Designing sustainable production systems that are respectful of the environment and produce safe food of quality is a challenge for the future. A critical step is to find the best combinations of genetic resources and cultural practices adapted to target environments. The objective of this work is to illustrate how an ecophysiological process-based simulation model could be possibly used to design genotypes and to propose innovative production systems, by applying a methodology of optimization. As example, we studied the peach-brown rot system and used the 'Virtual Fruit', a processbased model that has been extensively tested, to perform virtual experiments. The challenge was to optimize the trade-off between antagonistic criteria of major importance for both fruit quality (increasing fruit mass and sweetness) and sensitivity to brown rot (decreasing skin density of cracks) in four different cultural scenarios. A multiobjective evolutionary algorithm, namely NSGA-II, was applied to solve this multiobjective optimization problem based on the 'Virtual Fruit'. The optimized variables were six parameters of the 'Virtual Fruit', selected on the basis of a sensitivity analysis. This optimization method provided a large diversity of solutions among which the decision-maker can choose the best suited trade-off between criteria according to a particular objective. Most of the optimized solutions were distributed along Pareto fronts suggesting a good convergence of the algorithm. Moreover, it also provided some solutions located in noncrowded zones which constitute some original alternatives for the final decision-maker. The results confirmed the strong antagonism between the criteria considered. Large fruits had a weak sweetness and high crack density and for a given mass, those with improved sweetness had higher crack density. In a current breeding scheme, fruit mass would be the only criteria considered but alternative schemes could be considered for future, favoring organoleptic quality or environment friendly practices. In those cases, some interesting optimized solutions were identified.
The home health care (HHC) covers a wide range of health care services carried out in patients' home in case of illness, injury or aging. Each caregiver should as far as possible adhere to the schedule set by the decision maker. However, unforeseen events would sometimes occur and delay the delivery of care services, which will qualify the service as poor or even risky. Deterministic models ignore this uncertainty, which can arise at any time and will therefore lead to non-compliance with the predefined schedule. Furthermore, patients need several care activities per day, and some of them require to be simultaneous by their nature such as dressing, getting out of bed and bathing. In this work, a stochastic programming model with recourse (SPR model) is proposed to deal with the home health care routing and scheduling problem (HHCRSP) where uncertainties in terms of traveling and caring times that may occur as well as synchronization of services are considered. The objective is to minimize the transportation cost and the expected value of recourse, which is estimated using Monte Carlo simulation. The recourse is defined as a penalty cost for patients' delayed services and a remuneration for caregivers' extra working time. The deterministic model is solved by CPLEX, the genetic algorithm (GA) and the general variable neighborhood search (GVNS) based heuristics. The SPR model is solved by Monte Carlo simulation embedded into the GA. Computational results highlight the efficiency of GVNS and GA based heuristics and the complexity of the SPR model in terms of CPU running times.
KeywordsVariable neighborhood search • Genetic algorithm • Home health care • Routing and scheduling • Multiple synchronized services • Stochastic programming model with recourse B Mohammed Bazirha
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