No abstract

The importance of customer satisfaction was identified by many industries as a key factor of competitive advantage. So, for companies in the small package shipping industry, it can be reasonable to increase the service quality even at the expense of transportation cost to gain customer loyalty. These companies noticed that customer satisfaction can be increased by providing consistent service in the form of visiting customers with the same driver at approximately the same time of the day over a certain time period. Motivated by this real-world problem, the consistent vehicle routing problem (ConVRP) combines traditional vehicle routing constraints with the requirements for service consistency. This article presents a fast solution method called template-based adaptive large neighborhood search for the described problem. Compared to state-of-the-art heuristics, the developed algorithm is highly competitive on the available benchmark instances. Additionally, new test instances are provided. These seem to be more challenging due to the variation of different model parameters and consequently help to identify interesting effects. Finally, a relaxed variant of the original ConVRP is presented. In this variant, the departure times from the depot can be delayed to adjust the service times of the customers. Experiments show that allowing later departure times considerably improves the solution quality under tight consistency requirements.

T he consistent vehicle routing problem (ConVRP) takes customer satisfaction into account by assigning one driver to a customer and by bounding the variation in the arrival times over a given planning horizon. These requirements may be too restrictive in some applications. In the generalized ConVRP (GenConVRP), each customer is visited by a limited number of drivers and the variation in the arrival times is penalized in the objective function. The vehicle departure times may be adjusted to obtain stable arrival times. Additionally, customers are associated with AM/PM time windows. In contrast to previous work on the ConVRP, we do not use the template concept to generate routing plans. Our approach is based on a flexible large neighborhood search that is applied to the entire solution. Several destroy and repair heuristics have been designed to remove customers from the routes and to reinsert them at better positions. Arrival time consistency is improved by a simple 2-opt operator that reverses parts of particular routes.A computational study is performed on ConVRP benchmark instances and on new instances generated for the generalized problem. The proposed algorithm performs well on different variants of the ConVRP. It outperforms template-based approaches in terms of travel cost and time consistency. For the GenConVRP, we experiment with different input parameters and examine the trade-off between travel cost and customer satisfaction. Remarkable cost savings can be obtained by allowing more than one driver per customer.