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

Vargas, Angie Paola; Díaz, Danilo; Jaramillo, Santiago; Rangel, Francisco; Villa, Daniel; Villegas, Juan G.

doi:10.1590/0103-6513.20210037

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Research Article • Prod. 32 • 2022 • https://doi.org/10.1590/0103-6513.20210037 copy

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

Authorship SCIMAGO INSTITUTIONS RANKINGS

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.

Keywords
Optimization model; Metaheuristic algorithm; Discrete event simulation model; Waste management; Waste transfer station

Parameters
$n_{j}$ Number of visits required in frequency $j \in F$
$r_{i}$ Number of collections in the week required by route $i \in M$
$v_{k j}$ Binary value equal 1 if the frequency $j \in F$ includes the day $k \in D$ , 0 otherwise
$d_{i}$ Duration of the solid waste collection process in route $i \in M$
$t_{i}^{E T}$ Time from WTS to start of route $i \in M$
$t_{i}^{B O}$ Time from the depot to start of route $i \in M$
$f_{i}$ Time from route $i \in M$ to WTS
$t E T$ Service time in the WTS
$t B O$ Travel time from WTS to depot
$t n_{i}$ Average tonnes of solid waste collected in route $i \in M$
$f r e q_{i}$ Current collection frequency on route $i \in M$
$d v$ Time available of the compactor vehicles during each day to execute the solid waste collection process
$m c$ Maximum number of collection routes that can change their collection frequency
$ϵ$ Very small real number

Trailers	External trucks	Internal trucks	Compactor vehicles
Trailers	External trucks	Internal trucks	Maximum queue	Average queue	Average time in queue (minutes)	Average time in WTS (minutes)
21	18	4	35	3.0	20	40
29	18	4	18	2.0	15	35
35	20	5	12	0.5	2	23
40	20	5	12	0.17	2	22

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[1] * juan.villegas@udea.edu.co