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Abstract
This study established a truck dispatching model adopting 0-1 decision variables to rationally allocate truck transportation in open-pit mines, maximize the total loading and unloading volume of trucks, and solve the problem of the inability of the truck dispatching model to guide production in open-pit mines because of nonspecific results. The model considers loading and unloading logical relationships, working time constraints, loading and unloading volume constraints, traffic flow constraints, and loading and unloading capacity constraints to maximize the total loading and unloading volume. The operation of trucks between loading and unloading sites is taken as the decision variable. The results show multiple transportation routings of all trucks between loading and unloading sites in working time. Double decision variables are used to solve the expression problem of constraints. The mathematical model is solved using Lingo. The proposed algorithm was then used to optimize the truck dispatching. The application result of the total loading and unloading volume was 10950.6 m3, and the total loading and unloading number was 384. The optimization result could guide production effectively.
Highlights
System optimization is a part of mine intelligent construction [1,2,3]
Most high-yield coal mines are open-pit mining enterprises, mainly using the excavator-truck mining process, and truck transportation plays an important role in open-pit mining enterprises
E truck dispatching model established in this study takes the operation of trucks between loading and unloading sites as the decision variables and adopts 0-1 variables. e target value of the model is the maximum number of truck loadings and unloadings. e loading and unloading logical relationships, working time constraints, loading and unloading volume constraints, traffic flow constraints, and loading and unloading capacity constraints are considered, and two decision variable sets are introduced. e solution results can be used to determine the specific routings of multiple transportations between loading and unloading sites during working time
Summary
System optimization is a part of mine intelligent construction [1,2,3]. In the face of a changing market form, optimizing the production process and reducing enterprise costs are the key issues that each mining enterprise needs to solve urgently [4,5,6,7]. Erefore, it is important to establish a reasonable truck transportation dispatching model using an efficient algorithm to optimize and solve the problem and formulate a truck dispatching plan to meet the actual production needs of the mine. Coelho et al proposed three multiobjective heuristic algorithms, 2PPLS-VNS, MOVNS, and NSGA-II, which can be applied to the dynamic assignment problem of truck in open-pit mining operations [23]. E real-time truck dispatching model was proposed for strip mines based on the target flow saturation of traffic flow planning [29]. Taking the lowest total transportation cost as the objective function and considering the constraints of ore production, grade balance, and the shortest waiting time for trucks, the quantum particle swarm optimization algorithm was improved to solve the dispatching model [31]. E truck dispatching model established in this study takes the operation of trucks between loading and unloading sites as the decision variables and adopts 0-1 variables. e target value of the model is the maximum number of truck loadings and unloadings. e loading and unloading logical relationships, working time constraints, loading and unloading volume constraints, traffic flow constraints, and loading and unloading capacity constraints are considered, and two decision variable sets are introduced. e solution results can be used to determine the specific routings of multiple transportations between loading and unloading sites during working time
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