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22nd EPIA Conference on Artificial Intelligence (EPIA)

作者： Mota, Bruno Ramos, Daniel Faria, Pedro Ramos, Carlos GECAD Res Grp Intelligent Engn & Comp Adv Innovat Rua Dr Antonio Bernardino de Almeida 431 P-4200072 Porto Portugal Polytech Porto Rua Dr Antonio Bernardino de Almeida 431 P-4200072 Porto Portugal

ISBN: (纸本)9783031490101;9783031490118

With the remnants of a COVID-19 pandemic still crippling the European economy, and the Russo-Ukrainian war propagating this crisis even further, it has become more than crucial to invest in renewable energy resources to mitigate energy dependencies. As a result, these crises have lowered the competitiveness of European manufacturers when compared to the rest of the world. Nevertheless, machine longevity is also essential to consider in manufacturing environments, since maintenance costs due to poor load management can lead to considerable additional monetary costs in the long term. The premise of the present paper is to propose a production scheduling algorithm that focuses on optimizing the total energy costs and machine longevity in a flexible job shop manufacturing layout. To achieve this, a Genetic Algorithm is employed to shift tasks in order to reduce load during peak demand times, utilize locally generated energy to its potential, minimize single-machine task overload, and consider imposed constraints in the production schedule. To validate the proposed methodology, a case study from the literature that uses real-production data is explored and compared to the present paper's solution. Results show that the proposed methodology was capable of reducing single-machine task overload, that is, improving machine longevity, by 87.8%, while only increasing the energy costs, as a consequence, by 12.8%.

关键词： Energy cost Job shop Machine longevity production scheduling optimization Renewable energy resources

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Study on the Optimized production scheduling of Polymetallic Mines Considering the Vehicle Routing Problem

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IEEE ACCESS 2025年 13卷 76412-76436页

作者： Luo, Jianfu Wu, Taosuo Bai, Hongmei Jie, Hui Hulunbuir Univ Sch Engn Hailar 021008 Hulunbuir Peoples R China Hulunbuir Univ Sch Math & Phys Hailar 021008 Hulunbuir Peoples R China

The reasonable scheduling of production processes in polymetallic mines is crucial for comprehensively utilizing mineral resources and ensuring economic benefits. The simultaneous mining of high- and low-grade ores, scientific ore transportation, and reasonable ore blending are prerequisites for stable polymetallic beneficiation and guaranteed concentrate quality. Therefore, establishing an optimization model for this problem and designing algorithms to solve it have great significance in the theory of optimization algorithms and in production practices for polymetallic mining enterprises. Based on the production processes of polymetallic mines, this paper establishes a two-stage optimization model for mining, ore transportation, and ore blending production scheduling considering the vehicle routing problem in a truck transportation process. An improved imperialist competition algorithm is designed to solve the vehicle routing problem for the second stage model. The computational results show that the proposed algorithm reduces ore transportation costs by no less than 0.83% compared to other algorithms, which translates to an annual cost saving of at least 100,000 yuan for the mining enterprise of this case study. By solving the model, the Pareto front between unit cost and total grade fluctuation in the entire production process, as well as the rules for making decisions on production scheduling schemes, are obtained. The relevant numerical results of the production scheduling scheme corresponding to the "inflection point" on the final Pareto front are also provided.

关键词： Ores production Transportation Costs Vehicle routing Mineral resources Job shop scheduling Planning Process control optimization Polymetallic mine production scheduling optimization vehicle routing problem imperialist competitive algorithm

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Solving LTPSOP in open-pit mines using Gaussian process and human mental search

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OPSEARCH 2024年第3期61卷 1061-1092页

作者： Tolouei, Kamyar Moosavi, Ehsan Gholinejad, Mehran Islamic Azad Univ Dept Petr & Min Engn South Tehran Branch Tehran Iran Islamic Azad Univ Res Ctr Modeling & Optimizat Sci & Engn South Tehran Branch Tehran Iran

In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic mixed-integer programming framework (SMIP) is presented for the long-term production scheduling optimization problem (LTPSOP) in open-pit mines. The objective function maximizes the net present value and minimizes the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. The current study has proposed a hybrid model to solve the LTPSOP with the incorporation of augmented Lagrangian relaxation (ALR) and the human mental search metaheuristic algorithm (HMS). The ALR function is formulated and the HMS is used to update the Lagrange multipliers. Besides, a Gaussian process machine learning method is applied to estimate the grade in a mineral deposit. The results obtained from a supercomputer have indicated that the ALR-HMS method conducted on real case studies generates 18.29% and 31.56% more cumulative net present value;9.75% and 13.98% larger pit size than the conventional method, respectively. Finally, while improving the solvability and optimality gap, the analysis of the outcomes has demonstrated that the proposed model yields a near-optimal solution with a rational time.

关键词： Open-pit mine production scheduling optimization Grade uncertainty Metaheuristic algorithms Machine learning Lagrangian relaxation

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An effective MIP model based on grey wolf optimizer for lot-sizing LTPSOP in open-pit mines under uncertainty

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第17期14卷 1-17页

作者： Tolouei, Kamyar Moosavi, Ehsan Gholinejad, Mehran Islamic Azad Univ South Tehran Branch Dept Petr & Min Engn Tehran Iran Islamic Azad Univ Res Ctr Modeling & Optimizat Sci & Engn South Tehran Branch Tehran Iran

The design and scheduling of open-pit mines are considered as an important and intricate problem in mines planning. The optimal extraction sequence is obtained by the production schedule by maximizing the net present value. The long-term production scheduling optimization problem (LTPSOP) of open-pit mines is a lot-sizing, mixed-integer programming (MIP) problem that should be solved in reasonably limited time due to the operational requirements. The current paper presents a new solution approach capable of finding an optimal solution for the LTPSOP of open-pit mines due to the deterministic assumption and concerning the grade uncertainty. To speed up convergence to optimality for lot-sizing instances, the approach discovers the concept of meta-heuristics, a term typically used to denote an optimization algorithm hybridizing meta-heuristic with augmented Lagrangian relaxation (ALR) methods. The grey wolf optimizer (GWO) algorithm is planned to update the Lagrangian multipliers. The newly proposed model has been compared with the results of the hybrid methods gained from the classic Lagrangian relaxation (LR) and ALR methods integrated with the firefly algorithm, imperialist competitive algorithm, particle swarm optimization, genetic algorithm, the traditional sub-gradient method, and conventional method without using the Lagrangian approach. In terms of cumulative net present value and average ore grade in a 12-year period, the consequences achieved from the case study point out that a solution close to optimal can be presented by the ALR-GWO hybrid strategy in comparison with other methods. Actually, the proposed method allows the solution of extremely large problems in rational time with a high-quality solution.

关键词： Meta-heuristics Grey wolf optimizer algorithm Grade uncertainty Lagrangian relaxation production scheduling optimization Open-pit mine

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A stochastic mixed integer linear programming framework for oil sands mine planning and waste management in the presence of grade uncertainty

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CIM JOURNAL 2022年第1期13卷 16-37页

作者： Mbadozie, O. Ben-Awuah, E. Maremi, A. Laurentian Univ Sudbury ON Canada Laurentian Univ Min Engn Sudbury ON Canada

The primary purpose of oil sands mine planning and waste management is to provide ore from the mine pit to the processing plant while containing the tailings in an efficient manner in-pit. Incorporating waste management in the mine plan is essential to maximize the economic potential of the mineral reserve and minimize waste management costs. However, spatial variability such as grade uncertainty results in ore tonnage variations, which leads to fluctuations in the quantity of ore to be processed and waste to be managed. This paper investigates the application of a stochastic mixed integer linear programming (SMILP) on oil sands mine planning to integrate bitumen grade uncertainty and waste management. Sequential Gaussian simulation is employed to quantitatively model the spatial variability of bitumen grade in the oil sands deposit. Multiple simulated orebody models are used as inputs for the SMILP model to generate optimal mine plans in the presence of grade uncertainty. The results demonstrate that the SMILP schedule generates 14% and 17% improvements in net present value compared to the E-type and ordinary kriging schedules, respectively. These results indicate that the SMILP model is a robust tool for optimizing stochastic integrated oil sands production schedules and waste management. L'objectif principal de la planification des mines de sables bitumineux et de la gestion des d & eacute;chets est de fournir du minerai de la mine & agrave;l'usine de traitement tout en confinant les r & eacute;sidus de mani & egrave;re efficace dans la fosse. L'int & eacute;gration de la gestion des d & eacute;chets dans le plan minier est essentielle pour maximiser le potentiel & eacute;conomique de la r & eacute;serve min & eacute;rale et de minimiser les co & ucirc;ts de gestion des d & eacute;chets. Cependant, la variabilit & eacute;spatiale comme l'incertitude de la teneur se traduit par des variations du tonnage de minerai, ce qui entra & icirc;ne des fluctuations de la qu

关键词： Grade uncertainty Ordinary kriging production scheduling optimization Sequential Gaussian simulation Stochastic programming Uncertainty-based oil sands mine planning Waste management gaussien s & eacute quentiel simulation gestion des d & eacute chets incertitude de teneur krigeage ordinaire optimisation de l'ordonnancement de production programmation stochastique planification des mines de sables bitumineux fond & eacute e sur l'incertitude, simulation gaussienne s & eacute quentielle

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optimization OF PLANNING AND scheduling OF REFINERY PRODUCT BASED ON DOWNSTREAM REQUIREMENTS 11

OPTIMIZATION OF PLANNING AND SCHEDULING OF REFINERY PRODUCT ...

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11th International Pipeline Conference (IPC 2016)

作者： He, Guoxi Liang, Yongtu Fang, Limin Zheng, Qi Sun, Liying China Univ Petr Natl Engn Lab Pipeline Safety Beijing 102249 Peoples R China PetroChina Liaohe Oilfield Co Drilling & Produciton Technol Res Inst Panjin 124010 Peoples R China

ISBN: (纸本)9780791850275

The disconnect between the optimization systems of upstream production and downstream demand poses a legitimate problem for China's refined oil industry in terms of overproduction waste. Established methods only partially model the refinery system and are unable to integrate detailed production plans or meet market demands. Therefore, the research on production scheduling optimization combined with the demand of downstream pipeline network has very real applications that not only reduce the consumption of human/material resources, but also increase economic efficiency. This paper aims to optimize the production scheduling of refined oil transportation based on the demand of downstream product pipelines by analyzing the relationships between crude oil supply, refinery facility capacities and refinery tanks storage. The new model will minimize the refined production surplus therefore minimizing refinery costs and wastage. This is done by implementing models custom designed to optimize the three subsystems of the overall process: oil product blending scheduling optimization, producing and processing equipment scheduling optimization, and mixed crude oil scheduling optimization. We first analyzed the relationship between all the production units from the crude oil to the distributional destinations of oil products. A mathematical model of the refinery production scheduling was then built with minimum total surplus inventory as the objective function. We assumed a known downstream demand and used a step by step model to optimize oil stocks. The oil blending plan, production scheduling, amount of crude oil, and refined oil mixing ratios were all derived from the model using three methods: a nonlinear method called Particle Swarm optimization (PSO), the simplex method and the enumeration method. The evidence laid out in this paper verifies our models functionality and suggests that systems can be significantly optimized by using these methods which can provide solutions fo

关键词： Refined Oil Blending Based on Demand production scheduling optimization Mixed Crude Oil Refining Quality Indicator

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