Vehicle Routing with Stochastic Supply of Crowd Vehicles and Time Windows

2021 ◽  
Author(s):  
Fabian Torres ◽  
Michel Gendreau ◽  
Walter Rei
Keyword(s):  
Urban Areas ◽  
Time Window ◽  
Time Windows ◽  
Cost Effective ◽  
Branch And Price ◽  
Irrational Behavior ◽  
Last Mile ◽  
Stochastic Solution ◽  
The Cost ◽  
Price Algorithm

The growth of e-commerce has increased demand for last-mile deliveries, increasing the level of congestion in the existing transportation infrastructure in urban areas. Crowdsourcing deliveries can provide the additional capacity needed to meet the growing demand in a cost-effective way. We introduce a setting where a crowd-shipping platform sells heterogeneous products of different sizes from a central depot. Items sold vary from groceries to electronics. Some items must be delivered within a time window, whereas others need a customer signature. Furthermore, customer presence is not guaranteed, and some deliveries may need to be returned to the depot. Delivery requests are fulfilled by a fleet of professional drivers and a pool of crowd drivers. We present a crowd-shipping platform that standardizes crowd drivers’ capacities and compensates them to return undelivered packages back to the depot. We formulate a two-stage stochastic model, and we propose a branch and price algorithm to solve the problem exactly and a column generation heuristic to solve larger problems quickly. We further develop an analytical method to calculate upper bounds on the supply of vehicles and an innovative cohesive pricing problem to generate columns for the pool of crowd drivers. Computational experiments are carried out on modified Solomon instances with a pool of 100 crowd vehicles. The branch and price algorithm is able to solve instances of up to 100 customers. We show that the value of the stochastic solution can be as high as 18% when compared with the solution obtained from a deterministic simplification of the model. Significant cost reductions of up to 28% are achieved by implementing crowd drivers with low compensations or higher capacities. Finally, we evaluate what happens when crowd drivers are given the autonomy to select routes based on rational and irrational behavior. There is no cost increase when crowd drivers are rational and select routes that have a higher compensation first. However, when crowd drivers are irrational and select routes randomly, the cost can increase up to 4.2% for some instances.

Naïve Bayes classifier enabled home energy management scheme for cost-effective end-user comfort

2021 ◽  
Vol 40 (1) ◽  
pp. 403-413
Author(s):  
M. Firdouse Ali Khan ◽  
Ganesh Kumar Chellamani ◽  
Premanand Venkatesh Chandramani
Keyword(s):  
Energy Management ◽  
Time Window ◽  
Cost Effective ◽  
Pattern Generation ◽  
Home Appliances ◽  
Management Scheme ◽  
Home Energy Management ◽  
Maximum Comfort ◽  
The Cost ◽  
User Comfort

Under demand response enabled demand-side management, the home energy management (HEM) schemes schedule appliances for balancing both energy and demand within a residence. This scheme enables the user to achieve either a minimum electricity bill (EB) or maximum comfort. There is always the added burden on a HEM scheme to obtain the least possible EB with comfort. However, if a time window that contains comfortable time slots of the day for an appliance operation, is identified, and if the cost-effective schedule-pattern gets generated from these windows autonomously, then the burden can be reduced. Therefore, this paper proposes a two-level method that can assist the HEM scheme by generating a cost-effective schedule-pattern for scheduling home appliances. The first level uses a classifier to identify the comfortable time window from past ON and OFF events. The second level uses pattern generation algorithms to generate a cost-effective schedule-pattern from the identified window. The generated cost-effective schedule-pattern is applied to a HEM scheme as input to demonstrate the proposed two-level approach. The simulation results exhibit that the proposed approach helps the HEM scheme to schedule home appliances cost-effectively with a satisfactory user-comfort between 90% and 100%.

A Compressed Annealing Approach with Pre-Process for the Asymmetric Traveling Salesman Problem with Time Windows

2011 ◽  
Vol 5 (5) ◽  
pp. 669-678
Author(s):  
Tadanobu Mizogaki ◽  
◽  
Masao Sugi ◽  
Masashi Yamamoto ◽  
Hidetoshi Nagai ◽  
...  
Keyword(s):  
Traveling Salesman Problem ◽  
Feasible Solution ◽  
Time Window ◽  
Time Windows ◽  
Computing Time ◽  
Traveling Salesman ◽  
Asymmetric Traveling Salesman Problem ◽  
Insertion Method ◽  
Time Window Constraints ◽  
The Cost

This paper proposes a method of rapidly finding a feasible solution to the asymmetric traveling salesman problem with time windows (ATSP-TW). ATSP-TW is a problem that involves determining the route with the minimum travel cost for visiting n cities one time each with time window constraints (the period of time in which the city must be visited is constrained). “Asymmetrical” denotes a difference between the cost of outbound and return trips. For such a combinatorial optimization problem with constraints, we propose a method that combines a pre-process based on the insertion method with metaheuristics called “the compressed annealing approach.” In an experiment using a 3-GHz computer, our method derives a feasible solution that satisfies the time window constraints for all of up to about 300 cities at an average of about 1/7 the computing time of existing methods, an average computing time of 0.57 seconds, and a maximum computing time of 9.40 seconds.

A branch‐and‐price algorithm for the vehicle routing problem with time windows on a road network

Networks ◽  
2018 ◽  
Vol 73 (4) ◽  
pp. 401-417 ◽  
Author(s):  
Hamza Ben Ticha ◽  
Nabil Absi ◽  
Dominique Feillet ◽  
Alain Quilliot ◽  
Tom Van Woensel
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Road Network ◽  
Time Windows ◽  
Branch And Price ◽  
Routing Problem ◽  
Price Algorithm

scholarly journals Analysis of an Automated Vehicle Routing Problem in Logistics considering Path Interruption

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Zhang ◽  
Lei Shi ◽  
Jing Chen ◽  
Xuefeng Li
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Initial Period ◽  
Pso Algorithm ◽  
Automated Vehicles ◽  
Routing Problem ◽  
Automated Vehicle ◽  
Last Mile ◽  
The Cost

The application of automated vehicles in logistics can efficiently reduce the cost of logistics and reduce the potential risks in the last mile. Considering the path restriction in the initial stage of the application of automated vehicles in logistics, the conventional model for a vehicle routing problem (VRP) is modified. Thus, the automated vehicle routing problem with time windows (AVRPTW) model considering path interruption is established. Additionally, an improved particle swarm optimisation (PSO) algorithm is designed to solve this problem. Finally, a case study is undertaken to test the validity of the model and the algorithm. Four automated vehicles are designated to execute all delivery tasks required by 25 stores. Capacities of all of the automated vehicles are almost fully utilised. It is of considerable significance for the promotion of automated vehicles in last-mile situations to develop such research into real problems arising in the initial period.

A branch-and-price algorithm for the Vehicle Routing Problem with Deliveries, Selective Pickups and Time Windows

2010 ◽  
Vol 206 (2) ◽  
pp. 341-349 ◽  
Author(s):  
Gabriel Gutiérrez-Jarpa ◽  
Guy Desaulniers ◽  
Gilbert Laporte ◽  
Vladimir Marianov
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Branch And Price ◽  
Routing Problem ◽  
Price Algorithm

scholarly journals Cost-Effective Vibration Analysis through Data-Backed Pipeline Optimisation

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6678
Author(s):  
Artur Sokolovsky ◽  
David Hare ◽  
Jorn Mehnen
Keyword(s):  
Signal Processing ◽  
Vibration Analysis ◽  
Failure Modes ◽  
Time Window ◽  
Time Windows ◽  
Sampling Rate ◽  
Cost Effective ◽  
Classification Performance ◽  
Observation Time ◽  
Efficient Design

Vibration analysis is an active area of research, aimed, among other targets, at an accurate classification of machinery failure modes. The analysis often leads to complex and convoluted signal processing pipeline designs, which are computationally demanding and often cannot be deployed in IoT devices. In the current work, we address this issue by proposing a data-driven methodology that allows optimising and justifying the complexity of the signal processing pipelines. Additionally, aiming to make IoT vibration analysis systems more cost- and computationally efficient, on the example of MAFAULDA vibration dataset, we assess the changes in the failure classification performance at low sampling rates as well as short observation time windows. We find out that a decrease of the sampling rate from 50 kHz to 1 kHz leads to a statistically significant classification performance drop. A statistically significant decrease is also observed for the 0.1 s time window compared to the 5 s one. However, the effect sizes are small to medium, suggesting that in certain settings lower sampling rates and shorter observation windows might be worth using, consequently making the use of the more cost-efficient sensors feasible. The proposed optimisation approach, as well as the statistically supported findings of the study, allow for an efficient design of IoT vibration analysis systems, both in terms of complexity and costs, bringing us one step closer to the widely accessible IoT/Edge-based vibration analysis.

scholarly journals Conception and Development of a Last Mile Vehicle for Urban Areas

2021 ◽  
pp. 167-177
Author(s):  
Andreas Höfer ◽  
Erhard Esl ◽  
Daniel Türk ◽  
Veronika Hüttinger
Keyword(s):  
Urban Areas ◽  
Cost Effective ◽  
Drive Train ◽  
Maximum Speed ◽  
Vehicle Concept ◽  
Legal Regulations ◽  
Last Mile ◽  
Speed Up ◽  
Continuous Power ◽  
On Time Delivery

AbstractIn megacities, increasing globalization effects are leading to rapidly increasing prosperity and augmented purchasing power, and thus to a growing need for punctual, cost-effective, and environmentally friendly delivery of goods. A smart, small electric vehicle concept is presented that targets on meeting the requirements for the delivery of goods in urban areas and that is designed especially for the delivery on the last mile. This last mile vehicle (LMV) for cargo transportation is attached to a truck. Whenever it is needed, for example to deliver goods into narrow streets, in pedestrian areas or in case of traffic jams, it can be unfolded and unloaded from the truck and hereby guarantees a flexible and punctual delivery of goods. This flexible on-time delivery is possible because the last mile vehicle is designed, so that the legal regulations of the non-motorized vehicle lane, that is everywhere to be found in Asia, are met. The vehicle is designed with three wheels, a range of 40-60 km and an electric drive train with a continuous power of 2 × 250 W that enables a maximum speed up to 40 km/h of the vehicle. The drive train consists of a battery pack that can be charged electrically from the truck, two inverters, and two electric wheel hub motors. The LMV has been designed and constructed as a prototype and has been tested on non-public roads to prove the vehicle concept. For Europe, it can be classified as an L2e vehicle and with slight modifications; it can be applied on European roads as well.

scholarly journals Time Window-of-Opportunity Strategies for Oil Spill Planning and Response

1999 ◽  
Vol 71 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Atle B. Nordvik
Keyword(s):  
Decision Making ◽  
Oil Spill ◽  
Time Window ◽  
Time Windows ◽  
Cost Effective ◽  
Field Studies ◽  
Insurance Companies ◽  
Specific Response ◽  
Wide Range ◽  
Oil Spill Response

This paper presents an integrated scientific and engineering strategy to improve and bring planning and decision-making for marine oil spill response to a higher level of knowledge. The most efficient, environmentally preferred, and cost effective spill response is dependent on the following factors: chemistry of the spilled product, quantity, location, response time, environmental conditions, and effectiveness of available response technologies at various degrees of oil weathering.Time windows is a highly targeted process, in which the selection of response technologies will be more efficient, cost effective, technically correct, and environmentally sensitive and appropriate. The strategy integrates dynamic oil weathering data and performance effectiveness data for oil spill response technologies derived from laboratory, mesoscale, and experimental field studies. Performance data has been developed from a wide range of viscosities of different weathering stages of transported oils into a dynamic oil weathering database to identify and estimate time periods, called "technology windows-of-opportunity." In these windows, specific response methods, technologies, equipment, or products are more effective during clean-up operations for specific oils. The data bases represent the state of the art for response technologies and research in oil spill response.The strategy provides a standard foundation for rapid and cost effective oil spill response decision-making, and is intended for use by local, state, federal agencies, response planners, clean up organizations (responders), insurance companies, tanker owners, and transporters. It provides policy, planners and decision-makers with a scientifically based and documented "tool" in oil spill response that has not been available before.

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