# Optimization Model for Selective Harvest Planning Performed by Humans and Robots
## 2 Literature Review  
Harvest yields tend to be uncertain regarding quantity, quality, and timing, due to dynamic and unpredictable weather, soil, and water conditions [1; 41]. Moreover, the harvesting time of individual fruits or vegetables will influence the production yield and quality [11]. The harvest time is affected by many factors, such as cultivation practices, weather conditions, soil conditions, geographical location, rate of quality decay, storage capacity, the required processing needed, and transport considerations [1; 11; 41]. Given the yield and quality attributes and the transport and storage restrictions, mathematical optimization techniques, such as linear or integer programming, are often used to determine the best possible harvest time [8]. This type of programming _determines the best harvesting time_ for different objective functions, such as maximum profit or minimum expenses [2; 11]. To date, this _best harvesting time_ has been considered _at the field level only for non-selective harvesting_ (i.e., when to harvest a specific field with a single harvest). Another decision that must be made before harvesting concerns the number of workers to recruit for the harvest season. Labor costs are a significant expense in agricultural production, specifically in the harvesting process [42]. Harvesting often involves migrant seasonal workers and requires recruitment processes that take time and require planning [43]. Furthermore, it is necessary to maintain a balance between ensuring a sufficient number of workers to harvest the ripe products (not missing harvestable produce due to a lack of workers) and ensuring that workers are fully utilized to reduce operational costs. Usually, the harvest planning problem is modeled using a mathematical formulation and then solved by different operational research methods [2]. Integer linear programming, the most common method [2], has been used to find the optimal date for grape and apple harvesting, taking into consideration operational costs and fruit quality [44; 45]. In these studies, the harvest date was set for non-selective harvesting, i.e., harvesting the whole field at once. A different planning model for wine grape harvesting involving uncertainty combined integer linear programming and robust stochastic optimization [26]. Other studies have used dynamic programming [10] and simulation models [46] to solve problems in harvest planning. However, all of these studies addressed _decisions related to the whole field_ and _did not relate to individual fruits_. Any study related to the modeling of harvest planning must take the following features into consideration, as reviewed in [1]:  
* Time window constraints--The model should consider the optimal time for harvesting and the quality decay resulting from harvesting outside of that time window, as well as its effect on revenues. * Resource limitations--These include capacity and productivity constraints, together with labor and machine availability. * Yield perishability--The deterioration of fresh products during the post-harvest period must be taken into account. Yield perishability can be modeled in several ways, including continuous deterioration curves, a loss factor for each period after harvesting, and the effect of product deterioration on customer demand. * Uncertainty--There is uncertainty in the harvest yield (quantity and quality), due to unknown weather conditions and the inherent variability of agricultural processes. * Inventory control--The inventory should be considered in terms of holding costs, duration of keeping in the inventory constraints, or as a decision variable. Integrating all of the above elements into a single model is complicated and may be impossible to solve in reasonable computational times. Therefore, heuristics algorithms are usually developed to derive an operational solution [47]. None of the studies covered in the review [1] included all the aforementioned features, and _only two included four out of the five features_. [PERSON] et al.