Abstract
In order to solve the optimization problem of wet waste collection and transportation in Chinese cities, this paper constructs a chance-constrained low-carbon vehicle routing problem (CCLCVRP) model in waste management system and applies certain algorithms to solve the model. Considering the environmental protection point of view, the CCLCVRP model combines carbon emission costs with traditional waste management costs under the scenario of application of smart bins. Taking into the uncertainty of the waste generation rate, chance-constrained programming is applied to transform the uncertain model to a certain one. The initial optimal solution of this model is obtained by a proposed hybrid algorithm, that is, particle swarm optimization (PSO); and then the further optimized solution is obtained by simulated annealing (SA) algorithm, due to its global optimization capability. The effectiveness of PSOSA algorithm is verified by the classic database in a capacitated vehicle routing problem (CVRP). What’s more, a case of waste collection and transportation is applied in the model for acquiring reliable conclusions, and the application of the model is tested by setting different waste fill levels (WFLs) and credibility levels. The results show that total costs rise with the increase of credibility level reflecting dispatcher’s risk preference; the WFL value range between 0.65 and 0.75 can obtain the optimal solution under different credibility levels. Finally, according to these results, some constructive proposals are propounded for the government and the logistics organization dealing with waste collection and transportation.
Highlights
Solid waste management (SWM) has always been the most concerned issue in every region [1,2]which is composed of many stages including generation, collection and transportation, treatment and disposal [3,4]
This means that when collection vehicles depart from the depot according to the schedule and the amount of waste in smart waste bins, there will be an incremental of waste amount in smart waste bins as travelling time elapses, so a chance constraint method is applied to deal with this uncertainty and credibility levels are predefined to insure the probability of routes’ success
The objective function varies with the predefined credibility levels and the objective values of the same solution are different with different credibility levels
Summary
Solid waste management (SWM) has always been the most concerned issue in every region [1,2]. Vehicles produce emissions when driving, and when loading and unloading waste due to the necessity to keep their engines running, producing constant exhaust emissions [11,12] These considerations highlight the importance of optimizing vehicle routing to reduce the carbon emissions during the process of waste collection and transportation [13]. The use of fixed routes might lead to half-full waste bins, overflowing waste bins and high fuel consumption, which are very serious problems [9] For these reasons, many cities and regions are starting to use smart waste bins to reduce operating costs and improve residents’ satisfaction through real-time monitoring of waste volumes.
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