A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization

Abstract

Urban waste collection is one of the principal processes in municipalities with large expenses and laborious operations. Among the important issues raised in this regard, the lack of awareness of the exact amount of generated waste makes difficulties in the processes of collection, transportation and disposal. To this end, investigating the waste collection issue under uncertainty can play a key role in the decision-making process of managers. This paper addresses a novel robust bi-objective multi-trip periodic capacitated arc routing problem under demand uncertainty to treat the urban waste collection problem. The objectives are to minimize the total cost (i.e. traversing and vehicles’ usage costs) and minimize the longest tour distance of vehicles (makespan). To validate the proposed bi-objective robust model, the ε-constraint method is implemented using the CPLEX solver of GAMS software. Furthermore, a multi-objective invasive weed optimization algorithm is then developed to solve the problem in real-world sizes. The parameters of the multi-objective invasive weed optimization are tuned optimally using the Taguchi design method to enhance its performance. The computational results conducted on different test problems demonstrate that the proposed algorithm can generate high-quality solutions considering three indexes of mean of ideal distance, number of solutions and central processing unit time. It is proved that the ε-constraint method and multi-objective invasive weed optimization can efficiently solve the small- and large-sized problems, respectively. Finally, a sensitivity analysis is performed on one of the main parameters of the problem to study the behavior of the objective functions and provide the optimal policy.

A Capacitated Vehicle Routing Problem considering Satisfaction Level of Customers: A Genetic Algorithm

“…Since there are some major limitations in the study, future research directions can be designed accordingly. In the following, the most important recommendations are given: 1) Considering a time horizon to fulfill the demand of customers [25], 2) Extending the problem considering more real-world assumptions, such as time windows constraint [26], 3) Applying uncertainty techniques to study the uncertain nature of the parameters, such as fuzzy programming [27][28] and robust optimization [29][30], 4) Developing other algorithms to evaluate the performance of the proposed GA, such as runner root algorithm (RRA) [31], particle swarm optimization (PSO) algorithm [32] and cuckoo optimization algorithm (COA) [33]. 5) Considering other objectives (e.g., pollution minimization [34]) and applying efficient multiobjective meta-heuristic algorithms, such as non-dominated sorting genetic algorithm III (NSGA-III) [35].…”

Optimization of solid waste collection circuits

“…The problem is solved through a hybrid algorithme (heuristic algorithme and (SA) algorithme). In 2019, Tirkolaee et al [46] treated PCARP with demand uncertainty.…”

A robust green traffic‐based routing problem for perishable products distribution

“…Robust optimization approach has been studied by many researchers as an effective method to overcome the real‐world instability . This technique can efficiently control the uncertainty level and yield feasible solutions with a high probability …”

“…Robust optimization approach has been studied by many researchers as an effective method to overcome the real-world instability. [37][38][39][40][41][42][43][44] This technique can efficiently control the uncertainty level and yield feasible solutions with a high probability. 45 For a brief description, the robust optimization approach suggested by Bertsimas and Sim 45 is presented as follows.…”