Abstract
Waste collection is nowadays an increasingly important business. However, it is often an inefficient operation due to the high uncertainty associated with the real waste bins’ fill-levels. To deal with such uncertainty the use of sensors to transmit real time information is seen as possible solution. But, in order to improve operations’ efficiency, the sensors’ usage must be combined with optimization procedures that inform on the optimal collection routes to operationalize, so as to guarantee a maximization of the waste collected while also minimizing transportation costs. The present work explores this challenge and studies three operational management approaches to define dynamic optimal routes, considering the access to real-time information on the bins’ fill-levels. A real case study is solved and important results were found where significant profit improvements are observed when compared to the real operation. This shows the potential of the proposed approaches to build an expert system, which can support the operations manager's decisions.
Highlights
The Internet-of-Things (IoT) is an emergent research topic that is causing a positive impact in the way operations are managed (Atzori et al, 2017)
In order to improve operations’ efficiency, the sensors’ usage must be combined with optimization procedures that inform on the optimal collection routes to operationalize, so as to guarantee a maximization of the waste collected while minimizing transportation costs
This work introduces the Smart Waste Collection Routing Problem, where it is assumed that uncertainty regarding the amount of waste in each bin is reduced by installing sensors capable of reading and transmitting the bins’ fill-levels in real-time
Summary
The Internet-of-Things (IoT) is an emergent research topic that is causing a positive impact in the way operations are managed (Atzori et al, 2017). One of the areas where such conditions should be investigated is the waste management area where realtime information on bins’ fill-levels, provided by volumetric sensors installed in waste containers should be explored, as defined in the development of Smart Cities (Gruler et al, 2017) This access to real-time information on the bins’ fill-levels, if effectively used by operations managers, could result in an improvement of productivity. Efficient waste collection systems should be operated, where the role of in-place expert systems could represent a major cornerstone that would help to define optimal operational management procedures In this context, the access to bins’ fill-levels in real-time, through monitoring systems, emerges as a path to be explored, as this will allow the reduction of demand uncertainty (e.g. the amount of real waste level in the bins).
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