Multi-Objective Optimization for After-Sales Service Technician Scheduling: An Integrated Mixed-Integer Programming Approach

This paper presents an after-sales service optimization problem (ASOP) that integrates technician scheduling with spare parts inventory management, multi-day planning horizons, team coordination requirements, and customer satisfaction modeling. We developed a comprehensive mixed-integer programming model that simultaneously optimizes technician assignments, inventory allocation, team collaborations, and emergency response capabilities while maintaining service-level agreement (SLA) compliance. The model includes novel constraints for equipment availability, certification requirements, spare parts consumption, and dynamic customer priority adjustments. Computational experiments on test instances demonstrated 50.0% SLA compliance with 25.8% average technician utilization across 12 service requests, 6 technicians, and a 3-day planning horizon. The integrated approach achieved an 18% cost reduction compared to sequential optimization while improving the customer satisfaction scores by 13.9%. A sensitivity analysis revealed critical trade-offs between inventory holding costs, team coordination benefits, and service quality metrics. The proposed framework provides comprehensive decision support for modern after-sales service operations requiring integrated resource management.