Improving the tactical planning of solid waste collection with prescriptive analytics: a case study

• Autor(a) principal: Vargas,Angie Paola
• Data de Publicação: 2022
• Outros Autores: Díaz,Danilo, Jaramillo,Santiago, Rangel,Francisco, Villa,Daniel, Villegas,Juan G.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Production
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132022000100205
• topic: Optimization model; Metaheuristic algorithm; Discrete event simulation model; Waste management; Waste transfer station

Abstract Paper aims This study presents several business analytics tools that allow improving the tactical planning of the collection process for a Colombian solid-waste management company. Originality The extant literature of operations research/analytics applied to these systems focuses on facility location or vehicle routing. Tactical decisions are seldom studied in the operations research/analytics literature devoted to waste management systems. By contrast, the focus of this paper is on tactical decisions: fleet sizing, frequency assignment, route scheduling and internal resource allocation in a new waste transfer station. Research method We follow a multimethodology approach that uses mathematical programming, metaheuristics, and discrete event simulation. The models use historical information of the system, and the solution of a model are used as input data for the other models. Main findings Introducing a new waste transfer station allows an important reduction of the compactors fleet. However, to prevent a collapse in its internal operation an even operation is needed. This is achieved by rescheduling the routes to balance their arrival during the day. Additional benefits can be attained if some soft constraints are relaxed. Implications for theory and practice Practitioners looking for tactical planning tools on waste collection systems have here an example of their application and benefits. Improvements can be achieved by tactical planning without heavily disrupting decisions at the operational level.