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By L. F. Wright

Operations of these two mining companies have some superficial similarities-geographical location, production rates, comparatively large ore reserves; but any critical comparison of mining and milling procedures or of economics must take into account the striking differences in geological environment and mineralogy of the respective ore deposits. The properties are only about 20 miles apart on an upland surface of moderate relief (part of the Interior Plateau of central B. C.) which occupies an area east of Thompson River between the towns of Ashcroft and Merritt. Being in the "dry belt" of British Columbia, water conservation is necessary at both operations.

Jan 12, 1963

By J. R. Billingsley

The Sunro mine, operated by Cowichan Copper Co. Ltd., under lease from Sunro Mines Ltd., a Cominco subsidiary, is located on the south end of Vancouver Island. Exploration and limited development before 1960 established the existence of twelve mineralized zones. The "B" and "C" orebodies of the River zone contained an estimated 937,000 tons of 1.80r/, copper plus approximately 35 cents per ton of precious metals. On reviewing the indicated tonnage and the exploration possibilities in the River zone and the other eleven zones it was decided to construct a 1500-tpd mill. In August 1961, excavating of chambers for a crushing plant and concentrator located underground near the River zone began.

Jan 12, 1963

By W. E. Field

In the Coast Mountains approximately 110 miles north of Vancouver, the gold mine of Bralorne- Pioneer Mines Ltd. lies at an elevation of 3500 ft. The deepest or 41 level in the mine is at an elevation of 1400 ft below sea level, and the rock temperature on this level is 122.5°F. The thermal gradient is 1 "F increase in temperature for each 67.5 ft increase in depth. The mine produces between 500 and 600 tons per clay, utilizing an adit two internal shafts, and one service shaft from surface. The Queen shaft, extending from the 26 to the 41 level, IS the deepest, and most production now comes from this area. Stoping in by cut and fill using mill tailing hydraulic backfill.

Jan 12, 1963

By R. M. Porter

The Sullivan mine of Consolidated Mining and Smelting Co. of Canada Ltd. is located one mile west of Kimberley. With its daily production of 10,000 tons, the mine is the largest in British Columbia and one of the outstanding mines of the world. The Sullivan orebody dips from nearly horizontal to 45". The ore zone is a bedded replacement de- posit in argillite with a width varying from a few inches up to 300 ft. The main sulfide minerals are pyrrhotite, pyrite, galena, and sphalerite. The chief metals produced are lead and zinc, with less important qualities of iron, silver, tin, antimony, bismuth, cadmium, indium and sulfur.

Jan 12, 1963

In mid-November, a long awaited announcement was made by John Drybrough, President of Granduc Mines Ltd. In short, he said that the extensive exploration conducted by his company and its parent organization, Newmont Mining Corp., have indicated the feasibility of a commercial copper mining operation at the company's prospect located slightly north of Stewart, B.C., and just east of the Alaskan Panhandle. The financing of the project is now being planned. SUMMARY OF PROJECT Granduc reasonably assured ore reserves are estimated at 32,510,000 tons averaging 1.93% copper content before dilution, of which 20,670,000 tons averaging 2.08;:; copper content are situated above the proposed mine access tunnel at elevation 2475 above sea level. The life of the reserves at the planned rate of production is 13 1/2 years. The probability of finding substantial additions to these present reserves is considered good.

Jan 12, 1963

By J. W. Robinson

The Jersey mine, owned by Canadian Exploration Ltd., has produced nearly five million tons of lead- zinc ore from a flat-lying replacement orebody. A room and pillar method of trackless mining has been used, leaving a checkerboard pattern of 40-ft wide panels, with the hanging wall supported by pillars from 20 to 40 ft in diameter. About 35% of the remaining ore reserve is tied up in pillars. The pillars in the north end of the mine are smaller than the pillars in the south end and each contains about 500 to 1000 tons of ore. The larger pillars in the south end contain from 2000 to 8000 tons of ore each. The pillars vary from 15 to 60 ft high, depending on the thickness of the orebody at any given point. They are irregularly shaped, and from 20 to 40 ft in diameter.

Jan 12, 1963

By L. M. Kaas

Production planning techniques, particularly a production scheduling model are proposed for use in taconite mining in Minnesota. Applications of the scheduling model, on a two-year mine plan, which evaluates the equipment involved, are detailed. Data and tables are included. INTRODUCTION Fifty years ago Walter Swart, a mining engineer investigating the Minnesota taconites for the Jackling interests, made this report: "There is no doubt in my mind that this taconite mining is a game worth playing and that it can be played to a successful finish. It seems now only a question of men, money, methods and details."' The prosperous industry envisioned by Swart and other taconite pioneers is today a reality. Its growth has been spurred by technological advances in mining and beneficiation, depletion of natural ore reserves, and improvement of the economic climate. However, the massive problems that were solved to make this development possible are only a foreshadowing of the questions of "men, money, methods and details" that will be encountered in the future. The increasing demands for higher quality taconite pellets have already forced producers to examine better methods of quality control for their operations. THE NEED FOR PRODUCTION SCHEDULING The maintenance of fairly constant crude ore characteristics is of major importance to mill quality control. Through careful mixing of ore from storage bins, the concentrator staff can partially reduce the deviations of the feed analysis from the analysis desired. However, the limited effectiveness of bin mixing systems has caused some mining companies to utilize large capacity blending piles to reduce grade variations. Although the analysis of material being reclaimed from such a pile is relatively uniform, raw materials still must be scheduled from the mine into the pile in such a manner that the average analysis of all material is close to the feed analysis prescribed by the mill. PRODUCTION SCHEDULING MODEL Minnesota taconite mining companies have used three analyses to estimate how a certain ore will react to concentration. These factors are the crude magnetic iron content of the ore, the iron content of the plant concentrate produced from the ore at a standard grind, and the grindability index of the ore. The actual laboratory tests used to determine these factors have been described by Apuli.2 These data are obtained from analysis of diamond drill cores, and are the established criteria used by mine planning engineers to formulate taconite production schedules. Variations in the grade of mine output should be controlled by any production scheduling system. However, to be truly practical, production scheduling methods must also consider many of the less tangible variables that affect mine operation. These include pit layout, available equipment, and ownership of ore leases. The number and scope of the variables inherent in this type of mine planning suggested that a computer simulation could be used to solve the problem. The preparation of information for use with a computer simulation program was the initial phase of this project. Meaningful data was developed from logs of diamond drill cores. Analyses were related to their respective areas of influence by means of mine maps, Fig. 1. The three assay variables and tonnages for each area were punched on cards. In addition to these values, a location code number was punched in the first eight columns of the cards. This code is explained as follows: Column 1 Bench number Column 2 Quadrant location as determined by mine reference lines Columns 3-5 East-west coordinate Columns 6-8 North-south coordinate The heart of any simulation techniques is a well engineered mathematical model. To develop a model for taconite production scheduling, the relationship between the three assay variables had to be established. Because changes in each analysis factor affect the mill circuits in different manners, a weighting system was used to determine the relative im-

Jan 1, 1967

By C. B. Manula, H. Gezik

In planning modem-day mining operations, management needs to pass from the area of subjective decision-making to an area of objective decision-makirlg. Planning procedures currently being practiced by many mine managers depend primarily on common sense and trial-and-error methods which oftentimes never reach optimum solutions. For this reason, an attempt is made to provide an understanding of linear programming to solve production planning problems. More specifically, a linear programming model is proposed to solve the production scheduling problem as it was found to exist in the crushed stone industry. Optimal production scheduling is of prime concern here, since the industry has to cope with seasonal demands for its products. The solution, as determined through the application of the model, provides management not only with an optimal production schedule but also with the following information regarding the improvement of existing facilities: (I) the location of bottlenecks which limit production and profit; (2) the directions in which the company i fixed equipment should be expanded or modified; and (3) the evaluation of unused resources. Future trends in stone production will quite likely parallel those in the construction industry. Continued record expenditures for structures such as dams, highways, and buildings are the reasons for this accelerated growth. Production forecasts for the next decade are predicted to be around one billion tons with an approximate 7% annual increase. The problem of increasing production from 570 million tons in 1966 to 1 billion tons by 1976 offers no small challenge. This situation is further compounded by other problems and economic factors. Population expansion, government regulation, labor agreement, and changes in technology wd cause managers and operators to be confronted by many more complexities than characterize present-day operations. Some of these. problems and economic factors are: (1) In some metropolitan and other urban areas, easily accessible sources of stone are almost depleted. Transportation costs will be a prime concern of the industry. (2) Population growth has forced residential expansion to a point where many quarry operators must conform to urban and suburban regulations. (3) Government regulation in regard to air and water pollution, noise, and reclamation as well as zoning developments necessitate that crushed stone producers keep abreast of changing conditions and requirements. (4) The crushed stone industry operates under pressure of a highly competitive situation. Every company's objective is to retain a favorable marketing position by exercising great attention to production and marketing costs. This is rather difficult, since the crushed stone industry is one segment of modern mining which has to cope with a seasonal demand for its products. Among the problems posed here, the one of exercising great attention to production and marketing costs is of current interest to management. Under seasonal demand conditions, this problem creates further difficulties for managers who need to plan and forecast. STATEMENT OF THE PROBLEM Various methods of Operations Research have been developed to handle a problem whose genesis is explained above. It is very seldom, however, that these quantitative means are used in practice by the mining industry. This paper describes the use of Operations Research methods to solve a production planning problem when demand is seasonal. The seasonal production planning problem is an important one because of the substantial costs associated with changing production, employment, and inventory levels to adjust to seasonal sales patterns. Most companies try to operate within reasonable levels of employment changes and hold inventories within storage capacities. In general, little or no attempts were made to develop minimum cosl schedules or to provide objective means to compare various production policies. When demand varies within a fixed time period, 11:-oduction requirements may be met by following one

Jan 1, 1970

By W. B. Wise, H. D. Attra, W. M. Black

The purpose of this paper is to illustrate a comparatively new approach for solving a problem that has plagued oil producers for many years—how to make the most money with available field production capacity. In a very basic operation such as a top-allowable one-well field, a computer and staff of engineers are not required to determine that the best operation is to produce that well at its maximum for as long a time as it will produce. However, as the problem becomes more complex and the number of wells and number of reservoirs increase, it probably will be a wise choice to devote some engineering thought and calculation time to determine the best way to operate that field on both an ultimate and day-to-day basis. Continued developments in the field broadly referred to as "operations research" have led to a number of useful engineering analyses for optimizing field operational problems. One of these techniques, termed linear programing, is parlicularly interesting because of its potential applications to the study of complex oilfield operating problems. Linear programing is concerned with the problem of planning a complex set of interde~endenl activities in the best possible (optimal) fashion. A field application of linear programing is being used to maximize daily income from a multi-reservoir producing operation. The initial results of this study indicate that the linear programing technique (I) can be applied effectively to field problems, (2) can be used to detect unsuspected avenues for improving operations and (3) can effect a significantly improved means of controlling field operations through the use of the highspeed computer. INTRODUCTION The usual approach for studying complex field operational problems has been to look in detail at several basic plans suggested by judgement and experience and, then, to choose the best plan from those investigated. This approach is entirely satisfactory provided: (1) that judgment and experience alone will reliably pick the best or near the best program as one of the plans to be investigated; and (2), and most important, that the factors which control field production will not change. This paper deals with the large group of field operations which are at least one-step removed from a satisfactory solution using the conventional method of analysis. Problems such as pipeline- and gathering-system design, transportation studies, large field development projects and multi-reservoir producing operations fall in this latter category. To find the best program for handling these problems when the best program is not obvious, a complete optimization study is required, and high-speed computers are required to continually update the program once it is selected. Operations of this type have an extremely large number of possible solutions. However, only one will result in maximum economic return and, more important, this one program must be re-evaluated and adjusted constantly to accomodate such changing field factors as the number of producing days, the price of crude, the discovery of new reserves, declining reservoir pressure with subsequent decline in production and a host of other factors which characterize specific operations. One of the most widely used, direct methods of optimization is a numerical procedure termed "linear programing", which is designed to investigate systematically each of the possible solutions involved in a given problem, determining rapidly and uniquely the optimum answer. As implied by the name, linear programing requires that all field variables be either defined or expressed in a series of interdependent linear equalities or inequalities. These relationships are solved simultaneously, using a form of matrix solutions to determine the optimum values for each producing variable that will maximize net profits. The solution techniques used in a recent field case study is termed the "simplex method" and is discussed in full detail in Ref. 1.

By C. B. Manula, R. Venkataramani

Application of computers to present-day open-pit mining with bucket wheel excavators (BWE) is discussed. The development of the wheel excavators and their use in mining are discussed along with the necessity for building a computer model of the bucket wheel and the mathematical formulation of the problem. The simulation procedure, testing the model, and test results are summarized. Even though the mining industry in 1966 produced more ore than ever before, current extraction rates are only a fraction of what is expected in the later years of the 20th century. Nearly 90% of all metals and mineral products consumed last year was recovered by open-pit mining. This has placed great pressure on this segment of the industry which has, consequently, resulted in some spectacular developments. With increasing size of projects, the need for increased sophistication of engineering, planning, management, and administration of modern mining installations has never become more apparent. The design of complete systems for the mine and plant that fit the mold of today's business and social environments is undergoing an evolutionary process. Traditional concepts in mine development and operations are being sidestepped in favor of new ideas and principles. As the overburden thickness increases, materials handling presents a major problem to mining companies, especially those concerned with the mass production of ore and waste from low-grade deposits. The profit margin here is likely to be significantly less as to take chances with capital investment. Constant efforts are needed to improve upon productivity if the ore is to be economically mined. The development of vast low-grade deposits and thick overburden deposits calls for better tools to handle the enormous amount of materials. A natural solution to this problem is the use of bucket wheel excavators (BWE), which employ a continuous cutting head to feed the materials handling system. High productivity, versatility, economy of operation, and adaptability to most types of haulage systems combine to make BWE's attractive for large earth-moving operations. "Operating costs are being pushed down by the impact of giant haulage units, by high-speed conveyors, and computerized railroads. Matching all these with the continuous output of BWEs, one can visualize increased production at much lower costs." Historical Background The wheel excavator, which was patented in Germany in 1913, made its first appearance in an open-pit lignite mine in 1920. From this early beginning, however, BWEs were slow coming into practice. Initial developments were dampened by many design problems. From 1936 onward, major developments in design improved the wheel's ability, capacity, and versatility. A literature survey shows that wheel excavators are being used in Australia, Zambia, South Africa, the Congo, India, Indonesia, Czechoslovakia, Russia, Great Britain, Guyana, Yugoslavia, Morocco, Germany, Canada, and the U.S. for mining and loading chalk, lignite, clay, sandstone, phosphate, broken ore of iron, coal, shale, loose and semi-loose rock overburden.' A recent LMG* BWE at work in a German lignite mine weighs 6790 tons with an hourly capacity of 11,000 cu m. Although the BWE has wide applicability, its application to new mining areas poses a problem. Because of the large capital investment involved in BWE application and the narrow profit margins in mining low-grade ores or coal at depth, little margin of error can be tolerated in the selection, design, and operation of these machines. The questions that need to be answered prior to installation of a BWE for a mineable deposit are: 1.) What are the anticipated BWE performance characteristics? 2) Which method of BWE operation is most efficient? Attempts to answer these questions require a thorough knowledge of the mining system and the BWE operation. One approach is the building of a computer-ori-ented simulation model to determine how information and policy create the character of the BWE system under consideration. BWE Operation Modern BWEs generally excavate in blocks. Fig. la shows a BWE working in an established cut. The wheel is positioned to travel on the pit floor in line with the top edge of the old highwall. As it advances, a new highwall is exposed in the direction of excavation. Digging is done by rotating the wheel, swinging it from side to side in long parallel arcs, and "crowding" into the bank, by advancing the entire machine, or by the travel of the digging boom if an automatic crowd is available (Fig. lb). A second way by which the wheel can be advanced into the bank is by the falling cut method. A brief description of each of these methods follows. Cut with a Crowding Machine: At the end of every swing, the digging boom can be extended by the thickness of cut desired and the boom swung back in the reverse direction. Obviously, the thickness of bank excavated does not vary with the boom position; therefore, the slewing motion of the boom is fairly constant for uniform output. The thickness through which the digging boom can be advanced into the bank is theoretically calculated from the formula'

Jan 1, 1971

By T. J. Neil, C. B. Manula, O&apos

This paper describes a simulation model developed to analyze truck haulage problems as they are found in the open pit mining and construction industries. The model, using a computer, cycles trucks between their assigned loading and discharge points over a measured haulage route. Required parameters for model operation include the number and type of operating trucks and loading units, maximum truck acceleration and velocity, a realistic vehicular deceleration rate, and the ratio of ore to total material loaded at each origin. Equipment performance characteristics, system profile characteristics, and service time distributions are also included. Records are kept of all waiting times at the loading and discharge points and of any truck interference on the haulroads (moving queues), and a current journal of ore and waste production is maintained. In the pursuit of reduced truck haulage costs, management is confronted with three particular problems in truck and shovel mines. 1) What is the best assignment of trucks to shovels in the system? 2) If new equipment is needed, which new shovels and trucks will operate most effectively in the system? 3) Are any physical changes in the haulage layout warranted to increase productivity? For small open-pit mines the answers to these questions may be obvious. However, for larger operations complex problems are encountered with production and system configuration relationships usually being unknown. The mining simulator discussed here was developed to aid management in evaluating alternative pit-haulage schemes to reduce the risk involved in the selection and assignment of equipment in open-pit truck and shovel mining systems. There are two fundamental differences between this model and other truck haulage simulators: one is a difference of degree, the other a difference of kind. This model can simulate more complex systems than previous models by allowing transportation of material from multiple mine faces to multiple mine destinations and by being readily adaptable to a wide variety of mine layouts. The second difference that characterizes the model is the standard simulation of truck movements. This technique enables each vehicle to perform according to its mechanical capabilities and the physical profile of the haul road. Among the similarities between other truck haulage simulators and this model are the recognition of the probabilistic nature of certain service times in the system and the use of a digital computer to operate the model. The computer inherits the staggering computational and bookkeeping work loads and provides a means for compressing the time scale by allowing several days' operations to be simulated in a matter of minutes. DISTRIBUTION SYSTEM Basic aspects of the system analyzed here may be brought into focus by referring to Fig. 1, a schematic of the typical open pit truck-shovel operation programmed into the simulation model. A single stage materials handling scheme is employed to transport both ore and waste. Loaded trucks are directed to a crusher or stockpile if ore is being mined, while overburden is conveyed to a waste dump. Each shovel has a given number of assigned trucks and has two associated haulage routes, one for waste and one for ore. Up to 5 shovels and as many as 10 trucks per shovel (or a total of 50 trucks) can be operated in the model. Essentially the model, using a computer, cycles trucks between their assigned shovels and the proper discharge points over measured haulage routes. Required input parameters for model operation include the number of shifts to be simulated, operating time per shift, number and type of operating trucks and shovels, maximum truck acceleration and velocity, a realistic vehicular deceleration rate, and the ratio of ore to total material loaded at each shovel. Equipment performance characteristics, system profile characteristics, and service time distributions are

Jan 1, 1968

By Daniel T. Brian, O&apos

Exploration targets and results in the mining industry are commonly summarized in terms of tonnage and grade. Business appraisals require, as additional information, the expected profit or loss implied by the physical summay. The early incorporation of knowledge gained through financial analysis computations can provide a means for economically resolving many common exploration and development problems. Examples of such applications from the activities of exploration planning, reconnaissance, land acquisition, and development planning are presented. While prospecting for mineral deposits, geologists are frequently asked what they hope to find or have found. The response is usually descriptive, such as "50 million tons of 1% copper," and the economic implications are deferred until a capital investment decision is required. At that time, a financial analysis integrating the four analysis components is completed. These components are: 1) Tonnage and Average grade above cutoff grade 2) Capital and operating costs 3) Product value 4) Appropriate profit or loss accounting Close examination of these components reveals that there is no intrinsic reason for avoiding financial analyses at earlier stages in exploration, as long as the uncertainty of the estimate is realized. Tonnage and average grades are commonly estimated at early stages in exploration. For this component, the appropriate cutoff is the major uncertainty. Capital and operating costs are difficult to estimate before detailed quantitative information is available, but estimates useful for the types of applications pertinent to earlier exploration stages are possible. Product value estimates may or may not be difficult, depending on the pricing structure of the particular commodity. A simplified accounting procedure usually may be adopted without introducing any significant loss of validity. This paper describe some of the financial analysis applications which are pertinent during mineral exploration and deposit development. These applications lend additional insight during exploration planning, land acquisition, physical exploration, and development. The examples are oriented toward a hypothetical management investment policy which is based on a single criterion, a minimum 15% discounted cash flow rate of return. In reality, managements use multiple criteria. This simplification is convenient for purposes of presentation and does not invalidate the examples. Computers are useful, but not a requirement, for the applications described. The use of computers provides the results on a more timely basis at costs lower than equivalent manual computations. UNDERLYING PRINCIPLES AND ANALYSIS METHODS Data estimates are subject to uncertainty. The analyst may choose to assess this uncertainty by using one of two principles. Each principle has some advantages. The principle most commonly employed is "assumed certainty." Each financial analysis is based on one set of selected data, which may represent the average estimates, minimum expected conditions, etc. The critical data is identified by making successive calculations, varying the values used. Such studies, called sensitivity analyses, isolate the economic effect of various uncertainties, and guide the allocation of future efforts toward improving the overall accuracy of the analysis. A second principle, "assumed risk," examines the interactive effect of the current uncertainties by qualitatively assigning probability distributions to each data element. These probability distributions indicate the currently appraised chance that a given numerical value will prove to be true. The corresponding procedure, risk analysis, consists of processing many individual financial analyses, each analysis being based on randomly selected numerical values from the probabilistic distributions. The result is a numerical presentation of the uncertainty, but care must be exercised in the interpretation of results. The advantage of this procedure is the direct measurement of economic uncertainty caused by the interaction of uncertain estimates. Kennecott Copper Corp. has developed computer systems for accomplishing sensitivity and risk analyses.

Jan 1, 1970

By D. P. Harris

This paper surveys a few of the quantitative exploration models that might be of interest to an ex-plorationist seeking to apply methods of operations research to mineral exploration. A general development of each model is presented, and the model is evaluated relative to the exploration activity. Finally, a philosophy of exploration and mineral resources is presented, and a possible exploration strategy incorporating concepts of some of these models consistent with this philosophy is proposed. The high cost of conducting an effective exploration program has generated an increasing interest in methods of optimizing the allocation of the resources of a firm engaged in mineral exploration. As a step towards this, mathematical models have been formulated which are designed to quantify those variables of mineral exploration in such a manner that they can be integrated with all phases of the firm's operations so as to optimize its profit objective. QUANTITATIVE MODELS General: Exploration models express at least two concepts: mineral occurrence and effectiveness of search. In simplest terms, an exploration model might be formulated mathematically as follows: P(X) = F(X) . G(X), where P(X) = the unconditional probability of discovery of X deposits, F(X) = the probability of occurrence of X deposits, and G(X) = the probability of discovery of X deposits, conditional upon their existence in the search area. With these general models as a basis of reference, some of the specific exploration models are examined below. The Allais Model: DEVELOPMENT - M. Allaisl performed the first study in which a philosophy of exploration was formulated into a probability framework. His objective was to estimate the economic profitability of the exploration of the Algerian Sahara. Allais divided the Algerian Sahara into blocks of land (called cells) 10 km square and defined the random variable, X, as the number of mining districts per cell. Thus, F(X) would describe the probability of 1, 2, or n mining districts occurring within any cell of the search area. Allais reasoned that his best information as to the form of the distribution, F(X), was the number of mining districts per cell that had been found in well explored areas in the world, such as the Western States of the U.S. He found that the Poisson function produced a reasonably good fit to the distribution of districts in these explored areas: where? is a parameter, such as the average number of districts per cell. Thus, by determining the value

Jan 1, 1968

By Y. C. Kim, C. B. Manula

This paper is concerned with the details of the derivation of an operations research model, specifically linear programming, to solve production scheduling problems. While some results are presented for an actual study, the calculation of cases of a more general nature have not yet been completed. Production managers from those segments of the mining industry that experience a seasonal or highly variable sales demand find it rather difficult to develop overall producing plans for their organizations. In an attempt to solve this technical problem, demands are usually met by keeping production corresponding exactly to sales. This results in a fluctuating production schedule which is costly to maintain because of overtime premiums in periods of high requirements and because of costs associated with an idle mine plant during slack periods. An alternate solution may be to produce part of the desired amount and make up any deficiencies by using overproduction in previous time periods. This tends to smooth the production pattern through the use of a stockpile. However, because of associated storage costs, the solution may again be undesirable if it yields comparatively large surpluses. In general, this type of scheduling problem has an infinite number of solutions which satisfy the requirements. These are largely dependent on the extent to which an operation is geared to changes in production and on the size of its stockpiling facility. The determination of an efficient schedule is implied, therefore, as one lying between two extreme solutions, i.e., one that minimizes surpluses and one that minimizes output fluctuations. These conflicting objectives result in an economic balance problem between the costs of carrying surpluses forward from slack periods versus the cost of high production levels during peak demands. Various methods of operations research have been developed to handle problems whose genesis is explained above. It is very seldom, however, that these quantitative means are used in practice by the mining industry. Those who are familiar with mining operations know that most managers have not progressed satisfactorily in this area. Production planning systems which most mines employ today are often no more than rough records of management's mental planning. For small mines these means are normally sufficient; but as operations grow larger and become more complex, this type of planning ability is no longer adequate. Managers may find themselves losing control of cost relationships, taking longer to outline production sequences, and becoming forgetful of certain resource availabilities. ORIGIN AND SCOPE OF A SCHEDULING PROBLEM The more fundamental aspects of the production scheduling problem can be brought into focus by demonstrating how it exists in a bituminous coal mine* located in southwestern Pennsylvania. This mine, operating in the Pittsburgh seam, uses a definite room-and-pillar block system of mining, which is planned and carried out with only slight variations to meet local conditions. The coal seam, which is firm and varying in thickness from 8 to 9 ft with the bottom 6% ft being extracted, is under 500 ft of cover and opened by a shaft. The bulk of the coal (60%) is obtained from the pillaring activity with the mains and sections, which are considered primarily as development activities, contributing the balance of 12% and 28%, respectively. All coal is won by a ripper-type continuous miner in conjunction with shuttle-cars directly behind a loading machine. Track haulage is employed to move coal from these machine centers to the shaft bottom. The coal, thus mined, is shipped to one destination as part of a mixture of coals to be used in the making of steel. A material flow diagram of this distribution scheme is illustrated in Fig. 1. PROBLEM STATEMENT From the above types of shipments and mining limitations, the basic problem is to determine an economic means of producing and stockpiling a single coal product produced in three different machine centers (main, section, and pillar). Fig. 2 shows total demand for coal as a function of time. Because of the extremely high peak demands, slack period production must be stored and then distributed

Jan 1, 1969

By J. F. Abel

It can be concluded that rock mechanics instrumentation, geologic mapping, and operations research in combination will produce an accurate estimate of tunnel support requirements for establishing a steel-set load prediction model. This estimate can be used to increase safety and to reduce tunneling costs. Unnecessary supports can be eliminated; overloaded or insufficient supports can be replaced on a more scientific basis than previously possible. Only a limited number of measurements are required to develop a steel set load prediction model. The results of this study can be applied generally; but, every tunnel must be considered as a separate problem. The construction and geologic variables which were found to be significant in the prediction of rock loads in this tunnel were: steel section modulus, set blocking points, percentage of alteration, relative degree of faulting, relative water condition, distance to the nearest fault, and average joint spacing. INTRODUCTION A combined engineering geology and rock mechanics program was performed throughout the Straight Creek tunnel pilot bore in Colorado in order to develop a technique for estimation of steel tunnel support loads. The technique involves the construction of a statistical model from measured steel set loads, geologic and construction factors. The aim of the investigation was to produce a set of charts, based on these factors, which could be used by a trained man equipped with a hand lens and measuring tape, to specify a safe and economical steel size and set spacing. In order to be of practical value the measured parameters had to be rapidly measurable and capable of quantification. It was apparent from the beginning that quantitative values for many of the geologic factors would have to be arbitrary. It was impossible to ascertain in advance which of the readily determi-nable geologic and construction factors significantly affect steel set loading. As the investigation progressed the number of observations was systematically reduced in order to determine the minimum amount of data which would be necessary for a significant mathematical model. This paper presents an analysis of all the variables measured at each instrumented steel set. The statistical significance of the measured variables was determined. On the basis of this determination, a mathematical model was constructed and charts were prepared to select and space the steel tunnel supports at any given point in the tunnel. Once the effect of geologic and construction factors on set loading is sufficiently established, it is possible to exert close control over the selection of steel tunnel support size and spacing. The magnitude of this rock mechanics program can be appreciated when it is known that there were 44 instrumentation stations for which steel set loads were measured. For each of these sets there were three construction variables recorded, and nine geologic parameters determined (Table I). A rough approximation of the cost for both the collection and reduction of the data would be $50,000. This is in addition to the cost of geologic mapping.

Jan 1, 1967

By Sukumar Bandopadhyay

The escalation in micro-computer power during the past ten years has been instrumental in the realization of operations research and other mine planning tools as a practical mine design tool. Present day utilization of computer techniques varies across a wide spectrum of activities: from operations research to aid mine management through payroll processing to mine design, monitoring and control. With the advances in computers, increasing automation and robotization in mining, operations research techniques are being used in modeling and evaluation. The trend discernable in mine planning is increasing use of operations research models. In the next ten to fifteen years, nanotechnology will enter in our lives in a big way. There are several methods being pursued for information processing at the nanoscale. The mineral industry management in the 21st century will be faced with several new challenges and opportunities from these advances in the mineral industry applications, data and information acquisition and transmission technology

Aug 25, 2002

By Thomas V. Falkie

The use of operation research and computer techniques in the mineral industries continued to gain acceptance during 1966. There are generally at least five sources of information for researchers and practitioners in the field: (1) Technical periodicals like MINING ENGINEER- ING, Mining Congress Journal, etc. (2) Proceedings of the Symposium and Short Course on Computers and Operations Re- search in Mineral Industries, held last year at Penn. State University and jointly sponsored by the University of Arizona, Stanford University, Colorado School of Mines, Penn. State University and the Society of Mining Engineers of AIME. (3) Various local conferences, such as the 1966 Rocky Mountain Minerals Conference held in Denver. (4) United States Bureau of Mines publications. (5) The AIME annual meeting in New York and the SME Fall Meeting in Tampa.

Jan 2, 1967

By V. Srajer

Operations research includes the application of systems analysis to mining operations, the acquisition of field data and the creation and verification of computer models of these operations. These activities result in the generation of computerized mine planning tools which have a firm base-in observational facts. The Surface Mining Laboratory of the Canada Centre for Mineral and Energy Technology (CANMET), in Devon, Alberta has been engaged in operations research activities since 1982. The work includes computer simulation of truck/shovel, continuous mining equipment and dragline operations. Other examples include cooperative programs with industry (such as the demonstration and evaluation of an integrated truck management system), the development of a device to measure off-highway truck rolling resistance, and an automated remote data transmission network. The presentation outlines existing and proposed research projects of the Operations Research group.

Jan 1, 1989

By J. S. Aronofsky

The areas are identified where the techniques of operations research have been applied in the petroleum industry. Particular emphasis is given to the linear program models that have been used for solving management problems. Of the entire gamut of applications for linear programming, attention is focused on the models that represent underground reservoirs. Such models could be used to help schedule drilling rig operations and to schedule oil production. A framework is established for placing the model for underground reservoirs in the proper perspective along with the models which represent other facets of the oil business. One of the remarkable phenomena of this century is the rapid growth in the size and' complexity of human organizations. The mere size of modern business means that administrative decisions can affect vast quantities of capital and large number of people. Mistakes can be tremendously costly, and a single wrong decision can require years for rectification. Moreover, the pace of modern business is such that decisions are required more rapidly than ever before; the mere postponement of action can give a decided advantage to a competitor. It is not surprising that the increase in difficulty of decision making has been followed by efforts to put this activity on a more objective basis. The growing science of operations research, which relies heavily on the methods that have proved so successful in the physical sciences, is one aspect of these efforts.

Jan 1, 1962

By D. Gatzemeier

The Jewett mine is a surface mine in central Texas, producing about 8,000,000 tons/year of lignite. The mine covers 46,000 acres of land. Approximately 430 people are employed by Northwestern Resources Company and through their efforts, the Jewett Mine is one of the most productive mining operations in the country. The Jewett Mine utilizes a unique structure of staff to convert Operations Research into plans which can be implemented at the "front line" level of the Operations Team. This planning not only makes the mine cost effective and efficient, but promotes creativity in mining, which promotes ongoing research to lower the cost of mining.

Jan 1, 1993