Optimal Alignments for Designing Urban Transport Systems: Application to Seville

The achievement of some of the Sustainable Development Goals (SDGs) from the recent 2030 Agenda for Sustainable Development has drawn the attention of many countries towards urban transport networks. Mathematical modeling constitutes an analytical tool for the formal description of a transportation system whereby it facilitates the introduction of variables and the definition of objectives to be optimized. One of the stages of the methodology followed in the design of urban transit systems starts with the determination of corridors to optimize the population covered by the system whilst taking into account the mobility patterns of potential users and the time saved when the public network is used instead of private means of transport. Since the capture of users occurs at stations, it seems reasonable to consider an extensive and homogeneous set of candidate sites evaluated according to the parameters considered (such as pedestrian population captured and destination preferences) and to select subsets of stations so that alignments can take place. The application of optimization procedures that decide the sequence of nodes composing the alignment can produce zigzagging corridors, which are less appropriate for the design of a single line. The main aim of this work is to include a new criterion to avoid the zigzag effect when the alignment is about to be determined. For this purpose, a curvature concept for polygonal lines is introduced, and its performance is analyzed when criteria of maximizing coverage and minimizing curvature are combined in the same design algorithm. The results show the application of the mathematical model presented for a real case in the city of Seville in Spain.Ministerio de Economía y Competitividad MTM2015-67706-

Learning Technology in Scheduling Based on the Mixed Graphs

We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus

Pseudorandom number generator influence on the genetic algorithm performance to minimize maritime cargo delivery route length

We consider a problem of minimizing the maritime cargo delivery route length to reduce the delivery cost. In our model, the cost is equivalent to the sum of tour lengths of feeders used for the delivery to cover the route. Formulated as a multiple traveling salesman problem, we solve it with a genetic algorithm. The algorithm performance is dramatically influenced by the stream of pseudorandom numbers used for randomly generating the starting population and accomplishing random mutations. As the number of ports increases from 10 to 80, the route length variation intensifies from 3.5% to 22.5% on average. However, we increase the route length minimization accuracy by re-running the algorithm to solve the same problem until closely the best solution is obtained. The number of reruns is about 3 to 6 for up to 20 ports. For more than 20 ports the required number of algorithm reruns abruptly increases from 28 reruns for 30 ports to about 51 reruns within the range of 40 to 80 ports

A GA based meta-heuristic for capacitated vehicle routing problem with simultaneous pick-up and deliveries

In this study, we focus on the theoretical framework of a decision model for a real world problem. The problem reveals itself as simultaneous distribution of commodities and recollection of empty packages the same size as the initial state with a single depot and a fleet of uniform vehicles with limited capacities. Resembling instances pile a profound literature under the category of "pick-up and delivery problems with backhauls" and "rural postman problem." To solve the arousing NP-hard problem we use genetic algorithm approach. Computational efficiency and a good solution performance are sought. We have studied the wide literature of the vehicle routing problems, classified and briefly introduced the previous asserted algorithms, which provide considerably high quality solutions. We have developed a genetic algorithm based meta-heuristic on a linear IP model proposed by Dethloff (2001) and conducted tests to come up with a robust heusritic producing results with a reasonable quality. The models we studied were mainly taken from the machine scheduling literature and adapted to handle our problem. Our research has revealed that no resembling problem has ever been proposed to be solved using the genetic algorithms approach. Thus, this work is a first in its field. The improvement algorithm is found to be considerably good performing while the random keys method failed to produce reasonable solutions. We have tested our algorithm on two benchmark problems introduced by Min (1989) and Dethloff (2001). The latter is composed of 40 problem instances generated. We have performed parameter tests to tune our algorithm and shown that our algorithm produced the best ever solution for the first problem and considerably good solutions for the second one

Meta-Heuristics for Dynamic Lot Sizing: a review and comparison of solution approaches

Proofs from complexity theory as well as computational experiments indicate that most lot sizing problems are hard to solve. Because these problems are so difficult, various solution techniques have been proposed to solve them. In the past decade, meta-heuristics such as tabu search, genetic algorithms and simulated annealing, have become popular and efficient tools for solving hard combinational optimization problems. We review the various meta-heuristics that have been specifically developed to solve lot sizing problems, discussing their main components such as representation, evaluation neighborhood definition and genetic operators. Further, we briefly review other solution approaches, such as dynamic programming, cutting planes, Dantzig-Wolfe decomposition, Lagrange relaxation and dedicated heuristics. This allows us to compare these techniques. Understanding their respective advantages and disadvantages gives insight into how we can integrate elements from several solution approaches into more powerful hybrid algorithms. Finally, we discuss general guidelines for computational experiments and illustrate these with several examples

Research trends in combinatorial optimization

Acknowledgments This work has been partially funded by the Spanish Ministry of Science, Innovation, and Universities through the project COGDRIVE (DPI2017-86915-C3-3-R). In this context, we would also like to thank the Karlsruhe Institute of Technology. Open access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

Selection and Penalty Strategies for Genetic Algorithms Designed to Solve Spatial Forest Planning Problems

Genetic algorithms (GAs) have demonstrated success in solving spatial forest planning problems. We present an adaptive GA that incorporates population-level statistics to dynamically update penalty functions, a process analogous to strategic oscillation from the tabu search literature. We also explore performance of various selection strategies. The GA identified feasible solutions within 96%, 98%, and 93% of a nonspatial relaxed upper bound calculated for landscapes of 100, 500, and 1000 units, respectively. The problem solved includes forest structure constraints limiting harvest opening sizes and requiring minimally sized patches of mature forest. Results suggest that the dynamic penalty strategy is superior to the more standard static penalty implementation. Results also suggest that tournament selection can be superior to the more standard implementation of proportional selection for smaller problems, but becomes susceptible to premature convergence as problem size increases. It is therefore important to balance selection pressure with appropriate disruption. We conclude that integrating intelligent search strategies into the context of genetic algorithms can yield improvements and should be investigated for future use in spatial planning with ecological goals

Radiation exposure reduction effect model in urban street and its application for decision making with trees

학위논문(박사)--서울대학교 대학원 :환경대학원 협동과정 조경학,2019. 8. 이동근.Urban heat island and climate change have increased urban heat and threatened public health. Most streets are made of impervious surfaces and little vegetation. Also, cars and buildings are anthropogenic heat sources. Consequently, urban planners are considering heat mitigation strategies to prevent further increases in urban heating on streets. Critically, pedestrian thermal exposure depends on several factors in addition to air temperature, in particular the radiant environment in urban street canyons. Several strategies have been studied for reducing radiation exposure of pedestrian. Among them, street tree is a well-known strategy to effectively reduce radiant heat by shade effect. But, decision makers such as urban planners do not know how much radiation is reduced by their decision about reducing radiation exposure. And it is difficult to know which options are the most effective for improving street thermal environment. Therefore, this study aims to develop a decision making support tool that determines the effective radiation exposure reduction plans with trees. The study developed a model to estimate the pedestrian radiant heat load that is suitable for urban street with varying tree and building design. The model simulates shortwave and longwave radiation exchange for each urban element and area-weighted view factors, then finally obtains mean radiant temperature (MRT) of pedestrians on the sidewalk. Using this estimation model, the study examined the variation of MRT depending on the tree design parameters (e.g., tree size and interval). The results showed that as the tree interval decreased, MRT reduction was increased exponentially by small trees, while MRT reduction was increased linearly by large trees. Based on this results, decision maker can identify MRT reduction effect of trees and select most appropriate tree design parameters. A variety of MRT reduction strategies can be applied to one site. To reflect a real world, the study proposed a multi-strategies combination model. To find the effective combination plans consisting of tree, grass, albedo reduction of building walls and sidewalk, the objective functions were set: maximizing MRT reduction and minimizing the cost. The model provide a wide range of alternatives to satisfy these objectives, allowing decision makers to select plan tailored to their preference or site condition. This study seeks to develop useful decision support tool for urban planner by providing quantitative effect of tree and a range of options with cost-effective strategies combination. This will provide insights for sustainable urban planning by designing thermal-friendly streets with tree.도시열섬과 기후변화로 인한 도시의 열 증가는 도시민의 건강을 위협하고 있다. 대부분의 보행로는 식재 등의 투수성 포장이 아닌 불투수성 표면으로 이루어져 있고, 자동차와 건물 등에서 인공열이 발생하기 때문에 열 환경이 좋지 않다. 따라서 계획가들은 보행자의 열 스트레스를 낮추기 위한 열 완화 전략을 고려해야 한다. 특히나 열 스트레스는 공기 온도 이외에 도시 협곡의 복사열 환경에 크게 영향을 받기 때문에, 보행자 복사열 노출을 줄이기 위한 전략이 연구되어야 한다. 지금까지 복사열 노출을 막기 위한 여러 전략이 연구되어 왔는데, 그 중 가로수는 그늘 효과를 통해 보행자 복사열을 줄일 수 있는 효과적인 전략으로 알려져 있다. 그러나, 도시계획가와 같은 의사결정자는 그들의 결정에 의해 얼마나 많은 복사열이 감소되는지 알지 못하며, 가장 효과적으로 복사열을 저감하는데 필요한 가로수의 설계 옵션이 무엇인지 알기가 어렵다. 따라서 본 연구는 가로수를 이용한 보행자 복사열 저감 계획을 지원해주는 의사결정 도구를 개발하는 것을 목표로 한다. 본 연구는 가로수와 건물의 디자인에 따라 변화되는 보행자의 복사열을 추정할 수 있는 모델을 개발했다. 이 모델은 각 도시 요소 및 형태 계수를 통해 단파 및 장파 복사 교환을 모의한 후 보행자의 평균 복사 온도를 계산한다. 이러한 평가 모델을 활용하여, 본 연구에서는 첫 번째로 가로수 설계 변수인 나무 크기와 식재 간격에 따라 달라지는 평균복사온도를 확인하였다. 나무 간격이 감소할수록 작은 나무에서 평균복사온도 감소가 기하 급수적으로 증가하는 것을 밝혀냈다. 의사결정자는 위와 같은 결과를 바탕으로 그들이 선택한 설계 변수가 가질 복사열 저감 효과를 파악할 수 있을 것이다. 실제 보행로에는 가로수 외에 다양한 복사열 저감 전략이 적용될 수 있다. 이러한 현실 문제를 반영하기 위하여, 본 연구에서는 의사결정 지원 모델 중 하나로 다중 전략 조합 모델을 제안했다. 나무, 잔디, 건물 벽 및 보행로의 알베도 감소로 구성된 전략 중 효과적인 구성을 찾기 위해서, 평균복사온도를 최대로 저감하고 비용을 최소로 하는 것을 목적함수로 설정하였다. 이 모델은 두 가지 목적함수를 만족시키는 다양한 옵션을 제공함으로써 의사결정자가 그들의 선호도나 대상지 특성에 맞는 계획안을 선택할 수 있도록 하였다. 본 연구는 가로수 설계 변수에 따른 복사열 저감과 비용-효과적인 전략들의 조합을 제공함으로써 가장 효율적인 가로수 설계가 가능하도록 하는 의사결정 지원도구를 제안한다. 위 도구는 가로수를 이용하여 보행로의 열 환경을 개선함으로써 지속가능한 도시 계획에 대한 통찰력을 제공할 것이다.Introduction .................................................................................................. 1 CHAPTER 1: A multilayer mean radiant temperature model for pedestrians in a street canyon with trees............................................................... 7 Introduction ...................................................................................................7 Methods ....................................................................................................... 11 Results and discussions ............................................................................... 28 Conclusion .................................................................................................. 40 CHAPTER 2: Variations in pedestrian mean radiant temperature based on the spacing and size of street trees ....................................................... 43 Introduction ................................................................................................. 43 Methods ....................................................................................................... 47 Results and discussions ............................................................................... 55 3.4. Conclusion .................................................................................................. 66 CHAPTER 3: Optimal multi-strategies modeling reveals a range of options for reducing pedestrian radiation exposure that integrate four strategies................................................................................................................. 69 Introduction ................................................................................................. 69 Methods ....................................................................................................... 73 Results ......................................................................................................... 85 Discussion ................................................................................................... 90 Conclusions ................................................................................................. 95 Conclusions ................................................................................................. 97 Bibliography ............................................................................................... 98 APPENDICES ..................................................................................................... 117Docto

GIS and ant algorithm for multi-objective siting of emergency facilities

Master'sMASTER OF ENGINEERIN