A Heuristic Solution Based on Clarke & Wright's Savings Algorithm for the Optimization of Sludge Hauling: the case of a Portuguese company

Passos, Jeysa; Lourinho, Gonçalo; Alves, Octávio; Brito, Paulo

http://hdl.handle.net/10400.26/25185

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Sewage sludge originating from wastewater treatment plants (WWTPs) can be a major cause of environmental pollution and their appropriate management should be viewed as a priority. A critical aspect in sludge management practices is route optimization since significant costs are associated with the transportation of waste. In this work, we propose a heuristic solution based on Clarke-Wright savings method for the sludge collection problem of a Portuguese company within a perspective of reducing collection costs (transportation) and negative impacts on the environment. Two case studies were considered in the methodology: the first one focused on the comparison between the general weekly collection plan presently used by the company and the solution generated by CW algorithm (Case Study A); the second one explored a new hypothetical scenario centered on the expansion of the company’s activities (Case Study B). In general, the application of CW method led to a decrease in traveled distances and transportation costs, as well as carbon dioxide emissions. Specifically, with the adoption of the optimized plan in Case Study A we found that a single vehicle (instead of three) would be capable of performing sludge hauling operations in a given week leading to total weekly savings of 346 km, representing a decrease of almost 40% for both cost and pollutant emissions. Regarding Case Study B, the model suggests that for about 76% of the initial cost, the company would be capable of attending twice the number of customers, i.e., via route optimization, it is possible to expand their client portfolio while still creating savings. Moreover, a sensitivity analysis (SA) was carried out in order to check the robustness of results when undergoing changes in the input parameters. We found that vehicle capacity and fuel price are two important factors in route optimization with model results greatly influenced by changes in both parameters.