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Alvarez-Miranda, E., Campos-Valdes, C., Quiroga, M. M., Moreno-Faguett, M., & Pereira, J. (2020). A Multi-Criteria Pen for Drawing Fair Districts: When Democratic and Demographic Fairness Matter. Mathematics, 8(9), 27 pp.
Abstract: Electoral systems are modified by individuals who have incentives to bias the rules for their political advantage (i.e., gerrymandering). To prevent gerrymandering, legislative institutions can rely on mathematical tools to guarantee democratic fairness and territorial contiguity. These tools have been successfully used in the past; however, there is a need to accommodate additional meanings of the term fairness within the electoral systems of modern democracies. In this paper, we present an optimization framework that considers multiple criteria for drawing districts and assigning the number of representatives. Besides some typical districting criteria (malapportionment and contiguity), we introduce novel criteria for ensuring territorial equilibrium and incentives for candidates to deploy their representation efforts fairly during their campaign and period in office. We test the method, which we denote as Multi-criteria Pen, in a recent and a forthcoming reform of the Chilean electoral system. The results show the potential of our tool to improve the current territorial design and offers insights on the motivations, objectives, and deficiencies of both reform plans.
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Campos-Valdes, C., Alvarez-Miranda, E., Quiroga, M. M., Pereira, J., & Duran, F. L. (2021). The Impact of Candidates' Profile and Campaign Decisions in Electoral Results: A Data Analytics Approach. Mathematics, 9(8), 902.
Abstract: In recent years, a wide range of techniques has been developed to predict electoral results and to measure the influence of different factors in these results. In this paper, we analyze the influence of the political profile of candidates (characterized by personal and political features) and their campaign effort (characterized by electoral expenditure and by territorial deployment strategies retrieved from social networks activity) on the electoral results. This analysis is carried out by using three of the most frequent data analyitcs algorithms in the literature. For our analysis, we consider the 2017 Parliamentary elections in Chile, which are the first elections after a major reform of the electoral system, that encompassed a transition from a binomial to a proportional system, a modification of the districts' structure, an increase in the number of seats, and the requirement of gender parity in the lists of the different coalitions. The obtained results reveal that, regardless of the political coalition, the electoral experience of candidates, in particular in the same seat they are running for (even when the corresponding district is modified), is by large the most influential factor to explain the electoral results. However, the attained results show that the influence of other features, such as campaign expenditures, depends on the political coalition. Additionally, by means of a simulation procedure, we show how different levels of territorial deployment efforts might impact on the results of candidates. This procedure could be used by parties and coalitions when planning their campaign strategies.
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Opazo, D., Moreno, S., Alvarez-Miranda, E., & Pereira, J. (2021). Analysis of First-Year University Student Dropout through Machine Learning Models: A Comparison between Universities. Mathematics, 20(9), 2599.
Abstract: Student dropout, defined as the abandonment of a high education program before obtaining the degree without reincorporation, is a problem that affects every higher education institution in the world. This study uses machine learning models over two Chilean universities to predict first-year engineering student dropout over enrolled students, and to analyze the variables that affect the probability of dropout. The results show that instead of combining the datasets into a single dataset, it is better to apply a model per university. Moreover, among the eight machine learning models tested over the datasets, gradient-boosting decision trees reports the best model. Further analyses of the interpretative models show that a higher score in almost any entrance university test decreases the probability of dropout, the most important variable being the mathematical test. One exception is the language test, where a higher score increases the probability of dropout.
Keywords: machine learning; first-year student dropout; universities
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Alvarez-Miranda, E., Pereira, J., Torrez-Meruvia H., & Vila, M. (2021). A Hybrid Genetic Algorithm for the Simple Assembly Line Balancing Problem with a Fixed Number of Workstations. Mathematics, 9(17), 2157.
Abstract: The assembly line balancing problem is a classical optimisation problem whose objective is to assign each production task to one of the stations on the assembly line so that the total efficiency of the line is maximized. This study proposes a novel hybrid method to solve the simple version of the problem in which the number of stations is fixed, a problem known as SALBP-2. The hybrid differs from previous approaches by encoding individuals of a genetic algorithm as instances of a modified problem that contains only a subset of the solutions to the original formulation. These individuals are decoded to feasible solutions of the original problem during fitness evaluation in which the resolution of the modified problem is conducted using a dynamic programming based approach that uses new bounds to reduce its state space. Computational experiments show the efficiency of the method as it is able to obtain several new best-known solutions for some of the benchmark instances used in the literature for comparison purposes.
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Alvarez-Miranda, E., & Pereira, J. (2022). A Districting Application with a Quality of Service Objective. Mathematics, 10(1), 13.
Abstract: E-commerce sales have led to a considerable increase in the demand for last-mile delivery companies, revealing several problems in their logistics processes. Among these problems, are not meeting delivery deadlines. For example, in Chile, the national consumer service (SERNAC) indicated that in 2018, late deliveries represented 23% of complaints in retail online sales and were the second most common reason for complaints. Some of the causes are incorrectly designed delivery zones because in many cases, these delivery zones do not account for the demographic growth of cities. The result is an imbalanced workload between different zones, which leads to some resources being idle while others fail to meet their workload in satisfactory conditions. The present work proposes a hybrid method for designing delivery zones with an objective based on improving the quality of express delivery services. The proposed method combines a preprocess based on the grouping of demand in areas according to the structure of the territory, a heuristic that generates multiple candidates for the distribution zones, and a mathematical model that combines the different distribution zones generated to obtain a final territorial design. To verify the applicability of the proposed method, a case study is considered based on the real situation of a Chilean courier company with low service fulfillment in its express deliveries. The results obtained from the computational experiments show the applicability of the method, highlighting the validity of the aggregation procedure and improvements in the results obtained using the hybrid method compared to the initial heuristic. The final solution improves the ability to meet the conditions associated with express deliveries, compared with the current situation, by 12 percentage points. The results also allow an indicative sample of the critical service factors of a company to be obtained, identifying the effects of possible changes in demand or service conditions.
Keywords: districtin; glast-mile delivery; hybrid heuristics
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Yuraszeck, F., Mejia, G., Pereira, J., & Vila, M. (2022). A Novel Constraint Programming Decomposition Approach for the Total Flow Time Fixed Group Shop Scheduling Problem. Mathematics, 10(3), 329.
Abstract: This work addresses a particular case of the group shop scheduling problem (GSSP) which will be denoted as the fixed group shop scheduling problem (FGSSP). In a FGSSP, job operations are divided into stages and each stage has a set of machines associated to it which are not shared with the other stages. All jobs go through all the stages in a specific order, where the operations of the job at each stage need to be finished before the job advances to the following stage, but operations within a stage can be performed in any order. This setting is common in companies such as leaf spring manufacturers and other automotive companies. To solve the problem, we propose a novel heuristic procedure that combines a decomposition approach with a constraint programming (CP) solver and a restart mechanism both to avoid local optima and to diversify the search. The performance of our approach was tested on instances derived from other scheduling problems that the FGSSP subsumes, considering both the cases with and without anticipatory sequence-dependent setup times. The results of the proposed algorithm are compared with off-the-shelf CP and mixed integer linear programming (MILP) methods as well as with the lower bounds derived from the study of the problem. The experiments show that the proposed heuristic algorithm outperforms the other methods, specially on large-size instances with improvements of over 10% on average.
Keywords: scheduling; fixed group shop; group shop; constraint programming
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Pereira, J., & Alvarez-Miranda, E. (2018). An exact approach for the robust assembly line balancing problem. Omega-Int. J. Manage. Sci., 78, 85–98.
Abstract: This work studies an assembly line balancing problem with uncertainty on the task times. In order to deal with the uncertainty, a robust formulation to handle changes in the operation times is put forward. In order to solve the problem, several lower bounds, dominance rules and an enumeration procedure are proposed. These methods are tested in a computational experiment using different instances derived from the literature and then compared to similar previous approaches. The results of the experiment show that the method is able to solve larger instances in shorter running times. Furthermore, the cost of protecting a solution against uncertainty is also investigated. The results highlight that protecting an assembly line against moderate levels of uncertainty can be achieved at the expense of small quantities of additional resources (stations). (C) 2017 Elsevier Ltd. All rights reserved.
Keywords: Line balancing; Robust optimization; Lower bounds; Branch-and-bound
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Sandoval, G., Alvarez-Miranda, E., Pereira, J., Rios-Mercado, R. Z., & Diaz, J. A. (2022). A novel districting design approach for on-time last-mile delivery: An application on an express postal company. Omega-Int. J. Manage. Sci., 113, 102687.
Abstract: Last-mile logistics corresponds to the last leg of the supply chain, i.e., the delivery of goods to final cus-tomers, and they comprise the core activities of postal and courier companies. Because of their role in the supply chain, last-mile operations are critical for the perception of customers regarding the perfor-mance of the whole logistic process. In this sense, the sustained growth of e-commerce, which has been abruptly catalyzed by the irruption of the COVID-19 pandemic, has hanged the habits of customers and overtaxed the operational side of delivery companies, hindering their viability and forcing their adap-tation to the novel conditions. Many of these habits will remain after we overcome the sanitary crisis, which will permanently reshape the structure and emphasis of postal supply chains, demanding compa-nies to implement organizational and operational changes to adapt to these new challenges. In this work we address a last-mile logistic design problem faced by a courier and delivery company in Chile, although the same problem is likely to arise in the last-mile delivery operation of other postal companies, in particular in the operation of express delivery services. The operational structure of the company is based on the division of an urban area into smaller territories (districts) and the outsourcing of the delivery operation of each territory to a last-mile contractor. Because of the increasing volume of postal traffic and a decreasing performance of the service, in particular for the case of express deliveries, the company is forced to redesign its current territorial arrangement. Such redesign results in a novel optimization problem that resembles a classical districting problem with the additional quality of service requirements. This novel problem is first formulated as a mathematical programming model and then a specially tailored heuristic is designed for solving it. The proposed approach is tested on instances from the real-life case study, and the obtained results show significant improvements in terms of the percent-age of on-time deliveries achieved by the proposed solution when compared to the current districting design of the company. By performing a sensitivity analysis considering different levels of demand, we show that the proposed approach is effective in providing districting designs capable of enduring signifi-cant increases in the demand for express postal services.
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Melo, I. C., Alves, P. N., Queiroz, G. A., Yushimito, W., & Pereira, J. (2023). Do We Consider Sustainability When We Measure Small and Medium Enterprises' (SMEs') Performance Passing through Digital Transformation? Sustainability, 15(6), 4917.
Abstract: Small-medium enterprises (SMEs) represent 90% of business globally. Digital Transformation (DT) affects SMEs differently from larger companies because although SMEs have more flexibility and agility for adapting to new circumstances, they also have more limited resources and specialization capabilities. Thus, it is fundamental to measure SMEs' performance considering different perspectives. Here, we describe and analyze the state-of-the-art of DT in SMEs, focusing on performance measurement. We center on whether the tools used by SMEs encompass the triple bottom line of sustainability (i.e., environmental, social, and economic aspects). To do so, in December 2021, we performed a comprehensive systematic literature review (SLR) on the Web of Science and Scopus. In addition, we also explored a novel approach for SLR: topic modeling with a machine learning technique (Latent Dirichlet Allocation). The differences and interchangeability of both methods are discussed. The findings show that sustainability is treated as a separate topic in the literature. The social and environmental aspects are the most neglected. This paper contributes to sustainable development goals (SDGs) 1, 5, 8, 9, 10, and 12. A conceptual framework and future research directions are proposed. Thus, this paper is also valuable for policymakers and SMEs switching their production paradigm toward sustainability and DT.
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