A model based method for evaluation of crop operation scenarios in greenhouses

<strong>Abstract</strong> This research initiated a model-based method to analyse labour in crop production systems and to quantify effects of system changes in order to contribute to effective greenhouse crop cultivation systems with efficient use of human labour and technology. This method was gradually given shape in the discrete event simulation model GWorkS, acronym for Greenhouse Work Simulation. Model based evaluation of labour in crop operations is relatively new in greenhouse horticulture and could allow for quantitative evaluation of existing greenhouse crop production systems, analysis of improvements, and identification of bottlenecks in crop operations. The modelling objective was a flexible and generic approach to quantify effects of production system changes. Cut-rose was selected as a case-study representative for many cut-flowers and fruit vegetables. The first focus was a queueing network model of the actions of a worker harvesting roses in a mobile cultivation system. Data and observations from a state-of-art mobile rose production system were used to validate and test the harvesting model. Model experiments addressed target values of operational parameters for best system performance. The model exposed effects of internal parameters not visible in acquired data. This was illustrated for operator and gutter speed as a function of crop yield. The structure and setup of the GWorkS model was generic where possible and system specific where inevitable. The generic concept was tested by transferring GWorkS to harvesting a greenhouse section in a static growing system for cut-roses and extending it with navigation in the greenhouse, product handling, and multiple operator activity (up to 3 workers). Also for rose harvesting in a static growing system, the model reproduced harvesting accurately. A seven workday validation for an average skilled harvester showed a relative root mean squared error (RRMSE) under 5% for both labour time and harvest rate. A validation for 96 days with various harvesters showed a higher RRMSE, 15.2% and 13.6% for labour time and harvest rate respectively. This increase was mainly caused by the absence of model parameters for individual harvesters. Work scenarios were simulated to examine effects of skill, equipment, and harvest management. For rose yields of 0.5 and 3 harvested roses per m2, harvest rate was 346 and 615 stems h-1 for average skilled harvesters, 207 and 339 stems h-1 for new harvesters and 407 and 767 stems h-1 for highly skilled harvesters. Economic effects of trolley choice are small, 0-2 € per 1000 stems and two harvest cycles per day was only feasible if yield quality effects compensate for extra costs of 0.2-1.1 eurocents per stem. In a sensitivity analysis and uncertainty analysis, parameters with strong influence on labour performance in harvesting roses in a static system were identified as well as effects of parameter uncertainty on key performance indicators. Differential sensitivity was analysed, and results were tested for linearity and superposability and verified using the robust Monte Carlo method. The model was not extremely sensitive for any of the 22 tested input parameters. Individual sensitivities changed with crop yield. Labour performance was most affected by greenhouse section dimensions, single rose cut time, and yield. Throughput was most affected by cut time of a single rose, yield, number of harvest cycles, greenhouse length and operator transport velocity. In uncertainty analysis the coefficient of variation for the most important outputs labour time and throughput is around 5%. The main sources of model uncertainty were in parallel execution of actions and trolley speed. The uncertainty effect of these parameters in labour time, throughput and utilisation of the operator is acceptably small with CV less than 5%. The combination of differential sensitivity analysis and Monte Carlo analysis gave full insight in both individual and total sensitivity of key performance indicators. To realise the objective of model based improvement of the operation of horticultural production systems in resources constrained system, the GWorkS-model was extended for simultaneous crop operations by multiple workers analysis. This objective was narrowed down to ranking eight scenarios with worker skill as a central theme including a labour management scenario applied in practise. The crop operations harvest, disbudding and bending were considered, which represent over 90% of crop-bound labour time. New sub-models on disbudding and bending were verified using measured data. The integrated scenario study on harvest, disbudding and bending showed differences between scenarios of up to 5 s per harvested rose in simulated labour time and up to 7.1 € m-2 per year in labour costs. The simulated practice of the grower and the scenario with minimum costs indicated possible savings of 4 € m-2 per year, which equals 15% of labour cost for harvest, disbudding and bending. Multi-factorial assessment of scenarios pointed out that working with low skilled, low paid workers is not effective. Specialised workers were most time effective with -17.5% compared to the reference, but overall a permanent team of skilled generalists ranked best. Reduced diversity in crop operations per day improved labour organisational outputs but ranked almost indifferent. The reference scenario was outranked by 5 scenarios. Discrete event simulation, as applied in the GWorkS-model, described greenhouse crop operations mechanistically correct and predicts labour use accurately. This model-based method was developed and validated by means of data sets originating from commercial growers. The model provided clear answers to research questions related to operations management and labour organisation using the full complexity of crop operations and a multi-factorial criterion. To the best of our knowledge, the GWorkS-model is the first model that is able to simulate multiple crop operations with constraints on available staff and resources. The model potentially supports analysis and evaluation of design concepts for system innovation.