## 5 Conclusions  
In this paper, we presented an extended version of the HV model to improve multi-criteria ABC inventory classification. Our proposed nonlinear weighted product model (WPM) incorporates multiple criteria with different measurement units without converting the performance of each inventory item in terms of each criterion into a normalized attribute value. This represents an improvement over the model proposed by [PERSON]. The WPM could also be viewed as providing a more reasonable classification for inventory items from the illustrated example, presented and used to compare our model with the HV model. In this paper, we also presented the improved CCM algorithm for solving the WPM, in which nonconvex nonlinearity was present in both the objective function and the constraints. The strategy presented here involved greatly reducing the steps in choosing m variables among \((m+n)\) variables, such that the corresponding \(m\times m\) Jacobian matrix was nonsingular. Using the improved algorithm, we applied Gaussian elimination to the original matrix to determine which m variables to choose. Our second improvement was to remove solving the nonlinear differential equations system, which occurs in the line search method of the CCM algorithm. This paper demonstrates an efficient algorithm for solving nonlinear programming problems, in which the feasible solution set does not have to be convex. The practical implication of this study is to further improve the efficient nonlinear optimization solver based on the CCM by optimizing the quality of existing solutions, thus improving time and space efficiency. Future research should continue to investigate the feasibility of implementing this proposed CCM algorithm in discrete domain issues for engineering applications in order to decide if the algorithm could be superior to off-the-shelf software. Future studies could apply the CCM to other nonlinear programs that arise in practice. For instance, autonomous vehicles represent one of many developments that will influence future mobility needs and planning needs. Traffic assignment models seek the same \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Item** & **LINGO** & **CCM** & **Item** & **LINGO** & **CCM** & **Item** & **LINGO** & **CCM** & **Item** & **LINGO** & **CCM** \\ \hline
1 & 60 & 1720 & 13 & 60 & 1505 & 25 & 35 & 2482 & 37 & 60 & 1367 \\ \hline
2 & 60 & 1866 & 14 & 55 & 1852 & 26 & 60 & 1646 & 38 & 50 & 2369 \\ \hline
3 & 60 & 1207 & 15 & 60 & 1911 & 27 & 35 & 3213 & 39 & 50 & 1888 \\ \hline
4 & 35 & 1867 & 16 & 59 & 1673 & 28 & 55 & 1741 & 40 & 50 & 1657 \\ \hline
5 & 60 & 1627 & 17 & 60 & 1090 & 29 & 50 & 2024 & 41 & 58 & 1903 \\ \hline
6 & 60 & 1419 & 18 & 59 & 1377 & 30 & 35 & 3045 & 42 & 50 & 2452 \\ \hline
7 & 60 & 1388 & 19 & 57 & 1456 & 31 & 55 & 1900 & 43 & 55 & 1496 \\ \hline
8 & 60 & 1495 & 20 & 60 & 1729 & 32 & 55 & 2397 & 44 & 75 & 555 \\ \hline
9 & 58 & 1428 & 21 & 60 & 1287 & 33 & 55 & 1645 & 45 & 55 & 215 \\ \hline
10 & 57 & 1995 & 22 & 55 & 1802 & 34 & 60 & 1158 & 46 & 75 & 492 \\ \hline
11 & 60 & 1086 & 23 & 55 & 2009 & 35 & 50 & 2223 & 47 & 60 & 2712 \\ \hline
12 & 60 & 1105 & 24 & 60 & 1612 & 36 & 55 & 1932 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The number of iteration for LINGO and the canonical coordinates method (CCM) to find the local optimal solution. objective as route guidance strategies and provide turning points with information for implementing route guidance control strategies. Faster algorithms developed specifically for traffic assignment can be adapted and used in vehicle route guidance systems. The minimization of total travel time is a common goal, both globally and from a traffic administration perspective. The current road network manages more traffic by achieving system optimization. Some researchers have focused their efforts on dynamic traffic assignment because of the unrealistic assumptions of static traffic assignment.