A simulation model for truck-shovel operation

A truck-shovel mining system is a flexible mining method commonly used in surface mines. Both simulation and queuing models are commonly used to model the truckshovel mining operation. One fundamental problem associated with these types of models is that most of the models handle the truck haulage system as macroscopic simulation models, which ignore the fact that a truck as an individual vehicle unit dynamically interacts not merely with other trucks in the system but also with other elements of the traffic network. Some important operational factors, such as the bunching effect and the influence of the traffic intersections, are either over simplified or ignored in such a macroscopic model. This thesis presents a developed discrete-event truck-shovel simulation model, referred to as TSJSim (Truck and Shovel JaamSim Simulator), based on a microscopic traffic and truck-allocation approach. The TSJSim simulation model may be used to evaluate the Key Performance Indicators (KPIs) of the truck-shovel mining system in an open pit mine. TSJSim considers a truck as an individual traffic vehicle unit that dynamically interacts with other trucks in the system as well as other elements of the traffic network. TSJSim accounts for the bunching of trucks on the haul routes, practical rules at intersections, multiple decision points along the haul routes as well as the influence of the truck allocation on the estimated queuing time. TSJSim also offers four truck-allocation modules: Fixed Truck Assignment (FTA), Minimising Shovel Production Requirement (MSPR), Minimising Truck Waiting Time (MTWT) and Minimising Truck Semi-cycle Time (MTSCT) including Genetic Algorithm (GA) and Frozen Dispatching Algorithm (FDA)

High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization

Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.

A Goal Programming Model for Dispatching Trucks in an Underground Gold Mine

The cost of transporting mined material in an underground mine is major. This cost typically represents between 50 to 60 percent of a mine’s total operating costs. The problem of dispatching trucks in an underground gold mine is, therefore, of major economic importance and warrants the use of a decision support model. The developments of a realistic decision-support model for the dispatching problem in an underground gold mine is addressed in this paper. The problem must address multiple conflicting objectives and therefore a goal programming model was formulated. The model was applied to a case study, the Red Lake underground gold mine, in Ontario, Canada. The results showed major improvements in meeting the multiple objectives of this problem versus a single objective model. The results illustrate the flexibility that the dispatching problem (in underground gold mines) yields when solved for multiple objectives using a goal programming model

A Goal Programming Model for Dispatching Trucks in an Underground Gold Mine

The cost of transporting mined materials in an underground mine is major. This cost typically represents between 50 to 60 percent of a mine’s total operating costs. The problem of dispatching trucks in an underground gold mine is of major economic importance and warrants the use of a decision support model. The developments of a realistic decision-support model for the dispatching problem in an underground gold mine were addressed in this paper. The problem must address multiple conflicting objectives, and therefore, a goal programming model was formulated. The model was applied to a case study, the Red Lake underground gold mine, in Ontario, Canada. The results showed major improvements in meeting the multiple objectives of this problem versus a single objective model. The results also illustrate the flexibility that the dispatching problem (in underground gold mines) yields when solved for multiple objectives using a goal programming model

A Goal Programming Model for Dispatching Trucks in an Underground Gold Mine

The cost of transporting mined material in an underground mine is major. This cost typically represents between 50 to 60 percent of a mine’s total operating costs. The problem of dispatching trucks in an underground gold mine is, therefore, of major economic importance and warrants the use of a decision support model. The developments of a realistic decision-support model for the dispatching problem in an underground gold mine is addressed in this paper. The problem must address multiple conflicting objectives and therefore a goal programming model was formulated. The model was applied to a case study, the Red Lake underground gold mine, in Ontario, Canada. The results showed major improvements in meeting the multiple objectives of this problem versus a single objective model. The results illustrate the flexibility that the dispatching problem (in underground gold mines) yields when solved for multiple objectives using a goal programming model

Coping with the Curse of Dimensionality by Combining Linear Programming and Reinforcement Learning

Reinforcement learning techniques offer a very powerful method of finding solutions in unpredictable problem environments where human supervision is not possible. However, in many real world situations, the state space needed to represent the solutions becomes so large that using these methods becomes infeasible. Often the vast majority of these states are not valuable in finding the optimal solution. This work introduces a novel method of using linear programming to identify and represent the small area of the state space that is most likely to lead to a near-optimal solution, significantly reducing the memory requirements and time needed to arrive at a solution. An empirical study is provided to show the validity of this method with respect to a specific problem in vehicle dispatching. This study demonstrates that, in problems that are too large for a traditional reinforcement learning agent, this new approach yields solutions that are a significant improvement over other nonlearning methods. In addition, this new method is shown to be robust to changing conditions both during training and execution. Finally, some areas of future work are outlined to introduce how this new approach might be applied to additional problems and environments

A comparative study of algorithms for solving the multiobjective open-pit mining operational planning problems

This work presents a comparison of results obtained by different methods for the Multiobjective Open-Pit Mining Operational Planning Problem, which consists of dynamically and efficiently allocating a fleet of trucks with the goal of maximizing the production while reducing the number of trucks in operation, subject to a set of constraints defined by a mathematical model. Three algorithms were used to tackle instances of this problem: NSGA-II, SPEA2 and an ILS-based multiobjective optimizer called MILS. An expert system for computational simulation of open pit mines was employed for evaluating solutions generated by the algorithms. These methods were compared in terms of the quality of the solution sets returned, measured in terms of hyper volume and empirical attainment function (EAF). The results are presented and discussed

Production scheduling and mine fleet assignment using integer programming

Production Scheduling, extraction sequence of mining blocks in different production periods to maximize profit over the life of the mine and subjected to different constraints, is an important aspect of any mining activity. Mine production scheduling problem can be solved using various approaches, but the best approach is one which can give an optimal result. Production scheduling solely cannot result in a proper planning thus, fleet assignment problem needs to be incorporated into production scheduling problem to have a realistic mine plan. Proper fleet assignment ensures that the fleet is not under or over utilized. Fleet assignment problem is integer type programming since, size of fleet cannot be a floating number. In this thesis, production scheduling and fleet assignment problem are solved using branch and cut algorithm. Production schedule for 4736 blocks from a case study of coal mine is done with a production period of 5 years. Solution time for solving the production scheduling problem was 48.14 hours with an NPV value of Rs 4.45938x1011. Short terms production scheduling is done for one year and the NPV value obtained was Rs 7.59796x1010 with a solution time of 57.539 minutes. Fleet assignment is done for first year and is observed that the size of dumper fleet can be reduced to 30 thus saving huge amount of initial capital investment