Waste collection multi objective model with real time traceability data

Abstract

Waste collection is a highly visible municipal service that involves large expenditures and difficult operational problems, plus it is expensive to operate in terms of investment costs (i.e. vehicles fleet), operational costs (i.e. fuel, maintenances) and environmental costs (i.e. emissions, noise and traffic congestions). Modern traceability devices, like volumetric sensors, identification RFID (Radio Frequency Identification) systems, GPRS (General Packet Radio Service) and GPS (Global Positioning System) technology, permit to obtain data in real time, which is fundamental to implement an efficient and innovative waste collection routing model.

The basic idea is that knowing the real time data of each vehicle and the real time replenishment level at each bin makes it possible to decide, in function of the waste generation pattern, what bin should be emptied and what should not, optimizing different aspects like the total covered distance, the necessary number of vehicles and the environmental impact.

This paper describes a framework about the traceability technology available in the optimization of solid waste collection, and introduces an innovative vehicle routing model integrated with the real time traceability data, starting the application in an Italian city of about 100,000 inhabitants. The model is tested and validated using simulation and an economical feasibility study is reported at the end of the paper.

Introduction

In recent years, many municipalities, particularly in industrialized nations, have been forced to assess their solid waste management and examine its cost-effectiveness and environmental impact in terms of designing collection routes, due to a number of concerns such as cost, health and environment (Nuortio et al., 2006). The most common difficulties encountered in the management of waste collection are the optimization of resources for an efficient management system.

Specialty vehicles, with self-compactors, are usually designated to collect urban solid waste, with considerable operating expenses, hence designing efficient collection strategies is vital not only to reduce operating costs and vehicle emissions, but also to maximize the amount of re-cycling, while minimizing traffic congestion associated with refuse collection vehicles (RCV) operations (McLeod and Cherrett, 2008). While loading and unloading bins, trucks have to keep their engines running, producing constant exhaust emissions, but also causing noise and traffic congestion. The portion of time spent loading and unloading typically depends on different factors (the technology employed, the size and location of the collection operation, etc.), but in case of urban waste in cities with high population density and high traffic congestion, the non-transportation time, which includes time spent for load–unload operations and other idle times can reach 50% of the total time. This consideration highlights the importance not only to optimize the vehicle route, in order to reduce the transportation time, but also to reduce the number of load–unload stops.

The cost of collection of municipal solid waste is typically measured in terms of cost per ton, with an inverse relationship between the costs of collecting solid waste and the amount of materials collected, as a consequence moving bins that are only partially full seems an unnecessary misuse of resources, and an avoidable production of polluting emissions.

Waste collection business is divided into three major areas: commercial, residential and roll-on–roll-off. Each area includes municipal solid waste and re-cycling material, and each one is very different from the others. Residential waste collection generally involves servicing private homes, while the commercial waste collection involves servicing customers such as strip malls, restaurants and small office buildings. The difference between roll-on–roll-off collection and commercial collection is the size of the container. This paper considers a particular type of residential waste collection, largely diffused in Italy, in which waste is located in bins along the streets of a defined road network. Nuortio et al. (2006) observed that the amount of municipal solid waste for each garbage bin is variable and the accumulation of waste depends on several factors such as the number of inhabitants, lifestyle, time of the year, etc., therefore, the considered waste collection problem is stochastic by nature.

This paper aims to present a new multi objective routing model with real time data interchange for the residential waste collection, based on the integration of new technological traceability systems with a new heuristic routing model. The basic idea is that, if the real time position and replenishment level of each vehicle are known, as it is the real time waste level at each bin and which bins have been visited, it is possible to decide which bins should be emptied and which can be avoided at a certain time. This allows an optimization of the route plan and to minimize covered distance and number of vehicles needed, which, as a consequence, would minimize travel time, number of load–unload stops, exhaust emissions, noise and traffic congestion. Today, modern traceability devices, like volumetric sensors, identification RFID (Radio Frequency Identification), GPRS (General Packet Radio Service) and GPS (Global Positioning System) technology, can be used to obtain all the data defined before.

The potential benefits of this new approach are both economic and environmental:

• Reduction in investment costs for vehicles fleet, thanks to the ability to schedule on-demand pick-ups according to the effective need, with a consequent reduction in the number of vehicles.

• Reduction in operational costs (fuel, maintenance, etc.), thanks to the reduction of vehicles, covered distance and stationary load and unload times.

• The elimination of unnecessary stops, which means a reduction of engine emissions, produced both by sanitation vehicles and traffic congestion.

• The reduction of noise especially in urban areas.

After a brief review of literature on vehicle routing problems and their applications, developed in Section 2, Section 3 introduces a framework about the traceability technology available to waste collection and shows an application in the case study. It also presents the software application developed in order to obtain and manage real time data used as inputs for the proposed routing model. Later, in Section 4, the authors introduce the heuristics model for waste collection. Section 5 validates the model, simulating the results and comparing the new approach with other classical routing models in function of different patterns of waste generation. The section concludes the simulative study analyzing the optimization of a set of parameters necessary for the proposed routing model, such as the optimal bin replenishment level, which is the parameter that defines if a bin has to be emptied or not. Section 6 analyzes the economical feasibility of the real time traceability routing model in terms of costs/benefits versus the classical waste collection model, considering different scenario. Our conclusions wrap up the study in Section 7.