Simulation-based Algorithm for Determining Best Package Delivery
Alternatives under Three Criteria: Time, Cost and Sustainability
- URL: http://arxiv.org/abs/2106.11027v1
- Date: Sat, 5 Jun 2021 18:17:09 GMT
- Title: Simulation-based Algorithm for Determining Best Package Delivery
Alternatives under Three Criteria: Time, Cost and Sustainability
- Authors: Suchithra Rajendran and Aidan Harper
- Abstract summary: This paper develops a simulation algorithm that assists same-day package delivery companies to serve customers instantly.
The proposed recommender system provides the best solution with respect to three criteria: cost, time, and sustainability.
This paper also considers the best delivery alternative during the presence of a pandemic, such as COVID-19.
- Score: 6.85316573653194
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: With the significant rise in demand for same-day instant deliveries, several
courier services are exploring alternatives to transport packages in a cost-
and time-effective, as well as, sustainable manner. Motivated by a real-life
case study, this paper focuses on developing a simulation algorithm that
assists same-day package delivery companies to serve customers instantly. The
proposed recommender system provides the best solution with respect to three
criteria: cost, time, and sustainability, considering the variation in travel
time and cost parameters. The decision support tool provides recommendations on
the best alternative for transporting products based on factors, such as source
and destination locations, time of the day, package weight, and volume. Besides
considering existing new technologies like electric-assisted cargo bikes, we
also analyze the impact of emerging methods of deliveries, such as robots and
air taxis. Finally, this paper also considers the best delivery alternative
during the presence of a pandemic, such as COVID-19. For the purpose of
illustrating our approach, we consider the delivery options in New York City.
We believe that the proposed tool is the first to provide solutions to courier
companies considering evolving modes of transportation and under logistics
disruptions due to pandemic.
Keywords: Instant package delivery; Courier services; Simulation algorithm;
Recommender system; Emerging technologies; COVID-19 pandemic.
Related papers
- Analytical model for large-scale design of sidewalk delivery robot
systems [4.510000677649468]
We propose a model that captures both the initial cost and the operation cost of the delivery system and evaluates the impact of constraints and operation strategies on the deployment.
We then apply the model in neighborhoods in New York City to evaluate deploying the sidewalk delivery robot system in a real-world scenario.
arXiv Detail & Related papers (2023-10-26T15:26:12Z) - A Survey on Service Route and Time Prediction in Instant Delivery:
Taxonomy, Progress, and Prospects [58.746820564288846]
Route&Time Prediction (RTP) aims to estimate the future service route as well as the arrival time of a worker.
Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain.
We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction; (ii) model architecture, which encompasses sequence-based and graph-based models; and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement
arXiv Detail & Related papers (2023-09-03T14:43:33Z) - Movement Penalized Bayesian Optimization with Application to Wind Energy
Systems [84.7485307269572]
Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-making given side information.
In this setting, the learner receives context (e.g., weather conditions) at each round, and has to choose an action (e.g., turbine parameters)
Standard algorithms assume no cost for switching their decisions at every round, but in many practical applications, there is a cost associated with such changes, which should be minimized.
arXiv Detail & Related papers (2022-10-14T20:19:32Z) - A Real-Time Energy and Cost Efficient Vehicle Route Assignment Neural
Recommender System [0.0]
This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria.
We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes.
A complete recommendation logic is then developed to allow for real-time optimum assignment for each route.
arXiv Detail & Related papers (2021-10-21T04:17:35Z) - A Deep Reinforcement Learning Approach for Constrained Online Logistics
Route Assignment [4.367543599338385]
It is crucial for the logistics industry on how to assign a candidate logistics route for each shipping parcel properly.
This online route-assignment problem can be viewed as a constrained online decision-making problem.
We develop a model-free DRL approach named PPO-RA, in which Proximal Policy Optimization (PPO) is improved with dedicated techniques to address the challenges for route assignment (RA)
arXiv Detail & Related papers (2021-09-08T07:27:39Z) - Estimating the Robustness of Public Transport Systems Using Machine
Learning [62.997667081978825]
Planning public transport systems is a highly complex process involving many steps.
Integrating robustness from a passenger's point of view makes the task even more challenging.
In this paper, we explore a new way of such a scenario-based robustness approximation by using methods from machine learning.
arXiv Detail & Related papers (2021-06-10T05:52:56Z) - Selective Survey: Most Efficient Models and Solvers for Integrative
Multimodal Transport [0.0]
The main objective is to explore a beneficent selection of the existing research, methods and information in the field of multimodal transportation research.
The selective survey covers multimodal transport design and optimization in terms of: cost, time, and network topology.
The gap between theory and real-world applications should be further solved in order to optimize the global multimodal transportation system.
arXiv Detail & Related papers (2021-03-16T08:31:44Z) - Dynamic Bicycle Dispatching of Dockless Public Bicycle-sharing Systems
using Multi-objective Reinforcement Learning [79.61517670541863]
How to use AI to provide efficient bicycle dispatching solutions based on dynamic bicycle rental demand is an essential issue for dockless PBS (DL-PBS)
We propose a dynamic bicycle dispatching algorithm based on multi-objective reinforcement learning (MORL-BD) to provide the optimal bicycle dispatching solution for DL-PBS.
arXiv Detail & Related papers (2021-01-19T03:09:51Z) - Mathematical simulation of package delivery optimization using a
combination of carriers [0.0]
Authors analyzed and proposed a solution for the problem of cost optimization for packages delivery for long-distance deliveries using a combination of paths delivered by supplier fleets, worldwide and local carriers.
Experiment is based on data sources of the United States companies using a wide range of carriers for delivery services.
arXiv Detail & Related papers (2020-11-02T18:44:04Z) - An Image Processing Pipeline for Automated Packaging Structure
Recognition [60.56493342808093]
We propose a cognitive system for the fully automated recognition of packaging structures for standardized logistics shipments based on single RGB images.
Our contribution contains descriptions of a suitable system design and its evaluation on relevant real-world data.
arXiv Detail & Related papers (2020-09-29T07:26:08Z) - Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep
Reinforcement Learning Approach [88.45509934702913]
We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed.
We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS.
By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
arXiv Detail & Related papers (2020-02-21T07:29:15Z)
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.