Proposal of a Transport Planning Model for the Removal of Quarry Stone Using a Simulation

Abstract

This article describes a transport planning model which applies a simulation to support decision-making in quarry operations. The analysis of the transport system was used as input for creating the model and the subsequent research. There are five loading points in the quarry from which, according to the plan, the quarry stone is transported to the crusher, where it proceeds for further processing. The required daily capacity of the downstream technological line is 3800 t/day. Based on the analysis, it was found that it was impossible to fulfill this requirement from the two loading points which were the furthest from the unloading point. For this reason, two simulation models of the transport system were created. The first simulation model is a transport system (loading, removal and dumping of quarry stone) from one loading point. Simulation experiments were performed on this model for all loading points. The findings from the analysis confirmed the results of the simulation experiments. Subsequently, a model of the combined quarry stone removal from two loading points was proposed. The model was designed for two variants of dividing the work shift into two work sections. In the model, which had a tabular form, the combinations of loading points and values of the minimum number of unloaded cars for stone removal were suitable to ensure its necessary daily capacity. The last part of this research was expanding the original model with an additional loading point. Several experiments were performed on this model. The aim of these experiments was to verify the combinations of loading points presented in the proposed model and the combinations of unsatisfactory loading points. Based on the research results, the transport planning model and simulation models are suitable additional tools for the decision-making process in removing quarry stone.

1. Introduction

Internal transport in a company is an integral part of every technological process. Its technical level should correspond to the technical level of other machines and equipment so that it does not become an obstacle to the flow of technological process. Surface mining means of transport are subject to performance, economic and environmental requirements.

Continuous and cyclic transports are used in quarries. Continuous transport mainly uses means with a continuous flow of material (e.g., belt conveyors in coal mining) or a flow of material in regular batches (e.g., endless rope transport—cableway). Cyclical transport ensures material flow in cycles (e.g., car transport, rail transport).

The type of transport used also depends on the type of rocks mined. When mining cohesive rocks in quarries, cyclic transport systems are mainly used (often a combination of car transport and shovel loaders). Continuous modes of transport are also often used when mining coal and non-cohesive rocks.

The specific conditions of mining in quarries determine that automobile transport is the most used type of transport from the quarry wall to the crusher because of its advantages: mobility and work maneuverability.

Transport in quarries is one of the most frequent problems addressed in several scientific articles, the authors of whom have solved several complex or partial problems related to this topic.

One of the solved problems is the selection of suitable means of transport. The selection of suitable means of transport and their compliance with loading equipment impact the evaluation of the performance of the transport system and the entire company [1]. Technical, technological, economic, environmental, geological and organizational factors must be considered when selecting dump truck models to ensure the efficient operation of deep open pits [2]. The authors of [3] present a procedure for choosing loading and transportation machines for an open-pit complex ore mine using a statistical testing method (Monte Carlo technique). Also, the study [4] investigated the crucial factors and corresponding criteria that affect the selection of trucks. To solve the selection problem, the authors of this study used the analytic hierarchy process and the fuzzy weighted sum model for six different truck types and 20 selection criteria.

Another problem that has been solved is the cycle time analysis of dump trucks. Cycle time is a direct measure of the process and equipment performance in waste material transport for open-pit mines and other processes [5]. The study [6] demonstrates a practical cycle time analysis of dump trucks. It examines the possibility of minimizing the transport system’s cycle time and factors impacting the dump truck’s speed. The authors analyzed the data using mathematical regression methods via the Microsoft Excel program. Cycle time and the match factor method are used to evaluate loading and transport equipment in an andesite mine in the article [7]. The match factor (MF) equals one, indicating an efficient operation between loading and transport equipment. If the MF value is less than one (MF < 1), it demonstrates that the transport equipment works 100% effectively and the loading equipment allocates more time for waiting. The last scenario is when the MF value exceeds one (MF > 1). A higher value indicates that the loading equipment works effectively, but the hauling equipment generates waiting time.

Several articles and studies deal with optimizing the flow of trucks [8] and efficiently allocating trucks to the shovels in operation at open-pit mines. This aim of maximize production and minimize costs is subject to operational and physical constraints. To support these processes, different centralized dispatching solutions have been implemented based on mathematical programming, heuristic methods, or simulation modeling [9,10].

This research is focused on developing a transport planning model for a limestone quarry. Simulation modeling is applied to create and validate this model.

Modeling and model creation are fundamental digital approaches to research, analysis, design, and system projecting. Modeling is the process of creating and verifying a model to replace a dynamic system. Modeling and models are applied in different forms and used in different research areas. Authors use and present various types of models: mathematical [11], graphical [12,13], simulation [14] and heuristic [15].

Simulation models are used when it is either impossible or difficult to obtain an analytical solution to a specific investigated problem or when a comparison of analytical and simulation solutions is recommended [16]. Simulation models are applied to solve problems in various fields of research; for example, a simulation model of an assembly line production system for analyzing collaborative workplaces in detail [17], a simulation model for redesigning the production process [18], a simulation model of the production process for testing the energy consumption of selected equipment [19], a simulation model to assess the effectiveness of the delivery handling process [20], a simulation model of transport processes in a small cross-docking center [21], and a simulation traffic model to monitor and assess changes in the internal traffic circuit of cities [22].

Simulations are crucial for developing planning and exploratory models to optimize decision-making and the design and operations of complex and smart production systems. In manufacturing and logistics systems, modeling and simulation denote a set of methods and technological tools that allow experimentation on and validation of products, processes and systems design and the prediction of system performance [23].

Typical goals of industrial Discrete Event Simulation are identifying bottlenecks in manufacturing processes, finding operational characteristics (time and number of entities in the queue, service and system at each process stage) and simulating existing and new scenarios [24].

Simulation is the process of experimenting with a model. Experiments on the created model accumulate the information and statistics used in the real system.

Simulations on real process models aim to obtain information, one of today’s most valuable resources. Simulation eliminates the need for surplus financial resources from interventions in the real system. The information thus obtained is used to evaluate and improve the modeled system.

A simulation helps to thoroughly compare and evaluate the possible solution variants. Such model processing makes it possible to choose the most suitable variant of the solution and, in many cases, contributes to a more economically advantageous solution.

Simulation is also widely used in industries, including mining. A summary and overview of the digital technologies, including simulation, currently relevant to mining companies, as presented and discussed by mining magazines and media, and overview reports from leading consulting agencies across the mineral value chain, can be found in [25,26].

Simulation is also used in this industry to solve problems related to the mining process using various simulation tools. The work [27] presents a simulation study as an efficient tool for analyzing the times and costs of underground haulage systems (railway and vehicles) used in the mining processes with ARENA simulation software. The simulation model was developed to identify the number and size of the trucks needed to reach the future production targets using the SimMine simulation software [28].

Janic et al. [29] designed a mining planning model in the MATLAB/Simulink environment. The study [30] suggests modeling the disconnection process of mining wagons in the Tecnomatix Plant Simulation program using the SimTalk program. The article [31] describes a simulation model of the work cycle of loading and transporting the raw material at a quarry in the ExtendSim8 simulation software.

Krysa et al. applied discrete simulations to analyze the effectiveness of transporting raw materials when extracting low-quality deposits. They used Haulsim software to create a model of a cyclic transport system in an open-pit limestone mine. A discrete simulation of machine operation allowed a detailed analysis of the technological, operating and economic parameters for selected solutions [32].

A simulation model for obtaining the optimal truck–shovel configuration scheme under the existing transportation conditions of an open-pit mine based on Flexsim is given in [33].

Open-pit mining operations involve loading and hauling material from the pit to the processing plant or waste dump, depending on the ore grade. These two activities account for over 50% of a mine’s total operating costs [34].

Several published studies describe extracting/transporting mineral raw materials in quarries and their simulations, as shown in

Table 1. The list of research papers on the mentioned problems.

Research Problem	References	Comments
Selection of suitable means of transport	[2,3,4]	In [2], criteria for selecting suitable types of dump trucks are proposed. Ref. [3] applied the Monte Carlo technique for loader selection. The Fuzzy–WSM method and 20 criteria for the selection of freight vehicles are presented in [4].
Cycle time analysis of dump trucks	[5,6,7,31]	Manyele [5] processed the statistical analysis of transport cycle time data, and Enkhchuluun et al. [6] used regression models for temporal analysis. Adiansyah et al., in the publication [7], applied the coefficient of agreement (MF) method. The article [31] presents methods for loading and transport work cycle modeling.
Optimizing the material flow	[11,12,13,14,15,17,18,19,20,21,22]	To minimize costs, various types of models have been implemented: mathematical [11], graphical [12,13], simulation [14,16] and heuristic [15]. Simulation models of the production processes are described in [17,18,19,20,21,22].
Transport planning and capacity analysis using different types of software	[27,28,30,33]	Different types of software were applied in selected simulation studies; for example, Arena software [27] for cost simulation, SimMine software [28] for simulating the number of vehicles, and Tecnomatix software [30] and optimization for planning vehicle and loader interactions in [33] in Flexsim software.

. For example, this transport process is connected with strategies to increase its efficiency by calculating transport costs [1]. A vast space is devoted to hierarchical, analytical, or numerical models. Table 1 presents an overview of the literature in the given field of research.

A review of the published works suggests that data from real systems can only sometimes be obtained, as is the case in this study’s proposed simulation model. Therefore, this paper’s authors will base their research on developing hypothetical simulation models.

This article presents two simulation models for the selected transport system in a limestone quarry. The first simulation model is a transport system model for moving quarry stone from one loading point to one unloading point. The second simulation model will verify the transport planning model, removing quarry stone from two loading points using one loading device during the day. This study is based on data from an operating quarry. The computer simulation model was created using the ExtendSim 8LT simulation tool (v.8.0.2). This simulation tool uses connected blocks and determines the direction of the flows (dumper flow with quarry stone). Each block represents a part of a process or operation in the simulation model.

This article’s main contribution is the created model of a transport plan for a real transport system in a quarry. Another benefit is the modeling of the removal of the stone by combining two loading points in the simulation tool EXTENDSim8, performing simulation experiments for the specified conditions.

2. Materials and Methods

Internal transport in a mining company requires efficient transport systems (dumpers and shovel loaders). These systems must ensure the required quarry stone is available for further processing.

The transport system is made up of several elements. The first element of the system is the loader, which takes raw materials from the quarry stone dump and loads the stone into trucks. The second element is a dumper that ensures the relocation of the quarry stone. The third element of the system is the unloading point, where the stone is unloaded from the dumper (in this case, it is the primary crusher, which is the input element for the technological process of quarry stone processing). Another element is the transport roads in the quarry.

The input flow consists of tons of quarry stone in the quarry dump. Four main activities are performed in the given system. Figure 1 schematically shows these activities: loading quarry stone into a dumper with a shovel loader, moving the loaded dumper from the loading to the unloading point, unloading the quarry stone from the dumper and moving the empty dumper back to the loading point. The return of an empty dumper from the unloading point to the loading point occurs along the same route as for the removal of quarry stone.

The following basic operation parameters characterize the selected process:

• The type of mineral raw material and its properties;
• The parameters of the shovel loader (the size and load capacity of the shovel, the length of the loading cycle (dependent on the first factor as well));
• The technical parameters of the dumpers (carrying capacity, speed);
• The number of deployed dumpers;
• The number of deployed shovel loaders;
• The length of the transport route (routes);
• The number of loading and unloading points.

A sequence of steps solves the problem of removing quarry stone from two loading points using one shovel loader to ensure the daily capacity of quarry stone (Figure 2). In this case, the daily capacity is the amount of quarry stone in tons for the subsequent technological processing process.

The methodological steps are described below:

• Creation and verification of the simulation model. The simulation model is presented in the Results Section.
• Performing simulation experiments by changing the input parameters for several loading points and experiment analysis.
• Determination of the system capacity for each loading point based on simulation experiments.
• The proposal of the transport planning model for combined quarry stone removal from two loading points ensures the required capacity for the technological process, and narrow points based on point 4 are used.
• Expansion of the simulation model for removing quarry stone from two loading points.
• Performing simulation experiments for selected designs of combinations of two different loading points and experiment analysis.
• Proposals and recommendations.

This sequence of steps is preceded by an analysis of the transport system and the selection of a suitable simulation tool.

System analysis is suitable for finding out how a real system works, and subsequently, it is possible to derive individual results from its observations and evaluations. This analytical method is mainly used in cases where we want to improve the given system or completely replace it and create a new one [35].

A system analysis of the transport system was carried out in the given operation. From the system analysis, input data were obtained to create and fill the simulation model. The following results were obtained from the systemic analysis of the transport system:

• The transport system has one shovel loader at its disposal;
• The dumper loading time is 2.7–4 min;
• The dumper unloading time is 1.5–2 min;
• The dumper has a capacity of 35 tons;
• The transport system has three dumpers at its disposal;
• The operation has a total of five loading points, L1-L5, at its disposal;
• The average travel times from the loading point to the unloading point are 2.45 min (L1), 2.95 min (L2), 3.45 min (L3), 4.45 min (L4) and 4.95 min (L5);
• A total of 3800 tons of quarry stone are needed for the subsequent technological process (the system must remove and unload stone from 109 dumpers into the crusher daily);
• Based on the analysis, it was found that it was impossible to ensure the unloading of 3800 tons from the two loading points, L4 and L5, located farthest from the unloading point;
• The loading points were selected based on the gradual extraction of the deposit (exploration plan, mining) and the amount of stone at individual loading points.

Based on the analysis’s findings, a proposal to remove quarry stone by combining two loading points of the system was needed.

The ExtendSim8 LT was chosen to simulate the transport system. This simulation system combines discrete and continuous simulation capabilities and is a popular tool for PC_MS Windows and Macintosh computers. Using ExtendSim8 LT can develop dynamic models of existing or proposed processes in various fields. ExtendSim8 LT simulates any system or process by creating a logical representation in an easy-to-use format. ExtendSim8 LT is an easy-to-use yet potent tool for simulating processes. This tool contains a set of building blocks that allow model creators to build models quickly [36].

3. Results and Discussion

3.1. Simulation Model of Transport System

The simulation model was created according to the current rules of simulation modeling. This section only presents the created and verified model and describes its parts, as shown in Figure 3 and

Table 2. Block functions.

Block	Functions
	Create—dumpers generates input units. In this case, it generates dumpers. Create—quarry stone generates input units. In this case, it simulates the arrival of quarry stone at the capacity of a dumper.
	Queue 1 represents the front of generated dumpers. Queue 2 represents the front of dumpers waiting to be loaded. Queue 3 represents the front of dumpers waiting to be unloaded. Queue 4 represents the point of unloading.
	Select item splits the connection so that the returning empty dumpers can go through the cycle again.
	Batch combines the input unit quarry stone and the input unit dumper.
	Activity—loading represents the loading of quarry stone into a dumper. Activity—unloading represents the unloading of quarry stone from a dumper.
	Transport 1 represents the transfer of a load dumper to the unloading point. Transport 2 represents the return of an empty dumper to the point of loading.
	Information shows that a dumper has completed the entire transport cycle and sends it to the Holding Tank block (it indicates how many dumpers have completed the entire transport cycle).
	Holding Tank calculates the transport cycle using the passage of dumpers that complete the entire transport cycle.
	Random number generates inputs (numbers) for individual activity blocks from a time interval (operant A—time taken to load quarry stone into the dumper; operant B—time taken to unload the dumper).
	Exit represents the output requirements and how much quarry stone of the truck’s capacity was unloaded at the unloading point.
	Discrete Event, the block from the input values, draws graphs of the simulation and writes the values of the monitored inputs into the table.

. The simulation model created in ExtendSim 8LT is discrete and made up of building blocks. Each block used represents an activity in the simulation model. Blocks are connected using connectors that determine the direction of flows (quarry stone flow, dumper flow). The simulation model simulates the transport cycle (loading, transport (removal), unloading and transport back). Figure 3 shows the simulation model for moving quarry stone from one loading point to one unloading point. The functions of the blocks used are shown in Table 2.

Selected parameters from the system analysis are inserted into the model blocks. In the Create—dumpers block, the value of 3 is set, and the block will generate 3 dumpers that enter the system. In the block Create—quarry stone, the value is set to 100, and the block will generate 100 batches of quarry stone. One batch of quarry stone represents the capacity of one dumper, as mentioned above. The Random number block via connector 1 enters randomly generated values from a time interval <2.7–4> min (the time interval for loading the quarry stone into the dumper) into the Activity—loading block. In the Transport 1 block, representing transport to the unloading point, a value represents the average travel time from the loading point to the unloading point from the analysis. The Random number block via connector 2 enters randomly generated values from a time interval <1.5–2> min (the time interval for unloading the quarry stone from the dumper) from the analysis into the Activity—unloading block. In the Transport 2 block, representing the return transport to the loading point, a value represents the average travel time as in the Transport 1 block.

3.2. Simulation Experiment

Experiments were performed on the verified model for all five loading points L1-L5.

The experiments’ simulation time was 480 min, the net time of a work shift. The results are shown in

Table 3. Results obtained from experiments.

Parameter	Experiment 1	Experiment 2	Experiment 3	Experiment 4	Experiment 5
Loading point	L1	L2	L3	L4	L5
Number of loaded dumpers	139	127	117	102	95
Number of unloaded dumpers	138	126	116	101	94
Number of dumpers that completed the entire transport cycle	137	125	115	100	93
Time use of the loader, %	94.4	87.5	80	69	64.2

.

Figure 4 graphically represents the course of the number of unloaded dumpers from loading points L1 to L4 for the simulation time of 480 min.

3.3. Determination of System Capacity for Each Loading Point Based on Simulation Experiments

The next part of the article deals with the parameter of the number of unloaded dumpers per day. We multiply this parameter by the dump truck’s capacity and thus determine the number of tons of quarry stone unloaded into the crusher at the unloading point.

Table 4. Performance of the transport system for individual loading points.

Parameter	Experiment 1	Experiment 2	Experiment 3	Experiment 4	Experiment 5
Loading point	L1	L2	L3	L4	L5
Number of unloaded dumpers	138	126	116	101	94
Required daily capacity, tons	4830	4410	4060	3535	3290

shows that 3800 tons can be achieved per shift only from the first three loading points (green color), confirming the analysis. For this reason, removing material from points L4 and L5 must be designed with locations L1-L3 in mind.

3.4. Proposal for Combined Quarry Stone Removal from Two Loading Points

Proposals for the removal of quarry stone from two loading points during one shift based on Table 2 and Table 3 are performed for two variants of work shift distribution (length of working time—break—length of working time), provided that after loading the required number of dumpers at the first loading point, the shovel loader moves to the second loading point.

• Variants:
• Variant 1: 2 × 4 h (½ shift).
• Variant 2: a combination of periods of 4.5 × 3.5 h for two alternatives:
• Alternative 1 (A1): 4.5 h—time before moving the shovel loader (first loading point), and 3.5 h—time after moving the shovel loader (second loading point).
• Alternative 2 (A2): 3.5 h—the time before moving the shovel loader (first loading point), and 4.5 h after moving the shovel loader (second loading point).

First, the number of loaded dumpers for the selected periods was determined for each loading point, as shown in Table 4. According to the requirement, 54–55 dumpers should be loaded at each loading point for 4 h (½ shift). The points that do not meet this requirement are marked in color. They are the L4 and L5 points. A total of 61 dumpers should be loaded at each loading point for a 4.5 h period, and 48 dumpers in a 3.5 h period. The points that do not meet this requirement are marked in color in

Table 5. Numbers of loaded dumpers for the selected periods.

Period	L1	L2	L3	L4	L5
4 h	69	63	58	50	47
4.5 h	78	71	65	56	52

. Again, they are at points L4 and L5.

The next step was to create a pair of loading points, combining the L4 and L5 bottlenecks with L1, L2 and L3. The combinations of loading points L1, L2 and L3 with each other still need to be solved.

Table 6. Variant 1: Numbers of loaded dumpers for combinations of loading points.

Variant 1	2 × 4 h
Loading Points	Number of Dumpers	Loading Points	Number of Dumpers
L1-L4 L4-L1	119	L1-L5 L5-L1	116
L2-L4 L4-L2	113	L2-L5 L5-L2	110
L3-L4 L4-L3	108	L3-L5 L5-L3	105

shows the number of loaded dumpers for combinations of loading points based on Table 5 for Variant 1, and

Table 7. Variant 2: Numbers of loaded dumpers for combinations of loading points.

Variant 2		Number of Dumpers	Variant 2		Number of Dumpers
A1: 4.5 × 3.5 h	A2: 3.5 × 4.5 h		A1: 4.5 × 3.5 h	A2: 3.5 × 4.5 h
L1-L4	L4-L1	122	L1-L5	L5-L1	119
L4-L1	L1-L4	116	L5-L1	L1-L5	112
L2-L4	L4-L2	115	L2-L5	L5-L2	112
L4-L2	L2-L4	111	L5-L2	L2-L5	107
L3-L4	L4-L3	109	L3-L5	L5-L3	106
L4-L3	L3-L4	107	L5-L3	L3-L5	103

shows the number of loaded dumpers for Variant 2.

Table 6 shows that removing quarry stone using the combinations of loading points L3-L4 (L4-L3) and L3-L5 (L5-L3) does not meet the requirement of 109 unloaded dumpers per shift, marked in color. Other combinations, in some cases, meet the requirement with a margin.

From Table 7, which shows Variant 2, the color-marked combinations do not meet the given requirement. There are two combinations for the L4 point and three for the L5 point.

Based on the values in Table 6 and Table 7, it can be concluded that the combination of loading points L3 and L5 does not meet the condition of 109 loaded and subsequently unloaded dumpers. This means that under the given conditions, it is impossible to meet the daily required capacity of 3800 tons in any Variant with this combination of points.

Based on the results obtained, a model is created in Figure 5:

The first column combines stone removal from loading points L4 and L5 with points L1, L2 and L3 for Variant 1 and Variant 2 (A1, A2).

The right-hand part of the table shows the following information:

-

The periods of the work shift are assigned for individual variants and loading points;

-

The minimum number of unloaded dump trucks for each combination of loading points in a row for the removal of 3800 tons of quarry stone;

-

The red arrow shows the direction of movement of the loader during a work break.

3.5. Simulation Model for the Removal of Quarry Stone from Two Loading Points

Based on our proposal, a simulation model for removing quarry stones from two loading points was created by expanding the original simulation model, as shown in Figure 6.

Table 8. The other blocks used functions.

Block	Functions
	This block indicates whether the required number of “A” cars have been unloaded.
	The decision compares the number of unloaded dumpers with the value “A” (The value “A” for the correct redirection of dumpers represents the number of loaded dumpers from the first loading point, reduced by 2. After unloading the number of dumpers corresponding to the value “A”, the dumper is released to the second loading point and two other dump trucks are loaded).
	The gate checks whether the dumper is unloaded. If yes, it releases the dumper to the first loading point if the condition in the decision block is not met.

describes the other blocks used in the model.

The simulation model presented in this section is developed to verify the proposed transport planning model in Figure 5.

The model will also be used to carry out further simulation experiments, in which the parameters will be changed to determine how to meet the requirements for unsuitable combinations of load points.

3.6. Simulation Experiment for the Removal of Quarry Stone from Two Loading Points

Many experiments were performed on the created simulation model with a simulation time of 480 min. Some of the results are presented below, showing the results of the experiments performed for one combination of Variant 1 and Variant 2 and for a combination that does not meet the conditions below.

Experiment 6: Verification of the combination of points L2-L5 for Variant 1. During the simulation time, 110 dumpers were unloaded. The 63 dumpers were removed and the quarry stone was unloaded into the crusher in 245 min from the L2 loading point. The remaining 47 dumpers from loading point L5 were moved in 235 min. The results are summarized in Table 8.

Experiment 7: Verification of the combination of points L3-L4 for Variant 2: A1. During the simulation time, 109 dumpers were unloaded. The 65 dumpers from the first loading point, L3, moved and unloaded the quarry stone into the crusher in 273 min. The remaining 44 dumpers from loading point L4 were moved in 207 min. The results are summarized in

Table 9. Results obtained from experiments for the removal of quarry stone from 2 loading points.

Parameter	Experiment 6	Experiment 7
Variant	1	2: A1
Combination of loading points	L2-L5	L3-L4
Number of unloaded dumpers	63–47	65–44
Time use of the shovel loader, %	72	71
Time use of the unloading point %	46	46

.

Experiment 8 was carried out for Variant 2: A1, a combination of loading points L3-L5, as shown in Table 6. This combination of loading points turned out to be unsuitable. This experiment was carried out for the current state (Experiment 8A) and for changed input conditions (Experiment 8B and Experiment 8C), the task of which was to find out under what conditions it would be possible to reach the value of 109 unloaded dumpers.

Experiment 8A: The task of this experiment was to confirm that it is impossible to fulfill the given requirements by combining quarry stone removal from points L3-L5 for Variant 2. Only 106 dumpers were unloaded during the simulation time, as shown in Table 7. The 65 dumpers from the L3 loading point were unloaded in 275 min, two minutes more than in Experiment 7. Only 41 dumpers out of 44 were taken and unloaded from the loading point L5, as shown in Figure 7.

Experiment 8B: In this case, the simulation time was adjusted from 480 to 500 min. In this experiment, 109 dumpers were unloaded in 493 min. Based on this finding, this alternative is also applicable in operation, as there is a minimum extension of working time, in this case, 13 min. Several simulations of this experiment confirmed that the extension of working time was 8–14 min. For comparison, 65 dumpers from the L3 loading point were unloaded in 271 min.

Experiment 8C: The task of this experiment was to find out the number of unloaded dumpers during the removal of quarry stone when changing an input parameter—the number of dumpers—from 3 to 4. A total of 134 dumpers were unloaded in a simulation time of 480 min, as shown in Figure 7; 65 dumpers moved the quarry stone from the L3 loading point to the crusher and unloaded it in 220 min. When we compare this time with the time from Experiment 8A, the time is reduced by 50 min, or by 18%. A total of 109 dumpers were unloaded in 383 min, and 69 dumpers were unloaded from point L5.

Figure 7 shows the course of the number of unloaded dumpers for Experiment 8A and Experiment 8C. Figure 8 compares the results of Experiments 8A and 8C.

Based on the results of the experiments and their analysis, the proposed transport planning model is applicable in practice for the decision-making process in the removal of quarry stones.

The validation of the designed transport planning model is possible by testing it directly in operation. However, it should be remembered that with time, the model will have to be re-evaluated due to the progress of stone extraction and changes in the length of the transport route.

4. Conclusions

This article describes the methodology of the proposed transport planning model that applies a simulation to support decision-making in quarry operations. There are five loading points in the quarry from which, according to the plan, the quarry stone is transported to the crusher, where it proceeds for further processing. The required daily capacity of the downstream technological line is 3800 tons/day. Based on the analysis, it was found that it was impossible to fulfill this requirement for the two loading points which were the furthest from the unloading point. Experiments on the created simulation model also confirmed this fact.

For this reason, a proposal was created to combine transportation from two loading points, where one loading point is a point from which it is not possible to ensure daily capacity. Based on the results, the proposal for a combination of places (Figure 5) was created in tabular form. Subsequently, a second simulation model was created by expanding the original model. This model was used to verify the proposals from Figure 5 and combinations of unsatisfactory loading points. Based on the research results, the transport planning model and simulation models are a suitable additional tool for the decision-making process in removing quarry stones and evaluating existing transport systems.

The limitation of the proposed model is that it was created for a specific quarry operation. As mentioned above, its validity will have to be re-evaluated over time based on changes in working conditions due to the mining process.

The simulation models presented in this article can be applied to cyclically operating transport systems using similar devices by changing the input parameters of the models. The simulation model can be extended to include other loading points and other activities. In the future, the given simulation models can be used to simulate experiments related to, e.g., an increase in material removal by changing the system parameters or creating new loading points in an operation. Another possibility of use is verifying the selection of new transport system equipment. Of course, the equipment selection for the transportation system may also be analyzed from the perspective of the quality of the transported material.