A heuristic approach on a multi-objective vehicle routing problem with pick-up and delivery service

• Main Authors: Chen, Pohan, Pineda, Neilson-David L., Rollan, Miguel Roberto M.
• Format: text
• Language: English
• Published: Animo Repository 2013
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/10958
• Institution: De La Salle University

Vehicle Routing Problem has captured the interest mar researchers through the years. Studies with different types of VRP were developed such as VRP with time window, VRP with capacitated vehicle, VRP with pick-up and delivery service and the likes. These studies in the past would cater to different kinds of systems. In order to consider all parameters in a unique system, and to meet objectives in a VRP, this study was made. The Model development was first used in order to assess and determine the variables. From these variables, equations were made in order to clarify and express the constraints of the system. A discussion of the limitations of the system is later then used in the development of the heuristic. The development of heuristic is subjected of 3 phases. The first phase considers the maximization of its service level. Given the service level, the lowest transaction time of which generates an algorithm that which node to serve first. The 2nd phase generally proceeds to minimizing the cost of the route. Looking for the least cost that would able to serve the demand of which the route taken on phase 1 was done. Service level is still a main aspect, so the decision of getting the least cost should also accept the service level gotten from phase 1. The last phase would be the use of the sub-con. The travel cost of the sub-con is computed in a way that which route it takes. This will then be weighed with the penalty cost that was incurred in the 1st phase. A computer program was done given the data and a validation was prepared in order to see how close the optimality of the heuristic. The heuristic was able to get the most optimal solution for the given data. Sensitivity analysis was also done and the factors including the relationship of sub-con and owned vehicles, time window, and demand are known to be significant in affecting the response on the service level and operation cost.