A multi-objective location-allocation model in mass casualty events response

2018 ◽  
Vol 13 (1) ◽  
pp. 236-274 ◽  
Author(s):  
Mahsa Pouraliakbarimamaghani ◽  
Mohammad Mohammadi ◽  
Abolfazl Mirzazadeh
Keyword(s):  
Genetic Algorithm ◽  
Health Care ◽  
Emergency Operation ◽  
Mass Casualty ◽  
Nsga Ii ◽  
Content Type ◽  
Multi Objective ◽  
Mass Casualty Event ◽  
Personnel Shortages ◽  
Injured Patients

Purpose When designing an optimization model for use in a mass casualty event response, it is common to encounter the heavy and considerable demand of injured patients and inadequate resources and personnel to provide patients with care. The purpose of this study is to create a model that is more practical in the real world. So the concept of “predicting the resource and personnel shortages” has been used in this research. Their model helps to predict the resource and personnel shortages during a mass casualty event. In this paper, to deal with the shortages, some temporary emergency operation centers near the hospitals have been created, and extra patients have been allocated to the operation center nearest to the hospitals with the purpose of improving the performance of the hospitals, reducing congestion in the hospitals and considering the welfare of the applicants. Design/methodology/approach The authors research will focus on where to locate health-care facilities and how to allocate the patients to multiple hospitals to take into view that in some cases of emergency situations, the patients may exceed the resource and personnel capacity of hospitals to provide conventional standards of care. Findings In view of the fact that the problem is high degree of complexity, two multi-objective meta-heuristic algorithms, including non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA), were proposed to solve the model where their performances were compared in terms of four multi-objective metrics including maximum spread index (MSI), spacing (S), number of Pareto solution (NPS) and CPU run-time values. For comparison purpose, paired t-test was used. The results of 15 numerical examples showed that there is no significant difference based on MSI, S and NPS metrics, and NRGA significantly works better than NSGA-II in terms of CPU time, and the technique for the order of preference by similarity to ideal solution results showed that NRGA is a better procedure than NSGA-II. Research limitations/implications The planning horizon and time variable have not been considered in the model, for example, the length of patients’ hospitalization at hospitals. Practical implications Presenting an effective strategy to respond to a mass casualty event (natural and man-made) is the main goal of the authors’ research. Social implications This paper strategy is used in all of the health-care centers, such as hospitals, clinics and emergency centers when dealing with disasters and encountering with the heavy and considerable demands of injured patients and inadequate resources and personnel to provide patients with care. Originality/value This paper attempts to shed light onto the formulation and the solution of a three-objective optimization model. The first part of the objective function attempts to maximize the covered population of injured patients, the second objective minimizes the distance between hospitals and temporary emergency operation centers and the third objective minimizes the distance between the warehouses and temporary centers.

Multi-objective multi-facility green manufacturing closed-loop supply chain under uncertain environment

2019 ◽  
Vol 39 (1) ◽  
pp. 58-76 ◽  
Author(s):  
Behzad Karimi ◽  
Amir Hossein Niknamfar ◽  
Babak Hassan Gavyar ◽  
Majid Barzegar ◽  
Ali Mohtashami
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Closed Loop ◽  
Distribution Centers ◽  
Nsga Ii ◽  
Closed Loop Supply Chain ◽  
Content Type ◽  
Uncertain Environment ◽  
Multi Objective ◽  
Hierarchy Process

Purpose Today’s, supply chain production and distribution of products to improve the customer satisfaction in the shortest possible time by paying the minimum cost, has become the most important challenge in global market. On the other hand, minimizing the total cost of the transportation and distribution is one of the critical items for companies. To handle this challenge, this paper aims to present a multi-objective multi-facility model of green closed-loop supply chain (GCLSC) under uncertain environment. In this model, the proposed GCLSC considers three classes in case of the leading chain and three classes in terms of the recursive chain. The objectives are to maximize the total profit of the GCLSC, satisfaction of demand, the satisfactions of the customers and getting to the proper cost of the consumers, distribution centers and recursive centers. Design/methodology/approach Then, this model is designed by considering several products under several periods regarding the recovery possibility of products. Finally, to evaluate the proposed model, several numerical examples are randomly designed and then solved using non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. Then, they are ranked by TOPSIS along with analytical hierarchy process so-called analytic hierarchy process-technique for order of preference by similarity to ideal solution (AHP-TOPSIS). Findings The results indicated that non-dominated ranked genetic algorithm (NRGA) algorithm outperforms non-dominated sorting genetic algorithm (NSGA-II) algorithm in terms of computation times. However, in other metrics, any significant difference was not seen. At the end, to rank the algorithms, a multi-criterion decision technique was used. The obtained results of this method indicated that NSGA-II had better performance than ones obtained by NRGA. Originality/value This study is motivated by the need of integrating the leading supply chain and retrogressive supply chain. In short, the highlights of the differences of this research with the mentioned studies are as follows: developing multi-objective multi-facility model of fuzzy GCLSC under uncertain environment and integrating the leading supply chain and retrogressive supply chain.

Multi-objective structural optimization of a wind turbine blade using NSGA-II algorithm and FSI

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramazan Özkan ◽  
Mustafa Serdar Genç
Keyword(s):  
Structural Optimization ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Design Parameters ◽  
Aerodynamic Optimization ◽  
Nsga Ii ◽  
Content Type ◽  
Multi Objective ◽  
Optimization Study

Purpose Wind turbines are one of the best candidates to solve the problem of increasing energy demand in the world. The aim of this paper is to apply a multi-objective structural optimization study to a Phase II wind turbine blade produced by the National Renewable Energy Laboratory to obtain a more efficient small-scale wind turbine. Design/methodology/approach To solve this structural optimization problem, a new Non-Dominated Sorting Genetic Algorithm (NSGA-II) was performed. In the optimization study, the objective function was on minimization of mass and cost of the blade, and design parameters were composite material type and spar cap layer number. Design constraints were deformation, strain, stress, natural frequency and failure criteria. ANSYS Composite PrepPost (ACP) module was used to model the composite materials of the blade. Moreover, fluid–structure interaction (FSI) model in ANSYS was used to carry out flow and structural analysis on the blade. Findings As a result, a new original blade was designed using the multi-objective structural optimization study which has been adapted for aerodynamic optimization, the NSGA-II algorithm and FSI. The mass of three selected optimized blades using carbon composite decreased as much as 6.6%, 11.9% and 14.3%, respectively, while their costs increased by 23.1%, 29.9% and 38.3%. This multi-objective structural optimization-based study indicates that the composite configuration of the blade could be altered to reach the desired weight and cost for production. Originality/value ACP module is a novel and advanced composite modeling technique. This study is a novel study to present the NSGA-II algorithm, which has been adapted for aerodynamic optimization, together with the FSI. Unlike other studies, complex composite layup, fiber directions and layer orientations were defined by using the ACP module, and the composite blade analyzed both aerodynamic pressure and structural design using ACP and FSI modules together.

scholarly journals Selection methods and diversity preservation in many-objective evolutionary algorithms

2018 ◽  
Vol 52 (4) ◽  
pp. 502-519 ◽  
Author(s):  
Luis Martí ◽  
Eduardo Segredo ◽  
Nayat Sánchez-Pi ◽  
Emma Hart
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Euclidean Distance ◽  
Selection Methods ◽  
Selection Scheme ◽  
Content Type ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Diversity Preservation ◽  
Promising Line

Purpose One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III and a modified version of the Non-dominated Sorting Genetic Algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.

scholarly journals Analisis Perbandingan Aggregat Of Function (AOF) dengan Non-Dominated Sorting Genetic Algorithm (NSGA-II) dalam Menentukan Optimasi Multi-Objective pada Penjadwalan Mesin Produksi Flow Shop

2018 ◽  
Vol 17 (2) ◽  
pp. 158
Author(s):  
Fifin Sonata ◽  
Dede Prabowo Wiguna
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Total Tardiness ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Trade Off ◽  
Multi Objective

Penjadwalan mesin produksi dalam dunia industri memiliki peranan penting sebagai bentuk pengambilan keputusan. Salah satu jenis sistem penjadwalan mesin produksi adalah sistem penjadwalan mesin produksi tipe flow shop. Dalam penjadwalan flow shop, terdapat sejumlah pekerjaan (job) yang tiap-tiap job memiliki urutan pekerjaan mesin yang sama. Optimasi penjadwalan mesin produksi flow shop berkaitan dengan penyusunan penjadwalan mesin yang mempertimbangkan 2 objek yaitu makespan dan total tardiness. Optimasi kedua permasalahan tersebut merupakan optimasi yang bertolak belakang sehingga diperlukan model yang mengintegrasikan permasalahan tersebut dengan optimasi multi-objective A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimazitaion : NSGA-II. Dalam penelitian ini akan dibandingkan 2 buah metode yaitu Aggregat Of Function (AOF) dengan NSGA-II agar dapat terlihat nilai solusinya. Penyelesaian penjadwalan mesin produksi flow shop dengan algoritma NSGA-II untuk membangun jadwal dengan meminimalkan makespan dan total tardiness.Tujuan yang ingin dicapai adalah mengetahui bahwa model yang dikembangkan akan memberikan solusi penjadwalan mesin produksi flow shop yang efisien berupa solusi pareto optimal yang dapat memberikan sekumpulan solusi alternatif bagi pengambil keputusan dalam membuat penjadwalan mesin produksi yang diharapkan. Solusi pareto optimal yang dihasilkan merupakan solusi optimasi multi-objective yang optimal dengan trade-off terhadap seluruh objek, sehingga seluruh solusi pareto optimal sama baiknya.

scholarly journals Mathematical modeling for optimizing the blood supply chain network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amir Rahimzadeh Dehaghani ◽  
Muhammad Nawaz ◽  
Rohullah Sultanie ◽  
Tawiah Kwatekwei Quartey-Papafio
Keyword(s):  
Supply Chain ◽  
Blood Supply ◽  
Supply Chain Network ◽  
Distribution Centers ◽  
Nsga Ii ◽  
Content Type ◽  
Supply Chain Networks ◽  
Multi Objective ◽  
Blood Supply Chain ◽  
The Cost

PurposeThis research studies a location-allocation problem considering the m/m/m/k queue model in the blood supply chain network. This supply chain includes three levels of suppliers or donors, main blood centers (laboratories for separation, storage and distribution centers) and demand centers (hospitals and private clinics). Moreover, the proposed model is a multi-objective model including minimizing the total cost of the blood supply chain (the cost of unmet demand and inventory spoilage, the cost of transport between collection centers and the main centers of blood), minimizing the waiting time of donors in blood donating mobile centers, and minimizing the establishment of mobile centers in potential places.Design/methodology/approachSince the problem is multi-objective and NP-Hard, the heuristic algorithm NSGA-II is proposed for Pareto solutions and then the estimation of the parameters of the algorithm is described using the design of experiments. According to the review of the previous research, there are a few pieces of research in the blood supply chain in the field of design queue models and there were few works that tried to use these concepts for designing the blood supply chain networks. Also, in former research, the uncertainty in the number of donors, and also the importance of blood donors has not been considered.FindingsA novel mathematical model guided by the theory of linear programming has been proposed that can help health-care administrators in optimizing the blood supply chain networks.Originality/valueBy building upon solid literature and theory, the current study proposes a novel model for improving the supply chain of blood.

Kinematic Multi-Objective Optimization of Circular-Toothed Gerotor Pumps by Genetic Algorithm

2017 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Third Party ◽  
Low Flow ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Multi Objective ◽  
Pareto Curve

A gerotor gear generation algorithm has been developed that evaluates key performance objective functions to be minimized or maximized, and then an optimization algorithm is applied to determine the best design. Because of their popularity, circular-toothed gerotors are the focus of this study, and future work can extend this procedure to other gear forms. Parametric equations defining the circular-toothed gear set have been derived and implemented. Two objective functions were used in this kinematic optimization: maximize the ratio of displacement to pump radius, which is a measure of compactness, and minimize the kinematic flow ripple, which can have a negative effect on system dynamics and could be a major source of noise. Designs were constrained to ensure drivability, so the need for additional synchronization gearing is eliminated. The NSGA-II genetic algorithm was then applied to the gear generation algorithm in modeFRONTIER, a commercial software that integrates multi-objective optimization with third-party engineering software. A clear Pareto front was identified, and a multi-criteria decision-making genetic algorithm was used to select three optimal designs with varying priorities of compactness vs low flow variation. In addition, three pumps used in industry were scaled and evaluated with the gear generation algorithm for comparison. The scaled industry pumps were all close to the Pareto curve, but the optimized designs offer a slight kinematic advantage, which demonstrates the usefulness of the proposed gerotor design method.

Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jianzhong Cui ◽  
Hu Li ◽  
Dong Zhang ◽  
Yawen Xu ◽  
Fangwei Xie
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Characteristics ◽  
Transmission Efficiency ◽  
Outer Radius ◽  
Transmission Model ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Dynamic Transmission Model

Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

Solving Facility Location Problems with a Tol for Rapid Development of Multi-Objective Evolutionary Algorithms (MOEAs)

2007 ◽  
pp. 642-660
Author(s):  
A. L. Medaglia
Keyword(s):  
Genetic Algorithm ◽  
Facility Location ◽  
Rapid Development ◽  
Service Level ◽  
International Markets ◽  
Nsga Ii ◽  
Multi Objective ◽  
Location Models ◽  
Cost Cutting ◽  
Facility Location Models

The low price of coffee in the international markets has forced the Federación Nacional de Cafeteros de Colombia (FNCC) to look for cost-cutting opportunities. An alternative that has been considered is the reduction of the operating infrastructure by closing some of the FNCC-owned depots. This new proposal of the coffee supplier network is supported by (uncapacitated and capacitated) facility location models that minimize operating costs while maximizing service level (coverage). These bi-objective optimization models are solved by means of NSGA II, a multi-objective evolutionary algorithm (MOEA). From a computational perspective, this chapter presents the multi-objective Java Genetic Algorithm (MO-JGA) framework, a new tool for the rapid development of MOEAs built on top of the Java Genetic Algorithm (JGA). We illustrate MO-JGA by implementing NSGA II-based solutions for the bi-objective location models.

Analysis of Emergency Health Care Workforce and Service Readiness for a Mass Casualty Event in the Republic of Ireland

2018 ◽  
Vol 13 (02) ◽  
pp. 243-255 ◽  
Author(s):  
Tener Goodwin Veenema ◽  
Fiona Boland ◽  
Declan Patton ◽  
Tom O’Connor ◽  
Zena Moore ◽  
...  
Keyword(s):  
Health Care ◽  
Emergency Response ◽  
Large Scale ◽  
Mass Casualty ◽  
Republic Of Ireland ◽  
Emergency Responders ◽  
Mass Casualty Event ◽  
Service Readiness ◽  
The Republic ◽  
Professional Disciplines

ABSTRACTObjectiveUltimately, a country’s capacity for a large-scale major emergency response will be directly related to the competence of its health care provider (HCP) workforce and communication between emergency responders and hospitals. The purpose of this study was to assess HCP preparedness and service readiness for a major emergency involving mass casualties (mass casualty event or MCE) in Ireland.MethodsA cross-sectional study using a 53-item survey was administered to a purposive sample of emergency responders and HCPs in the Republic of Ireland. Data collection was achieved using the Qualtrics® Research Suite. Descriptive statistics and appropriate tests of comparison between professional disciplines were conducted using Stata 13.ResultsA total of 385 respondents, registered nurses (43.4%), paramedics (37.9%), medical doctors (10.1%), and administrators/managers (8.6%), participated in the study. In general, a level of knowledge of MCEs and knowledge of clinical response activities and self-assessed clinical competence varied drastically across many aspects of the survey. Knowledge and confidence also varied across professional disciplines (P<0.05) with nurses, in general, reporting the least knowledge and/or confidence.ConclusionsThe results demonstrate that serious deficits exist in HCP knowledge, skills, and self-perceived abilities to participate in a large-scale MCE. Results also suggest a poor knowledge base of existing major emergency response plans. (Disaster Med Public Health Preparedness. 2019;13:243–255)

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