A comparative study of algorithms for solving the multiobjective open-pit mining operational planning problems

This work presents a comparison of results obtained by different methods for the Multiobjective Open-Pit Mining Operational Planning Problem, which consists of dynamically and efficiently allocating a fleet of trucks with the goal of maximizing the production while reducing the number of trucks in operation, subject to a set of constraints defined by a mathematical model. Three algorithms were used to tackle instances of this problem: NSGA-II, SPEA2 and an ILS-based multiobjective optimizer called MILS. An expert system for computational simulation of open pit mines was employed for evaluating solutions generated by the algorithms. These methods were compared in terms of the quality of the solution sets returned, measured in terms of hyper volume and empirical attainment function (EAF). The results are presented and discussed

A Novel Multi-Objective Optimization Approach with Flexible Operation Planning Strategy for Truck Scheduling

Data Availability Statement: Not applicable.The transportation system plays an important role in the open-pit mine. As an effective solution, smart scheduling has been widely investigated to manage transportation operations and increase transportation capabilities. Some existing truck scheduling methods tend to treat the critical parameter (i.e., the moving speed of the truck) as a constant, which is impractical in real-world industrial scenarios. In this paper, a multi-objective optimization (MOO) algorithm is proposed for truck scheduling by considering three objectives, i.e., minimizing the queuing time, maximizing the productivity, and minimizing the financial cost. Specifically, the proposed algorithm is employed to search continuously in the solution space, where the truck moving speed and truck payload are chosen as the operational variables. Moreover, a smart scheduling application integrating the proposed MOO algorithm is developed to assist the user in selecting a suitable scheduling plan. Experimental results demonstrate that our proposed MOO approach is effective in tackling the truck scheduling problem, which could satisfy a wide range of transportation conditions and provide managers with flexible scheduling options.European Union’s Horizon 2020 Research and Innovation Programme under Grant 869529 (Dig_IT)

Otimização do teor de corte e do sequenciamento de minas subterrâneas

Orientador: Priscila Cristina Berbert RampazzoDissertação (mestrado profissional) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação CientíficaResumo: Métodos de lavra subterrânea são aplicados na extração de vários metais e minerais. O planejamento de métodos subterrâneos difere do planejamento de métodos de superfície pelo fato de que não é necessário extrair todas as áreas de produção dentro dos limites econômicos finais para se ter uma sequência factível, ou seja, nos métodos subterrâneos é fisicamente possível que algumas áreas permaneçam não lavradas mesmo estando dentro do limites econômicos finais. O planejamento estratégico é a área central do planejamento de longo prazo de uma mina e visa definir estratégias de escala de produção, métodos de lavra e de beneficiamento mineral, selecionar as áreas que serão lavradas e otimizar a sequência de lavra destas áreas de produção. Para garantir a viabilidade econômica do empreendimento, o planejamento estratégico deve considerar as características-chave dos empreendimentos de mineração, que são: a necessidade de capital intensivo, o longo período de retorno do investimento e o ativo (reserva) limitado. Essas características devem ser consideradas durante o processo de valoração de um empreendimento mineiro, que normalmente é feito através do cálculo do VPL, valor presente líquido. Dentre as principais alavancas do planejamento estratégico, o teor de corte utilizado na seleção dos blocos que serão lavrados e o sequenciamento de mina são os que geram maior número de opções, fazendo com que avaliações de cenários demandem muito tempo e se tornem inviáveis na prática dada a necessidade de respostas rápidas para tomadas de decisão. Neste trabalho, três diferentes modelos matemáticos são propostos para abordar, de forma conjunta, o problema da seleção dos blocos de lavra de uma mina subterrânea e a otimização do sequenciamento destes blocos. Tais modelos consideram o VPL como principal objetivo a ser maximizado e resultam no uso do teor de corte como fator que equilibra as capacidades de produção dos diferentes estágios de um sistema de mineração. A abordagem matemática adapta a modelagem clássica de problemas de sequenciamento considerando os blocos de lavra como tarefas e as atividades de escavação de galerias (desenvolvimento de acessos) e de produção de minério (lavra) como máquinas. Os modelos propostos são testados com base em casos reais, utilizando-se métodos de solução exata e um algoritmo genético. Os resultados computacionais mostram que o algoritmo genético é mais eficiente do que os métodos exatos, sobretudo para instâncias maiores, mais próximas da realidadeAbstract: Underground mining methods are used at the extraction of many metals and minerals. Underground mining planning differs from surface mining planning mainly because, in the first case, it is not necessary to extract all mining blocks within the ultimate economic limits to have a feasible sequence, i.e., it is physically possible to an underground mine to have some areas left \textit{in situ} even if they are inside the ultimate economic limits. Strategic planning is the core area of long-term mining planning and aims to define the scale of production, mining and processing methods, to select areas that will be mined, and to optimize the mining sequence. To guarantee the economic feasibility of a mining asset, strategic planners must also consider the key aspects of mining businesses, which are: capital-intensive requirements, long-term payback, and limited asset (reserves) life. These characteristics must be considered during the valuation process of a mining asset, which is normally conducted through NPV, net present value, calculations. Among the main strategic planning levers, cut-off grades (used at the selection of blocks that will be mined) and the mine sequencing are the ones that generate the greatest number of options. As scheduling multiple scenarios requires a great deal of time, this is infeasible in real situations given the need for quick responses. In this dissertation, three mathematical models are proposed to tackle, at the same time, two problems: the selection of the mining blocks in an underground mine, and the optimization of their sequence. These models consider NPV as the main objective to be maximized and result in using cut-off grades as a factor that balances the main capabilities of a mining system. The mathematical approach adapts classical scheduling models considering mining blocks as jobs; and tunnels excavation (access development) and ore production (mining) activities as machines. The proposed models are tested, with real cases, using exact-solution methods and a genetic algorithm. Results show that the genetic algorithm is more efficient than the exact methods, especially for greater instances that are similar to real problemsMestradoMatematica Aplicada e ComputacionalMestre em Matemática Aplicada e Computaciona

Editorial for the Special Issue on Combinatorial Optimization Problems

First paragraph: In combinatorial optimization, the goal is to find an optimal solution, according to some objective function, from a discrete search space. These problems arise widely in industry and academia and, unfortunately, many of them are NP-hard and no polynomial time algorithm can guarantee their solution to a certified optimality unless. Therefore, in the last decades researchers have investigated the use of stochastic search algorithms to find near optimal solutions to these problems. In particular, great research efforts have been devoted to the development and application of metaheuristic algorithms to solve combinatorial optimization problems

Preliminary hydrogeological modeling and optimal monitoring network design for a contaminated abandoned mine site area: application of developed monitoring network design software

In abandoned mine sites, i.e., mine sites where mining operations have ended, wide spread contaminations are often evident, but the potential sources and pathways of contamination especially through the subsurface, are difficult to identify due to inadequate and sparse geochemical measurements available. Therefore, it is essential to design and implement a planned monitoring net-work to obtain essential information required for establishing the potential contamination source locations, i.e., waste dumps, tailing dams, pits and possible pathways through the subsurface, and to design a remediation strategy for rehabilitation. This study presents an illustrative application of modeling the flow and transport processes and monitoring network design in a study area hydrogeologically resembling an abandoned mine site in Queensland, Australia. In this preliminary study, the contaminant transport process modeled does not incorporate the reactive geochemistry of the contaminants. The transport process is modeled considering a generic conservative contaminant for the illustrative purpose of showing the potential application of an optimal monitoring design methodology. This study aims to design optimal monitoring network to: 1) minimize the contaminant solute mass estimation error; 2) locate the plume boundary; 3) select the monitoring locations with (potentially) high concentrations. A linked simulation optimization based methodology is utilized for optimal monitoring network design. The methodology is applied utilizing a recently developed software package CARE-GWMND, developed at James Cook University for optimal monitoring network design. Given the complexity of the groundwater systems and the sparsity of pollutant concentration observation data from the field, this software is capable of simulating the groundwater flow and solute transport with spatial interpolation of data from a sparse set of available data, and it utilizes the optimization algorithm to determine optimum locations for implementing monitoring wells

Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign

Cross-selling campaigns seek to offer the right products to the set of customers with the goal of maximizing expected profit, while, at the same time, respecting the purchasing constraints set by investors. In this context, a bi-objective version of this NP-Hard problem is approached in this paper, aiming at maximizing both the promotion campaign total profit and the risk-adjusted return, which is estimated with the reward-to-variability ratio known as Sharpe ratio. Given the combinatorial nature of the problem and the large volume of data, heuristic methods are the most common used techniques. A Greedy Randomized Neighborhood Structure is also designed, including the characteristics of a neighborhood exploration strategy together with a Greedy Randomized Constructive technique, which is embedded in a multi-objective local search metaheuristic. The latter combines the power of neighborhood exploration by using a Pareto Local Search with Variable Neighborhood Search. Sets of non-dominated solutions obtained by the proposed method are described and analyzed for a number of problem instances

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

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.The authors wish to thank FCT for the financial support under the doctoral Grant SFRH/BD/71501/2010, as well as the construction company that kindly provided the real-world data. Also, we wish to thank Olaf Mersmann for kindly providing the R code for the SMS-EMOA algorithm

Development and evaluation of models for assessing geochemical pollution sources with multiple reactive chemical species for sustainable use of aquifer systems: source characterization and monitoring network design

Michael designed a groundwater flow and reactive transport optimization model. He applied this model to characterize contaminant sources in Australia's first large scale uranium mine site in the Northern Territory. He identified the contamination sources to the groundwater system in the area. His findings will assist planning actions and steps needed to implement the mitigation strategy of this contaminated aquifer

Application of nature-inspired optimization algorithms to improve the production efficiency of small and medium-sized bakeries

Increasing production efficiency through schedule optimization is one of the most influential topics in operations research that contributes to decision-making process. It is the concept of allocating tasks among available resources within the constraints of any manufacturing facility in order to minimize costs. It is carried out by a model that resembles real-world task distribution with variables and relevant constraints in order to complete a planned production. In addition to a model, an optimizer is required to assist in evaluating and improving the task allocation procedure in order to maximize overall production efficiency. The entire procedure is usually carried out on a computer, where these two distinct segments combine to form a solution framework for production planning and support decision-making in various manufacturing industries. Small and medium-sized bakeries lack access to cutting-edge tools, and most of their production schedules are based on personal experience. This makes a significant difference in production costs when compared to the large bakeries, as evidenced by their market dominance. In this study, a hybrid no-wait flow shop model is proposed to produce a production schedule based on actual data, featuring the constraints of the production environment in small and medium-sized bakeries. Several single-objective and multi-objective nature-inspired optimization algorithms were implemented to find efficient production schedules. While makespan is the most widely used quality criterion of production efficiency because it dominates production costs, high oven idle time in bakeries also wastes energy. Combining these quality criteria allows for additional cost reduction due to energy savings as well as shorter production time. Therefore, to obtain the efficient production plan, makespan and oven idle time were included in the objectives of optimization. To find the optimal production planning for an existing production line, particle swarm optimization, simulated annealing, and the Nawaz-Enscore-Ham algorithms were used. The weighting factor method was used to combine two objectives into a single objective. The classical optimization algorithms were found to be good enough at finding optimal schedules in a reasonable amount of time, reducing makespan by 29 % and oven idle time by 8 % of one of the analyzed production datasets. Nonetheless, the algorithms convergence was found to be poor, with a lower probability of obtaining the best or nearly the best result. In contrast, a modified particle swarm optimization (MPSO) proposed in this study demonstrated significant improvement in convergence with a higher probability of obtaining better results. To obtain trade-offs between two objectives, state-of-the-art multi-objective optimization algorithms, non-dominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm, generalized differential evolution, improved multi-objective particle swarm optimization (OMOPSO) and speed-constrained multi-objective particle swarm optimization (SMPSO) were implemented. Optimization algorithms provided efficient production planning with up to a 12 % reduction in makespan and a 26 % reduction in oven idle time based on data from different production days. The performance comparison revealed a significant difference between these multi-objective optimization algorithms, with NSGA-II performing best and OMOPSO and SMPSO performing worst. Proofing is a key processing stage that contributes to the quality of the final product by developing flavor and fluffiness texture in bread. However, the duration of proofing is uncertain due to the complex interaction of multiple parameters: yeast condition, temperature in the proofing chamber, and chemical composition of flour. Due to the uncertainty of proofing time, a production plan optimized with the shortest makespan can be significantly inefficient. The computational results show that the schedules with the shortest and nearly shortest makespan have a significant (up to 18 %) increase in makespan due to proofing time deviation from expected duration. In this thesis, a method for developing resilient production planning that takes into account uncertain proofing time is proposed, so that even if the deviation in proofing time is extreme, the fluctuation in makespan is minimal. The experimental results with a production dataset revealed a proactive production plan, with only 5 minutes longer than the shortest makespan, but only 21 min fluctuating in makespan due to varying the proofing time from -10 % to +10 % of actual proofing time. This study proposed a common framework for small and medium-sized bakeries to improve their production efficiency in three steps: collecting production data, simulating production planning with the hybrid no-wait flow shop model, and running the optimization algorithm. The study suggests to use MPSO for solving single objective optimization problem and NSGA-II for multi-objective optimization problem. Based on real bakery production data, the results revealed that existing plans were significantly inefficient and could be optimized in a reasonable computational time using a robust optimization algorithm. Implementing such a framework in small and medium-sized bakery manufacturing operations could help to achieve an efficient and resilient production system.Die Steigerung der Produktionseffizienz durch die Optimierung von Arbeitsplänen ist eines der am meisten erforschten Themen im Bereich der Unternehmensplanung, die zur Entscheidungsfindung beiträgt. Es handelt sich dabei um die Aufteilung von Aufgaben auf die verfügbaren Ressourcen innerhalb der Beschränkungen einer Produktionsanlage mit dem Ziel der Kostenminimierung. Diese Optimierung von Arbeitsplänen wird mit Hilfe eines Modells durchgeführt, das die Aufgabenverteilung in der realen Welt mit Variablen und relevanten Einschränkungen nachbildet, um die Produktion zu simulieren. Zusätzlich zu einem Modell sind Optimierungsverfahren erforderlich, die bei der Bewertung und Verbesserung der Aufgabenverteilung helfen, um eine effiziente Gesamtproduktion zu erzielen. Das gesamte Verfahren wird in der Regel auf einem Computer durchgeführt, wobei diese beiden unterschiedlichen Komponenten (Modell und Optimierungsverfahren) zusammen einen Lösungsrahmen für die Produktionsplanung bilden und die Entscheidungsfindung in verschiedenen Fertigungsindustrien unterstützen. Kleine und mittelgroße Bäckereien haben zumeist keinen Zugang zu den modernsten Werkzeugen und die meisten ihrer Produktionspläne beruhen auf persönlichen Erfahrungen. Dies macht einen erheblichen Unterschied bei den Produktionskosten im Vergleich zu den großen Bäckereien aus, was sich in deren Marktdominanz widerspiegelt. In dieser Studie wird ein hybrides No-Wait-Flow-Shop-Modell vorgeschlagen, um einen Produktionsplan auf der Grundlage tatsächlicher Daten zu erstellen, der die Beschränkungen der Produktionsumgebung in kleinen und mittleren Bäckereien berücksichtigt. Mehrere einzel- und mehrzielorientierte, von der Natur inspirierte Optimierungsalgorithmen wurden implementiert, um effiziente Produktionspläne zu berechnen. Die Minimierung der Produktionsdauer ist das am häufigsten verwendete Qualitätskriterium für die Produktionseffizienz, da sie die Produktionskosten dominiert. Jedoch wird in Bäckereien durch hohe Leerlaufzeiten der Öfen Energie verschwendet was wiederum die Produktionskosten erhöht. Die Kombination beider Qualitätskriterien (minimale Produktionskosten, minimale Leerlaufzeiten der Öfen) ermöglicht eine zusätzliche Kostenreduzierung durch Energieeinsparungen und kurze Produktionszeiten. Um einen effizienten Produktionsplan zu erhalten, wurden daher die Minimierung der Produktionsdauer und der Ofenleerlaufzeit in die Optimierungsziele einbezogen. Um optimale Produktionspläne für bestehende Produktionsprozesse von Bäckereien zu ermitteln, wurden folgende Algorithmen untersucht: Particle Swarm Optimization, Simulated Annealing und Nawaz-Enscore-Ham. Die Methode der Gewichtung wurde verwendet, um zwei Ziele zu einem einzigen Ziel zu kombinieren. Die Optimierungsalgorithmen erwiesen sich als gut genug, um in angemessener Zeit optimale Pläne zu berechnen, wobei bei einem untersuchten Datensatz die Produktionsdauer um 29 % und die Leerlaufzeit des Ofens um 8 % reduziert wurde. Allerdings erwies sich die Konvergenz der Algorithmen als unzureichend, da nur mit einer geringen Wahrscheinlichkeit das beste oder nahezu beste Ergebnis berechnet wurde. Im Gegensatz dazu zeigte der in dieser Studie ebenfalls untersuchte modifizierte Particle-swarm-Optimierungsalgorithmus (mPSO) eine deutliche Verbesserung der Konvergenz mit einer höheren Wahrscheinlichkeit, bessere Ergebnisse zu erzielen im Vergleich zu den anderen Algorithmen. Um Kompromisse zwischen zwei Zielen zu erzielen, wurden moderne Algorithmen zur Mehrzieloptimierung implementiert: Non-dominated Sorting Genetic Algorithm (NSGA-II), Strength Pareto Evolutionary Algorithm, Generalized Differential Evolution, Improved Multi-objective Particle Swarm Optimization (OMOPSO), and Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO). Die Optimierungsalgorithmen ermöglichten eine effiziente Produktionsplanung mit einer Verringerung der Produktionsdauer um bis zu 12 % und einer Verringerung der Leerlaufzeit der Öfen um 26 % auf der Grundlage von Daten aus unterschiedlichen Produktionsprozessen. Der Leistungsvergleich zeigte signifikante Unterschiede zwischen diesen Mehrziel-Optimierungsalgorithmen, wobei NSGA-II am besten und OMOPSO und SMPSO am schlechtesten abschnitten. Die Gärung ist ein wichtiger Verarbeitungsschritt, der zur Qualität des Endprodukts beiträgt, indem der Geschmack und die Textur des Brotes positiv beeinflusst werden kann. Die Dauer der Gärung ist jedoch aufgrund der komplexen Interaktion von mehreren Größen abhängig wie der Hefezustand, der Temperatur in der Gärkammer und der chemischen Zusammensetzung des Mehls. Aufgrund der Variabilität der Gärzeit kann jedoch ein Produktionsplan, der auf die kürzeste Produktionszeit optimiert ist, sehr ineffizient sein. Die Berechnungsergebnisse zeigen, dass die Pläne mit der kürzesten und nahezu kürzesten Produktionsdauer eine erhebliche (bis zu 18 %) Erhöhung der Produktionsdauer aufgrund der Abweichung der Gärzeit von der erwarteten Dauer aufweisen. In dieser Arbeit wird eine Methode zur Entwicklung einer robusten Produktionsplanung vorgeschlagen, die Veränderungen in den Gärzeiten berücksichtigt, so dass selbst bei einer extremen Abweichung der Gärzeit die Schwankung der Produktionsdauer minimal ist. Die experimentellen Ergebnisse für einen Produktionsprozess ergaben einen robusten Produktionsplan, der nur 5 Minuten länger ist als die kürzeste Produktionsdauer, aber nur 21 Minuten in der Produktionsdauer schwankt, wenn die Gärzeit von -10 % bis +10 % der ermittelten Gärzeit variiert. In dieser Studie wird ein Vorgehen für kleine und mittlere Bäckereien vorgeschlagen, um ihre Produktionseffizienz in drei Schritten zu verbessern: Erfassung von Produktionsdaten, Simulation von Produktionsplänen mit dem hybrid No-Wait Flow Shop Modell und Ausführung der Optimierung. Für die Einzieloptimierung wird der mPSO-Algorithmus und für die Mehrzieloptimierung NSGA-II-Algorithmus empfohlen. Auf der Grundlage realer Bäckereiproduktionsdaten zeigten die Ergebnisse, dass die in den Bäckereien verwendeten Pläne ineffizient waren und mit Hilfe eines effizienten Optimierungsalgorithmus in einer angemessenen Rechenzeit optimiert werden konnten. Die Umsetzung eines solchen Vorgehens in kleinen und mittelgroßen Bäckereibetrieben trägt dazu bei effiziente und robuste Produktionspläne zu erstellen und somit die Wettbewerbsfähigkeit dieser Bäckereien zu erhöhen