# Collaborative Planning in Non-Hierarchical Networks--An Intelligent Negotiation-Based Framework
## 3 Framework Proposal
### Intelligent Planning Module  
The intelligent planning module was designed to allow the decentralized collaborative planning negotiation process to improve as quickly as possible, in each iteration, the quality of the framing dates proposals and, as a result, achieve better solutions for the overall collaborative plan.  
As each partner participating in the negotiation is not willing to disclosure their capacity model information to the network partners, the responses to the brokers' request for quotation are discreet and limited in number. This means that each partner is only available to participate in a small number of requests for quotation, and, when that number is exceeded, the partner loses interest in the negotiation process.  
From the interviews on the industrial partners involved in the research project, it was concluded that, on average, each partner would only be available to respond to a maximum of five requests for quotation for each business opportunity.  
Considering the presented requirements, to the collaborative planning framework provide improved solutions, it needs to present intelligent features that would allow, in a small number of iterations, to improve the collaborative plan solutions in terms of the multicriteria evaluation.  
Aligned with this view, different strategies have been designed and evaluated for the intelligent planning module, using the limited information available on each iteration. Through this intelligent approach, it is possible to make the process of defining new framings more efficient when compared with the traditional random approach.  
Table 1 presents the list of the implemented strategies for the intelligent planning module.  
Each one of the implemented strategies takes into consideration specific aspects that potentially could generate a path of improvements in the collaborative planning solutions. The aspects that backed up the development of proposed strategies were as follows:  
* Average--in the studied industrial sectors, a large number of partners present similar seasonal load patterns; therefore, the average value of the partners' quotation replies tends to converge to periods of lower load in the capacity models and as a consequence of lower cost;
* Weighted average--this strategy substantiation is similar to the average strategy but, in order to value the lower cost of responses, takes into account the weighted average of the inverse of the cost on each previous iteration response;
* Best proposal--this strategy assumes that the best previous iteration response is a good suggestion for the next framing iteration;
* Genetic approach--this strategy is inspired in the genetic algorithm (GA) metaheuristic approach. This strategy approach uses the bio-inspired selection and crossover operators. It starts by selecting the best previous iteration solutions and then performs a crossing over operation, mixing these best solutions in hope to obtain better solutions in the next iteration.  
The developed strategies in the intelligent planning module were essentially conceived from the empirical experience of the planning managers of the collaborative networks studied.  
This module integrated in the collaborative planning framework was designed as a decision support tool and allow the application of a set of strategies to define the stage intervals accordingly to the choice of the broker process manager.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Strategy** & **Next Frame Proposal Based in** \\ \hline Average & Average values of previous replies \\ \hline Weighted average & Weighted average values of previous replies \\ \hline Best proposal & Previous best proposal \\ \hline Genetic approach & Crossover of previous best proposals \\ \hline \hline \end{tabular}
\end{table}
Table 1: Intelligent Planning Module Strategies.