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@@ -505,8 +505,6 @@ By prioritizing resource allocation to high-demand sites, the recommended scheme
Building upon the foundation of Model I, this chapter introduces a tripartite architectural framework centered on the "Symbiotic Sites" strategy to transcend the traditional one-truck-one-site constraint and unlock dormant logistical capacity. Building upon the foundation of Model I, this chapter introduces a tripartite architectural framework centered on the "Symbiotic Sites" strategy to transcend the traditional one-truck-one-site constraint and unlock dormant logistical capacity.
The model's logic initiates with a rigorous screening phase, where candidate site pairs are evaluated through a multi-dimensional filter encompassing spatial proximity, Demand Accumulation Moderation, and Demand Stability. Once viable pairs are identified, an internal resource allocation mechanism employs a multi-objective utility function to determine the optimal cargo distribution between the paired sites, effectively reconciling the intrinsic trade-off between service effectiveness and cross-site fairness. Ultimately, these symbiotic pairs are integrated as composite decision units into an enhanced global scheduling algorithm, which synchronizes their requirements with independent sites to generate a unified and optimized distribution timetable for the entire year.
\begin{figure}[H] \begin{figure}[H]
\centering \centering
\includegraphics[width=10cm]{flowchartmodel2.png} \includegraphics[width=10cm]{flowchartmodel2.png}
@@ -514,6 +512,8 @@ The model's logic initiates with a rigorous screening phase, where candidate sit
\label{flow chart of Model II} \label{flow chart of Model II}
\end{figure} \end{figure}
The model's logic initiates with a rigorous screening phase, where candidate site pairs are evaluated through a multi-dimensional filter encompassing spatial proximity, Demand Accumulation Moderation, and Demand Stability. Once viable pairs are identified, an internal resource allocation mechanism employs a multi-objective utility function to determine the optimal cargo distribution between the paired sites, effectively reconciling the intrinsic trade-off between service effectiveness and cross-site fairness. Ultimately, these symbiotic pairs are integrated as composite decision units into an enhanced global scheduling algorithm, which synchronizes their requirements with independent sites to generate a unified and optimized distribution timetable for the entire year.
\subsection{Model Building} \subsection{Model Building}
\subsubsection{Selection Criteria for Symbiotic Sites} \subsubsection{Selection Criteria for Symbiotic Sites}
@@ -737,24 +737,23 @@ Fig. \ref{Fairness diagnostics} presents the fairness diagnostics for 70 service
Sensitivity analysis is used to verify that the key conclusions---improved effectiveness under feasibility constraints and controlled risk---remain stable under reasonable perturbations of a small set of threshold parameters. Throughout this section, we interpret parameter choices using the following decision rule: we prefer settings that (i) maintain feasibility and the equity floor enforced by the scheduling constraints (e.g., minimum service frequency), (ii) keep shortfall risk below an acceptable level (e.g., $R1 \le 5\%$), and (iii) maximize distribution effectiveness, prioritizing quality-weighted service (E2) when trade-offs exist. Sensitivity analysis is used to verify that the key conclusions---improved effectiveness under feasibility constraints and controlled risk---remain stable under reasonable perturbations of a small set of threshold parameters. Throughout this section, we interpret parameter choices using the following decision rule: we prefer settings that (i) maintain feasibility and the equity floor enforced by the scheduling constraints (e.g., minimum service frequency), (ii) keep shortfall risk below an acceptable level (e.g., $R1 \le 5\%$), and (iii) maximize distribution effectiveness, prioritizing quality-weighted service (E2) when trade-offs exist.
\begin{table}[H] \begin{table}[H]
\footnotesize
\centering \centering
\caption{Key parameters and tested ranges in sensitivity analysis} \caption{Key parameters and tested ranges in sensitivity analysis}
\label{tab:sensitivity_params} \label{tab:sensitivity_params}
\begin{tabular}{@{}llllp{4.2cm}@{}} \begin{tabular}{lccc}
\toprule[1pt] \toprule[1pt]
Parameter & Meaning & Baseline & Tested range & Rationale for range \\ Parameter & Meaning & Baseline & Tested range \\
\midrule[0.6pt] \midrule[0.6pt]
$C_0$ & Capacity threshold for correction (Task 1) & 350 & 350--450 & Around observed ``capacity ceiling'' \\ $C_0$ & Capacity threshold for correction (Task 1) & 350 & 350--450 \\
$p^{trunc}_0$ & Truncation probability threshold (Task 1) & 0.1 & 0.01--0.10 & From conservative to permissive correction \\ $p^{trunc}_0$ & Truncation probability threshold (Task 1) & 0.1 & 0.01--0.10 \\
$\hat c$ & Quality threshold used in E2 (Task 1) & 250 & 200--300 & Covers typical quality cutoffs \\ $\hat c$ & Quality threshold used in E2 (Task 1) & 250 & 200--300 \\
Merge ratio & Portion merged into dual-stop routes (Task 3) & 0.5 & 0.1--0.9 & From light to aggressive merging \\ Merge ratio & Portion merged into dual-stop routes (Task 3) & 0.5 & 0.1--0.9 \\
$l_{max}$ & Maximum pairing distance (Task 3) & 50 & 10--100 & Feasible local pairing to relaxed pairing \\ $l_{max}$ & Maximum pairing distance (Task 3) & 50 & 10--100 \\
$\mu_{\text{sum}}$ & Cap on paired expected demand (Task 3) & 450 & 350--550 & Controls combined-stop overload risk \\ $\mu_{\text{sum}}$ & Cap on paired expected demand (Task 3) & 450 & 350--550 \\
CV threshold & Maximum volatility admitted for pairing (Task 3) & 0.5 & 0.1--1.0 & Filters unstable sites vs. broader admission \\ CV threshold & Maximum volatility admitted for pairing (Task 3) & 0.5 & 0.1--1.0 \\
\bottomrule[1pt] \bottomrule[1pt]
\end{tabular} \end{tabular}
\end{table} \end{table}
\subsection{Sensitivity Analysis for Task 1} \subsection{Sensitivity Analysis for Task 1}