Toplu taşıma sistemlerinin evrimsel algoritmalarla optimizasyonu

This study aims to examine, regulate, and update the land transportation of the Erzurum Metropolitan Municipality (EMM), Turkey using computerized calculation techniques. In line with these targets, some critical information has been obtained for study: the number of buses, the number of expeditions, the number of bus lines, and the number and maps of existing routes belonging to EMM. By using the information that has been obtained, this study aims at outlining specific outputs according to the input parameters, such as determining the optimal routes, the average travel, and the journey time. Once all of these situations were considered, various optimization algorithms were used to get the targeted outputs in response to the determined input parameters. In addition, the study found that the problem involved in modeling the land transport network of the EMM is in line with the so-called “traveling salesman problem,” which is a scenario about optimization often discussed in the literature. This study tried to solve this problem by using the genetic algorithm, the clonal selection algorithm, and the DNA computing algorithm. The location data for each bus stops on the bus lines selected for the study were obtained from the EMM, and the distances between these coordinates were obtained by using Google Maps via a Google API. These distances were stored in a distance matrix file and used as input parameters in the application and then were put through optimization algorithms developed initially on the MATLAB platform. The study’s results show that the algorithms developed for the proposed approaches work efficiently and that the distances for the selected bus lines can be shortened.Bu çalışma, Erzurum Büyükşehir Belediyesi'nin (EBB) Türkiye kara ulaşımını bilgisayarlı hesaplama teknikleri kullanarak incelemeyi, düzenlemeyi ve güncellemeyi amaçlamaktadır. Bu hedefler doğrultusunda, çalışma için bazı önemli bilgiler: otobüs sayısı, sefer sayısı, otobüs hattı sayısı ve EBB’ye ait mevcut güzergâh sayısı ve haritaları elde edilmiştir. Bu çalışma, elde edilen bilgileri kullanarak, optimal rotaların belirlenmesi, ortalama seyahat ve yolculuk süresi gibi girdi parametrelerine göre belirli çıktıların ana hatlarını çizmeyi amaçlamaktadır. Tüm bu durumlar göz önüne alındığında, belirlenen girdi parametrelerine karşılık hedeflenen çıktıları elde etmek için çeşitli optimizasyon algoritmaları kullanılmıştır. Çalışma, EBB’ nin ulaşım ağının modellenmesindeki problemin, literatürde sıklıkla tartışılan optimizasyonla ilgili bir senaryo olan “gezgin satıcı problemi” ile uyumlu olduğunu bulmuştur. Çalışmada genetik algoritma, klonal seçim algoritması ve DNA hesaplama algoritması kullanılarak bu problem çözülmeye çalışılmıştır. Çalışmada seçilen otobüs hatlarındaki her bir durak için konum bilgisi EBB'den alınmış ve bu koordinatlar arasındaki mesafeler bir Google API üzerinden Google Maps kullanılarak elde edilmiştir. Bu mesafeler bir mesafe matrisi dosyasında saklanmış ve uygulamada giriş parametreleri olarak kullanılmış daha sonra MATLAB platformunda geliştirilen optimizasyon algoritmalarına aktarılmıştır. Çalışmanın sonuçları, önerilen yaklaşımlar için geliştirilen algoritmaların verimli çalıştığını ve seçilen otobüs hatları için mesafelerin kısaltılabileceğini göstermektedir

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selection of appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer ReviewedPostprint (published version

Applications of network optimization

Includes bibliographical references (p. 41-48).Ravindra K. Ahuja ... [et al.]

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem.Peer Reviewe

A statistical learning based approach for parameter fine-tuning of metaheuristics

Metaheuristics are approximation methods used to solve combinatorial optimization problems. Their performance usually depends on a set of parameters that need to be adjusted. The selectionof appropriate parameter values causes a loss of efficiency, as it requires time, and advanced analytical and problem-specific skills. This paper provides an overview of the principal approaches to tackle the Parameter Setting Problem, focusing on the statistical procedures employed so far by the scientific community. In addition, a novel methodology is proposed, which is tested using an already existing algorithm for solving the Multi-Depot Vehicle Routing Problem

Solutions to decision-making problems in management engineering using molecular computational algorithms and experimentations

制度:新 ; 報告番号:甲3368号 ; 学位の種類:博士(工学) ; 授与年月日:2011/5/23 ; 早大学位記番号:新568

Supply chain complexity and risk mitigation – A hybrid optimization–simulation model

AbstractWith food safety a growing concern in agriculture, the structure and management of agricultural supply chains has become a significant policy issue. In turn, agricultural supply chains are often analytically complex, characterized by feedback and time sensitive, often random parameters. Modern commodity chains such as wheat handling in Canada are no exception. Recently, the Canadian government classes of wheat, replacing it by a new wheat segregation system that relies on trust and self-declaration of wheat type by individual farmers. To maintain food safety as well as operate cost-effectively in this new trust-based system, wheat handlers may be forced to develop a set of contamination testing strategies to maintain historical wheat quality and consistency. In contrast to much of the extant literature, this research builds a hybrid optimization-simulation model representing the new Canadian wheat supply chain, with the goal of identifying cost efficient varietal testing strategies. After solving for a base scenario, sensitivity analysis is conducted on key variables that influence wheat quality testing strategies. Our results validate the utility of currently employed wheat quality testing strategies in the Canadian supply chain

An ACO model for a non-stationary formulation of the single elevator problem

The Ant Colony Optimization (ACO) metaheuristic is a bio-inspired approach for hard combinatorial optimization problems for stationary and non-stationary environments. In the ACO metaheuristic, a colony of artificial ants cooperate for finding high quality solutions in a reasonable time. An interesting example of a non-stationary combinatorial optimization problem is the Multiple Elevators Problem (MEP) which consists in finding a sequence of movements for each elevator to perform in a building so that to minimize, for instance, the users waiting average time. Events like the arrival of one new user to the elevator queue or the fault of one elevator dynamically produce changes of state in this problem. A subclass of MEP is the the so called Single Elevator Problem (SEP). In this work, we propose the design of an ACO model for the SEP that can be implemented as an Ant Colony System (ACS). Keywords: Ant Colony Optimization, Single Elevator Problem, Non-stationary Problems, Ant Colony System design.Facultad de Informátic