Using Simulation to Assess the Opportunities of Dynamic Waste Collection

Mes, Martijn

doi:10.1007/978-3-642-28777-0_13

Cited by 17 publications

(20 citation statements)

References 28 publications

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“…The proposed model reduces the amounts of carbon dioxide released in the environment by making the dynamic routing more effective. Discrete event simulation is the technique adopted in (Mes, 2012). The authors present a model that applies dynamic planning to exploit information transmitted through motion sensors embedded in underground bins.…”

Section: Prior Workmentioning

confidence: 99%

Assessing dynamic models for high priority waste collection in smart cities

Anagnostopoulos

Kolomvatsos

Anagnostopoulos

et al. 2015

Journal of Systems and Software

103

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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.

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Section: Prior Workmentioning

confidence: 99%

Assessing dynamic models for high priority waste collection in smart cities

Anagnostopoulos

Kolomvatsos

Anagnostopoulos

et al. 2015

Journal of Systems and Software

103

View full text Add to dashboard Cite

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“…Much of the effort in the literature for waste collection and inventory routing problems has been spent on finding the best routes, throughout a given planning horizon with a given set of customers [43]. Also, sophisticated algorithms have been developed to handle the uncertainty in routing.…”

Section: Transportation Managementmentioning

confidence: 99%

“…This case study is described in detail in Mes [43], where a simulation study is carried out to study the benefits of a dynamic collection policy compared to the current static way of scheduling. After the study of Mes [43], the dynamic planning methodology has been implemented at the company and major savings have been reported.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…To evaluate the impact of specific parameter settings, we use a similar simulation model as described in Mes [43]. However, instead of using specific discrete-event simulation software, we now implemented the model in Delphi, a general-purpose programming language.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…In addition, to focus on the topic of parameter tuning, we consider a simplified collection problem here. In contrast with Mes [43], we assume (i) deterministic deposit volumes (still stochastic interarrival time between deposits), (ii) constant mean deposit rate and volume over the days in the week, and (iii) accurate sensor information on the volume of waste inside the containers. To provide general insights, we also consider variations to the real life setting, by varying the network density (depending on the size of the network and the number of containers located in it), the number of vehicles available to collect the waste, and how fast the containers fill up (rate and volume of waste deposits).…”

Section: Experimental Settingsmentioning

confidence: 99%

See 2 more Smart Citations

Inventory routing for dynamic waste collection

Mes¹,

Schutten²,

Rivera³

2014

Waste Management

Self Cite

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We consider the problem of collecting waste from sensor equipped underground containers. These sensors enable the use of a dynamic collection policy. The problem, which is known as a reverse inventory routing problem, involves decisions regarding routing and container selection. In more dense networks, the latter becomes more important. To cope with uncertainty in deposit volumes and with fluctuations due to daily and seasonal effects, we need an anticipatory policy that balances the workload over time. We propose a relatively simple heuristic consisting of several tunable parameters depending on the day of the week. We tune the parameters of this policy using optimal learning techniques combined with simulation. We illustrate our approach using a real life problem instance of a waste collection company, located in The Netherlands, and perform experiments on several other instances. For our case study, we show that costs savings up to 40% are possible by optimizing the parameters.

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