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Heuristic strategy for scheduling farm operations
BOOK
REVIEW
van Elderen, E., Heuristic Strategy for Scheduling Farm Operations, Centre for Agricultural Publishing and Documentation, Wageningen, The Netherlands, 1977, 217 pp. Mathematical models of crop production systems can be used for two basic purposes. The most common is to provide information on overall system performance. This can provide an understanding of the importance of the different inputs and a means for comparing the performance of systems and the way they should be operated under different conditions. For example, for harvesting a given area of cereals, the combine size, operating speed and the harvest starting date which will minimise the cost of the harvesting operation and the grain losses can be determined. A second basic purpose for which information from mathematical models can be used is to control an operating system. For cereal harvesting this will concern decisions as to when to combine. It is necessary not only to formulate and obtain input data for a model providing control information but also to determine how the model is to be used--for example, whether computer terminals are available and whether farmers can be trained to use them. If a large number of farms in a given area were producing a similar crop, day to day advice as to the best decisions for the more likely sets of circumstances could be made available as a dialled telephone recording. There are few reports of mathematical models which provide control information for agricultural systems and no report of them being used commercially. Heuristic Strategy for Scheduling Farm Operations describes a simulation model intended to provide control information on how to schedule the machinery and labour as grain harvesting progresses. The author does not seem to have given any consideration as to how it would be used in practice. Operational research heuristic procedures are intuitively designed and unlikely to give optimal solutions. Dynamic programming does provide an optimal solution and is the usual technique for making a sequence of interrelated decisions in an optimal way. The author feels that to use dynamic programming for such a complex problem as scheduling machinery and labour for grain harvesting would be difficult to programme and would require excessive computer time to run. The operational 155 Agricultural Systems (4) (1979)--© Applied Science Publishers Ltd, England, 1979 Printed in Great Britain
156
BOOK REVIEW
research technique chosen, however, should be related to the purpose of the information provided and in many cases a simple model is adequate. The simulation model describes in some detail the interaction between a maturing grain crop, a harvesting system of men and machines and, in the examples given, seasonal sets of weather data. The model includes decision routines which take into account the state of the harvest, the crop and weather forecast at each decision interval. An analysis of weather data for a single site 1902-1951 (Table 3) is used to forecast weather based on the amount of rain, atmospheric pressure and wind direction. The suitability of this method of forecasting does not seem to have been examined. While the structure of this simulation model can permit it to be used to provide current operational information during the harvest, the examples are of overall system performance. It is stated on page 151 that: 'The aim of using the grain harvest to perform experiments with the simulation model was to learn how such an approach behaved and to find out whether the approach is worthwhile in even more complicated environments such as a grassland farm'. It is unlikely, however, that such a simulation can be used to represent other cropping systems without drastic rewriting though no doubt parts of the program could be re-used. The author justifies the correctness of his model on the basis of a satisfactory use of equipment and variable costs and the correct representation of the flow of material (page 155). Logical performance must at least partly justify the validity of a model. It is, however, unrealistic to claim that the results obtained for a complex simulation model are more genuine because a comparatively simple linear program underestimates these costs. The comparison is spurious as the linear program is more suitable for providing information on how the system should be optimised and the corresponding overall optimal result. By definition a linear program should provide a better result than a sub-optimal model. While 1 appreciate the tremendous effort that has gone into the development and description of this simulation model, I am unable to recommend this book. I do not believe the author has shown how this model can be used to provide information to control agricultural production systems. I believe that in many situations a simpler approach would permit a dynamic program to provide adequate optimal information. As a reader I found the book exceedingly difficult to follow due to the way the material was presented. The tables and figures are also unsatisfactory. For those interested in operational research applications to agricultural production systems 1 would recommend first one of the basic texts in OR and then appropriate published papers on particular applications. D. S. BOYCE
REVIEW
van Elderen, E., Heuristic Strategy for Scheduling Farm Operations, Centre for Agricultural Publishing and Documentation, Wageningen, The Netherlands, 1977, 217 pp. Mathematical models of crop production systems can be used for two basic purposes. The most common is to provide information on overall system performance. This can provide an understanding of the importance of the different inputs and a means for comparing the performance of systems and the way they should be operated under different conditions. For example, for harvesting a given area of cereals, the combine size, operating speed and the harvest starting date which will minimise the cost of the harvesting operation and the grain losses can be determined. A second basic purpose for which information from mathematical models can be used is to control an operating system. For cereal harvesting this will concern decisions as to when to combine. It is necessary not only to formulate and obtain input data for a model providing control information but also to determine how the model is to be used--for example, whether computer terminals are available and whether farmers can be trained to use them. If a large number of farms in a given area were producing a similar crop, day to day advice as to the best decisions for the more likely sets of circumstances could be made available as a dialled telephone recording. There are few reports of mathematical models which provide control information for agricultural systems and no report of them being used commercially. Heuristic Strategy for Scheduling Farm Operations describes a simulation model intended to provide control information on how to schedule the machinery and labour as grain harvesting progresses. The author does not seem to have given any consideration as to how it would be used in practice. Operational research heuristic procedures are intuitively designed and unlikely to give optimal solutions. Dynamic programming does provide an optimal solution and is the usual technique for making a sequence of interrelated decisions in an optimal way. The author feels that to use dynamic programming for such a complex problem as scheduling machinery and labour for grain harvesting would be difficult to programme and would require excessive computer time to run. The operational 155 Agricultural Systems (4) (1979)--© Applied Science Publishers Ltd, England, 1979 Printed in Great Britain
156
BOOK REVIEW
research technique chosen, however, should be related to the purpose of the information provided and in many cases a simple model is adequate. The simulation model describes in some detail the interaction between a maturing grain crop, a harvesting system of men and machines and, in the examples given, seasonal sets of weather data. The model includes decision routines which take into account the state of the harvest, the crop and weather forecast at each decision interval. An analysis of weather data for a single site 1902-1951 (Table 3) is used to forecast weather based on the amount of rain, atmospheric pressure and wind direction. The suitability of this method of forecasting does not seem to have been examined. While the structure of this simulation model can permit it to be used to provide current operational information during the harvest, the examples are of overall system performance. It is stated on page 151 that: 'The aim of using the grain harvest to perform experiments with the simulation model was to learn how such an approach behaved and to find out whether the approach is worthwhile in even more complicated environments such as a grassland farm'. It is unlikely, however, that such a simulation can be used to represent other cropping systems without drastic rewriting though no doubt parts of the program could be re-used. The author justifies the correctness of his model on the basis of a satisfactory use of equipment and variable costs and the correct representation of the flow of material (page 155). Logical performance must at least partly justify the validity of a model. It is, however, unrealistic to claim that the results obtained for a complex simulation model are more genuine because a comparatively simple linear program underestimates these costs. The comparison is spurious as the linear program is more suitable for providing information on how the system should be optimised and the corresponding overall optimal result. By definition a linear program should provide a better result than a sub-optimal model. While 1 appreciate the tremendous effort that has gone into the development and description of this simulation model, I am unable to recommend this book. I do not believe the author has shown how this model can be used to provide information to control agricultural production systems. I believe that in many situations a simpler approach would permit a dynamic program to provide adequate optimal information. As a reader I found the book exceedingly difficult to follow due to the way the material was presented. The tables and figures are also unsatisfactory. For those interested in operational research applications to agricultural production systems 1 would recommend first one of the basic texts in OR and then appropriate published papers on particular applications. D. S. BOYCE