Amina Lamghari

Bio: Amina Lamghari is an academic researcher from McGill University. The author has contributed to research in topics: Metaheuristic & Tabu search. The author has an hindex of 11, co-authored 22 publications receiving 498 citations. Previous affiliations of Amina Lamghari include Université du Québec à Trois-Rivières & Université de Montréal.

A variable neighbourhood descent algorithm for the open-pit mine production scheduling problem with metal uncertainty

Abstract: Uncertainty is an inherent aspect of the open-pit mine production scheduling problem (MPSP); however, little is reported in the literature about solution methods for the stochastic versions of the problem. In this paper, two variants of a variable neighbourhood descent algorithm are proposed for solving the MPSP with metal uncertainty. The proposed methods are tested and compared on actual large-scale instances, and very good solutions, with an average deviation of less than 3% from optimality, are obtained within a few minutes up to a few hours.

A hybrid method based on linear programming and variable neighborhood descent for scheduling production in open-pit mines

Abstract: Production scheduling of open-pit mines is an important problem arising in surface mine planning as it determines the raw materials to be produced yearly over the life of the mine, assesses the value of the mine, and contributes to the sustainable utilization of mineral resources Finding the optimal schedule is a complex task, involving large data sets and multiple constraints This paper introduces a two-phase hybrid solution method The first phase relies on solving a series of linear programming problems to generate an initial solution In the second phase, a variable neighborhood descent procedure is applied to improve the solution Upper bounds provided by CPLEX are used to evaluate the efficiency of the proposed method Its performance is also assessed by comparing it to recent solution methods proposed in the literature and to an alternate method implemented in commercial mine planning software commonly used by professional mine planners The results of these computational experiments indicate the efficiency of the proposed method and its superiority over the other methods It finds excellent solutions (within less than 32 % of optimality on average) for large instances of the problem in a few seconds up to a few minutes It also provides new best-known solutions for benchmark instances from the literature, and it can solve instances recently-published algorithms have found intractable

Global optimization of open pit mining complexes with uncertainty

Abstract: Graphical abstractDisplay Omitted HighlightsA stochastic global optimization framework for open pit mining complexes is proposed.The method simultaneously optimizes production schedules and downstream processes.The modeling is flexible and may be applied to numerous types of mining complexes.Three combinations of metaheuristics are tested.Results from an example show a substantial economic benefit when using this approach. Global optimization for mining complexes aims to generate a production schedule for the various mines and processing streams that maximizes the economic value of the enterprise as a whole. Aside from the large scale of the optimization models, one of the major challenges associated with optimizing mining complexes is related to the blending and non-linear geo-metallurgical interactions in the processing streams as materials are transformed from bulk material to refined products. This work proposes a new two-stage stochastic global optimization model for the production scheduling of open pit mining complexes with uncertainty. Three combinations of metaheuristics, including simulated annealing, particle swarm optimization and differential evolution, are tested to assess the performance of the solver. Experimental results for a copper-gold mining complex demonstrate that the optimizer is capable of generating designs that reduce the risk of not meeting production targets, have 6.6% higher expected net present value than the deterministic-equivalent design and 22.6% higher net present value than an industry-standard deterministic mine planning software.

Optimization-Based Adaptive Large Neighborhood Search for the Production Routing Problem

Abstract: Operational problems arising in the planning of integrated supply chains have been increasingly studied in the past decade. Among these, the production routing problem (PRP) is a difficult problem that aims to jointly optimize production, inventory, distribution, and routing decisions in order to satisfy the dynamic demand of customers and minimize the overall system cost. This paper introduces an optimization-based adaptive large neighborhood search heuristic for the PRP. In this heuristic, binary variables representing setup and routing decisions are handled by an enumeration scheme and upper-level search operators, respectively, and continuous variables associated with production, inventory, and shipment quantities are set by solving a network flow subproblem. Extensive computational experiments have been performed on benchmark instances from the literature. The results show that our algorithm generally outperforms existing heuristics for the PRP and can produce high-quality solutions in short computin...