topology optimization for continuum structures with random loads

2018 ◽  
Vol 35 (2) ◽  
pp. 710-732 ◽  
Author(s):  
Jie Liu ◽  
Guilin Wen ◽  
Qixiang Qing ◽  
Fangyi Li ◽  
Yi Min Xie
Keyword(s):  
Topology Optimization ◽  
Optimization Method ◽  
Computational Time ◽  
Optimization Approach ◽  
Random Load ◽  
Content Type ◽  
Continuum Structures ◽  
Robust Topology Optimization ◽  
Random Loads ◽  
Expansion Technique

Purpose This paper aims to tackle the challenge topic of continuum structural layout in the presence of random loads and to develop an efficient robust method. Design/methodology/approach An innovative robust topology optimization approach for continuum structures with random applied loads is reported. Simultaneous minimization of the expectation and the variance of the structural compliance is performed. Uncertain load vectors are dealt with by using additional uncertain pseudo random load vectors. The sensitivity information of the robust objective function is obtained approximately by using the Taylor expansion technique. The design problem is solved using bi-directional evolutionary structural optimization method with the derived sensitivity numbers. Findings The numerical examples show the significant topological changes of the robust solutions compared with the equivalent deterministic solutions. Originality/value A simple yet efficient robust topology optimization approach for continuum structures with random applied loads is developed. The computational time scales linearly with the number of applied loads with uncertainty, which is very efficient when compared with Monte Carlo-based optimization method.

A recursive implementation of the ALIENOR optimization method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daniele Peri
Keyword(s):  
Design Methodology ◽  
Large Scale ◽  
Optimization Problem ◽  
Design Space ◽  
Optimization Method ◽  
Fine Tuning ◽  
Content Type ◽  
Design Variables ◽  
Scale Optimization ◽  
Similar Paper

PurposeA recursive scheme for the ALIENOR method is proposed as a remedy for the difficulties induced by the method. A progressive focusing on the most promising region, in combination with a variation of the density of the alpha-dense curve, is proposed.Design/methodology/approachALIENOR method is aimed at reducing the space dimensions of an optimization problem by spanning it by using a single alpha-dense curve: the curvilinear abscissa along the curve becomes the only design parameter for any design space. As a counterpart, the transformation of the objective function in the projected space is much more difficult to tackle.FindingsA fine tuning of the procedure has been performed in order to identity the correct balance between the different elements of the procedure. The proposed approach has been tested by using a set of algebraic functions with up to 1,024 design variables, demonstrating the ability of the method in solving large scale optimization problem. Also an industrial application is presented.Originality/valueIn the knowledge of the author there is not a similar paper in the current literature.

A multi-actor multi-objective optimization approach for locating temporary logistics hubs during disaster response

2018 ◽  
Vol 8 (1) ◽  
pp. 2-21 ◽  
Author(s):  
Rajali Maharjan ◽  
Shinya Hanaoka
Keyword(s):  
Mathematical Model ◽  
Disaster Response ◽  
Decision Makers ◽  
New Method ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Multiple Decision Makers ◽  
Relief Distribution

Purpose The purpose of this paper is to develop a mathematical model that determines the location of temporary logistics hubs (TLHs) for disaster response and proposes a new method to determine weights of the objectives in a multi-objective optimization problem. The research is motivated by the importance of TLHs and the complexity that surrounds the determination of their location. Design/methodology/approach A multi-period multi-objective model with multi-sourcing is developed to determine the location of the TLHs. A fuzzy factor rating system (FFRS) under the group decision-making (GDM) condition is then proposed to determine the weights of the objectives when multiple decision makers exist. Findings The interview with decision makers shows the heterogeneity of decision opinions, thus substantiating the importance of GDM. The optimization results provide useful managerial insights for decision makers by considering the trade-off between two non-commensurable objectives. Research limitations/implications In this study, decision makers are considered to be homogeneous, which might not be the case in reality. This study does not consider the stochastic nature of relief demand. Practical implications The outcomes of this study are valuable to decision makers for relief distribution planning. The proposed FFRS approach reveals the importance of involving multiple decision makers to enhance sense of ownership of established TLHs. Originality/value A mathematical model highlighting the importance of multi-sourcing and short operational horizon of TLHs is developed. A new method is proposed and implemented to determine the weights of the objectives. To the best of the authors’ knowledge, the multi-actor and multi-objective aspects of the TLH location problem have not thus far been considered simultaneously for one particular problem in humanitarian logistics.

Two-Level Structural Optimization Method for Multi-Processor Environment

1990 ◽  
Author(s):  
Ching-Kuo Hsiung ◽  
Mohamed E. M. El-Sayed
Keyword(s):  
Structural Optimization ◽  
Lower Bounds ◽  
Optimization Problem ◽  
Optimization Method ◽  
Global Constraints ◽  
Optimization Approach ◽  
Final Solution ◽  
Local Constraints ◽  
Numerical Examples ◽  
Design Variables

Abstract In this paper a two-level structural optimization approach is presented. At the first level, the objective of the optimization problem is to minimize the total weight of the whole structure subject to the global constraints. At the second level, the optimization problem is divided into several subproblems, each subproblem represents a substructure. The objective of each subproblem is to minimize the weight of the assigned substructure subject to its local constraints. To assure that the final solution will not violate the global constraints, the optimum values of the design variables from the first level are used to update the lower bounds on these variables at the second level. Two numerical examples are included to demonstrate the approach and its application in a multi-processor environment.

Sign in / Sign up

Export Citation Format

Share Document