Optimizing warehouse operations through an integrated planning system

In today’s highly demanding supply chains, optimizing warehouse operations through advanced planning systems has become essential to ensure efficiency and responsiveness. However, many enterprises continue to rely on traditional or fragmented planning methods, which struggle to optimize operational sequences and dynamically allocate resources in real-time. This often results in inefficient utilization of docks, equipment, and labor, as well as increased vehicle waiting times and operational costs. To address these challenges, this study proposes an integrated planning approach that combines the Genetic Algorithm (GA) with the Dynamic Order-Based with Threshold Scheduling (DOBTS) algorithm. GA is first employed to generate an optimal sequence of warehouse operations, which is then used as input for DOBTS to dynamically schedule tasks based on real-time conditions and threshold values. By integrating GA’s global optimization capabilities with DOBTS’s adaptive scheduling mechanism, the proposed system aims to enhance workflow efficiency, reduce congestion and idle time, and ultimately lower logistics costs while improving overall competitiveness.