Research On Cross Docking Dynamic Scheduling Based On Glowworm Swarm Optimization And Ontology Knowledge

With the rapid development of national economy and logistics technology,as the well as the rise of e-commerce industry in recent years,the demand for social logistics increased significantly,making competition between major enterprises getting fiercer.In order to improve its own competitiveness in the market,each enterprise is looking for a supply chain management strategy that is in accordance with its development.In the process of supply chain,the scheduling of logistics vehicles and the warehousing management are the key factors influencing the efficiency and cost of transportation.Cross-docking is a practice in logistics only to serve its receiving and shipping function without any inventory to be stored in any delivery nodes(warehouse or distribution center),thus to realize the fastest possible delivery from inbound to outbound the distribution center,to minimize the operation time,reduce warehousing costs and improve delivery efficiency.The Cross-docking problem can be described as a practice of optimizing the interests through rationally allocating vehicles and docks under some constraints.It is also a typical Combinatorial Optimization topic.This dissertation is to research Cross-docking dynamic scheduling with time windows with the objective to minimize operation time by scheduling docks and sequencing trucks which is,the arrival time in Cross-docking center of vehicles i is known as [Tai,Tbi],yet the actual arrival time is unknown.Based on this,the whole scheduling process is down to two parts in the dissertation:1)Static scheduling by using Glowworm Swarm Optimization,GSO.2)Dynamic scheduling by using ontology knowledge and rules.In the part of Static scheduling,the dissertation postulate that the arrival time of cross-docking vehicles i is(Tai+Tbi)/2 thus has turn it to a concrete problem and set up the related mathematical model and find the solution by using Glowworm Swarm Optimization,GSO.The dissertation designed GSO coding strategy and Tabu Search strategy to produce initial population,proposing an algorithm that can give solutions to multi-dock Cross-docking scheduling.Then the dissertation classify the scheduling into 3 scales based on the quantities of docks and vehicles,and later compare the results with the one Simple Genetic Algorithm produces,and finally the dissertation verified the feasibility of GSM in Cross-docking scheduling solutions.In the part of Dynamic scheduling,the dissertation taking actual conditions,such as early arrival or late arrival as scheduled,arrival of unknown vehicles,malfunction ofvehicles,the unpredictable into consideration.Static scheduling sometimes need to change echoing with these emergencies.This dissertation,using Protégé to build ontology,has set up corresponding cross-docking ontology knowledge base,mainly describes classes?property and examples of Cross-docking area.Then it builds the rules of SWRL to direct the dynamic scheduling,stores the rules in the base and design the scheduling strategies that responding to different dynamic emergencies.At last,this dissertation,based on ontology and rules,creates a dynamic Cross-docking vehicle scheduling system which simulates the process of dynamic Cross-docking scheduling by using inference engine JESS and Protégé-OWL API.This system is user-friendly with simple interface and is capable of helping Cross-docking scheduler to upgrade scheduling strategy in no time,thus to improve efficiency.