Artículos (Organización Industrial y Gestión de Empresas II)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11407

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Insight Indoor Adaptive Behavioural Adjustments Considering A Machine Learning-Based Method
(OmniaScience, 2025) Barbadilla Martín, Elena; Aparicio Ruiz, Pablo; Ragel Bonilla, Juan Carlos; Guadix Martín, José; Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Instituto Andaluz de Prevención de Riesgos Laborales; European Union (UE); TEP127: Ingeniería de Organización
Purpose: Research focused on indoor thermal comfort has increased significantly in recent years. This is mainly due to the relationship of said variable with energy efficiency, derived from an optimal operation of conditioning systems. Related to this, it is important to highlight the relevance of the clothing insulation level, due to its connection with thermal comfort. Design/methodology/approach: The present study proposes a machine learning-based method, by applying four Machine Learning algorithms. For this purpose, the ASHRAE Global Thermal Comfort Database II was used, and nineteen features were selected as input variables. Findings: Among the four algorithms considered, Multi-Layer Perception more accurately predicted the clothing insulation level of the participants (R2=0,883). Moreover, as for the relevance of the input variables, those related to indoor and outdoor climatic conditions had the greatest effect on the estimation of the observed output. Originality/value: A Machine Learning-based approach to delve into the analysis of the clothing insulation level, as opposed to other studies that rely on regression models, is proposed. Moreover, it is not based on a single technique but carries out a systematic comparative analysis of several of the most commonly in the field of thermal comfort. Additionally, it seeks to encompass a wide range of input variables to investigate their relationship with the clothing insulation level.
A data-driven decision support system for service completion prediction in last mile logistics
(Elsevier, 2023-10) Pegado Bardayo, Ana; Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Aparicio Ruiz, Pablo; Organización Industrial y Gestión de Empresas II; Junta de Andalucía; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; TEP127: Ingeniería de Organización
The growing demand for last mile services (deliveries and pickups) often results in the work overload of couriers, who are unable to complete all their assigned services within their working day. Uncompleted services are a source of strong dissatisfaction by customers, particularly since they were probably aware that their requested service was scheduled for the day. The possibility of predicting how many and which are going to be these uncompleted services becomes an effective decision-making tool that would allow carriers to increase their perceived service levels without increasing the number of couriers and vehicles. This issue is addressed through the combination of two models. Firstly, machine learning techniques are applied to estimate how many services will remain uncompleted on a given route. Secondly, the use of clustering techniques is proposed as the basis to predict the routes to be followed by couriers, thus identifying potentially uncompleted services as the last ones in each route. The posited methodology is illustrated with a case study comprising four regions in Spain, obtaining promising results in terms of the predictive capacity and the accuracy of the models.
Aerospace sector innovation in Portugal and Andalusia: a search for cross-border collaboration opportunities
(Taylor & Francis, 0007-07) Lorenzo Espejo, Antonio; Muñoz Díaz, María Luisa; Muñuzuri, Jesús; Ribeiro, Bernardo; Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; TEP127: Ingeniería de Organización
The aerospace industry is one of the main drivers of the economies of Portugal and Andalusia (Spain), where it generates more than 30,000 jobs and sales of over 4000 million euros. This study analyses the needs and capacities regarding innovation of companies and R&D centres in the aerospace sector of both regions. 277 entities of Portugal and Andalusia provided valid responses to a questionnaire concerning 107 innovation resources. Regional and cross-border collaboration opportunities have been detected utilizing the survey data. The results show that the productivity and innovation capacity of companies in the sector could increase in both territories through better use of the existing resources. A significant set of identified needs can be addressed by capacities existing in the neighbouring region, which supports the urgency to enhance cross-border collaboration. Additionally, a basis for the prioritization of future investments is provided through the identification of several fields with weak innovation capacity.
A mobile App-Based approach to profiling e-commerce users for detecting more convenient delivery time slots
(Universidad Politécnica de Madrid, 2025) Escudero Santana, Alejandro; Cortés Muñoz, Juan Carlos; Flores Huamán, Kenny Jesús; García Llanera, Alfredo Casimiro; Robles-Velasco, Alicia; Romero Ternero, María del Carmen; Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia, Innovación y Universidades (MICIU). España
The escalating complexity of B2C e-commerce logistics, particularly in last-mile delivery, coupled with the growing consumer preference for home delivery, presents a significant challenge: the need to reduce disruptions during the delivery process. These disruptions not only diminish perceived service quality but also escalate distribution costs and environmental emissions. This paper introduces a novel initiative to tackle delivery failures resulting from customer absences by enabling users to generate and share location statistics with delivery companies, contingent upon their explicit consent. This feature allows users to define a list of preferred locations for tracking, thereby enhancing coordination between customers and delivery providers. By fostering this collaborative approach, users gain greater control over their data, contributing to a more efficient and customer-centric e-commerce logistics ecosystem. Preliminary results demonstrate the application’s potential to reduce delivery failures by up to 45%. This strategy not only mitigates disruptions and lowers costs but also helps minimize environmental impact.
A cuckoo search algorithm to improve a routing problem adapted to last mile e-commerce logistics
(Springer, 2025-12) Escudero Santana, Alejandro; Onieva, Luis; Cortés Muñoz, Juan Carlos; Muñoz Diaz, M. Luisa; Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia e Innovación (MICIN). España
The rapid growth of e-commerce as an alternative to traditional face-to-face commerce has highlighted the logistical challenges of efficiently delivering goods to end consumers. A major problem is that recipients are often not available at the designated delivery locations during the delivery process, leading to reduced efficiency in last-mile logistics. This research works on a novel last-mile logistics framework where customers can provide multiple delivery points with different time windows. This logistical challenge represents a new and more complex variant of the vehicle routing problem with time windows. To address this problem, we present a metaheuristic approach using the cuckoo search algorithm.The results demonstrate that cuckoo search algorithm improves the costs on previous results achieved by other methodologies, allowing for greater efficiency in last-mile logistics planning.
Solving an Unrelated Parallel Machines Scheduling Problem with machine- and job-dependent setups and precedence constraints considering Support Machines
(Elsevier, 2024-03) Muñoz Diaz, M. Luisa; Escudero Santana, Alejandro; Lorenzo Espejo, Antonio; Organización Industrial y Gestión de Empresas II; Agencia de Innovación y Desarrollo de Andalucía (IDEA)
Single-stage production planning problems are common in the academic literature and in real-world environments. One of the least studied scenarios in the literature for this environment is that with Unrelated Parallel Machines. In this work, this type of problem is inspired by a real station within the wind tower production process. The problem presents characteristics that have already been studied, such as mandatory precedences and setup times that depend on the machine and the job. However, a new and real feature is presented: the existence of “support machines”, which are machines that can continue to work but cannot complete jobs due to reduced capacity for some operational reason. In this case, instead of abandoning their work, support machines can still be used to assist other machines by performing partial jobs before handing them over to full-capacity machines for completion. This innovative concept of support machines, never before presented in this context of production planning, introduces a unique approach to dealing with reduced-capacity machines without sacrificing their operational potential. A Mixed-Integer Linear Programming (MILP) model is formulated to mathematically represent this problem. This work explores the impact of these support machines on production planning. For this purpose, Tabu Search and Simulated Annealing metaheuristics have been adapted for their solution, and a novel Constructive Heuristic has been developed based on the real and manual process currently performed in the aforementioned factory. These three algorithms are run and compared on a real database in order to minimise the makespan. Their analysis shows that although the use of support machines generally gives positive results, an improvement is not achieved in all cases. Furthermore, contrary to what might be expected, the use of full-capacity machines in the role of support machines sometimes improves completion times.
Enhancing process lead time forecasting with machine learning and upstream process data: A case study in wind tower manufacturing
(Elsevier, 2025-11) Flores-Huamán, Kenny-Jesús; Lorenzo Espejo, Antonio; Muñoz Diaz, M. Luisa; Escudero Santana, Alejandro; Organización Industrial y Gestión de Empresas II; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Ministerio para la Transformación Digital y de la Función Pública
Accurate lead time prediction is critical for optimizing sequential manufacturing processes, particularly in industries with high variability such as wind turbine tower production. This paper proposes a machine learning-based system to estimate lead times for two pivotal sequential operations: bending and longitudinal welding (LW). A distinctive feature of this system is its innovative integration strategy, where the predictive output from the bending model, specifically, the predicted bending lead time and its associated error, is leveraged as an input feature for the LW lead time estimation model. This approach explicitly models and enhances the representation of inter-process dependencies. While bending predictions show moderate performance, their inclusion as inputs demonstrably and significantly improves LW lead time estimation accuracy. A key contribution of this work is the comparative analysis between the ML-based LW predictions and traditional engineering methods. Our results demonstrate that the integrated ML model for LW achieves a 54% reduction in MAE (from 11.36 to 2.03 h) and a 74% lower RMSE (from 12.01 to 3.13 h) compared to engineering estimates, validating its superior accuracy. To enhance interpretability, SHAP (SHapley Additive Explanations) identifies critical factors such as sheet thickness, personnel experience, and upstream process quality, including the impact of the integrated bending predictions. The system’s low execution time enables real-time scheduling adjustments, offering a practical solution for production planning. These findings highlight the transformative potential of ML, particularly through such sequential predictive integration, in replacing outdated engineering heuristics and providing actionable insights for complex manufacturing environments.
A framework for analyzing service disruptions in last-mile and first-mile reverse logistics
(Elsevier, 2024-12) Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Pegado Bardayo, Ana; Guadix Martín, José; Organización Industrial y Gestión de Empresas II; Agencia Estatal de Investigación. España; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
The growth of e-commerce in recent years has altered the way in which retailers and customers interact with each other. The effect on freight distribution has been particularly noticeable: nowadays, customers may have their orders delivered at or close to the point of consumption and, thus, the volume of freight distributed by logistic operators has increased substantially. This has led to a growing number of carrier companies and an increasing demand for couriers, which can sometimes struggle to fulfill their assigned services. In this article, a study of the current state of last-mile and first-mile reverse logistics in Spain is presented based on the analysis of the operation of 20 carriers located in 15 different Spanish provinces. To this effect, a framework for the classification of service disruptions during last-mile delivery and first-mile collection routes is posited. Eight different potential disruption types are proposed, and methods for managers to identify and categorize service incompletions as such are provided. The results of the analysis highlight the importance of service and courier traceability to improve the handling of service disruptions and, overall, show a considerable improvement margin in the operational performance of carriers, for which several managerial implications are provided.
Mapping last mile logistics in Spain
(Elsevier, 2024) Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Gardrat, Mathieu; Toilier, Florence; Organización Industrial y Gestión de Empresas II; Agencia Estatal de Investigación. España; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Ministerio de Universidades
In this work, a depiction and analysis of the state of last mile logistics in Spain is presented. Through the study of the data from 12 Spanish provinces gathered by an IT service provider that enables the operation of 12 different carriers, the main characteristics of each region in Spain are analysed and discussed, portraying not only the geographical and demographic distinctions between the areas, but also the differences in operational efficiency between carriers. Furthermore, the posited metrics are collated with the population density indicators of the provinces, in an attempt to identify significant correlations and patterns that may bring light to the effect on the operation of the agencies of geographic concentration.
Measuring couriers individual efficiency in last-mile logistics
(Universitat Politècnica de València, 2025) Pegado Bardayo, Ana; Muñuzuri, Jesús; Cortés, Pablo; Lorenzo Espejo, Antonio; Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía; TEP127: Ingeniería de Organización
The last mile is a critical segment in logistics, significantly impacting the efficiency and profitability of delivery operations. However, managing courier workflows remains a challenge, especially given the variations in work patterns and external factors such as route deviations or traffic conditions. This study analyzes the workflows of couriers, and provides insights into couriers’ individual efficiency and the underlying factors influencing its performance. Using real-world data from a Spanish logistics company, the study measures the impact of work shifts, workload density, and contextual elements on courier efficiency. The research seeks to serve as a tool to identify potential bottlenecks, optimize task allocation, and improve overall service efficiency.
A methodology for incident detection in sectorized waterdistribution networks based on pressure and flow data
(Wiley, 2025) Robles-Velasco, Alicia; Onieva, Luis; Guadix Martín, José; Cortés, Pablo; Organización Industrial y Gestión de Empresas II; TEP127: Ingeniería de Organización
This study presents an intelligent system for predicting incident reports (IRs) insectorized water distribution networks, such as drains in sidewalks, lack of pres-sure, lack of water, leaks, or others, based on pressure and flow data. Currently,incident detection in the industry is highly inefficient, as it is always performedreactively—only after an incident has already occurred and its negative conse-quences are visible to users. Since these data are recorded at 5- to 15-min intervals,a methodology is proposed to integrate them with daily IRs. After processing thedata, a supervised classification learning system is developed with a binary out-put variable indicating the likelihood of an incident at a specific time step. Themethodology is validated using 2 years of data from a real network divided intoeight sectors. The system predicts 51.3% of IRs, with 78.9% accuracy, highlightingthe strong influence of daily mean and maximum flows on incidents.
Integration of the Adaptive Approach in HVAC System Operation: A Case Study
(MDPI, 2025-01) Aparicio Ruiz, Pablo; Ragel Bonilla, Juan Carlos; Barbadilla Martín, Elena; Guadix Martín, José; Organización Industrial y Gestión de Empresas II; Agencia Estatal de Investigación. España; TEP127: Ingeniería de Organización
Although different investigations have been carried out on the analysis of adaptive thermal comfort in naturally ventilated buildings, fewer have focused on mixed mode operation. Moreover, there is limited research as for the implementation of adaptive comfort models into the control system of buildings. Therefore, this paper investigates how the application of a setpoint based on adaptive comfort control (ACC) would affect occupants’ comfort considering mixed mode operation and based on the results of a longitudinal field study in an academic office building of a tertiary educational institution in southern Spain. The manuscript analyses the Thermal Preference Vote over 12 months in a mixed mode room with an HVAC system whose setpoint is adjusted with a previously calculated adaptive algorithm for the building. For that, a thorough analysis was conducted in which users identified situations regarding thermal comfort and the operation of the conditioning system was collected. The results indicate that it is possible to develop adaptive comfort models that ensure the thermal well-being of occupants. Moreover, this study highlights the need for further research to assess the implications of ACC in terms of comfort and energy consumption as well as addressing the future improvements and the limitations of the work carried out.
Investment Strategies to Maintain the State of Water Networks
(2024) Robles-Velasco, Alicia; Aparicio Ruiz, Pablo; Cortés, Pablo; Onieva, Luis; Organización Industrial y Gestión de Empresas II; Junta de Andalucía; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); TEP127: Ingeniería de Organización
Purpose: This article focuses on the problem of deciding the annual investment that a water company should allocate to the rehabilitation of its distribution and sanitation networks. The objective is to find the investment amount necessary to maintain an adequate quality and sustainability of the infrastructure. It is not a simple decision, as there are different criteria that may be of interest to the different agents involved. In this paper, we consider four criteria related to the reliability of individual pipes and the complete network. These indicators are the infrastructure value index, the average age of network pipes, the risk index and the probability of failure. Design/methodology/approach: A methodology is proposed to estimate the best annual investment by analysing the evolution of these indicators. Concretely, two strategies are tested. The first one is a minimax-based approach that seeks a balanced solution for all the indicators. The second one is named as minimal deviation strategy and seeks to minimise the deviation of all the indicators in the last year of the time horizon compared to the initial year. Findings: In order to obtain a realistic sample of the performance of both strategies, 201 scenarios, i.e. 201 different annual investments have been simulated. According to the first strategy, an annual investment of 55.5 M€ is the best option, while the minimal deviation strategy presents an annual investment of 39.5 M€ as the best decision. The study reveals that different evaluation functions lead to completely different annual investment. Concretely, the minimax evaluation function is more conservative than the minimal deviation strategy. Originality/value: This study proposes an original approach to address the decision problem of investments in asset management. To the best of the authors’ knowledge, it is the first attempt to treat that problem using this kind of evaluation functions. However, it is still a relatively straightforward proposal and there are many possible options to continue this line of research.
A production-inventory model to optimize the operation of distributed energy resource networks in a rolling horizon
(Elsevier, 2024-11) Cortés, Pablo; Escudero Santana, Alejandro; Barbadilla Martín, Elena; Guadix Martín, José; Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia, Innovación y Universidad (MICIU). España; TEP127: Ingeniería de Organización
The recent advancements in energy production, storage, and distribution are creating unprecedented opportunities in the field. Major consumers can benefit from the implementation of distributed energy resource networks capable of generating electricity or heating from sources, often renewable ones, in close proximity to the point of use, rather than relying on centralized generation sources from power plants. In this paper, we introduce a pioneering model designed to determine the optimal set of energy commands in a distributed energy resource network, minimizing operational costs in a time horizon. Indeed, we propose an innovative mixed-integer linear programming formulation rooted in the production-inventory models commonly employed in aggregate production planning. The system integrates diverse energy generation sources, storage facilities, and demand points, encompassing both electric and heating commodities. The optimum of the model is achieved for all analyzed instances of the test library (2 scenarios-20 instances) in an exceptionally short time, outperforming other approaches previously presented in the literature. We employed the Gurobi optimizer to solve the model, obtaining rapid responses that ensure real-time decision-making and facilitate effective control of the distributed energy resource network within a three-days’ rolling horizon, as discussed in a simulated real-life application case study. Indeed, the proposed model solves in less than 1 s, enabling near-instantaneous decision-making. This swift solution time surpasses any known references in the field, effectively shifting the bottleneck in DER network operation from the decision-making process to the forecasting of demand and weather conditions. While forecasting typically requires a minimum of 15 min, our approach suggests that a reduction in this forecasting time could further enhance the control system's response time, given the model's ability to deliver optimal solutions almost immediately. The real-time availability of optimal solutions allows for the seamless incorporation of stochastic elements into the control loop via a rolling horizon process.
A predictive framework for last-mile delivery routes considering couriers’ behavior heterogeneity
(Elsevier, 2024-12) Pegado Bardayo, Ana; Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Onieva, Luis; Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia, Innovación y Universidades (MICINN). España
Last-mile route prediction is a powerful tool for freight delivery companies that can be essential in the development of new features such as arrival time prediction or accurate workload allocation. Existing methodologies propose prediction models that, on the one hand, require external information, such as traffic data or a prior division of work areas, and, on the other hand, assume courier homogeneity in the search for travel patterns. However, this assumption may introduce noise in the predictions since different couriers may be subject to different routing habits. This study proposes a comprehensive predictive framework for delivery routes, which allows identifying the routing profiles that best suit each courier and work area, and which starts from basic information available for any carrier, ensuring the scalability of the tool. The analysis is supported by a case study, where the results obtained bring to light the heterogeneity in the routing decisions of the different drivers and show that the proposed approach produces consistent and accurate predictions for the vast majority of them.
Near-optimal operation of the distributed energy resources in a smart microgrid district
(Elsevier, 2020-04) Cortés, Pablo; Auladell León, Paloma; Muñuzuri, Jesús; Onieva, Luis; Organización Industrial y Gestión de Empresas II; Ministerio de Economía y Competitividad (MINECO). España
This paper considers the case study of a smart microgrid district at Graciosa Island in the Canary Islands. The smart energy microgrid district consists of several households and a public use building (school) that includes renewable energy sources (photovoltaic), Li-ion batteries for electric energy storage, domestic hot water heaters acting as thermal energy storage, a pool for balancing energy consumption and supplies, and the connection to the electric grid. We have modelled such a problem as a nonlinear mathematical programming model that is linearly approximated using special ordered sets of type 2. The linear approximation is solved using Gurobi optimization software, providing close-to-optimum solutions within an interval of 15 min that allows near-real-time operation of the smart energy district. The obtained results allow advancement of the net-zero energy neighbourhood concept in all the evaluated scenarios within a daily horizon and a positive energy balance in wider horizons. Obviously, these results are obtained in part due to the magnificent insolation conditions of the Canary Islands, but they allow justifying that the appropriate use of renewable energy resources and energy storage systems together with a balancing mechanism at the district level (such as the pool in our case study) may also lead to near-net-zero energy neighbourhoods in other geographical locations.
Using IoT data and applications to improve port-based intermodal supply chains
(Elsevier, 2020-01) Muñuzuri, Jesús; Onieva, Luis; Cortés, Pablo; Guadix Martín, José; Organización Industrial y Gestión de Empresas II; Fundación FIUS; Ministerio de Economía y Competitividad (MINECO). España
The complexity of port terminal operations and intermodal transshipments has pushed the introduction of information and communication technologies (ICT) to assist the multiple stakeholders involved in the multiple decision-making processes. More recently the development of systems based on the Internet of Things (IoT) represents a further step in the automation of data acquisition and processing. We present here an IoT system designed to optimize, manage and monitor container transport operations along an intermodal corridor, combining rail scheduling and inland vessel navigation. The system has a modular design, enabling the interaction with external systems, independently of their nature, through the cloud-based FIWARE platform. We provide a description of the three inter-related subsystems (container tracking, rail management and inland navigation), and a description of the expected supply chain benefits derived from the implementation of the system, both in terms of shippers and of terminal integration in the logistics flow. This system is currently operative at the Port of Seville, the main transshipment node in the Madrid – Seville – Canary Islands intermodal corridor in Spain.
Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach
(MDPI, 2024-08) Flores-Huamán, Kenny-Jesús; Escudero Santana, Alejandro; Muñoz Díaz, María Luisa; Cortés, Pablo; Organización Industrial y Gestión de Empresas II; Ministerio de Ciencia e Innovación (MICIN). España; TEP127: Ingeniería de Organización
This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available.
Exploring the correlation between courier workload, service density and distance with the success of last-mile and first-mile reverse logistics
(Springer, 2024) Lorenzo Espejo, Antonio; Muñuzuri, Jesús; Onieva, Luis; Muñoz Díaz, María Luisa; Organización Industrial y Gestión de Empresas II; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Agencia Estatal de Investigación. España; Ministerio de Universidades. España; TEP127: Ingeniería de Organización
Given the recent surge in online sales, particularly accentuated by the health crisis in 2020 and 2021, companies operating in the retail sector have increasingly recognised the importance of business-to-consumer (B2C) distribution. Consequently, last-mile logistics optimization has garnered increased attention in both academic and industry contexts. In this study, we examine the relationship between the workloads of couriers and their proficiency in executing assigned services in a B2C last-mile and first-mile reverse logistics environment. Additionally, we evaluate the connection between service density in an area and the distance between warehouses and service points with completion rates among couriers. By analysing a dataset corresponding to the deliveries and collections made in Madrid in 2021, we identify significant and moderate correlations between the couriers’ workloads and service completion rate. It should be noted that the correlations of completion rate with distance and visit frequency to each area are weak, yet statistically significant
Double deck elevator group control systems using evolutionary algorithms: interfloor and lunchpeak traffic analysis
(Elsevier, 2021-05) Cortés, Pablo; Muñuzuri, Jesús; Vázquez Ledesma, Alejandro; Onieva, Luis; Organización Industrial y Gestión de Empresas II; TEP127: Ingeniería de Organización
The continuous development of high-rise buildings around the world requires the installation of efficient elevator systems able to vertically transport the different passengers along the buildings in their daily journeys. Double deck elevators can increase the efficiency of these vertical transportation systems. Double deck elevators consist of two adjacent cabins that are joined and travel together along the same shaft, so the handling capacity of the system can be improved by allowing the dispatch of passengers with destination to two consecutive floors at the same instant. This type of architecture emerges as especially appropriate for uppeak traffic conditions. However, its suitability has not been sufficiently analysed for non-dominant (up or down) traffic patterns, such as interfloor and lunchpeak traffic. Our paper deals with conventionally controlled double deck elevators, where the Elevator Group Control System (EGCS) requires specific car-landing call allocation algorithms able to manage such special car architectures. Along this line, we propose a genetic algorithm that demonstrated a good performance when compared to a tabu search algorithm that was used as benchmark for comparison, taking into account different fitness evaluation functions (overall dispatching time and nearest call). The analysis was undertaken for interfloor and lunchpeak traffics and the average waiting, transit and journey times, and the energy consumption are reported as performance indexes of the vertical transportation system. The algorithms produced efficient results outperforming the considered benchmark and emerged as very competitive algorithms considering all the performance indexes as a whole. Results were tested using ELEVATE, the standard simulation software for vertical transportation.

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