Transportation costs account for a large portion of business expense in any logistics firm; thus, achieving proper solutions that manage those transportation activities well and reduce such expense should be the number one priority for the business. Essentially, such logistics management involves the routing plans for company vehicles that perform delivery/pick up and also the number of vehicles utilized. This study investigated and compared the optimization performances of routing algorithms using simulated geographic data based in Chiang Rai, Thailand, emulating the post office operation which had 1 post office, 4 delivery vehicles and 2 delivery zones (2 vehicles per zone): 65 customer locations for zone A and 74 for zone B. The major objective of this particular routing problem, called Vehicle Routing Problem (VRP), was that the total delivery distance of those 4 delivery vehicles combined should be minimized; moreover, those vehicles mush finish their delivery operation within a time constraint, set at 2
hours. The optimization algorithms, employed for the routing procedures, were Large Neighborhood Search (LNS), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Ant Colony Optimization (ACO), combined into 3 hybrid algorithms (LNS|PSO, LNS|DE and LNS|ACO). Those hybrid algorithms including pure ones (DE, PSO and ACO) were compared for their optimization performances; and the results showed that LNS|ACO hybrid algorithm was superior than the other two hybrid ones and also far better than pure DE, PSO and ACO algorithms at various parameter variants. Additionally, most algorithms (hybrid and pure ones) finished the delivery routing
within the time constraint of 2 hours except only for the pure algorithms at minimum parameter variant.
Professors and the "Ordinary and Necessary" Business Expense
