DSpace Repository :: Browsing by Author "Basso, Franco"

Browsing by Author "Basso, Franco"

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A deep learning approach for real-time crash prediction using vehicle-by-vehicle data
(2021) Basso, Franco; Pezoa, Raúl; Varas, Mauricio; Villalobos, Matías
In road safety, real-time crash prediction may play a crucial role in preventing such traffic events. However, much of the research in this line generally uses data aggregated every five or ten minutes. This article proposes a new image-inspired data architecture capable of capturing the microscopic scene of vehicular behavior. In order to achieve this, an accident-prediction model is built for a section of the Autopista Central urban highway in Santiago, Chile, based on the concatenation of multiple-input Convolutional Neural Networks, using both the aggregated standard traffic data and the proposed architecture. Different oversampling methodologies are analyzed to balance the training data, finding that the Deep Convolutional Generative Adversarial Networks technique with random undersampling presents better results when generating synthetic instances that permit maximizing the predictive performance. Computational experiments suggest that this model outperforms other traditional prediction methodologies in terms of AUC and sensitivity values over a range of false positives with greater applicability in real life.
A home hospitalization assignment and routing problem with multiple time windows, mandatory returns and perishable biological samples: A Chilean case study
(2024) Varas, Mauricio; Baesler, Felipe; Basso, Franco; Contreras, Juan Pablo; Pezoa, Raúl; Rojas-Goldsack, María Francisca
The increase in life expectancy and formal care has fostered the demand for home care services, including home hospitalization. For this service, decision-makers must allocate the staff and route the visits as efficiently as possible. To tackle this problem, in this paper, we devise a new mixed-integer programming formulation that incorporates several industry-specific features, including matching patients to medical specialties and synchronized visits of multiple specialists. Moreover, the proposed formulation also includes three features that have not been tackled simultaneously in the previous literature: multiple time windows, mandatory lunch breaks at the hospital, and fast delivery of perishable biological samples. The proposed model can be reduced to a vehicle routing problem with multiple times windows, known as NP-hard. Therefore, for solving large instances, we design a heuristic procedure composed of a constructive heuristic coupled with an improvement heuristic, which builds on a local branching scheme. To test the applicability of our approach, we conduct a case study focusing on the actual operations of Hospital Padre Hurtado of Santiago, Chile. Our computational experiments show that the model provides fully implementable solutions. Moreover, the heuristic procedure provides high-quality routes (regarding quality and solution times), making it a promising alternative to experience-based scheduling methods and state-of-the-art solvers.
A horizontal collaborative approach for planning the wine grape harvesting
(2022) Basso, Franco; Varas, Mauricio; Bosch, Paul; Contreras, Juan Pablo; Pezoa, Raúl
Horizontal collaboration is a strategy that has increasingly been used for improving supply chain operations. In this paper, we analyze the benefits of using a collaborative approach when optimally planning the wine grape harvesting process. Particularly, we assess how labor and machinery collaborative planning impacts harvesting costs. We model cooperation among wineries as a coalitional game with transferable costs for which the characteristic function vector is computed by solving a new formulation for planning the wine grape harvesting. In order to obtain stable coalitions, we devise an optimization problem that incorporates both rationality and efficiency conditions and uses the Gini index as a fairness criterion. Focusing on an illustrative case, we develop several computational experiments that show the positive effect of collaboration in the harvesting process. Moreover, our computational results reveal that the results depend strongly on the fairness criteria used. The Gini index, for example, favors the formation of smaller coalitions compared to other fairness criteria such as entropy.
A multi-objective approach for supporting wine grape harvest operations
(2020) Varas, Mauricio; Basso, Franco; Maturana, Sergio; Osorio, David; Pezoa, Raúl
In this paper, we present a novel multi-objective mixed-integer linear programming model to support wine grape harvesting. The proposed model considers the opposing nature of operational cost minimization and grape quality maximization, subject to several constraints, such as grape requirements and routing decisions. Based on the operations of a winery we worked with, we develop a negotiation protocol that can lead to an agreed final harvest schedule. The protocol includes an initial Pareto optimal solution obtained through the augmented weighted Tchebycheff method. Then, the solutions are presented to the two decision-makers and, if no agreement is reached, we conduct an iterative process, which includes finding Pareto optimal solutions in a neighborhood using the augmented ∊ -constraint method. Finally, we choose, within this set, the solution following a substitution rate criteria. We illustrate our procedure using an educational example.
A New Genetic Algorithm Encoding for Coalition Structure Generation Problems
(2020) Contreras, Juan Pablo; Bosch, Paul; Varas, Mauricio; Basso, Franco
Genetic algorithms have proved to be a useful improvement heuristic for tackling several combinatorial problems, including the coalition structure generation problem. In this case, the focus lies on selecting the best partition from a discrete set. A relevant issue when designing a Genetic algorithm for coalition structure generation problems is to choose a proper genetic encoding that enables an efficient computational implementation. In this paper, we present a novel hybrid encoding, and we compare its performance against several genetic encoding proposed in the literature. We show that even in difficult instances of the coalition structure generation problem, the proposed approach is a competitive alternative to obtaining good quality solutions in reasonable computing times. Furthermore, we also show that the encoding relevance increases as the number of players increases.
A vehicle-by-vehicle approach to assess the impact of variable message signs on driving behavior
(2021) Basso, Franco; Cifuentes, Álvaro; Pezoa, Raúl; Varas, Mauricio
Variable Message Signs (VMS) provide real-time information on traffic conditions, making it possible to guide drivers through electronic signs along the road. Relevant literature has proved VMS to be effective, especially for diverting traffic during incidents in the highway or inducing a speed reduction. Previous efforts, however, usually involve off-highway experiences, including the use of simulators or stated preference surveys, or the measurement of aggregate values of traffic through technologies that are prone to a higher failure rate, such as loop detectors. For bridging this gap, in this research, we propose a novel vehicle-by-vehicle approach (VBV), that differentiate by vehicle type, to assess the impact of VMS on drivers’ road behavior patterns along a section of a Chilean urban highway during risky situations. In addition to the messaging information, we use full traffic data obtained from free-flow gates equipped with automatic vehicle identification (AVI) technology. We conduct statistical analyses to study two potential messaginginduced behavioral changes, namely speed reduction and lane changes. For the speed reduction behavior, in 87.50% of the studied messages, the results indicate that the messages failed to induce the desired change in behavior. This value decreases to 71.85% for lane changes. The results indicate that heavy vehicle drivers and low-mileage drivers are more likely to follow lane change messages
Accessibility to opportunities based on public transport gps-monitored data: The case of Santiago, Chile
(2020) Basso, Franco; Frez, Jonathan; Martínez, Luis; Pezoa, Raúl; Varas, Mauricio
We study buses' accessibility to education, health, and job opportunities in Santiago, Chile. Our approach computes travel times during a week using full real-world GPS data for the 6681 buses of the public transport system. The use of such disaggregated data allows us to calculate accessibility based on real operating conditions rather than planned schedules, as most previous contributions do. To develop our analysis, we divide the city into 1390 walkable zones, and we compute travel times between them. Then, we calculate the number of opportunities reachable from each zone to the rest of the zones, and we aggregate them at a municipality level. Our main finding is that public transport is not able to alleviate the inequality given by the geographical distribution of opportunities in the city. We also find that accessibility for public opportunities is quite more homogenous throughout the city compared to private opportunities. The center and north-east part of the city, where the wealthier municipalities locate, attain the highest levels of accessibility to jobs and private health institutions. The west part of the city shows worrying poor accessibility to complex hospitals, while the south part is excluded from job opportunities. Overall, policies should aim to mitigate these inequalities by improving the quality of public transport services. Conventional alternatives include increasing bus service frequency and expanding the dedicated infrastructure for public transport. In the long term, better city planning is required to facilitate spreading the opportunities all over the city.
An optimization approach and a heuristic procedure to schedule battery charging processes for stackers of palletized cargo
(2019) Basso, Franco; Epstein, Leonardo David; Pezoa, Raúl; Varas, Mauricio
This paper proposes an approach to develop schedules for charging batteries in a battery center. This problem arises in warehouses and logistics centers that attempt to provide uninterrupted operations with battery powered machinery such as stackers. These operations often require recharging batteries on location, a process that involves two decisions: determining charging start-times and assigning batteries to chargers. Prices of grid-provided electric energy vary by hour of the day and can be almost negligible for energy available from photovoltaic solar collectors. Thus, efficient schedules should recharge batteries during time intervals with low tariffs while minimizing the time batteries spend in queue waiting for an available charger. In this situation, batteries arrive at the battery center during the operation. The model assumes that the counts of arriving batteries in time bands are known. The objective is to determine a charging schedule that minimizes a weighted sum of the costs of energy and delays. We develop a MIP model that incorporates the main features of the battery charging process. Unfortunately, computation times to solve these MIPs are too long to be practical. To overcome this limitation, we develop a constructive heuristic that finds a feasible solution in a matter of seconds, even for large-sized instances, with a relatively low GAP of 9.67%.
Assessing influential factors for lane change behavior using full real-world vehicle-by-vehicle data
(2021) Basso, Franco; Cifuentes, Álvaro; Cuevas-Pavincich, Francisca; Pezoa, Raúl; Varas, Mauricio
Understanding the underlying reasons for potential human risky driving behaviors is crucial for improving road safety. Recent technologies allow the analysis of driving behaviors at a microscopic level, allowing a naturalistic observation of such phenomenon through information collected unobtrusively. This paper assesses the factors that influence discretionary lane changes on an urban highway in Santiago, Chile, employing an interpretable machine learning approach. We use full real-world vehicle-by-vehicle data gathered from Automatic Vehicle Identification technology and individual public information of the vehicles and their owners, which allows us to understand patterns that might have different characteristics when performed in simulated environments. We provide insights about the causes that increase the likelihood of lane changes. For example, we find that: (i) the older the car, the less likely it is to change lane, (ii) younger drivers change lane more often, and (iii) motorcycles drivers are the most likely to change lane.
Collaborative job scheduling in the wine bottling process
(2018) Basso, Franco; Guajardo, Mario; Varas, Mauricio
This paper proposes a horizontal collaborative approach for the wine bottling scheduling problem. The opportunities for collaboration in this problem are due to the fact that many local wine producers are usually located around the same region and that bottling is a standard process. Collaboration among wineries is modeled as a cooperative game, whose characteristic function is derived from a mixed integer linear programming model. Real world instances of the problem are, however, unlikely to be solved to optimality due to its complex combinatorial structure and large dimension. This motivates the introduction of an approximated version of the original game, where the characteristic function is computed through a heuristic procedure. Unlike the exact game, the approximated game may violate the subadditivity property. Therefore, it turns relevant not only to nd a stable cost allocation but also to fi nd a coalition structure for selecting the best partition of the set of rms. We propose a maximum entropy methodology which can address these two problems simultaneously. Numerical experiments illustrate how this approach applies, and reveal that collaboration can have important positive e ects in wine bottling scheduling decreasing delay by 33.4 to 56.9% when improvement heuristic solutions are used. In contrast to the exact game in which the grand coalition is always the best outcome, in the approximated game companies may be better forming smaller coalitions. We also devise a simple procedure to repair the characteristic function of the approximated game so that it recovers the subadditivity property.
Crowding on public transport using smart card data during the COVID-19 pandemic : New methodology and case study in Chile
(2023) Basso, Franco; Hernández, Hugo; Frez, Jonathan; Leiva, Víctor; Pezoa, Raúl; Varas, Mauricio
Most crowding measures in public transportation are usually aggregated at a service level. This type of aggregation does not help to analyze microscopic behavior such as exposure risk to viruses. To bridge such a gap, our paper proposes four novel crowding measures that might be well suited to proxy virus exposure risk at public transport. In addition, we conduct a case study in Santiago, Chile, using smart card data of the buses system to compute the proposed measures for three different and relevant periods of the COVID-19 pandemic: before, during, and after Santiago’s lockdown. We find that the governmental policies diminished public transport crowding considerably for the lockdown phase. The average exposure time when social distancing is not possible passes from 6.39 min before lockdown to 0.03 min during the lockdown, while the average number of encountered persons passes from 43.33 to 5.89. We shed light on how the pandemic impacts differ across various population groups in society. Our findings suggest that poorer municipalities returned faster to crowding levels similar to those before the pandemic.
Estimation of the Origin-Destination Matrix for Trucks That Use Highways: A Case Study in Chile
(2022) Basso, Franco; Pezoa, Raúl; Tapia, Nicolás; Varas, Mauricio
Nowadays, freight transport is crucial in the functioning of cities worldwide. To dig further into the understanding of urban freight transport movements, in this research, we conducted a case study in which we estimated an origin-destination matrix for the trucks traveling on Autopista Central, one of Santiago de Chile’s most important urban highways. To do so, we used full real-world vehicle-by-vehicle information of freight vehicles’ movements along the highway. This data was collected from several toll collection gates equipped with free-flow and automatic vehicle identification technology. However, this data did not include any vehicle information before or after using the highway. To estimate the origins and destinations of these trips, we proposed a multisource methodology that used GPS information provided by SimpliRoute, a Chilean routing company. Nevertheless, this GPS data involved only a small subset of trucks that used the highway. In order to reduce the bias, we built a decision tree model for estimating the trips’ origin, whose input data was complemented by other public databases. Furthermore, we computed trip destinations using proportionality factors obtained from SimpliRoute data. Our results showed that most of the estimated origins belonged to outskirt municipalities, while the estimated destinations were mainly located in the downtown area. Our findings might help improve freight transport comprehension in the city, enabling the implementation of focused transport policies and investments to help mitigate negative externalities, such as congestion and pollution.
Estimation of trip purposes in public transport during the COVID-19 pandemic: The case of Santiago, Chile
(2023) Pezoa, Raúl; Basso, Franco; Quilodrán, Paulina; Varas, Mauricio
The COVID-19 pandemic strongly affected the mobility of people. Several studies have quantified these changes, for example, measuring the effectiveness of quarantine measures and calculating the decrease in the use of public transport. Regarding the latter, however, a low level of understanding persists as to how the pandemic affected the distribution of trip purposes, hindering the design of policies aimed at increasing the demand for public transport in a post-pandemic era. To address this gap, in this article, we study how the purposes of trips made by public transport evolved during the COVID-19 pandemic in the city of Santiago, Chile. For this, we develop an XGBoost model using the latest available origin-destination survey as input. The calibrated model is applied to the information from smart payment cards during one week in 2018, 2020, and 2021. The results show that during the week of maximum restriction, that is, during 2020, the distribution of trips by purpose varied considerably, with the proportion of trips to work increasing, recreational trips decreasing, and trips for health purposes remaining unchanged. In sociodemographic terms, in the higher-income communes, the decrease in the proportion of trips for work purposes was much greater than that in the communes with lower income. Finally, with the gradual return to in-person activities in 2021, the distribution of trip purposes returned to values similar to those before the pandemic, although with a lower total amount, which suggests that unless relevant measures are taken, the low use of public transportation could be permanent.
Horizontal collaboration in the wine supply chain planning: A Chilean case study
(2023) Basso, Franco; Ibarra, Guillermo; Pezoa, Raúl; Varas, Mauricio
The wine industry faces a highly competitive environment, making cost-effective management of the wine supply chain essential. Literature has shown that this objective can be achieved with the implementation of horizontal collaboration strategies in logistics. In this strategy, firms located at the same level of the supply chain cooperate to reduce costs, improve quality of service and mitigate environmental externalities. This paper analyses the implementation impacts of a horizontal collaboration policy in the wine supply chain. To do so, we propose a cooperative game with transferable costs, in which the characteristic function is obtained by solving a novel linear programming formulation that models the joint planning of the wine supply chain. To evaluate the benefits of collaboration, we conduct a case study involving three of Chile’s largest wineries. The results show that the use of collaborative frameworks leads to significant reductions in the logistics costs of the wine supply chain. Furthermore, we find that the grand coalition reduces the costs by 10.17% compared to the non-collaborative case. This reduction comes mainly from a decrease in the bulk wine inventory cost. We also analyse the impact of coordination costs on the savings and conduct a sensitivity analysis.
Managing premium wines using an (s- 1 , s) inventory policy: a heuristic solution approach
(2019) Varas, Mauricio; Basso, Franco; Lüer-Villagra, Armin; Mac Cawley, Alejandro; Maturana, Sergio
Operations research models are increasingly being used to support decision making in the wine industry. However, they have not yet been used to support inventory management decisions. In this paper, we develop a heuristic procedure for managing the stock of premium wines motivated by the operations of a small export-focused winery we worked with. Following an (s - 1; s) inventory policy, we assume that the decision maker aims to minimize the steady-state expected values of work in process, overage, and underage costs. The developed heuristic is as follows. First, we approximate the dynamics of the labeling process by a group scheduling policy to obtain the mean delays for each labeled product. Then, we address the problem of setting the inventory positions for the whole product portfolio by solving one newsvendor-type problem for each end-product. We provide some theoretical insights, a numerical example, and we analyze the accuracy of our procedure.
Measuring efficiency in the Chilean wine industry: a robust DEA approach
(2021) Varas, Mauricio; Basso, Franco; Maturana, Sergio; Pezoa, Raúl; Weyler, Marcelo
The Chilean wine industry has been quite innovative in terms of winemaking and trading. Yet, to survive in this competitive industry, wine managers should be aware of the relevance of monitoring their performance. In this paper, we assess how the five wineries listed on the Santiago Stock Exchange of Chile are efficient while using their critical resources for making profits. Particularly, we apply data envelopment analysis (DEA) to benchmark and rank these wineries’ technical efficiency based on four inputs and one output. We use data gathered from consolidated financial statements that are prepared using estimates, judgements, and assumptions. To account for some level of ex-post adjustments in data, we evaluate these wineries’ relative efficiency using a robust DEA model, which deals with ambiguous, imprecise, and uncertain input-output parameters. We analyse several levels of variability suitable for this data source, and we evaluate how changing the conservatism level affects technical efficiency and the rankings of the wineries. We also conduct a comparison between the five Chilean wineries and nine others from the New World. As the main conclusion, we found that Chilean wineries keep their efficiency level when including international firms in the analysis.
Measuring efficiency in the Chilean wine industry: A robust DEA approach
(2020) Varas, Mauricio; Basso, Franco; Maturana, Sergio; Pezoa, Raúl; Weyler, Marcelo
The Chilean wine industry has been quite innovative in terms of winemaking and trading. Yet, to survive in this competitive industry, wine managers should be aware of the relevance of monitoring their performance. In thispaper, we assess how the five wineries listed on the Santiago Stock Exchange of Chile are efficient while using their critical resources for making profits. Particularly, we apply data envelopment analysis (DEA) to benchmark and rank these wineries’ technical efficiency based on four inputs and one output. We use data gathered from consolidated financial statements that are prepared using estimates, judgments, and assumptions. To account for some level of ex-post adjustments in data, we evaluate these wineries’ relative efficiency using a robust DEA model, which deals with ambiguous, imprecise, and uncertain input-output parameters. We analyze several levels of variability suitable for this data source, and we evaluate how changing the conservatism level affects technical efficiency and the rankings of the wineries. We also conduct a comparison between the five Chilean wineries and nine others from the New World. As the main conclusion, we found that Chilean wineries keep their efficiency level when including international firms in the analysis.
Optimizing the wine transportation process from bottling plants to ports
(2023) Basso, Franco; Contreras, Juan Pablo; Pezoa, Raúl; Troncozo, Alejandro; Varas, Mauricio
The wine industry is a highly competitive sector for which any efficiency improvement in the wine supply chain plays a critical role in maintaining or increasing profitability. Literature shows several successful applications of operational research tools at each stage of the wine production process. However, unlike other stages, the transportation and distribution phase has not been given the same attention in the specialized literature. To bridge this gap, this article proposes an integer linear programming model to jointly determine a plan for the bottling and transportation of products to ports in order to minimize inventory, freight, and delay costs. This model can be optimally solved in less than one day for small instances of up to 25 jobs. In practice, however, some industrial instances can easily exceed 200 jobs, which precludes the use of this model to support decision-making. To cope with this issue, we devise a two-stage procedure that generates good-quality solutions for industrial-size instances of this problem in reasonable computing times. Particularly, we show that the GAP of the proposed heuristic solution is relatively low for a wide range of instances. Finally, a case study is conducted on a medium-sized Chilean winery we worked with, where the planning generated by the proposed heuristic reduces the costs corresponding to the transportation stage by 45.3% in the best case, compared to the initial planning of the winery.
The impact of bus punctuality on users’ decisions and welfare
(2022) Feres, Fernando; Basso, Franco; Pezoa. Raúl; Varas, Mauricio; Vargas- Estrada, Eusebio
This paper proposes a public transport users’ scheduling model that considers crowding inside vehicles, waiting time, and punctuality as a reliability measure. Commuters simultaneously make two choices: the preferred bus and the timing to arrive at the bus stop (on time or late). Public transport punctuality is the probability of being on time or late, generating a parameter of public transport reliability. We compute users’ equilibrium, social optimum, and first-best pricing and analytically devise a methodology to obtain the second-best pricing. Using numerical analysis, we show that (i) punctuality plays an essential role in the commuter strategy modifying according to its level, (ii) commuters’ strategy changes according to how reliable the system is, and (iii) second-best pricing is efficient only for limited cases.
The impact of electromobility in public transport: An estimation of energy consumption using disaggregated data in Santiago, Chile
(2024) Basso, Franco; Feijoo, Felipe; Pezoa, Raúl; Varas, Mauricio; Vidal, Brian
Electromobility in public transport has become a promising way to reduce environmental pollution. Several contributions have sought to estimate the energy consumption of buses in public transport. However, most of these efforts use measurements collected from controlled or simulated experiments, or that do not characterize the entire bus network. Unlike these studies, this article estimates the energy consumption of all the electric buses that circulate in the city of Santiago, Chile, during the studied period using full disaggregated GPS data and empirical measurements on some sensorized electric buses. The methodology considers a feature selection phase and the development of energy consumption prediction models using physics based and machine learning approaches. The performances of both models are compared with each other, and then, the best one is used to measure the impact of electromobility in the city. This analysis allows decision-makers to target investment by determining the buses with higher energy consumption savings in the face of budget constraints.

Browsing by Author "Basso, Franco"

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