Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
- URL: http://arxiv.org/abs/2005.13865v1
- Date: Thu, 28 May 2020 09:29:05 GMT
- Title: Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
- Authors: Jakob Bossek, Christian Grimme, G\"unter Rudolph, Heike Trautmann
- Abstract summary: We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time.
A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider a dynamic bi-objective vehicle routing problem, where a subset of
customers ask for service over time. Therein, the distance traveled by a single
vehicle and the number of unserved dynamic requests is minimized by a dynamic
evolutionary multi-objective algorithm (DEMOA), which operates on discrete time
windows (eras). A decision is made at each era by a decision-maker, thus any
decision depends on irreversible decisions made in foregoing eras. To
understand effects of sequences of decision-making and
interactions/dependencies between decisions made, we conduct a series of
experiments. More precisely, we fix a set of decision-maker preferences $D$ and
the number of eras $n_t$ and analyze all $|D|^{n_t}$ combinations of
decision-maker options. We find that for random uniform instances (a) the final
selected solutions mainly depend on the final decision and not on the decision
history, (b) solutions are quite robust with respect to the number of unvisited
dynamic customers, and (c) solutions of the dynamic approach can even dominate
solutions obtained by a clairvoyant EMOA. In contrast, for instances with
clustered customers, we observe a strong dependency on decision-making history
as well as more variance in solution diversity.
Related papers
- A revision on Multi-Criteria Decision Making methods for Multi-UAV
Mission Planning Support [4.198865250277024]
Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications.
One of the main problems considered is the Mission Planning for multiple UAVs.
A Decision Support System (DSS) has been designed to order and reduce the optimal solutions.
arXiv Detail & Related papers (2024-02-28T22:54:08Z) - Online Decision Mediation [72.80902932543474]
Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior.
In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
arXiv Detail & Related papers (2023-10-28T05:59:43Z) - On solving decision and risk management problems subject to uncertainty [91.3755431537592]
Uncertainty is a pervasive challenge in decision and risk management.
This paper develops a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them.
arXiv Detail & Related papers (2023-01-18T19:16:23Z) - R(Det)^2: Randomized Decision Routing for Object Detection [64.48369663018376]
We propose a novel approach to combine decision trees and deep neural networks in an end-to-end learning manner for object detection.
To facilitate effective learning, we propose randomized decision routing with node selective and associative losses.
We name this approach as the randomized decision routing for object detection, abbreviated as R(Det)$2$.
arXiv Detail & Related papers (2022-04-02T07:54:58Z) - Modularity in Reinforcement Learning via Algorithmic Independence in
Credit Assignment [79.5678820246642]
We show that certain action-value methods are more sample efficient than policy-gradient methods on transfer problems that require only sparse changes to a sequence of previously optimal decisions.
We generalize the recently proposed societal decision-making framework as a more granular formalism than the Markov decision process.
arXiv Detail & Related papers (2021-06-28T21:29:13Z) - Discovering Diverse Solutions in Deep Reinforcement Learning [84.45686627019408]
Reinforcement learning algorithms are typically limited to learning a single solution of a specified task.
We propose an RL method that can learn infinitely many solutions by training a policy conditioned on a continuous or discrete low-dimensional latent variable.
arXiv Detail & Related papers (2021-03-12T04:54:31Z) - Evolutionary Multi-Objective Optimization Algorithm Framework with Three
Solution Sets [7.745468825770201]
It is assumed that a final solution is selected by a decision maker from a non-dominated solution set obtained by an EMO algorithm.
In this paper, we suggest the use of a general EMO framework with three solution sets to handle various situations.
arXiv Detail & Related papers (2020-12-14T08:04:07Z) - The bi-objective multimodal car-sharing problem [0.0]
The aim of the BiO-MMCP is to determine the optimal mode of transport assignment for trips.
As user satisfaction is a crucial aspect in shared mobility systems, we consider user preferences in a second objective.
We develop a branch-and-cut algorithm which is embedded in two bi-objective frameworks.
arXiv Detail & Related papers (2020-10-18T13:48:17Z) - Inverse Active Sensing: Modeling and Understanding Timely
Decision-Making [111.07204912245841]
We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure.
We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
arXiv Detail & Related papers (2020-06-25T02:30:45Z) - Solution Subset Selection for Final Decision Making in Evolutionary
Multi-Objective Optimization [7.745468825770201]
We discuss subset selection from a viewpoint of the final decision making.
We show that the formulated function is the same as the IGD plus indicator.
arXiv Detail & Related papers (2020-06-15T06:26:58Z) - Dynamic Bi-Objective Routing of Multiple Vehicles [0.0]
Vehicle-Routing-Problems imply repeated decision making on dynamic customer requests.
We study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions.
arXiv Detail & Related papers (2020-05-28T09:35:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.