Optimization of logistics business process decision model based on multi-objective optimization algorithm

Logistics is becoming harder due to several difficulties. Globalization, changing client needs, and complex supply networks are examples. Traditional planning often disappoints. The inability to be adaptable, flexible, and rapid in finding effective solutions is the biggest issue. This research found that adaptive algorithms and artificial intelligence allow the Logistic Intelligence Multiple-Objective Decisions Model (LIMOD) to optimize complicated logistical business processes. After that, the study results were revealed. Several competing objectives are considered when designing the LIMOD. Goals are to shorten time to delivery, save money, and maximize resource use. LIMOD’s real-time priority adjusting technology is advanced. This mechanism represents a major LIMOD advancement. The concept’s dynamic adaptability makes it valuable in many logistical situations, especially those including supply chain uncertainty. This is particularly true for ambiguous supply networks. Production, online retail, and transportation companies with multi-layer supply chains could profit from LIMOD. It can connect without affecting business applications and be customized for a domain. Logistics managers can maximize decision possibilities and trade-offs with LIMOD, improving strategic planning. This improves talents. This study advances intelligent, sustainable logistics optimization systems. Businesses can confidently navigate global supply networks with this technology. Experiments showed LIMOD’s better performance. These tests used various datasets and logistical scenarios. Addressing three or more objective simultaneously, LIMOD provides 18% greater solution quality, 27% quicker completion times, and 23% greater effectiveness than typical optimization processes. Compared to other optimization methods. Learn about LIMOD here.