A heuristic solution method for node routing based solid waste collection problems

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

“…In Angelelli and Speranza (2002a) such an algorithm was used to measure the operating cost of three different waste-collection systems. Recently, Hemmelmayr et al (2011) studied a real world application in waste collection. In this paper, they proposed a successful VNS algorithm for the PVRP-IF and the multi-depot vehicle routing problem with inter-depot routes.…”

Section: Related Workmentioning

confidence: 99%

“…As described in Section 3.1 above, the routing problem is a PVRP-IF-SC, therefore we propose for its solution a VNS framework that extends to the PVRP-IF-SC the successful algorithm proposed by Hemmelmayr et al (2011) to efficiently solve the PVRP-IF. The modifications we introduced are aimed at incorporating the service choice into the previous framework without altering too much its overall structure, thus possibly preserving its effectiveness.…”

Section: Vns Algorithm For the Routing Partmentioning

confidence: 99%

“…Then 2-opt is used to improve the routes, where only solutions that are feasible with respect to capacity can be accepted. See Hemmelmayr et al (2011) for more details.…”

Section: Local Searchmentioning

confidence: 99%

“…The first set of instances is obtained by extending a set of PVRP-IF instances from the literature (see Hemmelmayr et al (2011)). In the dataset there are ten different instances, ranging from 48 to 288 collection points, with a time horizon T of 4 and 6 days, respectively.…”

Section: Synthetic Instancesmentioning

confidence: 99%

See 2 more Smart Citations

Models and Algorithms for the Integrated Planning of Bin Allocation and Vehicle Routing in Solid Waste Management

Hemmelmayr

Doerner

Hartl

et al. 2014

Transportation Science

Self Cite

The efficient organization of waste collection systems based on bins located along the streets involves the solution of several tactical optimization problems. In particular, the bin configuration and sizing at each collection site as well as the service frequency over a given planning horizon have to be decided. In this context, a higher service frequency leads to higher routing costs, but at the same time less or smaller bins are required which lead to lower bin allocation investment costs. The bins used have different types and different costs and there is a limit on the space at each collection site as well as a limit on the total number of bins of each type that can be used. In this paper we consider the problem of designing a collection system consisting in the combination of a vehicle routing and a bin allocation problem in which the trade-off between the associated costs has to be considered. The solution approach combines an effective VNS metaheuristic for the routing part with a MILP-based exact method for the solution of the bin allocation part. We propose hierarchical solution procedures in which the two decision problems are solved in sequence, as well as an integrated approach where the two problems are considered simultaneously. Extensive computational testing on synthetic and real-world instances with hundreds of collection sites show the benefit of the integrated approaches with respect to the hierarchical ones.

Forty years of periodic vehicle routing

2013

The periodic vehicle routing problem (PVRP) first appeared in 1974 in a paper about garbage collection (Beltrami and Bodin, Networks 4 (1974), 65–74). The wide applicability and versatility of the problem has led to a vast body of literature addressing both novel applications and solution methods. This article discusses the wide array of circumstances and settings in which the PVRP has been applied and describes the development of solution methods, both exact and heuristic, for the PVRP. As with many core research problems, many variants have been proposed. We will describe additional problem variants and extensions, as well as discuss the future of research for the PVRP. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 63(1), 2–15 2014