A Discrete and Improved Bat Algorithm for solving a medical goods distribution problem with pharmacological waste collection

Abstract

The work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's posthoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem.

Nature-inspired metaheuristics for optimizing information dissemination in vehicular networks

“…The main rationale behind the use of these nature-inspired metaheuristics relies in their reputed efficiency for solving other combinatorial optimization problems along recent years. Some examples of successful application cases can be found in [23,25,27,28]. The good acceptance of these previous studies have led us to the hypothesis that these methods may be promising also for the problem approached in this work.…”

“…The BA, introduced by Xin-She Yang in [38], is a bio-inspired method based on the echolocation behavior of microbats, which can find their prey and discriminate different kinds of insects even in complete darkness. Despite it was firstly proposed for solving continuous optimization problems, it has been adapted several times before [27,33,41] for solving discrete problems. Following the same philosophy, an adaptation has been made in the present research.…”

“…Then, the best movement carried out on x is chosen as output. To properly select the movement operator, and with the aim of enhancing the exploration capacity of the method, the inclination mechanism recently proposed in works, such as [23,25,27], is also used considering UC p (x, y) as wide movement and SW (x, c) as short movement.…”

“…Finally, PSO have been configured as described in Section 4. For the development and parameterization of these methods, the guidelines given in [24,[26][27][28] have been followed. Regarding GA_NAVI, the population size was also 100, the tournament size was set to 5, the crossover rate to 0.5 and the mutation rate to 0.015.…”

Nature-inspired optimization algorithms: Challenges and open problems

“…However, their main differences are in the ways of generating ∆x t i and ∆v t i from the population of the existing solutions. Most recently, new variants and applications are appearing regularly, including local ant system for allocating robot swarms [Khaluf et al(2019)Khaluf, Vanhee, and Simoens], hybrid ant and firefly algorithms [Goel and Maini(2018)], usability feature selection by MBBAT [Gupta and Ahlawat(2017)], vehicle routing (Osaba et al, 2017;Osaba et al, 2019) and others (Alyasseri et al, 2018;Abdel-Basset and Shawky, 2019).…”