409
Production scheduling optimization focuses mainly on decreasing costs and maximizing profits in a specific period of time. Regarding the optimization of production indices,
Mohammadi et al. (2017) conducted a study on fineness modulus optimization. Moreover,
Xu et al. (2018) carried out a study on the environmental optimization of mine production
scheduling. Ramazan (2007) employed three basic algorithms of mine production scheduling to develop a new algorithm based on linear scheduling for the whole blocks of minerals. For this purpose, it was necessary to decrease the number of integer variables and
constraints required in the integer scheduling formulation. Likewise, Ahmadi and Shahabi
(2018) used the genetic algorithm to optimize the fineness modulus of ores extracted from
mines. Yu et al. (2013) proposed a method to provide a raw combination of ore deposits
efficiently for decision-making (based on the features of ore deposits). Xu et al. (2018) optimized production indices based on dynamic scheduling. In a study on production scheduling improvement, Taylor defined how production scheduling operated. Janet prepared an
appropriate flowchart for the production scheduling problem, and Johnson focused on a
mathematic formulation of the scheduling problem (Herrmann et al. 2007). Chai et al. (2011)
proposed an integrative optimization method justifying case-based reasoning (CBR) with a
multi-objective evolutionary algorithm (MOEA). In their method, CBR decision-making of
operational indices was based on the operational experiences of engineers; however, MOEA
benefited from the multiple optimization of production indices. Ding et al. (2013) solved a
multi-bench optimization problem and proposed a multi-objective optimization solution in
order to achieve the appropriate efficiency. In practice, the optimization of operational indices is usually a dynamic problem.
Operational indices result from the decomposition and optimization of production scheduling indices. Based on these indices, there are unknown relationships between existing
constraints, evaluation of different scenarios, and input data improvement. Therefore, it is
important to coordinate decisions resulting in unique operational indices bringing about production scheduling indices. Fig. 4 indicates an approach along with a typical framework for
the optimization of operational indices. It also shows the open-loop optimization structure
including long-term, mid-term, and short-term production scheduling intervals.
6 CONCLUSIONS
Over the last decade research into scheduling, particularly in its most common operational
form of production scheduling, has risen in importance due to the demands of open pit mine.
While much progress has been made on an academic front, doubts remain over the transfer
of the science to fit the flexibility requirements of modern production scheduling. Production scheduling has received much attraction in the literature as the solution is made more
difficult through the requirement to satisfy the conflicting demands of both objective function and production constraints. All of the production parameters should be optimized for
mine production scheduling optimization. For this purpose, the initial structure of the mine
production problem was dealt with in this paper to focus on the optimization of mineral production indices. If priorities are identified and complied with correctly in decision-making, it
will be possible to modify and improve every operation separately. In fact, technical parameters of production indices are uncertain, and it is important to improve input data in each
section. To this end, appropriate structures were proposed to emphasize different sections of
description and monitoring problems. It is also recommended that decision-making sections
should be integrated with scheduling for the optimization of operational indices, processes,
and control in future studies.
REFERENCES
Ahmadi, M.R. & Shahabi, R.S. 2018. cut-off grades optimization in open pit mines using genetic algorithm. Resourch Policy 55: 184–191.
Production scheduling optimization focuses mainly on decreasing costs and maximizing profits in a specific period of time. Regarding the optimization of production indices,
Mohammadi et al. (2017) conducted a study on fineness modulus optimization. Moreover,
Xu et al. (2018) carried out a study on the environmental optimization of mine production
scheduling. Ramazan (2007) employed three basic algorithms of mine production scheduling to develop a new algorithm based on linear scheduling for the whole blocks of minerals. For this purpose, it was necessary to decrease the number of integer variables and
constraints required in the integer scheduling formulation. Likewise, Ahmadi and Shahabi
(2018) used the genetic algorithm to optimize the fineness modulus of ores extracted from
mines. Yu et al. (2013) proposed a method to provide a raw combination of ore deposits
efficiently for decision-making (based on the features of ore deposits). Xu et al. (2018) optimized production indices based on dynamic scheduling. In a study on production scheduling improvement, Taylor defined how production scheduling operated. Janet prepared an
appropriate flowchart for the production scheduling problem, and Johnson focused on a
mathematic formulation of the scheduling problem (Herrmann et al. 2007). Chai et al. (2011)
proposed an integrative optimization method justifying case-based reasoning (CBR) with a
multi-objective evolutionary algorithm (MOEA). In their method, CBR decision-making of
operational indices was based on the operational experiences of engineers; however, MOEA
benefited from the multiple optimization of production indices. Ding et al. (2013) solved a
multi-bench optimization problem and proposed a multi-objective optimization solution in
order to achieve the appropriate efficiency. In practice, the optimization of operational indices is usually a dynamic problem.
Operational indices result from the decomposition and optimization of production scheduling indices. Based on these indices, there are unknown relationships between existing
constraints, evaluation of different scenarios, and input data improvement. Therefore, it is
important to coordinate decisions resulting in unique operational indices bringing about production scheduling indices. Fig. 4 indicates an approach along with a typical framework for
the optimization of operational indices. It also shows the open-loop optimization structure
including long-term, mid-term, and short-term production scheduling intervals.
6 CONCLUSIONS
Over the last decade research into scheduling, particularly in its most common operational
form of production scheduling, has risen in importance due to the demands of open pit mine.
While much progress has been made on an academic front, doubts remain over the transfer
of the science to fit the flexibility requirements of modern production scheduling. Production scheduling has received much attraction in the literature as the solution is made more
difficult through the requirement to satisfy the conflicting demands of both objective function and production constraints. All of the production parameters should be optimized for
mine production scheduling optimization. For this purpose, the initial structure of the mine
production problem was dealt with in this paper to focus on the optimization of mineral production indices. If priorities are identified and complied with correctly in decision-making, it
will be possible to modify and improve every operation separately. In fact, technical parameters of production indices are uncertain, and it is important to improve input data in each
section. To this end, appropriate structures were proposed to emphasize different sections of
description and monitoring problems. It is also recommended that decision-making sections
should be integrated with scheduling for the optimization of operational indices, processes,
and control in future studies.
REFERENCES
Ahmadi, M.R. & Shahabi, R.S. 2018. cut-off grades optimization in open pit mines using genetic algorithm. Resourch Policy 55: 184–191.