Functional areas: this category comprises production, harvest, storage and distribution
Purpose of the chain: this category includes the scope of the decisions made: such as harvest planning and optimization.
Environmental factors: these include the planting environment with uncertainties and risks (countries with water shortage or natural calamities).
Fruit characteristics, such as (1) highly perishable and (2) long shelf life.
1.3. Common Concepts and Dominant Approaches
Several modeling approaches for the fresh fruit supply chain were conceived based on various settings, constraints, challenges and influencing elements (shown in Figure 2):.
Figure 2. Model classification framework for fresh fruit supply chains.
Most papers dealing with FFSC in a deterministic context implement LP or MIP formulations to make tactical and/or operational decisions. Additionally, the agricultural activities such as planting, harvesting and storing are covered more than the others. Besides, almost all authors only consider one kind of fruit as a case study to evaluate their model. Diverse decision-making levels and stages of the FFSC need to be considered more. Monoculture is known to be detrimental to soil health. Thus, future models should deal with polyculture farming and its SC implications.
Traditional deterministic models using linear programming or MIP are generally unable to deal with problems that involve uncertainties or give solutions with a high level of risk. This is particularly true in agricultural with several uncertain factors starting with weather conditions. Stochastic programming and robust programming (both extensions of linear programming) can address uncertainties in the parameters of linear or MIP optimization models for production and logistics planning in agri-food industries.
The L-shaped method is considered an effective tool to solve the stochastic problem. In addition, most of the authors believed that the two-stage stochastic model was a good choice for making tactical and operational decisions. Hence, two-stage stochastic models will still be used to deal with risks and uncertainties in the FFSC. However, new developments in robustness should be considered and applied to support decision making under uncertainty.
In many fresh fruit supply chains, uncertain elements include the time to harvest, quantity for packing, cost for shortage, etc. Such uncertainties can be modelled in ways other than stochastic programming as Fuzzy programming, Simulation and Non-linear programming.
2. Robustness and Limitations of Existing Models
In the general view, the following are the main criteria on which researchers conceive and structure their models for the planning and logistics of the fresh fruit supply chain:
Relationship between price and demand
Planting/harvesting times and shipping/transporting factors
Operational decision-making styles
The fruit species under consideration were varied but very commonly consumed on a daily basis such as tomatoes, apples, grapes, bananas, etc. However, many tropical fruits were not covered as extensively in the literature.
Figure 3 shows the coverage of the fresh fruit supply chain research in the past, focusing on several common species in the market.
Statistics related to the fresh fruit supply chain research in term of fruit species from 1978–2017.
It can be observed that apples, grapes and tomatoes are the most used in case studies. From the chart in Figure 3, it is noticeable that most cases covered in the literature are perennial fruit or one-year lifetime trees. Meanwhile, fast-growing perennial trees are less considered than one year trees.
Figure 4 shows that most of the papers deal with tactical decisions (17 articles) followed by the operational level (11 articles). Only six articles focused on the strategic decision level. However, the coupled models seemed the most favorite approach in dealing with the fresh fruit supply chain, including 24 articles at different levels for combined decisions, such as strategic—tactical (7 articles) and tactical—operational (17 articles).
Number of model categories in fresh fruit supply chain optimization.