ABSTRACT
The unexpected crisis posed by the COVID-19 pandemic in 2020 caused that items such as face shields and ear savers were highly demanded. In the Barcelona area, hundreds of volunteers employed their home 3D-printers to produce these elements. After the lockdown, they had to be collected by a reduced group of volunteer drivers, who transported them to several consolidation centers. These activities required a daily agile design of efficient routes, especially considering that routes should not exceed a maximum time threshold to minimize drivers' exposure. These constraints limit the number of houses that could be visited. Moreover, travel and service times are considered as random variables. This logistics challenge is modeled as a stochastic team orienteering problem. Our main performance indicator is the collected reward, which should be maximized. This problem is solved by employing a biased-randomized simheuristic algorithm, which is capable of generating high-quality solutions in short computing times. © 2021 IEEE.
ABSTRACT
The growth of e-commerce and the on-demand economy in urban and metropolitan areas has been accelerated by the recent COVID-19 pandemic. As a consequence, logistics and transportation operators are subject to a noticeable pressure to develop efficient delivery systems. These systems are also influenced by the global trend towards more sustainable transportation and mobility, which implies changes in urban policies and technological innovations-e.g., the substitution of traditional diesel petrol-drive vehicles by electric ones. This paper analyzes the current and predicted needs of logistics operators in the Barcelona metropolitan area. To do so, urban regulations are analyzed and key shareholders are interviewed. The analysis of these interviews promote a discussion on how the use of new 'agile' optimization algorithms-which are based on the combination of biased-randomized heuristics, computer parallelization techniques, and IoT / 5G technologies-can contribute to enhance urban distribution practices. Finally, we present a case study in which the effect of different configurations of working/resting times and parking areas availability on routing solutions is studied. Our research aims to provide comprehensive knowledge to managers and policy-makers, and to offer them with powerful tools capable of generating real-Time solutions to complex last-mile delivery challenges under dynamic conditions. © 2021 Elsevier B.V.. All rights reserved.
ABSTRACT
This chapter describes a case study regarding the use of ‘agile’ computational intelligence for supporting logistics in Barcelona’s hospitals during the COVID-19 crisis in 2020. Due to the lack of sanitary protection equipment, hundreds of volunteers, the so-called “Coronavirus Makers” community, used their home 3D printers to produce sanitary components, such as face covers and masks, which protect doctors, nurses, patients, and other civil servants from the virus. However, an important challenge arose: how to organize the daily collection of these items from individual homes, so they could be transported to the assembling centers and, later, distributed to the different hospitals in the area. For over one month, we have designed daily routing plans to pick up the maximum number of items in a limited time—thus reducing the drivers’ exposure to the virus. Since the problem characteristics were different each day, a series of computational intelligence algorithms was employed. Most of them included flexible heuristic-based approaches and biased-randomized algorithms, which were capable of generating, in a few minutes, feasible and high-quality solutions to quite complex and realistic optimization problems. This chapter describes the process of adapting several of our ‘heavy’ route-optimization algorithms from the scientific literature into ‘agile’ ones, which were able to cope with the dynamic daily conditions of real-life routing problems. Moreover, it also discusses some of the computational aspects of the employed algorithms along with several computational experiments and presents a series of best practices that we were able to learn during this intensive experience. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.