A Memetic Algorithm for Waste Collection Vehicle Routing Problem with Time Windows and Conflicts

Kolomvatsos

Journal of Systems and Software

et al. 2015

103

A B S T R A C TWaste Management (WM) represents an important part of Smart Cities (SCs) with significant impact on modern societies. WM involves a set of processes ranging from waste collection to the recycling of the collected materials. The proliferation of sensors and actuators enable the new era of Internet of Things (IoT) that can be adopted in SCs and help in WM. Novel approaches that involve dynamic routing models combined with the IoT capabilities could provide solutions that outperform existing models. In this paper, we focus on a SC where a number of collection bins are located in different areas with sensors attached to them. We study a dynamic waste collection architecture, which is based on data retrieved by sensors. We pay special attention to the possibility of immediate WM service in high priority areas, e.g., schools or hospitals where, possibly, the presence of dangerous waste or the negative effects on human quality of living impose the need for immediate collection. This is very crucial when we focus on sensitive groups of citizens like pupils, elderly or people living close to areas where dangerous waste is rejected. We propose novel algorithms aiming at providing efficient and scalable solutions to the dynamic waste collection problem through the management of the trade-off between the immediate collection and its cost. We describe how the proposed system effectively responds to the demand as realized by sensor observations and alerts originated in high priority areas. Our aim is to minimize the time required for serving high priority areas while keeping the average expected performance at high level. Comprehensive simulations on top of the data retrieved by a SC validate the proposed algorithms on both quantitative and qualitative criteria which are adopted to analyze their strengths and weaknesses. We claim that, local authorities could choose the model that best matches their needs and resources of each city.

Section: Prior Workmentioning

confidence: 99%

Assessing dynamic models for high priority waste collection in smart cities

Kolomvatsos

Journal of Systems and Software

et al. 2015

103

IEEE Trans. Evol. Computat.

“…Exact methods were applied to solve WCPs modeled as a CVRP (e.g., [23]) but suffer from the curse of dimensionality. Hence, heuristics and metaheuristics have been widely applied to optimize WCPs (e.g., [24], [25]). Minh et al [24] tackled WCPs with time windows with MA.…”

Section: Related Workmentioning

confidence: 99%

Region-Focused Memetic Algorithms With Smart Initialization for Real-World Large-Scale Waste Collection Problems

Lan

Peijun

et al. 2022

Memetic algorithm (MA) is widely applied to optimize routing problems as it provides one way to combine local search with global search. However, the local search in MA needs to be carefully designed according to the problem's characteristics. In this article, we consider a real-world large-scale waste collection problem with multiple depots, multiple disposal facilities, multiple trips, and working time constraints. Vehicles with a limited capacity and working time can start from different depots, collect waste at different sites, and make multiple trips to different disposal facilities to empty the waste and return to its origin. While the existing work considered problems with multiple trips and time constraints, none have tackled problems with multiple depots, multiple disposal facilities, multiple trips, as well as working time constraints. The change from "singledepot" to "multidepot" not only reflects better the situation in real life but also leads to a qualitative different and more complex problem. In this article, we first model this complex problem mathematically. Then, a novel region-focused MA is proposed to tackle this new challenge. Compared to classic MA, this region-focused one is enhanced by two major components: 1) a new heuristic-assisted solution initialization algorithm and 2) a region-focused local search with novel heuristics. Comprehensive computational studies show that our proposed approaches significantly outperform several state-of-the-arts on our real problem of thousands of tasks. The new local search procedure and solution initialization method significantly improve the search ability in combination with global search ability of MA.

“…Authors in [21] introduce a memetic algorithm to perform routing enforced with time windows and conflicts context. Model incorporates a combination of flow and set partitioning formulation to achieve multi-objective optimization.…”

Section: Related Workmentioning

confidence: 99%

Robust waste collection exploiting cost efficiency of IoT potentiality in Smart Cities

2015 International Conference on Recent Advances in Internet of Things (RIoT)

Zaslavsky

Medvedev

2015

Smart Cities constitute the future of civil habitation.Internet of Things (loT) enable innovative services exploiting sensor data from sensors embedded in the city. Waste collection is treated as a potential loT service which exploits robustness and cost efficiency of a heterogeneous fleet. In this paper we propose a dynamic routing algorithm which is robust and copes when a truck is overloaded or damaged and need replacement. We also incorporate a system model which assumes two kinds of trucks for waste collection, the Low Capacity Trucks (LCTs) and the High Capacity Trucks (HCTs). By incorporating HCTs we achieve reduction of the waste collection operational costs because route trips to the dumps are reduced due to high waste storage capacity of these trucks. Finally, the proposed models are evaluated on synthetic and real data from the city municipality of St. Petersburg, Russia. The models demonstrate consistency and correctness.