A simulated annealing algorithm for zoning in planning using parallel computing

[Abstract] There is an increasing demand for tools that support land use planning processes, particularly the design of zoning maps, which is one of the most complex tasks in the field. In this task, different land use categories need to be allocated according to multiple criteria. The problem can be formalized in terms of a multiobjective problem. This paper generalizes and complements a previous work on this topic. It presents an algorithm based on a simulated annealing heuristic that optimizes the delimitation of land use categories on a cadastral parcel map according to suitability and compactness criteria. The relative importance of both criteria can be adapted to any particular case. Despite its high computational cost, the use of plot polygons was decided because it is realistic in terms of technical application and land use laws. Due to the computational costs of our proposal, parallel implementations are required, and several approaches for shared memory systems such as multicores are analysed in this paper. Results on a real case study conducted in the Spanish municipality of Guitiriz show that the parallel algorithm based on simulated annealing is a feasible method to design alternative zoning maps. Comparisons with results from experts are reported, and they show a high similarity. Results from our strategy outperform those by experts in terms of suitability and compactness. The parallel version of the code produces good results in terms of speed-up, which is crucial for taking advantage of the architecture of current multicore processors.Ministerio de Educacion y Ciencia; 2013-41129PXunta de Galicia; GRC2014/008Xunta de Galicia; EM2013/04

Research Trends and Outlooks in Assembly Line Balancing Problems

This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works

Spatial optimization for land use allocation: accounting for sustainability concerns

Land-use allocation has long been an important area of research in regional science. Land-use patterns are fundamental to the functions of the biosphere, creating interactions that have substantial impacts on the environment. The spatial arrangement of land uses therefore has implications for activity and travel within a region. Balancing development, economic growth, social interaction, and the protection of the natural environment is at the heart of long-term sustainability. Since land-use patterns are spatially explicit in nature, planning and management necessarily must integrate geographical information system and spatial optimization in meaningful ways if efficiency goals and objectives are to be achieved. This article reviews spatial optimization approaches that have been relied upon to support land-use planning. Characteristics of sustainable land use, particularly compactness, contiguity, and compatibility, are discussed and how spatial optimization techniques have addressed these characteristics are detailed. In particular, objectives and constraints in spatial optimization approaches are examined

Vehicle routing for public transport with adapted simulated annealing

This paper presents an Adapted Simulated Annealing (ASA) algorithm to solve an instance of the vehicle routing problem (VRP): the intercity public transport problem (IPTP). This combinatorial optimization problem was effectively solved by means of a robust method. Its performance was achieved thanks to the incorporation of an auxiliary memory and a novel choice of the neighbours. The model is based on initial random solutions capable of generating appropriate bus routes and frequencies in a large solution space. We have established a search strategy that provides excellent responses at the process level. The intercity line linking Bahía Blanca and Punta Alta was chosen with the intention of evaluating ASA performance. The real traffic-behavior has been represented by means of the simulation software called SUMO. The computational results clearly indicate that the proposed approach constitutes an improvement in the ability to search for high quality solutions that facilitates the convergence.Fil: Rodriguez, D. A.. Universidad Nacional de Salta. Facultad de Ciencias Exactas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahia Blanca. Planta Piloto de Ingeniería Química (I). Grupo Vinculado al Plapiqui - Investigación y Desarrollo en Tecnología Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; ArgentinaFil: Olivera, Ana Carolina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Brignole, Nélida Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahia Blanca. Planta Piloto de Ingeniería Química (I). Grupo Vinculado al Plapiqui - Investigación y Desarrollo en Tecnología Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Laboratorio de Investigación y Desarrollo en Computación Científica; Argentin

Supervised regionalization methods, a survey.

This paper reviews almost four decades of contributions on the subject of supervised regionalization methods. These methods aggregate a set of areas into a predefined number of spatially contiguous regions while optimizing certain aggregation criteria. The authors present a taxonomic scheme that classifies a wide range of regionalization methods into eight groups, based on the strategy applied for satisfying the spatial contiguity constraint. The paper concludes by providing a qualitative comparison of these groups in terms of a set of certain characteristics, and by suggesting future lines of research for extending and improving these methods.regionalization, constrained clustering, analytical regions.

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Copyright © 2014 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 31 January 2014, available online: http://www.tandfonline.com/10.1080/00207543.2013.879618Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed by Ozcan et al. (Balancing parallel two-sided assembly lines, International Journal of Production Research, 48 (16), 4767-4784, 2010). The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles explained and discussed

Balancing of parallel U-shaped assembly lines

Copyright © 2015 Elsevier. This is a PDF file of an unedited manuscript that has been accepted for publication in Computers & Operations Research (doi: 10.1016/j.cor.2015.05.014). As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Please cite this article as: Ibrahim Kucukkoc, David Z. Zhang, Balancing of parallel U-shaped assembly Lines, Computers & Operations Research, http://dx.doi.org/10.1016/j.cor.2015.05.014A new hybrid assembly line design, called Parallel U-shaped Assembly Line system, is introduced and characterised along with numerical examples for the first time. Different from existing studies on U-shaped lines, we combine the advantages of two individual line configurations (namely parallel lines and U-shaped lines) and create an opportunity for assigning tasks to multi-line workstations located in between two adjacent U-shaped lines with the aim of maximising resource utilisation. Utilisation of crossover workstations, in which tasks from opposite areas of a same U-shaped line can be performed, is also one of the main advantages of the U-shaped lines. As in traditional U-shaped line configurations, the newly proposed line configuration also supports the utilisation of crossover workstations. An efficient heuristic algorithm is developed to find well-balanced solutions for the proposed line configurations. Test cases derived from existing studies and modified in accordance with the proposed system in this study are solved using the proposed heuristic algorithm. The comparison of results obtained when the lines are balanced independently and when the lines are balanced together (in parallel to each other) clearly indicates that the parallelisation of U-shaped lines helps decrease the need for workforce significantly.Balikesir UniversityTurkish Council of Higher Educatio

Mathematical model and agent based solution approach for the simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Copyright © 2014 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Production Economics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Production Economics, DOI: 10.1016/j.ijpe.2014.08.010One of the key factors of a successfully implemented mixed-model line system is considering model sequencing problem as well as the line balancing problem. In the literature, there are many studies, which consider these two tightly interrelated problems individually. However, we integrate the model sequencing problem in the line balancing procedure to obtain a more efficient solution for the problem of Simultaneous Balancing and Sequencing of Mixed-Model Parallel Two-Sided Assembly Lines. A mathematical model is developed to present the problem and a novel agent based ant colony optimisation approach is proposed as the solution method. Different agents interact with each other to find a near optimal solution for the problem. Each ant selects a random behaviour from a predefined list of heuristics and builds a solution using this behaviour as a local search rule along with the pheromone value. Different cycle times are allowed for different two-sided lines located in parallel to each other and this yields a complex problem where different production cycles need to be considered to build a feasible solution. The performance of the proposed approach is tested through a set of test cases. Experimental results indicate that considering model sequencing problem with the line balancing problem together helps minimise line length and total number of required workstations. Also, it is found that the proposed approach outperforms other three heuristics tested

Zone design of specific sizes using adaptive additively weighted voronoi diagrams

Territory or zone design processes entail partitioning a geographic space, organized as a set of areal units, into different regions or zones according to a specific set of criteria that are dependent on the application context. In most cases, the aim is to create zones of approximately equal sizes (zones with equal numbers of inhabitants, same average sales, etc.). However, some of the new applications that have emerged, particularly in the context of sustainable development policies, are aimed at defining zones of a predetermined, though not necessarily similar, size. In addition, the zones should be built around a given set of seeds. This type of partitioning has not been sufficiently researched; therefore, there are no known approaches for automated zone delimitation. This study proposes a new method based on a discrete version of the adaptive additively weighted Voronoi diagram that makes it possible to partition a two-dimensional space into zones of specific sizes, taking both the position and the weight of each seed into account. The method consists of repeatedly solving a traditional additively weighted Voronoi diagram, so that each seed?s weight is updated at every iteration. The zones are geographically connected using a metric based on the shortest path. Tests conducted on the extensive farming system of three municipalities in Castile-La Mancha (Spain) have established that the proposed heuristic procedure is valid for solving this type of partitioning problem. Nevertheless, these tests confirmed that the given seed position determines the spatial configuration the method must solve and this may have a great impact on the resulting partition

Spatial Optimization Methods And System For Redistricting Problems

Redistricting is the process of dividing space into districts or zones while optimizing a set of spatial criteria under certain constraints. Example applications of redistricting include political redistricting, school redistricting, business service planning, and city management, among many others. Redistricting is a mission-critical component in operating governments and businesses alike. In research fields, redistricting (or region building) are also widely used, such as climate zoning, traffic zone analysis, and complex network analysis. However, as a combinatorial optimization problem, redistricting optimization remains one of the most difficult research challenges. There are currently few automated redistricting methods that have the optimization capability to produce solutions that meet practical needs. The absence of effective and efficient computational approaches for redistricting makes it extremely time-consuming and difficult for an individual person to consider multiple criteria/constraints and manually create solutions using a trial-and-error approach. To address both the scientific and practical challenges in solving real-world redistricting problems, this research advances the methodology and application of redistricting by developing a new computational spatial optimization method and a system platform that can address a wide range of redistricting problems, in an automated and computation-assisted manner. The research has three main contributions. First, an efficient and effective spatial optimization method is developed for redistricting. The new method is based on a spatially constrained and Tabu-based heuristics, which can optimize multiple criteria under multiple constraints to construct high-quality optimization solutions. The new approach is evaluated with real-world redistricting applications and compared with existing methods. Evaluation results show that the new optimization algorithm is more efficient (being able to allow real-time user interaction), more flexible (considering multiple user-expressed criteria and constraints), and more powerful (in terms of optimization quality) than existing methods. As such, it has the potential to enable general users to perform complex redistricting tasks. Second, a redistricting system, iRedistrict, is developed based on the newly developed spatial optimization method to provide user-friendly visual interface for defining redistricting problems, incorporating domain knowledge, configuring optimization criteria and methodology parameters, and ultimately meeting the needs of real-world applications for tackling complex redistricting tasks. It is particularly useful for users of different skill levels, including researchers, practitioners, and the general public, and thus enables public participation in challenging redistricting tasks that are of immense public interest. Performance evaluations with real-world case studies are carried out. Further computational strategies are developed and implemented to handle large datasets. Third, the newly developed spatial optimization method is extended to solve a different spatial optimization problem, i.e., spatial community structure detection in complex networks, which is to partition networks to discover spatial communities by optimizing an objective function. Moreover, a series of new evaluations are carried out with synthetic datasets. This set of evaluations is different from the previous evaluations with case studies in that, the optimal solution is known with synthetic data and therefore it is possible to evaluate (1) whether the optimization method can discover the true pattern (global optima), and (2) how different data characteristics may affect the performance of the method. Evaluation results reveal that existing non-spatial methods are not robust in detecting spatial community structure, which may produce dramatically different outcomes for the same data with different characteristics, such as different spatial aggregations, sampling rates, or noise levels. The new optimization method with spatial constraints is significantly more stable and consistent. In addition to evaluations with synthetic datasets, a case study is also carried out to detect urban community structure with human movements, to demonstrate the application and effectiveness of the approach