Interactive Reference Point-Based Guided Local Search for the Bi-objective Inventory Routing Problem
This work addresses preference elicitation for decision makers in logistics optimization, but it is incremental as it builds on existing methods like local search and Clarke & Wright savings heuristic.
The authors tackled the bi-objective Inventory Routing Problem by proposing an interactive method that uses a reference point to guide local search, achieving computational results on benchmark data to demonstrate applicability.
Eliciting preferences of a decision maker is a key factor to successfully combine search and decision making in an interactive method. Therefore, the progressively integration and simulation of the decision maker is a main concern in an application. We contribute in this direction by proposing an interactive method based on a reference point-based guided local search to the bi-objective Inventory Routing Problem. A local search metaheuristic, working on the delivery intervals, and the Clarke & Wright savings heuristic is employed for the subsequently obtained Vehicle Routing Problem. To elicit preferences, the decision maker selects a reference point to guide the search in interesting subregions. Additionally, the reference point is used as a reservation point to discard solutions outside the cone, introduced as a convergence criterion. Computational results of the reference point-based guided local search are reported and analyzed on benchmark data in order to show the applicability of the approach.