A population-based iterated greedy algorithm for the delimitation and zoning of rural settlements

[Abstract] In this paper we present a Population-Based Iterated Greedy (PBIG) algorithm for delimiting and zoning rural settlements. Each cadastral plots is allocated to a category (traditional–historical, common or none) considering restrictions such as the characteristics of the existing edifications and the building density. Since the problem has multiple solutions, heuristic search algorithms, as PBIG, are a good strategy to solve it. Besides the resolution of the problem according to the requirements of the laws, our work explores also new methods of delimitation. The comparison between both types of solutions can help to improve the current methodology. The algorithm, implemented using the Java programming language and integrated into an open-source GIS software, has been tested in rural settlements with different morphological characteristics, providing adjustable solutions to the specific needs of each rural settlement.Xunta de Galicia; 2010/06Xunta de Galicia; CN2012/323Xunta de Galicia; CN2012/211Xunta de Galicia; 08SIN011291P

Balancing and Sequencing of Mixed Model Assembly Lines

Assembly lines are cost efficient production systems that mass produce identical products. Due to customer demand, manufacturers use mixed model assembly lines to produce customized products that are not identical. To stay efficient, management decisions for the line such as number of workers and assembly task assignment to stations need to be optimized to increase throughput and decrease cost. In each station, the work to be done depends on the exact product configuration, and is not consistent across all products. In this dissertation, a mixed model line balancing integer program (IP) that considers parallel workers, zoning, task assignment, and ergonomic constraints with the objective of minimizing the number of workers is proposed. Upon observing the limitation of the IP, a Constraint Programming (CP) model that is based on CPLEX CP Optimizer is developed to solve larger assembly line balancing problems. Data from an automotive OEM are used to assess the performance of both the MIP and CP models. Using the OEM data, we show that the CP model outperforms the IP model for bigger problems. A sensitivity analysis is done to assess the cost of enforcing some of the constraint on the computation complexity and the amount of violations to these constraints once they are disabled. Results show that some of the constraints are helpful in reducing the computation time. Specifically, the assignment constraints in which decision variables are fixed or bounded result in a smaller search space. Finally, since the line balance for mixed model is based on task duration averages, we propose a mixed model sequencing model that minimize the number of overload situation that might occur due to variability in tasks times by providing an optimal production sequence. We consider the skip-policy to manage overload situations and allow interactions between stations via workers swimming. An IP model formulation is proposed and a GRASP solution heuristic is developed to solve the problem. Data from the literature are used to assess the performance of the developed heuristic and to show the benefit of swimming in reducing work overload situations

Sparse Partitioning Around Medoids

Partitioning Around Medoids (PAM, k-Medoids) is a popular clustering technique to use with arbitrary distance functions or similarities, where each cluster is represented by its most central object, called the medoid or the discrete median. In operations research, this family of problems is also known as facility location problem (FLP). FastPAM recently introduced a speedup for large k to make it applicable for larger problems, but the method still has a runtime quadratic in N. In this chapter, we discuss a sparse and asymmetric variant of this problem, to be used for example on graph data such as road networks. By exploiting sparsity, we can avoid the quadratic runtime and memory requirements, and make this method scalable to even larger problems, as long as we are able to build a small enough graph of sufficient connectivity to perform local optimization. Furthermore, we consider asymmetric cases, where the set of medoids is not identical to the set of points to be covered (or in the interpretation of facility location, where the possible facility locations are not identical to the consumer locations). Because of sparsity, it may be impossible to cover all points with just k medoids for too small k, which would render the problem unsolvable, and this breaks common heuristics for finding a good starting condition. We, hence, consider determining k as a part of the optimization problem and propose to first construct a greedy initial solution with a larger k, then to optimize the problem by alternating between PAM-style "swap" operations where the result is improved by replacing medoids with better alternatives and "remove" operations to reduce the number of k until neither allows further improving the result quality. We demonstrate the usefulness of this method on a problem from electrical engineering, with the input graph derived from cartographic data

Automaattisen varastointi- ja keruujärjestelmän ohjausperiaatteet

Automated storage and retrieval systems (AS/RS) are popular for storing semi-fast moving items in distribution centers. They are costly systems whose design involves many critical decisions, which affect the overall performance of the system. This work is focused on the crane control policies of a double-deep dual-shuttle AS/RS. The goal of the thesis is to find out how operating performance of a crane can be improved with storage location assignment, dwell-point positioning and request sequencing. Alternative control rules were developed based on AS/RS literature and tested against those implemented by the supplier of the system. The comparison was carried out with a discrete-event simulation tool, which was built as a part of the thesis. The system was simulated under two different workload scenarios and rack fill levels. The simulation results indicate that sequencing and cycle formation algorithms can have a significant effect on system throughput in periods of high utilization. The effect was found larger with the lower 70 % fill level. The linear programming sequencing algorithm developed in this thesis was found to reduce the average cycle time by 5.3 % compared to the algorithm used by the supplier. In the on-shift scenario, the optimal dwell point strategy could reduce the average crane response time by 10 % compared to the policy used by the supplier. However, this difference was not noticeable when the average request turnover time was used as a measure.Jakelukeskuksissa yleiset hyllystöhissijärjestelmät ovat kalliita investointeja. Niiden keräilytehoa voidaan osaltaan parantaa tehostamalla varastohissin ohjausmenetelmiä. Tämän diplomityön tutkimuksen kohteena on hyllystöhissijärjestelmä, jossa on tuplasyvät hyllyt sekä hissi, jolla on kahden laatikon kantokapasiteetti. Työn tavoitteena oli selvittää, miten järjestelmän suorituskykyä voidaan parantaa hyllypaikkojen allokoinnin, hissin odotuspaikan valinnan, sekä hissitehtävien sekvensoinnin avulla. Järjestelmätoimittajan ohjausperiaatteiden hyvyyttä arvioitiin vertaamalla niitä kirjallisuuden pohjalta kehitettyihin ohjausmenetelmiin. Ohjausten vertailu tehtiin simulointityökalulla, joka rakennettiin työn aikana. Testiskenaarioissa simuloitiin järjestelmää kahdessa eri kuormitustilanteessa ja kahdella eri täyttöasteella. Simulointitulosten perusteella sekvensointi- ja syklinmuodostusalgoritmeilla huomattiin olevan merkittävä vaikutus tilanteissa, joissa hissillä on tehtäväjonoja. Vaikutus oli suurempi matalammalla 70 % täyttöasteella. Työssä kehitetty sekalukuoptimointiin perustuva sekvensointialgoritmi lyhensi keskimääräistä sykliaikaa 5,3 % toimittajan käyttämään algoritmiin verrattuna. Matalan käyttöasteen skenaariossa optimaalisen odotuspaikan valinta lyhensi hissin liikkumisaikaa seuraavan tehtävään 10 %, mutta ero oli käytännössä olematon, kun mittarina käytettiin keskimääräistä aikaa tehtävän saapumisesta sen suorittamiseen

Multi-level automated sub-zoning of water distribution systems

Water distribution systems (WDS) are complex pipe networks with looped and branching topologies that often comprise of thousands of links and nodes. This work presents a generic framework for improved analysis and management of WDS by partitioning the system into smaller (almost) independent sub-systems with balanced loads and minimal number of interconnections. This paper compares the performance of three classes of unsupervised learning algorithms from graph theory for practical sub-zoning of WDS: (1) Graph clustering – a bottom-up algorithm for clustering n objects with respect to a similarity function, (2) Community structure – a bottom-up algorithm based on network modularity property, which is a measure of the quality of network partition to clusters versus randomly generated graph with respect to the same nodal degree, and (3) Graph partitioning – a flat partitioning algorithm for dividing a network with n nodes into k clusters, such that the total weight of edges crossing between clusters is minimized and the loads of all the clusters are balanced. The algorithms are adapted to WDS to provide a decision support tool for water utilities. The proposed methods are applied and results are demonstrated for a large-scale water distribution system serving heavily populated areas in Singapore