Optimization of Human Resource Allocation Considering Customer Relationship Management Criteria and Uncertainty Conditions in Automotive Dealerships

Abstract

The present study employs a mixed-integer mathematical model to optimize human resource allocation in the automotive industry. The objective function of the proposed model aims to minimize the maximum waiting time of customers in service queues, while the constraints involve workforce allocation and time calculations for each service at every center. Most previous studies have relied on survey methods and interviews with experts and organizational elites. Given the complexity of designing an optimal model for Customer Relationship Management (CRM), such methods may distort the obtained results due to errors in interviews and questionnaires. Hence, this research utilizes mathematical optimization methods. For solving small-scale problems, the BARON method was applied using GAMS software. Due to the NP-hard nature of the allocation problem, metaheuristic algorithms were employed to handle larger-scale cases. Since these algorithms are designed based on natural elements, a stochastic procedure was implemented to generate initial solutions and enhance the final solution process. Thus, appropriate comparisons must be conducted to ensure the accuracy of such a procedure. To this end, three metaheuristic algorithms – Genetic Algorithm (GA), Harmony Search, and Grey Wolf Optimizer (GWO) – were employed to solve the final problem. The computational results indicated that the GOW outperformed the other algorithms in terms of efficiency, making it more practical for solving real numerical instances.