Automated framework for optimized path-planning for pile foundation drilling machines based on 4D BIM modelling

<p>While the Building Information Modeling (BIM) method allows accurate information modelling and thus more robust predictions, it often needs to be combined with tasks beyond the model or modelling phase, especially if the goal is a model-based construction phase. This study proposes an optimization workflow for the construction of pile foundations, since they are part of a varying range of building and infrastructure projects. Pile foundation drilling is an extensive construction process, which can be optimized significantly by reducing the execution length through an effective drilling path plan and automated data transfer. This was achieved through the combination of optimization algorithms, which were linked to the 3D BIM model and selected the shortest distance between piles using Ant Colony Optimization (ACO) algorithm, based on the Travelling Salesperson Problem (TSP). Subsequently the script created separate security distance-compliant tours for drilling machines, calculated construction times and converted the resulting paths into schedules, which in turn could be updated to the 3D BIM model to generate a 4D animation of the construction process. The developed optimization framework and script were tested with a construction company focused on special foundations based in Germany.</p>

Feature-Based Diversity Optimization for Problem Instance Classification

Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Travelling Salesperson Problem (TSP). In this article, we present a general framework that is able to construct a diverse set of instances which are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances which are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.

OPTIMASI RUTE PENGANTARAN PAKET MENGGUNAKAN METODE GENETIC ALGORITHM (GA)

Meningkatnya jumlah pengguna smartphone pada saat ini, sejalan dengan jumlah pengakses toko online melalui web atau aplikasi. Beberapa kemudahan yang ditawarkan oleh jenis pembelanjaan online menyebabkan perubahan perilaku konsumen dalam berbelanja, dari jenis pembelanjaan konvensional ke online. Hal ini akan meningkatkan jumlah pengantaran dari penyedia jasa pengantaran paket ke konsumen atau pembeli. Permasalahan TSP (Travelling Salesperson Problem) adalah penentuan rute terbaik dari tempat asal ke tempat tujuan dan kembali ke tempat asal. Pada penentuan rute pengantaran suatu paket, tanpa harus mendatangi tempat yang sama lebih dari satu kali, jika hanya 10 tempat mudah untuk diselesaikan. Namun jika lebih dari 30 tempat yang harus didatangi, maka ada banyak kemungkinan rute. Beberapa teknik optimasi dapat digunakan untuk mengatasi masalah tersebut, diantaranya adalah Genetic Algorithm merupakan metode yang mengadaptasi teori evolusi Darwin yaitu genetika dan seleksi. Proses dari metode Genetic Algorithm meliputi inisialisasi populasi awal, crossover, mutasi, perhitungan nilai Fitness, evaluasi, seleksi hingga di peroleh nilai populasi yang baru. Dari implementasi optimasi menggunakan algoritma genetika diperoleh hasil rute dengan akumulasi jarak yang lebih pendek dibandingkan jika menggunakan metode optimasi yang lain.

Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem

The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which metaheuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a cluster-based approach and a node-based approach, have been proposed by Hu and Raidl ( 2008 ) for solving this problem. In this article, local search algorithms and simple evolutionary algorithms based on these approaches are investigated from a theoretical perspective. For local search algorithms, we point out the complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches when initialized on a particular point of the search space, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time. Then we turn our attention to analysing the behaviour of simple evolutionary algorithms that use these approaches. We show that the node-based approach solves the hard instance of the cluster-based approach presented in Corus et al. ( 2016 ) in polynomial time. Furthermore, we prove an exponential lower bound on the optimization time of the node-based approach for a class of Euclidean instances.