Production schedules that provide optimal operating strategies while meeting practical,technical,and environmental constraints are an inseparable part of mining operations.Relying only on manual planning methods or co...Production schedules that provide optimal operating strategies while meeting practical,technical,and environmental constraints are an inseparable part of mining operations.Relying only on manual planning methods or computer software based on heuristic algorithms will lead to mine schedules that are not the optimal global solution.Mathematical mine planning models have been proved to be very effective in supporting decisions on sequencing the extraction of material in mines.The objective of this paper is to develop a practical optimization framework for caving operations’production scheduling.To overcome the size problem of mathematical programming models and to generate a robust practical near-optimal schedule,a multi-step method for long-term production scheduling of block caving is presented.A mixed-integer linear programming(MILP)formulation is used for each step.The formulations are developed,implemented,and verifed in the TOMLAB/CPLEX environment.The production scheduler aims to maximize the net present value of the mining operation while the mine planner has control over defned constraints.Application and comparison of the models for production scheduling using 298 drawpoints over 15 periods are presented.展开更多
In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to ...In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to optimize the SCC production scheduling(SCCPS) problem. Based on the CE method, a matrix encoding scheme was proposed and a backward decoding method was used to generate a reasonable schedule. To describe the distribution of the solution space, a probability distribution model was built and used to generate individuals. In addition, the probability updating mechanism of the probability distribution model was proposed which helps to find the optimal individual gradually. Because of the poor stability and premature convergence of the standard cross entropy(SCE) algorithm, the improved cross entropy(ICE) algorithm was proposed with the following improvements: individual generation mechanism combined with heuristic rules, retention mechanism of the optimal individual, local search mechanism and dynamic parameters of the algorithm. Simulation experiments validate that the CE method is effective in solving the SCCPS problem with complicated technological routes and the ICE algorithm proposed has superior performance to the SCE algorithm and the genetic algorithm(GA).展开更多
One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term produ...One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit mines.Deterministic and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this problem.Within the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling problem.Although the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational costs.In the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade uncertainty.It is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the convergence.Moreover,the Lagrangian coefficients are updated by using PSO and BA.The presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian approach.The results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid solution.Hence,it is more effectual than the conventional method.Furthermore,the time and cost of computation are diminished by the proposed hybrid strategies.The CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.展开更多
Optimization of long-term mine production scheduling in open pit mines deals with the management of cash flows, typically in the order of hundreds of millions of dollars. Conventional mine scheduling utilizes optimiza...Optimization of long-term mine production scheduling in open pit mines deals with the management of cash flows, typically in the order of hundreds of millions of dollars. Conventional mine scheduling utilizes optimization methods that are not capable of accounting for inherent technical uncertainties such as uncertainty in the expected ore/metal supply from the underground, acknowledged to be the most critical factor. To integrate ore/metal uncertainty into the optimization of mine production scheduling a stochastic integer programming(SIP) formulation is tested at a copper deposit. The stochastic solution maximizes the economic value of a project and minimizes deviations from production targets in the presence of ore/metal uncertainty. Unlike the conventional approach, the SIP model accounts and manages risk in ore supply, leading to a mine production schedule with a 29% higher net present value than the schedule obtained from the conventional, industry-standard optimization approach, thus contributing to improving the management and sustainable utilization of mineral resources.展开更多
Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it pos...Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it poses become an NP-hard problem.This problem has major practical significance because the effectiveness of the schedules obtained has strong economical impact for any mining project.Despite of the rapid theoretical and technical advances in this field,heuristics is still the only viable approach for large scale industrial applications.This work presents an approach combining genetic algorithms(GAs) and Lagrangian relaxation(LR) to optimally determine the CLTPSP of open pit mines.GAs are stochastic,parallel search algorithms based on the natural selection and the process of evolution.LR method is known for handling large-scale separable problems; however,the convergence to the optimal solution can be slow.The proposed Lagrangian relaxation and genetic algorithms(LR-GAs) combines genetic algorithms into Lagrangian relaxation method to update the Lagrangian multipliers.This approach leads to improve the performance of Lagrangian relaxation method in solving CLTPSP.Numerical results demonstrate that the LR method using GAs to improve its performance speeding up the convergence.Subsequently,highly near-optimal solution to the CLTPSP can be achieved by the LR-GAs.展开更多
Commodity prices have fallen sharply due to the global financial crisis. This has adversely affected the viability of some mining projects, including leading to the possibility of bankruptcy for some companies. These ...Commodity prices have fallen sharply due to the global financial crisis. This has adversely affected the viability of some mining projects, including leading to the possibility of bankruptcy for some companies. These price falls reflect uncertainties and risks associated with mining projects. In recent years, much work has been published related to the application of real options pricing theory to value life-of-mine plans in response to long term financial uncertainty and risk. However, there are uncertainties and risks associated with medium/short-term mining operations. Real options theory can also be applied to tactical decisions involving uncertainties and risks. This paper will investigate the application of real options in the mining industry and present a methodology developed at University of Queensland, Australia, for integrating real options into medium/short-term mine planning and production scheduling. A case study will demonstrate the validity and usefulness of the methodology and techniques developed.展开更多
In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil fl...In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wells in a given oil reservoir,subject to a number of constraints such as minimum up/down time limits and well grouping.The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost.Due to the NP-hardness of the problem,an improved particle swarm optimization(PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately.Computational experiments on randomly generated instances were carried out to evaluate the performance of the model and the algorithm's effectiveness.Compared with the commercial solver CPLEX,the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.展开更多
The aim of this paper is to compare block-structured linear programming （LP） models against other practical optimization methods for solving downstream product refinery problems using a solution method different fro...The aim of this paper is to compare block-structured linear programming （LP） models against other practical optimization methods for solving downstream product refinery problems using a solution method different from the existing ones （like mixed integer linear programming （MILP） method）. The work X-rays the Nigerian petroleum refining industries and their channel of distribution in the local setting and identifies the critical features of scheduling and allocation of refined crude products; either for distribution within the country or for exportation to the international market. Applying our model to the distribution model, the computational results reveal a better route with lowest transportation cost for the scheduling problem and the best optimal blend with higher revenue for the production problem.展开更多
Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of ...Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity, dynamicity, distributivity, and compatibility. The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors, the distribution of various resources to achieve rational organization, scheduling and management of production activities. This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system. Using the hybrid modeling method, the resources and functions of production system are encapsulated, and the agent-based production system model is established. A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper.展开更多
A matrix encoding scheme for the steelmaking continuous casting( SCC) production scheduling( SCCPS) problem and the corresponding decoding method are proposed. Based on it,a cross entropy( CE) method is adopted and an...A matrix encoding scheme for the steelmaking continuous casting( SCC) production scheduling( SCCPS) problem and the corresponding decoding method are proposed. Based on it,a cross entropy( CE) method is adopted and an improved cross entropy( ICE) algorithm is proposed to solve the SCCPS problem to minimize total power consumption. To describe the distribution of the solution space of the CE method,a probability model is built and used to generate individuals by sampling and a probability updating mechanism is introduced to trace the promising samples. For the ICE algorithm,some samples are generated by the heuristic rules for the shortest makespan due to the relation between the makespan and the total power consumption,which can reduce the search space greatly. The optimal sample in each iteration is retained through a retention mechanism to ensure that the historical optimal sample is not lost so as to improve the efficiency and global convergence. A local search procedure is carried out on a part of better samples so as to improve the local exploitation capability of the ICE algorithm and get a better result. The parameter setting is investigated by the Taguchi method of design-of-experiment. A number of simulation experiments are implemented to validate the effectiveness of the ICE algorithm in solving the SCCPS problem and also the superiority of the ICE algorithm is verified through the comparison with the standard cross entropy( SCE) algorithm.展开更多
In this study,an optimization model of a single machine system integrating imperfect preventive maintenance planning and production scheduling based on game theory is proposed.The costs of the production department an...In this study,an optimization model of a single machine system integrating imperfect preventive maintenance planning and production scheduling based on game theory is proposed.The costs of the production department and the maintenance department are minimized,respectively.Two kinds of three-stage dynamic game models and a backward induction method are proposed to determine the preventive maintenance(PM)threshold.A lemma is presented to obtain the exact solution.A comprehensive numerical study is provided to illustrate the proposed maintenance model.The effectiveness is also validated by comparison with other two existed optimization models.展开更多
Based on the concept of operation flexibility, we study the relationship among multiple operation sequences and provide a flexibility measure for operation sequences. A criterion is proposed to prioritize each operati...Based on the concept of operation flexibility, we study the relationship among multiple operation sequences and provide a flexibility measure for operation sequences. A criterion is proposed to prioritize each operation (rather than sequence). Under the multi-agent architecture the criterion can be used to guide the decision-making procedure during production scheduling so that there is an adequate flexibility at each decision point. Experimental results demonstrate the efficiency of the criterion when it is used as a scheduling heuristic. It can increase flexibility of manufacturing systems, and consequently improve the performance of the systems.展开更多
Ship outfitting is a key process in shipbuilding.Efficient and high-quality ship outfitting is a top priority for modern shipyards.These activities are conducted at different stations of shipyards.The outfitting plan ...Ship outfitting is a key process in shipbuilding.Efficient and high-quality ship outfitting is a top priority for modern shipyards.These activities are conducted at different stations of shipyards.The outfitting plan is one of the crucial issues in shipbuilding.In this paper,production scheduling and material ordering with endogenous uncertainty of the outfitting process are investigated.The uncertain factors in outfitting equipment production are usually decision-related,which leads to difficulties in addressing uncertainties in the outfitting production workshops before production is conducted according to plan.This uncertainty is regarded as endogenous uncertainty and can be treated as non-anticipativity constraints in the model.To address this problem,a stochastic two-stage programming model with endogenous uncertainty is established to optimize the outfitting job scheduling and raw material ordering process.A practical case of the shipyard of China Merchants Heavy Industry Co.,Ltd.is used to evaluate the performance of the proposed method.Satisfactory results are achieved at the lowest expected total cost as the complete kit rate of outfitting equipment is improved and emergency replenishment is reduced.展开更多
In the context of reducing its carbon emissions,the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability.The optimization of production planning ...In the context of reducing its carbon emissions,the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability.The optimization of production planning and scheduling plays a pivotal role in realizing these objectives such as improving production efficiency,saving energy,reducing carbon emissions,and enhancing quality.However,current practices in steel enterprises are largely dependent on experience-driven manual decision approaches supported by information systems,which are inadequate to meet the complex requirements of the industry.This study explores the current situation in production planning and scheduling,analyzes the characteristics and limitations of existing methods,and emphasizes the necessity and trends of intelligent systems.It surveys the current literature on production planning and scheduling in steel enterprises and analyzes the theoretical advancements and practical challenges associated with combinatorial and sequential optimization in this field.A key focus is on the limitations of current models and algorithms in effectively addressing the multi-objective and multiconstraint characteristics of steel produc-tion.To overcome these challenges,a novel framework for intelligent production planning and scheduling is proposed.This framework leverages data-and knowledge-driven decision-making and scenario adaptability,enabling the system to respond dynamically to real-time production conditions and market fluctuations.By integrating artificial intelligence and advanced optimization methodologies,the proposed framework improves the efficiency,cost-effectiveness,and environmental sustainability of steel manufacturing.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
The optimal scheduling of multi-product batch process is studied and a new mathematics model targeting the maximum profit is proposed, which can be solved by the modified genetic algorithm (MGA) with mixed coding (seq...The optimal scheduling of multi-product batch process is studied and a new mathematics model targeting the maximum profit is proposed, which can be solved by the modified genetic algorithm (MGA) with mixed coding (sequence coding and decimal coding) developed by us. In which, the partially matched cross over (PMX) and reverse mutation are used for the sequence coding, whereas the arithmetic crossover and heteropic mutation are used for the decimal coding. In addition, the relationship between production scale and production cost is analyzed and the maximum profit is always a trade-off of the production scale and production cost. Two examples are solved to demonstrate the effectiveness of the method.展开更多
Taking the seamless tube plant of Baoshan Iron & Steel Complex in China as the background,we analyze the characters of hot rolling seamless steel tube:multi varieties,low volume,complicated production process,flex...Taking the seamless tube plant of Baoshan Iron & Steel Complex in China as the background,we analyze the characters of hot rolling seamless steel tube:multi varieties,low volume,complicated production process,flexible production routes.Then integrated scheduling problem for hot rolling seamless steel tube production is studied,which covers two key points;order-grouping problem and solution method for flowshop/jobshop scheduling problem.On the basis of these two problems,integrated scheduling decision system is developed.The design idea,function flow sheet,data processing method,and functional module of visualized human-computer interactive scheduling system implemented in seamless steel tube plant of Shanghai Baoshan Iron & Steel Complex are described into detail.Compared with manual system,the performance of system shows the applicability and superiority in several criteria.展开更多
With the application of various information technologies in smart manufacturing,new intelligent production mode puts forward higher demands for real-time and robustness of production scheduling.For the production sche...With the application of various information technologies in smart manufacturing,new intelligent production mode puts forward higher demands for real-time and robustness of production scheduling.For the production scheduling problem in large-scale manufacturing environment,digital twin(DT)places high demand on data processing capability of the terminals.It requires both global prediction and real-time response abilities.In order to solve the above problem,a DT-based edge-cloud collaborative intelligent production scheduling(DTECCS)system was proposed,and the scheduling model and method were introduced.DT-based edge-cloud collaboration(ECC)can predict the production capacity of each workshop,reassemble customer orders,optimize the allocation of global manufacturing resources in the cloud,and carry out distributed scheduling on the edge-side to improve scheduling and tasks processing efficiency.In the production process,the DTECCS system adjusts scheduling strategies in real-time,responding to changes in production conditions and order fluctuations.Finally,simulation results show the effectiveness of DTECCS system.展开更多
An increasing number of novel and highly specialized computer-aided decision-making technologies for short-term production scheduling in oil refineries has emerged and evolved over the past two decades, thereby encour...An increasing number of novel and highly specialized computer-aided decision-making technologies for short-term production scheduling in oil refineries has emerged and evolved over the past two decades, thereby encouraging refiners to permanently rethink the way the refining business is operated and managed. In this report,we discuss the key lessons learned from one of the pioneering, yet daring, enterprise-wide programs entirely implemented in an energy company devoted to developing and implementing an advanced refinery production scheduling(RPS) technology, i.e., the RPS system of Petrobras. Apart from mathematical and information technology issues, the long-term sustainability of a successful RPS project is, we argue, the outcome of a virtuous cycle grounded on permanent actions devoted to improving technical education inside the organization,reinspecting organizational cultures and operational paradigms, and developing working processes.展开更多
This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage ti...This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.展开更多
文摘Production schedules that provide optimal operating strategies while meeting practical,technical,and environmental constraints are an inseparable part of mining operations.Relying only on manual planning methods or computer software based on heuristic algorithms will lead to mine schedules that are not the optimal global solution.Mathematical mine planning models have been proved to be very effective in supporting decisions on sequencing the extraction of material in mines.The objective of this paper is to develop a practical optimization framework for caving operations’production scheduling.To overcome the size problem of mathematical programming models and to generate a robust practical near-optimal schedule,a multi-step method for long-term production scheduling of block caving is presented.A mixed-integer linear programming(MILP)formulation is used for each step.The formulations are developed,implemented,and verifed in the TOMLAB/CPLEX environment.The production scheduler aims to maximize the net present value of the mining operation while the mine planner has control over defned constraints.Application and comparison of the models for production scheduling using 298 drawpoints over 15 periods are presented.
基金Project(ZR2014FM036)supported by Shandong Provincial Natural Science Foundation of ChinaProject(ZR2010FZ001)supported by the Key Program of Shandong Provincial Natural Science Foundation of China
文摘In order to increase productivity and reduce energy consumption of steelmaking-continuous casting(SCC) production process, especially with complicated technological routes, the cross entropy(CE) method was adopted to optimize the SCC production scheduling(SCCPS) problem. Based on the CE method, a matrix encoding scheme was proposed and a backward decoding method was used to generate a reasonable schedule. To describe the distribution of the solution space, a probability distribution model was built and used to generate individuals. In addition, the probability updating mechanism of the probability distribution model was proposed which helps to find the optimal individual gradually. Because of the poor stability and premature convergence of the standard cross entropy(SCE) algorithm, the improved cross entropy(ICE) algorithm was proposed with the following improvements: individual generation mechanism combined with heuristic rules, retention mechanism of the optimal individual, local search mechanism and dynamic parameters of the algorithm. Simulation experiments validate that the CE method is effective in solving the SCCPS problem with complicated technological routes and the ICE algorithm proposed has superior performance to the SCE algorithm and the genetic algorithm(GA).
文摘One of the surface mining methods is open-pit mining,by which a pit is dug to extract ore or waste downwards from the earth’s surface.In the mining industry,one of the most significant difficulties is long-term production scheduling(LTPS)of the open-pit mines.Deterministic and uncertainty-based approaches are identified as the main strategies,which have been widely used to cope with this problem.Within the last few years,many researchers have highly considered a new computational type,which is less costly,i.e.,meta-heuristic methods,so as to solve the mine design and production scheduling problem.Although the optimality of the final solution cannot be guaranteed,they are able to produce sufficiently good solutions with relatively less computational costs.In the present paper,two hybrid models between augmented Lagrangian relaxation(ALR)and a particle swarm optimization(PSO)and ALR and bat algorithm(BA)are suggested so that the LTPS problem is solved under the condition of grade uncertainty.It is suggested to carry out the ALR method on the LTPS problem to improve its performance and accelerate the convergence.Moreover,the Lagrangian coefficients are updated by using PSO and BA.The presented models have been compared with the outcomes of the ALR-genetic algorithm,the ALR-traditional sub-gradient method,and the conventional method without using the Lagrangian approach.The results indicated that the ALR is considered a more efficient approach which can solve a large-scale problem and make a valid solution.Hence,it is more effectual than the conventional method.Furthermore,the time and cost of computation are diminished by the proposed hybrid strategies.The CPU time using the ALR-BA method is about 7.4%higher than the ALR-PSO approach.
基金funded from the National Science and Engineering Research Council of Canada,Collaborative R&D Grant CRDPJ 335696 with BHP Billiton and NSERC Discovery Grant 239019 to R. Dimitrakopoulos
文摘Optimization of long-term mine production scheduling in open pit mines deals with the management of cash flows, typically in the order of hundreds of millions of dollars. Conventional mine scheduling utilizes optimization methods that are not capable of accounting for inherent technical uncertainties such as uncertainty in the expected ore/metal supply from the underground, acknowledged to be the most critical factor. To integrate ore/metal uncertainty into the optimization of mine production scheduling a stochastic integer programming(SIP) formulation is tested at a copper deposit. The stochastic solution maximizes the economic value of a project and minimizes deviations from production targets in the presence of ore/metal uncertainty. Unlike the conventional approach, the SIP model accounts and manages risk in ore supply, leading to a mine production schedule with a 29% higher net present value than the schedule obtained from the conventional, industry-standard optimization approach, thus contributing to improving the management and sustainable utilization of mineral resources.
文摘Constrained long-term production scheduling problem(CLTPSP) of open pit mines has been extensively studied in the past few decades due to its wide application in mining projects and the computational challenges it poses become an NP-hard problem.This problem has major practical significance because the effectiveness of the schedules obtained has strong economical impact for any mining project.Despite of the rapid theoretical and technical advances in this field,heuristics is still the only viable approach for large scale industrial applications.This work presents an approach combining genetic algorithms(GAs) and Lagrangian relaxation(LR) to optimally determine the CLTPSP of open pit mines.GAs are stochastic,parallel search algorithms based on the natural selection and the process of evolution.LR method is known for handling large-scale separable problems; however,the convergence to the optimal solution can be slow.The proposed Lagrangian relaxation and genetic algorithms(LR-GAs) combines genetic algorithms into Lagrangian relaxation method to update the Lagrangian multipliers.This approach leads to improve the performance of Lagrangian relaxation method in solving CLTPSP.Numerical results demonstrate that the LR method using GAs to improve its performance speeding up the convergence.Subsequently,highly near-optimal solution to the CLTPSP can be achieved by the LR-GAs.
文摘Commodity prices have fallen sharply due to the global financial crisis. This has adversely affected the viability of some mining projects, including leading to the possibility of bankruptcy for some companies. These price falls reflect uncertainties and risks associated with mining projects. In recent years, much work has been published related to the application of real options pricing theory to value life-of-mine plans in response to long term financial uncertainty and risk. However, there are uncertainties and risks associated with medium/short-term mining operations. Real options theory can also be applied to tactical decisions involving uncertainties and risks. This paper will investigate the application of real options in the mining industry and present a methodology developed at University of Queensland, Australia, for integrating real options into medium/short-term mine planning and production scheduling. A case study will demonstrate the validity and usefulness of the methodology and techniques developed.
基金Supported by National High Technology Research and Development Program of China(2013AA040704)the Fund for the National Natural Science Foundation of China(61374203)
文摘In this paper,an oil well production scheduling problem for the light load oil well during petroleum field exploitation was studied.The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wells in a given oil reservoir,subject to a number of constraints such as minimum up/down time limits and well grouping.The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost.Due to the NP-hardness of the problem,an improved particle swarm optimization(PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately.Computational experiments on randomly generated instances were carried out to evaluate the performance of the model and the algorithm's effectiveness.Compared with the commercial solver CPLEX,the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.
文摘The aim of this paper is to compare block-structured linear programming （LP） models against other practical optimization methods for solving downstream product refinery problems using a solution method different from the existing ones （like mixed integer linear programming （MILP） method）. The work X-rays the Nigerian petroleum refining industries and their channel of distribution in the local setting and identifies the critical features of scheduling and allocation of refined crude products; either for distribution within the country or for exportation to the international market. Applying our model to the distribution model, the computational results reveal a better route with lowest transportation cost for the scheduling problem and the best optimal blend with higher revenue for the production problem.
基金supported by Fundamental Research Funds for the Central Universities (No. N090403005)
文摘Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity, dynamicity, distributivity, and compatibility. The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors, the distribution of various resources to achieve rational organization, scheduling and management of production activities. This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system. Using the hybrid modeling method, the resources and functions of production system are encapsulated, and the agent-based production system model is established. A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper.
基金Key Project of Shandong Provincial Natural Science Foundation,China(No.ZR2010FZ001)National High-Tech Research and Development Program of China(863 Program)(No.2007AA04Z157)
文摘A matrix encoding scheme for the steelmaking continuous casting( SCC) production scheduling( SCCPS) problem and the corresponding decoding method are proposed. Based on it,a cross entropy( CE) method is adopted and an improved cross entropy( ICE) algorithm is proposed to solve the SCCPS problem to minimize total power consumption. To describe the distribution of the solution space of the CE method,a probability model is built and used to generate individuals by sampling and a probability updating mechanism is introduced to trace the promising samples. For the ICE algorithm,some samples are generated by the heuristic rules for the shortest makespan due to the relation between the makespan and the total power consumption,which can reduce the search space greatly. The optimal sample in each iteration is retained through a retention mechanism to ensure that the historical optimal sample is not lost so as to improve the efficiency and global convergence. A local search procedure is carried out on a part of better samples so as to improve the local exploitation capability of the ICE algorithm and get a better result. The parameter setting is investigated by the Taguchi method of design-of-experiment. A number of simulation experiments are implemented to validate the effectiveness of the ICE algorithm in solving the SCCPS problem and also the superiority of the ICE algorithm is verified through the comparison with the standard cross entropy( SCE) algorithm.
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.72061022 and 72171037).
文摘In this study,an optimization model of a single machine system integrating imperfect preventive maintenance planning and production scheduling based on game theory is proposed.The costs of the production department and the maintenance department are minimized,respectively.Two kinds of three-stage dynamic game models and a backward induction method are proposed to determine the preventive maintenance(PM)threshold.A lemma is presented to obtain the exact solution.A comprehensive numerical study is provided to illustrate the proposed maintenance model.The effectiveness is also validated by comparison with other two existed optimization models.
基金This work was supported by the National Natural Science Foundation of China(Grant No.59990470) the Outstanding Youth Foundation of China(Grant No.59725514)
文摘Based on the concept of operation flexibility, we study the relationship among multiple operation sequences and provide a flexibility measure for operation sequences. A criterion is proposed to prioritize each operation (rather than sequence). Under the multi-agent architecture the criterion can be used to guide the decision-making procedure during production scheduling so that there is an adequate flexibility at each decision point. Experimental results demonstrate the efficiency of the criterion when it is used as a scheduling heuristic. It can increase flexibility of manufacturing systems, and consequently improve the performance of the systems.
基金supported in part by the High-tech ship scientific research project of the Ministry of Industry and Information Technology of the People’s Republic of China,and the National Nature Science Foundation of China(Grant No.71671113)the Science and Technology Department of Shaanxi Province(No.2020GY-219)the Ministry of Education Collaborative Project of Production,Learning and Research(No.201901024016).
文摘Ship outfitting is a key process in shipbuilding.Efficient and high-quality ship outfitting is a top priority for modern shipyards.These activities are conducted at different stations of shipyards.The outfitting plan is one of the crucial issues in shipbuilding.In this paper,production scheduling and material ordering with endogenous uncertainty of the outfitting process are investigated.The uncertain factors in outfitting equipment production are usually decision-related,which leads to difficulties in addressing uncertainties in the outfitting production workshops before production is conducted according to plan.This uncertainty is regarded as endogenous uncertainty and can be treated as non-anticipativity constraints in the model.To address this problem,a stochastic two-stage programming model with endogenous uncertainty is established to optimize the outfitting job scheduling and raw material ordering process.A practical case of the shipyard of China Merchants Heavy Industry Co.,Ltd.is used to evaluate the performance of the proposed method.Satisfactory results are achieved at the lowest expected total cost as the complete kit rate of outfitting equipment is improved and emergency replenishment is reduced.
基金supported by the Key Program of the National Natural Science Foundation of China(Nos.52334008 and 51734004).
文摘In the context of reducing its carbon emissions,the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability.The optimization of production planning and scheduling plays a pivotal role in realizing these objectives such as improving production efficiency,saving energy,reducing carbon emissions,and enhancing quality.However,current practices in steel enterprises are largely dependent on experience-driven manual decision approaches supported by information systems,which are inadequate to meet the complex requirements of the industry.This study explores the current situation in production planning and scheduling,analyzes the characteristics and limitations of existing methods,and emphasizes the necessity and trends of intelligent systems.It surveys the current literature on production planning and scheduling in steel enterprises and analyzes the theoretical advancements and practical challenges associated with combinatorial and sequential optimization in this field.A key focus is on the limitations of current models and algorithms in effectively addressing the multi-objective and multiconstraint characteristics of steel produc-tion.To overcome these challenges,a novel framework for intelligent production planning and scheduling is proposed.This framework leverages data-and knowledge-driven decision-making and scenario adaptability,enabling the system to respond dynamically to real-time production conditions and market fluctuations.By integrating artificial intelligence and advanced optimization methodologies,the proposed framework improves the efficiency,cost-effectiveness,and environmental sustainability of steel manufacturing.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
文摘The optimal scheduling of multi-product batch process is studied and a new mathematics model targeting the maximum profit is proposed, which can be solved by the modified genetic algorithm (MGA) with mixed coding (sequence coding and decimal coding) developed by us. In which, the partially matched cross over (PMX) and reverse mutation are used for the sequence coding, whereas the arithmetic crossover and heteropic mutation are used for the decimal coding. In addition, the relationship between production scale and production cost is analyzed and the maximum profit is always a trade-off of the production scale and production cost. Two examples are solved to demonstrate the effectiveness of the method.
文摘Taking the seamless tube plant of Baoshan Iron & Steel Complex in China as the background,we analyze the characters of hot rolling seamless steel tube:multi varieties,low volume,complicated production process,flexible production routes.Then integrated scheduling problem for hot rolling seamless steel tube production is studied,which covers two key points;order-grouping problem and solution method for flowshop/jobshop scheduling problem.On the basis of these two problems,integrated scheduling decision system is developed.The design idea,function flow sheet,data processing method,and functional module of visualized human-computer interactive scheduling system implemented in seamless steel tube plant of Shanghai Baoshan Iron & Steel Complex are described into detail.Compared with manual system,the performance of system shows the applicability and superiority in several criteria.
基金supported by the 2020 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R.Chinathe State Grid Liaoning Electric Power Supply Co.,Ltd.,Comprehensive Security Defense Platform Project for Industrial/Enterprise Networks。
文摘With the application of various information technologies in smart manufacturing,new intelligent production mode puts forward higher demands for real-time and robustness of production scheduling.For the production scheduling problem in large-scale manufacturing environment,digital twin(DT)places high demand on data processing capability of the terminals.It requires both global prediction and real-time response abilities.In order to solve the above problem,a DT-based edge-cloud collaborative intelligent production scheduling(DTECCS)system was proposed,and the scheduling model and method were introduced.DT-based edge-cloud collaboration(ECC)can predict the production capacity of each workshop,reassemble customer orders,optimize the allocation of global manufacturing resources in the cloud,and carry out distributed scheduling on the edge-side to improve scheduling and tasks processing efficiency.In the production process,the DTECCS system adjusts scheduling strategies in real-time,responding to changes in production conditions and order fluctuations.Finally,simulation results show the effectiveness of DTECCS system.
文摘An increasing number of novel and highly specialized computer-aided decision-making technologies for short-term production scheduling in oil refineries has emerged and evolved over the past two decades, thereby encouraging refiners to permanently rethink the way the refining business is operated and managed. In this report,we discuss the key lessons learned from one of the pioneering, yet daring, enterprise-wide programs entirely implemented in an energy company devoted to developing and implementing an advanced refinery production scheduling(RPS) technology, i.e., the RPS system of Petrobras. Apart from mathematical and information technology issues, the long-term sustainability of a successful RPS project is, we argue, the outcome of a virtuous cycle grounded on permanent actions devoted to improving technical education inside the organization,reinspecting organizational cultures and operational paradigms, and developing working processes.
基金Thailand Research Fund (Grant #MRG5480176)National Research University Project of Thailand Office of Higher Education Commission
文摘This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.
