Research on Disturbance Management in Logistics Distribution Based on Corporate Core Values

This paper addresses the challenges faced by fresh and frozen product supply companies in the delivery process, particularly when disruptions such as refrigeration equipment failures or vehicle breakdowns render the original delivery plan unfeasible, leading to potential damage to the company’s brand reputation, decreased customer satisfaction, and reduced economic benefits. This study proposes the ECVB algorithm, which optimizes logistics by balancing core values, supplier-customer interests, and enhancing delivery efficiency. The algorithm’s key strengths lie in its adaptability to disruptions, efficient multi-objective optimization, and intelligent exit strategy, significantly reducing computational complexity while maintaining high accuracy. Its core value-driven framework and modular design enable adaptation to other combinatorial problems (e.g., routing, scheduling) via redefined objectives/constraints. First, a mathematical model based on Prospect Theory is proposed to evaluate factors such as supplier brand reputation, customer satisfaction, and supplier economic benefits in the context of supplier supply models. Next, a function model is designed that integrates two key indicators---supplier brand reputation and supplier economic benefits---by adjusting a fusion coefficient. Third, based on the Corporate Core Competence Theory, a function model is developed to balance the relationships between supplier brand reputation, customer satisfaction, and supplier economic benefits. Fourth, to solve the multi-objective optimization problem with customer satisfaction, enterprise competitiveness, and supplier satisfaction as objectives, a novel algorithm is designed to address this issue. Fifth, to optimize the computational complexity of the proposed algorithm and enhance the efficiency of solving the problem, an intelligent exit strategy is developed to conclude the iterative process of exploring the optimal solution. Experimental results, verified on public datasets, demonstrate that the ECVB algorithm achieves an HV value of 303---outperforming comparative algorithms---and an IGD value of 408 (ranking 2nd among 4 algorithms) in multi-objective optimization. These results show that the proposed solution effectively maintains the supplier’s enterprise core value, increases customer satisfaction, and improves economic profitability, especially when refrigeration equipment fails. These findings underscore the algorithm’s practical value in minimizing losses and enhancing operational resilience for cold chain logistics. Additionally, the algorithm is applied to real-world logistics data from companies in the Shaoguan region. By analyzing the system’s recommended delivery solutions, several phenomena in logistics practice were observed, leading to related management suggestions for logistics companies.