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
Basing on the advanced foreign management theory and methods, introducing MRP-Ⅱ having DSS function and combining Chinese conditions, we present in this paper a method of embedding the block of “lot size” producti...Basing on the advanced foreign management theory and methods, introducing MRP-Ⅱ having DSS function and combining Chinese conditions, we present in this paper a method of embedding the block of “lot size” production in MRP-Ⅱ.展开更多
The complexity of an open pit production scheduling problem is increased by grade uncertainty. A method is presented to calculate the cost of uncertainty in a production schedule based on deviations from the target pr...The complexity of an open pit production scheduling problem is increased by grade uncertainty. A method is presented to calculate the cost of uncertainty in a production schedule based on deviations from the target production. A mixed integer linear programming algorithm is formulated to find the min- ing sequence of blocks from a predefined pit shell and their respective destinations, with two objectives： to maximize the net present value of the operation and to minimize the cost of uncertainty. An efficient clustering technique reduces the number of var/ables to make the problem tractable. Also, the parameters that control the importance of uncertainty in the optimization problem are studied. The minimum annual mining capacity in presence of grade uncertainty is assessed. The method is illustrated with an oil sand deposit in northern Alberta.展开更多
An integral connection exists among the mine production planning, the mined material destination, and the ultimate pit limit （UPL） in the mining engineering economy. This relation is reinforced by real information a...An integral connection exists among the mine production planning, the mined material destination, and the ultimate pit limit （UPL） in the mining engineering economy. This relation is reinforced by real information and the benefits it engenders in the mining economy. Hence, it is important to create optimizing algorithms to reduce the errors of economic calculations. In this work, a logical mathematical algorithm that considers the important designing parameters and the mining economy is proposed. This algorithm creates an optimizing repetitive process among different designing constituents and directs them into the maximum amount of the mine economical parameters. This process will produce the highest amount of ores and the highest degree of safety. The modeling produces a new relation between the concept of the cutoff grade, mine designing, and mine planning, and it provides the maximum benefit by calculating the destination of the ores. The proposed algorithm is evaluated in a real case study. The results show that the net present value of the mine production is increased by 3% compared to previous methods of production design and UPL.展开更多
A collaborative planning framework based on the Lagrangian Relaxation was developed to coordinate and optimize the production planning of independent partners in multiple tier supply chains. Linking constraints and de...A collaborative planning framework based on the Lagrangian Relaxation was developed to coordinate and optimize the production planning of independent partners in multiple tier supply chains. Linking constraints and dependent demand constraints were added to the monolithic Multi-Level, multi-item Capacitated Lot Sizing Problem (MLCLSP). MLCLSP was Lagrangian relaxed and decomposed into facility-separable subproblems. Surrogate gradient algorithm was used to update Lagrangian multipliers, which coordinate decentralized decisions of the facilities. Production planning of independent partners could be appropriately coordinated and optimized by this framework without intruding their decisionities and private information. Experimental results show that the proposed coordination mechanism and procedure come close to optimal results as obtained by central coordination.展开更多
Automotive manufacturing is complex and includes the coordination of design in the manufacturing system. The manufacturing approaches over the past few years, disassembly have been a key issue, and it seems that simul...Automotive manufacturing is complex and includes the coordination of design in the manufacturing system. The manufacturing approaches over the past few years, disassembly have been a key issue, and it seems that simulation models are usually tailored to address a narrow set of industrial issues. This paper describes the development of the production line in the automobile manufacturing system through design, operation, and maintenance, based on multi-objectives of Algorithm and Simulation Model called MOA-SM. The both models are being developed at three different levels： the production line in the body and assembly shop, supply chain, and the production plan. The optimization tries to involve more objectives to solve the issues in manufacturing system. A solution that may optimize one performance measure may deteriorate since other performance solutions are difficult. The resulting algorithms are comparable to the simulation and multi-object in terms of success rate, assembly times, peak forces, moments, and have assembly times superior to those of a benchmark blind search algorithm.展开更多
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Basing on the advanced foreign management theory and methods, introducing MRP-Ⅱ having DSS function and combining Chinese conditions, we present in this paper a method of embedding the block of “lot size” production in MRP-Ⅱ.
文摘The complexity of an open pit production scheduling problem is increased by grade uncertainty. A method is presented to calculate the cost of uncertainty in a production schedule based on deviations from the target production. A mixed integer linear programming algorithm is formulated to find the min- ing sequence of blocks from a predefined pit shell and their respective destinations, with two objectives： to maximize the net present value of the operation and to minimize the cost of uncertainty. An efficient clustering technique reduces the number of var/ables to make the problem tractable. Also, the parameters that control the importance of uncertainty in the optimization problem are studied. The minimum annual mining capacity in presence of grade uncertainty is assessed. The method is illustrated with an oil sand deposit in northern Alberta.
文摘An integral connection exists among the mine production planning, the mined material destination, and the ultimate pit limit （UPL） in the mining engineering economy. This relation is reinforced by real information and the benefits it engenders in the mining economy. Hence, it is important to create optimizing algorithms to reduce the errors of economic calculations. In this work, a logical mathematical algorithm that considers the important designing parameters and the mining economy is proposed. This algorithm creates an optimizing repetitive process among different designing constituents and directs them into the maximum amount of the mine economical parameters. This process will produce the highest amount of ores and the highest degree of safety. The modeling produces a new relation between the concept of the cutoff grade, mine designing, and mine planning, and it provides the maximum benefit by calculating the destination of the ores. The proposed algorithm is evaluated in a real case study. The results show that the net present value of the mine production is increased by 3% compared to previous methods of production design and UPL.
文摘A collaborative planning framework based on the Lagrangian Relaxation was developed to coordinate and optimize the production planning of independent partners in multiple tier supply chains. Linking constraints and dependent demand constraints were added to the monolithic Multi-Level, multi-item Capacitated Lot Sizing Problem (MLCLSP). MLCLSP was Lagrangian relaxed and decomposed into facility-separable subproblems. Surrogate gradient algorithm was used to update Lagrangian multipliers, which coordinate decentralized decisions of the facilities. Production planning of independent partners could be appropriately coordinated and optimized by this framework without intruding their decisionities and private information. Experimental results show that the proposed coordination mechanism and procedure come close to optimal results as obtained by central coordination.
文摘Automotive manufacturing is complex and includes the coordination of design in the manufacturing system. The manufacturing approaches over the past few years, disassembly have been a key issue, and it seems that simulation models are usually tailored to address a narrow set of industrial issues. This paper describes the development of the production line in the automobile manufacturing system through design, operation, and maintenance, based on multi-objectives of Algorithm and Simulation Model called MOA-SM. The both models are being developed at three different levels： the production line in the body and assembly shop, supply chain, and the production plan. The optimization tries to involve more objectives to solve the issues in manufacturing system. A solution that may optimize one performance measure may deteriorate since other performance solutions are difficult. The resulting algorithms are comparable to the simulation and multi-object in terms of success rate, assembly times, peak forces, moments, and have assembly times superior to those of a benchmark blind search algorithm.
