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Heuristic and Metaheuristic Techniques for Optimization
Published in Michael W. Carter, Camille C. Price, Ghaith Rabadi, Operations Research, 2018
Michael W. Carter, Camille C. Price, Ghaith Rabadi
Metaheuristic algorithms are typically used for problems that are computationally complex and messy to model and solve using structured modeling approaches such as mathematical programming. Hence, they often need to be tailored to the problem at hand. Therefore, it is no surprise that there are not many canned metaheuristic software systems as they need to be customized to specific problems. Nevertheless, the internet is full of codes and binaries in different computer languages that can be utilized in software development, the vast majority of which are freely available for download. COIN-OR (Computational Infrastructure for Operations Research), for example, includes some open source libraries and frameworks for metaheuristic development. Similarly, Google offers a suite of portable software called Google Optimization Tools for solving combinatorial problems. It is almost impossible to list all software sources that pertain to various metaheuristics and the readers are encouraged to refer to the book’s website and conduct their own online search. The website GitHub.com contains open source code for several algorithms of interest.
Benefit maximisation based on aggregated condition indices: drawbacks for selection of pavement treatments
Published in International Journal of Pavement Engineering, 2022
Valentin Donev, Markus Hoffmann, Ronald Blab
Next, the solution quality achieved by the heuristic algorithm IBC is evaluated. Figure 10(a) displays the annual costs and network condition for an annual budget of €0.40 million. As a result of the tight budget, the do-nothing strategy must be selected for 23% of all sections, implying that these sections will not receive any treatments in the entire planning period. Still, the available budget in the last years of the planning period is not fully utilised. An optimisation approach can evaluate all possible combinations of strategies, while IBC evaluates the strategies in a sequence according to a specific order. So, a multi-year BIP (see Figure 3(a)) model is formulated, where each of the 3976 strategies is represented by a binary variable. The problem is solved using the branch-and-cut solver (COIN-OR-CBC) supported by @OpenSolver 2.9.0, an open-source add-in for Microsoft Excel (Forrest and Lougee-Heimer 2005, Mason 2012). Figure 10(b) presents the results, revealing that the budget is better utilised with this solution and only 13% of the network remains without funding (10% less!). In the literature it is argued that the IBC approach produces close to optimal results. In light of the already mentioned difficulties with the interpretation of benefits based on OCI, the finding here suggests that the difference between IBC and true optimisation might be considerable (depending on the situation), measured in terms of reducing the backlog demand (cf. Haas et al. 1985).
A heuristic approach for scheduling activities with ‘OR’-precedence constraints at an underground mine
Published in International Journal of Mining, Reclamation and Environment, 2020
Pierre Nancel-Penard, Nelson Morales, Valentina Rojas, Tomás González
To implement the heuristic approach, we used the UDESS library developed at Delphos Mine Planning Laboratory at Universidad de Chile [24], which implements data structures to store the activities, ‘AND’/‘OR’-precedences and the other constraints. This library also implements the mixed-integer program presented in Section 3. The code of the heuristic approach is written in C++, but the module also provides a wrapper to use it from Python. This code calls the coin-or Clp that is an open-source solver through the coin-or/Cbc library version 2.9 [25] to solve the linear programming relaxation of the mixed-integer program of Section 3. This implementation obtained a feasible schedule with all the activities of the base plan scheduled within 11.84 months, with a run time of 108 seconds on a notebook Intel core i7 2.60 GHz with 8 GB of RAM.
A linear optimization-based method for data privacy in statistical tabular data
Published in Optimization Methods and Software, 2019
Jordi Castro, José A. González
The lexicographic approach based on LO-CTA has been implemented within the FP7-INFRA-2010-262608 ‘Data without Boundaries’ European Union project. This code has been included in the τ-Argus package since version 4.1.0 [13,19] (which can be freely obtained from http://neon.vb.cbs.nl/casc/tau.htm), used by European (and some non-European) statistical agencies. The code may use up to five different solvers, both commercial and open source. In this work we will only provide results with the commercial solvers CPLEX and XPRESS, and the open source solver Clp [14] from the COIN-OR project. All the runs were carried out on a Fujitsu Primergy RX300 server with two 3.33 GHz Intel Xeon X5680 CPUs (each CPU with 12 cores) and 144 GB of RAM, under a GNU/Linux operating system (Suse 11.4), without exploitation of multithreading capabilities.