The Experts below are selected from a list of 457797 Experts worldwide ranked by ideXlab platform
Paul Hyland - One of the best experts on this subject based on the ideXlab platform.
-
energy efficient scheduling of open pit coal mine trucks
European Journal of Operational Research, 2017Co-Authors: Samuel Patterson, Erhan Kozan, Paul HylandAbstract:Abstract Mining companies are increasingly being challenged to improve energy efficiency, as a method of reducing both the cost and environmental impact of their operations. The haulage activity at an open-pit mine represents a large proportion of total energy consumption. In many other industries, state-of-the-art operations research Techniques, such as advanced scheduling, have been applied to support energy efficiency improvements. Despite this, only a limited amount of research using these Techniques has been conducted, to face the challenge of energy efficiency in mining. This research contributes an original mixed integer linear programming formulation that schedules haulage activity to minimise the truck and shovel energy consumption required to meet production targets. Since solving the model is found to be NP-hard and intractable for exact methods, a constructive algorithm and tabu search Solution Technique is developed to solve the model quickly enough for practical use. An operating mine in South East Queensland is used as a case study, to verify and validate the proposed model and Solution Technique using sensitivity and scenario analysis where significant potential for improvement is found. Several opportunities for using the model as a decision support tool are discussed, including examples of how it can be used for short, medium and long-term decision making.
-
Energy efficient scheduling of open-pit coal mine trucks
Science & Engineering Faculty, 2017Co-Authors: Samuel Patterson, Erhan Kozan, Paul HylandAbstract:Higlights - Open-pit coal mine truck activity is scheduled to minimise energy consumption. - A Solution Technique is developed to solve the model in reasonable time. - A case study is performed to validate the practical utility of the methodologies. - Opportunities for aiding short to long term decisions are presented. Abstract Mining companies are increasingly being challenged to improve energy efficiency, as a method of reducing both the cost and environmental impact of their operations. The haulage activity at an open-pit mine represents a large proportion of total energy consumption. In many other industries, state-of-the-art operations research Techniques, such as advanced scheduling, have been applied to support energy efficiency improvements. Despite this, only a limited amount of research using these Techniques has been conducted, to face the challenge of energy efficiency in mining. This research contributes an original mixed integer linear programming formulation that schedules haulage activity to minimise the truck and shovel energy consumption required to meet production targets. Since solving the model is found to be NP-hard and intractable for exact methods, a constructive algorithm and tabu search Solution Technique is developed to solve the model quickly enough for practical use. An operating mine in South East Queensland is used as a case study, to verify and validate the proposed model and Solution Technique using sensitivity and scenario analysis where significant potential for improvement is found. Several opportunities for using the model as a decision support tool are discussed, including examples of how it can be used for short, medium and long-term decision making.
Kai Sundmacher - One of the best experts on this subject based on the ideXlab platform.
-
dynamic optimization of constrained semi batch processes using pontryagin s minimum principle an effective quasi newton approach
Computers & Chemical Engineering, 2017Co-Authors: Erdal Aydin, Dominique Bonvin, Kai SundmacherAbstract:Abstract This work considers the numerical optimization of constrained batch and semi-batch processes, for which direct as well as indirect methods exist. Direct methods are often the methods of choice, but they exhibit certain limitations related to the compromise between feasibility and computational burden. Indirect methods, such as Pontryagin’s Minimum Principle (PMP), reformulate the optimization problem. The main Solution Technique is the shooting method, which however often leads to convergence problems and instabilities caused by the integration of the co-state equations forward in time. This study presents an alternative indirect Solution Technique. Instead of integrating the states and co-states simultaneously forward in time, the proposed algorithm parameterizes the inputs, and integrates the state equations forward in time and the co-state equations backward in time, thereby leading to a gradient-based optimization approach. Constraints are handled by indirect adjoining to the Hamiltonian function, which allows meeting the active constraints explicitly at every iteration step. The performance of the Solution strategy is compared to direct methods through three different case studies. The results show that the proposed PMP-based quasi-Newton strategy is effective in dealing with complicated constraints and is quite competitive computationally.
-
Dynamic Optimization of Constrained Semi-Batch Processes using Pontryagin's Minimum Principle-An Effective Quasi-Newton based Approach
2016Co-Authors: Erdal Aydin, Kai SundmacherAbstract:This work considers the numerical optimization of constrained batch and semi-batch processes, for which direct as well as indirect methods exist. Direct methods are often the methods of choice, but they exhibit certain limitations related to the compromise between feasibility and computational burden. Indirect methods, such as Pontryagin’s Minimum Principle (PMP), reformulate the optimization problem. The main Solution Technique is the shooting method, which however often leads to convergence problems and instabilities caused by the integration of the co-state equations forward in time. This study presents an alternative indirect Solution Technique. Instead of integrating the states and co-states simultaneously forward in time, the proposed algorithm parameterizes the inputs, and integrates the state equations forward in time and the co-state equations backward in time, thereby leading to a gradient-based optimization approach. Constraints are handled by indirect adjoining to the Hamiltonian function, which allows meeting the active constraints explicitly at every iteration step. The performance of the Solution strategy is compared to direct methods through three different case studies. The results show that the proposed PMP-based quasi-Newton strategy is effective in dealing with complicated constraints and is quite competitive computationally.
Mohammad A Hoque - One of the best experts on this subject based on the ideXlab platform.
-
a heuristic Solution Technique to attain the minimal total cost bounds of transporting a homogeneous product with varying demands and supplies
European Journal of Operational Research, 2014Co-Authors: Z A M S Juman, Mohammad A HoqueAbstract:Abstract Transportation of a product from multi-source to multi-destination with minimal total transportation cost plays an important role in logistics and supply chain management. Researchers have given considerable attention in minimizing this cost with fixed supply and demand quantities. However, these quantities may vary within a certain range in a period due to the variation of the global economy. So, the concerned parties might be more interested in finding the lower and the upper bounds of the minimal total costs with varying supplies and demands within their respective ranges for proper decision making. This type of transportation problem has received attention of only one researcher, who formulated the problem and solved it by LINGO. We demonstrate that this method fails to obtain the correct upper bound Solution always. Then we extend this model to include the inventory costs during transportation and at destinations, as they are interrelated factors. The number of choices of supplies and demands within their respective ranges increases enormously as the number of suppliers and buyers increases. In such a situation, although the lower bound Solution can be obtained methodologically, determination of the upper bound Solution becomes an NP hard problem. Here we carry out theoretical analyses on developing the lower and the upper bound heuristic Solution Techniques to the extended model. A comparative study on Solutions of small size numerical problems shows promising performance of the current upper bound Technique. Another comparative study on results of numerical problems demonstrates the effect of inclusion of the inventory costs.
-
an optimal Solution Technique to the single vendor multi buyer integrated inventory supply chain by incorporating some realistic factors
European Journal of Operational Research, 2011Co-Authors: Mohammad A HoqueAbstract:This paper develops two generalized integrated inventory models to deliver a single product from a vendor to multiple buyers. To minimize the total cost of set up, ordering, inventory holding and transportation, the production flow is synchronized by transferring the lot with equal and/or unequal (either all are equal or all are unequal or a combination of equal and unequal) sized batches (sub-lots), each of which incurs a transportation cost. For easy implementation of the models, we relax some unrealistic assumptions in the existing models such as unlimited capacities of the transport equipment and buyers' storage, insignificant set up and transportation times, unlimited lead time and batch sizes. A common optimal Solution Technique to the models is derived and their performances are analyzed. Potential significances of the Solution method are highlighted with Solutions of some numerical problems. The importance of the relaxed factors and limitation of the models are discussed.
-
an alternative heuristic Solution Technique for efficient management of the serial supply chain
International Journal of Business Innovation and Research, 2008Co-Authors: Mohammad A Hoque, S K GoyalAbstract:The serial supply chain management has received considerable attention in the literature. A few years back, the literature had been enriched by the presentation of serial supply chain models, both for single and sequence dependent multicomponent supply chain management, and their heuristic Solution procedures. This paper demonstrates that the sequencing rule applied to select the appropriate sequence of products in determining minimal total cost does not meet the purpose. In addition, it finds that though the author presented generalised models, the Solution procedures are restricted to a particular case. In this paper, the models are reorganised and alternative generalised heuristic Solution procedures are presented so that they can never be worse than the original one. Then, we carry out a comparative study of our methods with the original ones on two numerical problems (illustrated in the original paper) to show cost reductions with reduced cycle times by our method.
-
an optimal Solution Technique for the joint replenishment problem with storage and transport capacities and budget constraints
European Journal of Operational Research, 2006Co-Authors: Mohammad A HoqueAbstract:Abstract The joint replenishment problem, which is concerned with the problem of coordinating the replenishment of a group of items that may be ordered jointly, has been studied extensively and many heuristic Solution procedures have been presented in the literature. To obtain the minimum of the total cost, the main complexity lies in determining the appropriate lower bound of the basic cycle time. Also there is a lack of a global optimal Solution Technique of the problem. This paper presents its extended model to include some practical issues and develops a simple procedure to calculate the appropriate lower bound of the basic cycle time. By a comparative study of a numerical example, it demonstrates the inabilities of the available lower bound formulae in the literature. It also develops a generalized global optimal Solution algorithm of the extended model and illustrates this with a numerical example. Then a comparative study of the results of seven numerical examples is carried out to highlight the global optimality of the Solution Technique.
Samuel Patterson - One of the best experts on this subject based on the ideXlab platform.
-
energy efficient scheduling of open pit coal mine trucks
European Journal of Operational Research, 2017Co-Authors: Samuel Patterson, Erhan Kozan, Paul HylandAbstract:Abstract Mining companies are increasingly being challenged to improve energy efficiency, as a method of reducing both the cost and environmental impact of their operations. The haulage activity at an open-pit mine represents a large proportion of total energy consumption. In many other industries, state-of-the-art operations research Techniques, such as advanced scheduling, have been applied to support energy efficiency improvements. Despite this, only a limited amount of research using these Techniques has been conducted, to face the challenge of energy efficiency in mining. This research contributes an original mixed integer linear programming formulation that schedules haulage activity to minimise the truck and shovel energy consumption required to meet production targets. Since solving the model is found to be NP-hard and intractable for exact methods, a constructive algorithm and tabu search Solution Technique is developed to solve the model quickly enough for practical use. An operating mine in South East Queensland is used as a case study, to verify and validate the proposed model and Solution Technique using sensitivity and scenario analysis where significant potential for improvement is found. Several opportunities for using the model as a decision support tool are discussed, including examples of how it can be used for short, medium and long-term decision making.
-
Energy efficient scheduling of open-pit coal mine trucks
Science & Engineering Faculty, 2017Co-Authors: Samuel Patterson, Erhan Kozan, Paul HylandAbstract:Higlights - Open-pit coal mine truck activity is scheduled to minimise energy consumption. - A Solution Technique is developed to solve the model in reasonable time. - A case study is performed to validate the practical utility of the methodologies. - Opportunities for aiding short to long term decisions are presented. Abstract Mining companies are increasingly being challenged to improve energy efficiency, as a method of reducing both the cost and environmental impact of their operations. The haulage activity at an open-pit mine represents a large proportion of total energy consumption. In many other industries, state-of-the-art operations research Techniques, such as advanced scheduling, have been applied to support energy efficiency improvements. Despite this, only a limited amount of research using these Techniques has been conducted, to face the challenge of energy efficiency in mining. This research contributes an original mixed integer linear programming formulation that schedules haulage activity to minimise the truck and shovel energy consumption required to meet production targets. Since solving the model is found to be NP-hard and intractable for exact methods, a constructive algorithm and tabu search Solution Technique is developed to solve the model quickly enough for practical use. An operating mine in South East Queensland is used as a case study, to verify and validate the proposed model and Solution Technique using sensitivity and scenario analysis where significant potential for improvement is found. Several opportunities for using the model as a decision support tool are discussed, including examples of how it can be used for short, medium and long-term decision making.
Erhan Kozan - One of the best experts on this subject based on the ideXlab platform.
-
energy efficient scheduling of open pit coal mine trucks
European Journal of Operational Research, 2017Co-Authors: Samuel Patterson, Erhan Kozan, Paul HylandAbstract:Abstract Mining companies are increasingly being challenged to improve energy efficiency, as a method of reducing both the cost and environmental impact of their operations. The haulage activity at an open-pit mine represents a large proportion of total energy consumption. In many other industries, state-of-the-art operations research Techniques, such as advanced scheduling, have been applied to support energy efficiency improvements. Despite this, only a limited amount of research using these Techniques has been conducted, to face the challenge of energy efficiency in mining. This research contributes an original mixed integer linear programming formulation that schedules haulage activity to minimise the truck and shovel energy consumption required to meet production targets. Since solving the model is found to be NP-hard and intractable for exact methods, a constructive algorithm and tabu search Solution Technique is developed to solve the model quickly enough for practical use. An operating mine in South East Queensland is used as a case study, to verify and validate the proposed model and Solution Technique using sensitivity and scenario analysis where significant potential for improvement is found. Several opportunities for using the model as a decision support tool are discussed, including examples of how it can be used for short, medium and long-term decision making.
-
Energy efficient scheduling of open-pit coal mine trucks
Science & Engineering Faculty, 2017Co-Authors: Samuel Patterson, Erhan Kozan, Paul HylandAbstract:Higlights - Open-pit coal mine truck activity is scheduled to minimise energy consumption. - A Solution Technique is developed to solve the model in reasonable time. - A case study is performed to validate the practical utility of the methodologies. - Opportunities for aiding short to long term decisions are presented. Abstract Mining companies are increasingly being challenged to improve energy efficiency, as a method of reducing both the cost and environmental impact of their operations. The haulage activity at an open-pit mine represents a large proportion of total energy consumption. In many other industries, state-of-the-art operations research Techniques, such as advanced scheduling, have been applied to support energy efficiency improvements. Despite this, only a limited amount of research using these Techniques has been conducted, to face the challenge of energy efficiency in mining. This research contributes an original mixed integer linear programming formulation that schedules haulage activity to minimise the truck and shovel energy consumption required to meet production targets. Since solving the model is found to be NP-hard and intractable for exact methods, a constructive algorithm and tabu search Solution Technique is developed to solve the model quickly enough for practical use. An operating mine in South East Queensland is used as a case study, to verify and validate the proposed model and Solution Technique using sensitivity and scenario analysis where significant potential for improvement is found. Several opportunities for using the model as a decision support tool are discussed, including examples of how it can be used for short, medium and long-term decision making.
-
open pit block sequencing optimization a mathematical model and Solution Technique
Engineering Optimization, 2016Co-Authors: Amin Mousavi, Erhan Kozan, Shi Qiang LiuAbstract:This study presents a comprehensive mathematical formulation model for a short-term open-pit mine block sequencing problem, which considers nearly all relevant technical aspects in open-pit mining. The proposed model aims to obtain the optimum extraction sequences of the original-size (smallest) blocks over short time intervals and in the presence of real-life constraints, including precedence relationship, machine capacity, grade requirements, processing demands and stockpile management. A hybrid branch-and-bound and simulated annealing algorithm is developed to solve the problem. Computational experiments show that the proposed methodology is a promising way to provide quantitative recommendations for mine planning and scheduling engineers.
