Supporting multi-depot and stochastic waste collection management in clustered urban areas via simulation–optimization
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Waste collection is one of the most critical logistics activities in modern cities with considerable impact on the quality of life, urban environment, city attractiveness, traffic flows and municipal budgets. Despite the problem’s relevance, most existing work addresses simplified versions where container loads are considered to be known in advance and served by a single vehicle depot. Waste levels, however, cannot be estimated with complete certainty as they are only revealed at collection. Furthermore, in large cities and clustered urban areas, multiple depots from which collection routes originate are common, although cooperation among vehicles from different depots is rarely considered. This paper analyses a rich version of the waste collection problem with multiple depots and stochastic demands by proposing a hybrid algorithm combining metaheuristics with simulation. Our ‘simheuristic’ approach allows for studying the effects of cooperation among different depots, thus quantifying the potential savings this cooperation could provide to city governments and waste collection companies.
Keywordssimheuristics simulation–optimization waste collection management multi-depot vehicle routing problem horizontal cooperation stochastic demands
This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P and TRA2015-71883-REDT) and FEDER. Likewise, we want to acknowledge the support received from the Department of Universities, Research and Information Society of the Catalan Government (2014-CTP-00001) and the doctoral grant of the UOC.
Statement of contribution
This paper presents a simulation–optimization approach to support efficient waste collection management under scenarios characterized by the existence of multiple depots and stochastic demands (waste levels). Despite the fact that these scenarios are common in clustered urban areas and large cities, they had never been analysed before in the scientific literature. This is probably due to their inherent complexity, which requires the use of hybrid simulation–optimization methods. Thus, our approach relies on the combination of biased (oriented) randomization techniques, metaheuristics and Monte Carlo simulation. The proposed hybrid algorithm was tested on a set of large-sized WCP instances. Furthermore, the possible benefits of HC among different waste management service providers are analysed. These benefits refer to savings in total costs, number of used vehicles and number of opened depots. Although it is evident that these savings are highly dependent on the specific topology and characteristics of each instance, the computational experiments show that our algorithm is able to quantify them for all the proposed benchmarks, thus supporting the idea that sharing resources (i.e. containers, landfills, depots and/or vehicles) can provide significant savings in those cases in which depots and their assigned containers are geographically scattered.
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