# An Integrated Model for the Harvest, Storage, and Distribution of Perishable Crops
## 2 Literature Review
The computational results and a sensitivity analysis show that the model can be a very useful tool for planning supply chain activities, when dealing with perishable items. [PERSON] et al. (2018) consider a supply chain characterized by a production facility and multiple distribution centers. With the aim to minimize the total cost, the proposed production-inventory-routing model supports the decision-making about the production quantities, the distribution centers to be visited, the quantities to be delivered. To solve the problem efficiently, the authors divide it into two sub-problems, which deal, respectively, with the production and distribution activities. The first one is optimally solved, while a particle swarm heuristic is designed to tackle the distribution submodel. Computational experiments on a set of randomly generated instances prove the goodness of the proposed approach in terms of solution quality and time performance. [PERSON] et al. (2018) use a MILP model to define a production-inventory-routing problem where quality of perishable products is explicitly considered, and profit maximized. They test their generic supply chain model on some randomly generated instances and analyze how food perishability impacts on the solution. [PERSON] et al. (2018) propose a mathematical programming approach to integrate some common operational decisions, like production scheduling, inventory management, and vehicle routing. In particular, the considered production system is multi-stage and multi-site, while at the delivery level different transporting vehicles with different capacities are taken into account. The multi-period nature and the use of time windows make the problem quite difficult to be optimally solved. Therefore, two heuristic and meta-heuristic algorithms are proposed and applied to some benchmark instances, with good results. [PERSON] et al. (2018) present a multi-product and multi-period integrated production-inventory-routing problem with time windows, where the fleet of vehicles is heterogenous. The uncertainty in customers demand is tackled through two fuzzy approaches. The proposed model is tested and validated on a set of randomly generated numerical examples, which are solved optimally. [PERSON] et al. (2018) address a production-inventory-routing-problem in a meat supply chain, where the producer has a single meat processing centre with several production lines, and a fleet of vehicles for the distribution to the customers. The authors take into account many real-life features such as product family setups, food perishability, delivery time windows. Since the dimension of the problem is very large, a three-phase methodology is proposed, in order to find good solutions in a reasonable time. At the first step, the size of the problem is reduced, then an initial solution is found and iteratively improved with a fix-and-optimize based matheuristic. The approach is tested both on some simple instances from the literature and on a real-life case study. [PERSON] et al. (2018) present a generalized production-inventory-routing model with perishable inventory. With the aim of making their model close to reality, they discuss and analyze three different delivery, and selling priority policies, for a total of nine combinations of inventory management policies. An exact branch-and-cut algorithm is developed to solve the model, which can significanlty improve the current operating conditions of a food company located in China. The use of different work scenarios allows the identification of useful managerial implications. With the aim to determine an integrated food production, inventory, and distribution plan, [PERSON] et al. (2018) formulate a bi-objective MILP model which considers two main objectives: the minimization of production, inventory, and transportations costs, the maximization of average food quality. The high computational complexity of the problem requires the use of heuristic approaches to solve it in a reasonable time.