Artículos (Organización Industrial y Gestión de Empresas I)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11402

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A stochastic approach for solving the operating room scheduling problem
(Springer, 2018-06) Molina Pariente, José Manuel; Hans, Erwin W.; Framiñán Torres, José Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía
We address a stochastic operating room scheduling problem which consists of assigning an intervention date and operating room to surgeries on the waiting list, minimizing the under- and overtime costs of the operating rooms, and the cost of exceeding the capacity constraints of the system. Uncertainties in surgeries duration, in the arrivals of emergency surgeries and in surgeons’ capacity are considered. To solve the problem we propose a Monte Carlo optimization method based on the sample average approximation method, which combines an iterative greedy local search method and Monte Carlo simulation. The performance of the iterative greedy local search method is evaluated against an exact method and two existing heuristic methods for solving the deterministic version of the problem, using testbeds generated based on the literature. Finally, numerical experiments are presented to evaluate the performance of the Monte Carlo optimization method in a stochastic setting. The results show that the objective function value converges with exponential rates when the number of samples increases, obtaining an optimality index value around 1 %. By comparing the results against the solution obtained by the corresponding deterministic expected value problem, we conclude that an important cost reduction can be obtained by solving the stochastic problem rather than the deterministic one.
A Decision Support System for Operating Room scheduling
(2015-10) Dios, Manuel; Molina Pariente, José Manuel; Fernández-Viagas Escudero, Víctor; Andrade Pineda, José Luis; Framiñán Torres, José Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía
In this paper we present a Decision Support System (DSS) for surgery scheduling which is currently in use in several Surgical Units in one of the largest hospitals in Spain. This system embeds a number of optimization procedures (both exact and approximate) to support decisions related to the assignment of dates and Operating Rooms to the interventions of patients in a waiting list. The system is capable of producing a medium-term – i.e. up to six months – plan in order to estimate the week in which each patient would be intervened, so that the material and human resources needed for their intervention can be arranged. A short-term (two weeks) plan can be obtained to determine optimal or quasi-optimal intervention dates taking into account the surgical resources, patients’ availability, as well as a number of specific constraints and features imposed by the Hospital’s Managers (such as e.g. the maximum number of ORs in which a surgeon can intervene within a shift). The proposed DSS allows users to manually modify the plans in order to adapt them to last-minute changes via an interactive graphical user interface. The system has been successfully employed for surgery scheduling.
Generalised accelerations for insertion-based heuristics in permutation flowshop scheduling
(2020-05) Fernández-Viagas Escudero, Víctor; Molina Pariente, José Manuel; Framiñán Torres, José Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP134; Organización Industrial
The scheduling literature is abundant on approximate methods for permutation flowshop scheduling, as this problem is NP-hard for the majority of objectives usually considered. Among these methods, some of the most efficient ones use an insertion-type of neighbourhood to construct high-quality solutions. It is not then surprising that using accelerations to speed up the computation of the objective function can greatly reduce the running time of these methods, since a good part of their computational effort is spent in the evaluation of the objective function. Undoubtedly, the best-known of these accelerations has been employed for makespan minimisation (commonly denoted as Taillard’s accelerations). These accelerations have been extended to other related problems, but they cannot be employed for the classical permutation flowshop problem if the objective is other than the makespan. In these cases, other types of accelerations have been proposed, but they are not able to achieve a substantial reduction of the computational effort. In this paper, we propose a new speed-up procedure for permutation flowshop scheduling using objectives related to completion times. We first present some theoretical insights based on the concept of critical path. We also provide an efficient way to compute the critical path (indeed Taillard’s accelerations appear as a specific case of these results). The results show that the computational effort is substantially reduced for total completion time, total tardiness, and total earliness and tardiness, thus outperforming the existing accelerations for these problems.
Integrated operating room planning and scheduling problem with assistant surgeon dependent surgery durations
(Elsevier, 2015-04) Molina Pariente, José Manuel; Fernández-Viagas Escudero, Víctor; Framiñán Torres, José Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía
There is evidence in the literature that most surgeries in hospitals are performed by a team composed of two surgeons, and that their experience largely influences the surgery duration. However, to the best our knowledge, only one contribution has addressed the operating room planning and scheduling problem with surgical teams, but in such case surgery durations did not depend on the experience of surgeons. In this paper we address an integrated operating room planning and scheduling problem with surgical teams composed by one or two surgeons where surgery durations depend on their experience and skills. We propose a mixed integer linear programming (MILP) model to optimally solve this problem. Given the high computation requirements of our MILP model, we also propose an iterative constructive method. In order to evaluate the performance of both exact and approximate methods, an extensive test bed is generated. The computational experience shows that the proposed algorithm is able to find feasible solution for all problems requiring shorter CPU time and average relative percentage deviation than the MILP model. Finally, the robustness of the so-obtained surgical schedules is analyzed using simulation.
Network impact of increasing distributed PV hosting: A utility-scale case study
(Elsevier, 2021-03) Tévar Bartolomé, Gabriel; Gómez Expósito, Antonio; Arcos Vargas, Ángel; Rodríguez Montañés, Manuel; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Junta de Andalucía, Consejería de Economía y Conocimiento; European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER); Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
This paper presents a methodology to assess the cost of upgrading the LV network so that it can host increasing levels of PV distributed generation. The cost is valued in terms of investment, operation and maintenance and losses. A real scale case study (80,000 customers from urban and rural areas, 460 GWh of demand and 3,600 km of MV&LV lines/cables) is thoroughly analysed, using the actual network topology along with the hourly demand of each supply point, provided by smart meters. With the help of a planning-oriented load flow solver, technical violations (congestions and overvoltages) are quantified and classified for different PV penetration scenarios. Then, a heuristic procedure is implemented aimed at identifying the most cost-effective investment solution that sequentially eliminates the previously ranked excessive congestions, assuming that mild congestions and other operational problems, can be addressed without extra investments, either through network switching practices or through the implementation of local flexibility markets. Specifically, it has been found that the direct costs (investment and O&M) arising from the massive deployment of PV generation amount to 2.73 €/kWp and 10.18 €/kWp for scenarios with 30% and 50% PV penetration levels, respectively. No additional investments needed in digitization, monitoring, automation and systems have been considered.
CO2 price effects on the electricity market and greenhouse gas emissions levels: an application to the Spanish market
(Springer, 2023-04) Arcos Vargas, Ángel; Núñez Hernández, Fernando; Ballesteros Gallardo, Juan Antonio; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
This research proposes an empirical method to estimate the impact on the wholesale electricity market of an increase in the price of CO2 emission allowances. The current literature in this field is mainly focused on long-term simulation analyses, while this study carries out a short-term analysis with microdata from the electricity market. A higher price of CO2 implies an increase in the electricity generation costs of polluting units and therefore an increase in the price of the electricity market. When CO2 becomes more expensive, polluting electricity generators are shifted in the hourly electricity supply curve towards less competitive positions (in favour of less polluting/cheaper units). Displaced polluting units could even be taken out of the market, which would imply a reduction in CO2 emissions. These short-term movements can be reproduced with our microdata of the day-ahead electricity market –data Provided by the Spanish Market Operator (OMIE). According to our results, increases in the carbon price of 10, 20, or 30 € per ton, respectively, cause increases of 1.8%, 4.2% and 5.3% in the electricity price (year 2018), while the negative effect on emissions is relatively small. Our analysis concludes with the estimation of an ARIMA-SARIMA model that looks for the main determinants of the variations in the hourly energy prices and the carbon emissions. The estimations show that the marginal supply technology in the electricity market is important in explaining these variations.
Influence of rooftop PV generation on net demand, losses and network congestions: A case study
(Elsevier, 2019-03) Tévar Bartolomé, Gabriel; Gómez Expósito, Antonio; Arcos Vargas, Ángel; Rodríguez Montañés, Manuel; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
This paper presents a practicable methodology allowing the impact of rooftop photovoltaic (PV) panels on distribution networks to be thoroughly assessed. Based on granular information (both in space and time) about the network and the demand, and a minimum but realistic set of assumptions concerning the available rooftop surface, hourly scenarios of PV production and consumption are created for increasing PV penetration levels. A load flow solution provides, at the feeder level, the voltage profile, the net power flowing through each feeder section and the total losses. The methodology is then applied to a real case study comprising over 80,000 customers from urban and rural areas, with an annual consumption of 460 GWh and served by over 3600 km of MV&LV lines/cables, large enough to safely extrapolate the attained conclusions to the general case. In a nutshell, network losses increase with respect to the base case scenario, and network congestions begin to show up, when the PV penetration exceeds 30% of the maximum installable power, which is roughly equivalent to 45% of the PV power that yields zero net demand all year round.
Self-sufficient renewable energy supply in urban areas: Application to the city of Seville
(Elsevier, 2019-04) Arcos Vargas, Ángel; Gómez Expósito, Antonio; Gutiérrez García, Francisco José; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
This paper proposes a methodology that, using only publicly available information, assesses the capacity of urban agglomerations to be self-sufficient and energy sustainable. The methodology evaluates the potential of the urban surface to accommodate thermal and PV solar facilities, as well as the energy storage requirements on a year-round basis. When applied to the city of Seville, considering not only the current electricity demand but also the one arising from the electrification of both urban transport and the entire thermal energy currently supplied by gas, the conclusion is that it is possible to have a self-supplied energy system based almost exclusively on PV and thermal panels. Given the high cost of seasonal storage, the resulting system is economically unviable, so it makes sense to keep the transmission grid as a back-up system to feed power into the city at certain times of the year (up to 8% of annual consumption). In the case study, the renewable energy comes mainly from PV facilities roof-mounted (72%), or ground-mounted in the surrounding urban lots (25%), plus a modest contribution from biogas (wastewater). A discussion is included regarding the role of regulated utilities, that should be reconsidered in the light of these upcoming scenarios.
Impact of battery technological progress on electricity arbitrage: An application to the Iberian market
(Elsevier, 2020-02) Arcos Vargas, Ángel; Canca Ortiz, José David; Núñez Hernández, Fernando; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
Recent technological advances in power electronics and electrical storage have increased interest in the arbitrage business based on Battery Energy Storage Systems. With this objective, the present work develops a Mixed-Integer Linear Programming model for obtaining optimal electricity sale\purchase strategies with batteries. For each configuration (battery size /inverter size), the model provides an optimal trading strategy. Using this strategy for different configurations and with the current market prices, some financial indicators are calculated in order to select the optimal configuration. Finally, our analysis considers the significant technological progress that has occurred in recent years, the effects on profitability of a reduction in the battery cost, and of an improvement both in the round-trip efficiency and in the battery's lifetime (in terms of the number of cycles). The results indicate that, with current technology, the optimal inverter size for a 10 MWh battery is 3 MW, although, if technological progress continues at the current rate, the arbitrage of electricity by using batteries is expected to be viable from 2024 onwards. Additionally, the effects that different technological improvements (cost, useful life and losses) will have on profitability are calculated,; for example, it is observed that an improvement of 1.6% of the round-trip efficiency and an increase of 1000 life cycles will provide an average increase of 16,000 € and 75,000 €, respectively, in terms of Net Present Value.
Implementation of the Asset Management, Operational Reliability and Maintenance Survey in Recycled Beverage Container Manufacturing Lines
(MDPI, 2024-12-06) Parra, Carlos; Morán, Carlos; Pizarro, Félix; Duque, Pablo; Aránguiz, Andrés; González-Prida, Vicente; Parra, Jorge; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I
The effectiveness of a comprehensive maintenance and reliability management process can be assessed through an in-depth analysis of various factors that collectively represent the maintenance contribution to the operational and production processes of an industrial asset. There are no simple formulas for designing an integrated maintenance and reliability model within an asset management framework (in accordance with the ISO 55001 standard), nor are there fixed or universal rules that apply equally to all production assets over time. In light of this, the primary goal of this article is to provide an overview of the implementation project of the AMORMS (Asset Management, Operational Reliability and Maintenance Survey), based on the maintenance management model (MMM) developed by INGEMAN, at the SINEA PERU plant, a leading company in Latin America specializing in the industrial production of recycled plastic containers for commercial beverages (PET preforms—Polyethylene Terephthalate). Lastly, the article outlines the recommendations and high-impact action plans that will support SINEA PERU in strengthening the efficiency of its maintenance and reliability management processes while effectively optimizing the value of its industrial assets throughout its life cycle.
A sustainable electricity market design: Application to the Iberian market
(2024-11) Dorado Galatoire, Erick Andrés; Núñez Hernández, Fernando; Arcos Vargas, Ángel; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; European Commission (EC). Plan de Recuperación, Transformación y Resiliencia de la UE (Next Generation EU/PRTR); European Commission (EC). Fondo Social Europeo Plus (ESF+); Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
Current electricity markets are not moving fast enough towards decarbonization, nor are they ensuring the economic sustainability of the different supply technologies in the short-term –cannibalization and depredation problems–. To solve these problems, this study presents an innovative market design to achieve a sustainable electricity system based on cleaner production. Based on the assumption that the energy supplied by manageable and non-manageable generators can be considered as two different goods, we propose a two-market system within the day-ahead market where non-manageable generators operate in the first market through long-term contracts and manageable generators operate in the (marginalist) second market. Under this system, the regulator appropriates an economic surplus that is given by the difference –produced in the first market– between what is received from the buyers in this market (second market's equilibrium price) and what is paid to each non-manageable generator (levelized cost of energy). This public surplus will be used to cover part of the regulated costs of the electricity system, lowering the final price of electricity. To simulate empirically our two-market design, we use hourly microdata from the supply and demand curves of the Iberian day-ahead market. The results of this case study are promising, showing a decrease in the electricity prices and CO2 emissions, an increase in the amount of energy exchanged and the obtainment of a surplus for the regulator of 5,753 M€ in 2021 and 9,944 M€ in 2022. The proposed design ensures the economic sustainability of intermittent renewables as well as provides signals for long-term investment in the deployment of flexible carbon-free resources.
Additional revenues estimation in a market-based redispatch: An opportunity for flexibility
(2024-10) Dorado Galatoire, Erick Andrés; Arcos Vargas, Ángel; Martínez Ramos, José Luis; Universidad de Sevilla. Departamento de Ingeniería Eléctrica; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia, Innovación y Universidades (MICIU). España; Agencia Estatal de Investigación. España; European Commission (EC). Plan de Recuperación, Transformación y Resiliencia de la UE (Next Generation EU/PRTR); European Commission (EC). Fondo Social Europeo Plus (ESF+); Universidad de Sevilla. TEP196: Sistemas de Energía Eléctrica
The technical constraints in the transmission networks are exerting significant strain on the functioning of electricity markets as the share of variable renewable energy sources is increasing. To manage this issue, the transition to a nodal pricing market has been proposed in the literature. Given that the current structure of the European Union electricity market corresponds to a zonal pricing, the transition to nodal pricing would imply fundamental changes in its market structure. In order to avoid the latter, this study develops a method to identify locational price signals in a decentralized market with zonal pricing and subsequent market-based redispatch. To this end, first, nodes with structural technical constraints are identified –using energy programmes–. Then, the additional revenues of physical units located at these nodes are estimated –using electricity prices–. Finally, this strategic information will guide the development of flexibility solutions –manageable generation capacity and demand response– in these geographic locations, mimicking the main advantage of nodal pricing. Our methodology has been empirically tested on the Spanish power system, using hourly data from the System Operator and Market Operator for four full years, from 2019 to 2022. The results show a higher and constant economic revenue in certain nodes of this system –upward redispatch price is 74.90% higher than day-ahead market price–. Thus, this method can be used by market participants to evaluate publicly available data from a decentralized market with zonal pricing and subsequent market-based redispatch, and obtain essential information to make the best investment decisions.
Assessing the level of centralisation in scheduling decisions: The role of hybrid approaches
(Elsevier, 2024-10) Framiñán Torres, José Manuel; Pérez González, Paz; Fernández-Viagas Escudero, Víctor; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP134: Organización Industrial y Gestión de Empresas
Decentralised scheduling has been recently advocated as a promising alternative to overcome the difficulties of scheduling jobs in today’s complex and uncertain manufacturing scenarios using the traditional (i.e. centralised) approach consisting of developing a global schedule under the assumption that all necessary data are available and known in advance. In the decentralised approach, machines in the shop floor interact among them to negotiate the jobs to be processed according to a weighted combination of indicators that reflect the current shop floor status. While earlier works have shown the potential advantages of this approach as compared to the centralised one, often these studies are too specific to obtain more generalisable conclusions, since they focus on a single illustrative case study with a small number of jobs/machines, and conduct a limited comparison of both approaches. Furthermore, while its ability to capture current shop floor conditions makes decentralised scheduling more adaptative and flexible than its centralised counterpart, its local (myopic) nature results in a low performance under certain conditions. Therefore, another aspect worth of investigation is to study if a combination of both approaches could ideally result in a more efficient hybrid scheduling mechanism for a wider range of scenarios. Our paper aims at studying these two aspects. First, we compare the performance of centralised and decentralised approaches in a set of medium-sized job shop instances with different levels of processing times variability, and with the objective of minimising the makespan. Each approach is compared under their best conditions, i.e. the optimal deterministic solution for the centralised approach and the best weights for the decentralised one. Second, we propose a hybrid approach that uses the centralised solution as an additional indicator in the decentralised approach. The extensive computational experience carried out shows that the decentralised approach outperforms the centralised one for the scenarios with some degree of variability and that the proposed hybrid approach exhibits the best overall performance, being able to combine the advantages of both centralised and decentralised methods. The sensitivity of the decentralised and hybrid approaches to the coefficients that weight the different indicators is also investigated.
A new composite heuristic to minimize the total tardiness for the single machine scheduling problem with variable and flexible maintenance
(Elsevier, 2025-01) Costa, Antonio; Corsini, Roberto R.; Pagano, Daniele; Fernández-Viagas Escudero, Víctor; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; European Union (UE); Universidad de Sevilla- TEP134: Organización Industrial
Inspired by a real-world maintenance/job scheduling issue coming from the semiconductor industry, the present paper proposes a new heuristic algorithm structure for the single machine scheduling T-problem with flexible and variable maintenance, job release dates and sequence dependence setup times. Considering the typical short-term production planning needs, a single maintenance problem has to be scheduled within a certain time interval, along with a set of jobs so as to minimize the total tardiness. A twofold contribution emerges from the present paper. First, four mixed-integer linear programming models are developed for the problem at hand and compared in terms of time to convergence and computational complexity. Second, a novel heuristic algorithm, which has been configured into three distinct variants, has been compared with 17 alternative heuristics from the relevant literature based on a comprehensive experimental campaign. The numerical results allow the selection of the most suitable MILP model and confirm the effectiveness of the proposed heuristic approach.
A complex network analysis of groundwater wells in and around the Doñana Natural Space, Spain
(Elsevier, 2024-11) Rodríguez Alarcón, R.; Lozano Segura, Sebastián; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Agencia Estatal de Investigación. España; Universidad de Sevilla. TEP216: Tecnologías de la Información e Ingeniería de Organización
This paper proposes the use of Complex Network Analysis tools to study the spatial distribution and temporal evolution of groundwater abstraction wells within a region of interest. To that end a weighted undirected network connecting wells within a certain distance was built. The edge weights take into account the distance between the wells as well as their corresponding abstraction volumes. In addition to the conventional complex network metrics, two specific abstraction-based centrality indexes are proposed, based on the total impact of a groundwater abstraction well on its surrounding wells and on the total impact of surrounding wells on a specific location. The proposed approach has been applied to the area of the Doñana Natural Space, one of the most important wetlands in Europe. The topology of the network reveals a large number of small, disconnected components and a few large components. This is reflected also in the degree distribution, which follows a Power Law distribution. The network has a large average clustering coefficient, a small average path length and a low level of centralisation. Global and average local network efficiency are also high. The network is assortative in terms of degree-degree and strength-strength correlations. Eigenvector centrality of the nodes reveals the location of influential wells.
Predicting Rail Corrugation Based on Convolutional Neural Networks Using Vehicle’s Acceleration Measurements
(2024-07) Haghbin, Masoud; Chiachío Ruano, Juan; Muñoz Moreno, Sergio; Escalona Franco, José Luis; Guillén López, Antonio Jesús; Crespo Márquez, Adolfo; Cantero-Chinchilla, Sergio; Universidad de Sevilla. Departamento de Ingeniería y Ciencia de los Materiales y del Transporte; Universidad de Sevilla. Departamento de Ingeniería Mecánica y de Fabricación; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Junta de Andalucía; Universidad de Sevilla. TEP123: Metalurgia e Ingeniería de los Materiales; Universidad de Sevilla. TEP134: Organización Industrial; Universidad de Sevilla. TEP111: Ingeniería Mecánica
This paper presents a deep learning approach for predicting rail corrugation based on on-board rolling-stock vertical acceleration and forward velocity measurements using One-Dimensional Convolutional Neural Networks (CNN-1D). The model’s performance is examined in a 1:10 scale railway system at two different forward velocities. During both the training and test stages, the CNN-1D produced results with mean absolute percentage errors of less than 5% for both forward velocities, confirming its ability to reproduce the corrugation profile based on real-time acceleration and forward velocity measurements. Moreover, by using a Gradient-weighted Class Activation Mapping (Grad-CAM) technique, it is shown that the CNN-1D can distinguish various regions, including the transition from damaged to undamaged regions and one-sided or two-sided corrugated regions, while predicting corrugation. In summary, the results of this study reveal the potential of data-driven techniques such as CNN-1D in predicting rails’ corrugation using online data from the dynamics of the rolling-stock, which can lead to more reliable and efficient maintenance and repair of railways.
Integrating Change Management with a Knowledge Management Framework: A Methodological Proposal
(MDPI, 2024-07) Picado Argüello, Bernal; González-Prida, Vicente; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. TEP134: Organización industrial
This study proposes the integration of change management with a knowledge management framework to address knowledge retention and successful change management in the context of Industry 5.0. Using the ADKAR model, it is suggested to implement strategies for training and user acceptance testing. The research highlights the importance of applying the human capital life cycle in knowledge and change management, demonstrating the effectiveness of this approach in adapting to Industry 5.0. The methodology includes a review of the state of the art in intangible asset management, change management models, and the integration of change and knowledge management. In addition, a case study is presented in a food production company that validates the effectiveness of the ADKAR model in implementing digital technologies, improving process efficiency and increasing employee acceptance of new technologies. The results show a significant improvement in process efficiency and a reduction in resistance to change. The originality of the study lies in the combination of the ADKAR model with intangible asset and knowledge management, providing a holistic solution for change management in the Industry 5.0 era. Future implications suggest the need to explore the applicability of the ADKAR model in different industries and cultures, as well as its long-term effects on organisational sustainability and innovation. This comprehensive approach can serve as a guide for other organisations seeking to implement successful digital transformations.
Matching inventory and demand in a Fast Moving Consumer Goods company: A Decision Support System
(Elsevier, 2024-08) Framiñán Torres, José Manuel; Guerrero, Francisco; Pérez González, Paz; Toscano, Sonia; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Corporación Tecnológica de Andalucía; Universidad de Sevilla. TEP134: Organización Industrial
Demand fulfilment and its core, order promising, is a key business function that directly impacts the company's ability to generate revenue through sales. For companies operating under a Make-To-Stock (MTS) production strategy, it is tantamount to matching both on-hand and projected stock to customer demand. The prevalent manner to carry out this task is to use either First-Come-First-Served policies, or simple allocation rules embedded in commercially available software. However, order promising is, in general, quite complex due to uncertainties both in demand and supply, and it can become extremely challenging when demand exceeds the stock. Therefore, several contributions have established the advantages of order promising using optimisation models and/or sophisticated algorithms. However, these works assume that customers can be clustered according to their profitability/importance and it remains open to test whether this also holds for homogeneous customers. Furthermore, due to the exploratory nature of most studies, they assume rather stylised scenarios (such as e.g. single product-orders, partial shipments) that do not clarify how model-based order promising would perform in a realistic setting. In this paper we present a decision framework for order promising in a Fast Moving Consumer Goods (FMCG) company in the brewery sector where customers cannot be clustered and that incorporates many realistic features (such as e.g. multi-product orders, on-full delivery, or perishability constraints). The decision framework relies on several iterations of a MILP model that allocates the stock to customer and reserves some portion of the stock for orders arriving later on. These functionalities are embedded in a Decision Support System (DSS) that also helps with the renegotiation of the orders that cannot be served. Results reported from the usage of the DSS show its ability to obtain high service levels and to favourably compare to other order promising policies. Furthermore, in order to analyse the instance sizes that can be solved in reasonable time, and the parameters that influence the performance and results of the MILP model, an extensive computational experience has been carried out using a testbed that covers a wide range of business scenarios.
Ant colony algorithms for minimizing costs in multi-mode resource constrained project scheduling problems with spatial constraints
(Growing Science, 2024) Pascual de la Pisa, Miguel; Molina Gómez, José Carlos; Eguía Salinas, Ignacio; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Universidad de Sevilla. TEP216: Tecnologías de la Información e Ingeniería de Organización.
This paper addresses the problem of activity scheduling and operator assignment in workstations of aerospace assembly lines. The problem is modelled as a new variant of the Multi-Mode Resource Constrained Project Scheduling Problem (MRCPSP), which incorporates practical features from aerospace workstations in assembly lines. These workstations have a substantial number of activities to be scheduled within a given assembly cycle time. It introduces particularities which are not usually addressed such as considering additional workers for performing activities, different workers’ proficiency, and spatial limitations in work zones. The objective is to schedule the activities of an aerospace workstation, minimising the total labour cost, while satisfying the cycle time and the zone’s limitations. The problem is initially formulated by employing mixed-integer linear programming methods with mathematical modelling and solved using two different algorithms: an Ant Colony System (ACS) and a memetic ACS. Given the novelty of the problem presented, new sets of benchmark cases of different sizes for this problem are also proposed and solved. To assess the performance of the algorithms, the solutions for the small-sized instances are compared in terms of deviation with the results obtained by an optimisation modelling software. Further experimentation with the algorithms is carried out with medium and large instances, showing good performance and providing reasonably good results in realistic problems.
A metafrontier network DEA approach for water usage efficiency assessment in the light of SDG target 6.4
(Elsevier, 2024-10) Lozano Segura, Sebastián; Borrego Marín, María del Mar; Universidad de Sevilla. Departamento de Economía Aplicada III; Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TEP216: Tecnologías de la Información e Ingeniería de Organización
The efficient use of water must be enhanced and promoted to achieve the Sustainable Development Goal (SDG) 6. Thus, to contribute to the achievement of SDG Target 6.4 and close a research gap in SDGs’ progress, this paper carries out a water usage efficiency analysis of 126 countries leveraging the AQUASTAT database maintained by the Food and Agriculture Organization of the United Nations (FAO), providing information that can help assess and promote the sustainable use and management of water. The methodology uses a conceptual model that considers a Water Withdrawal (WW) stage and a Water Productivity (WP) stage, each one with its own set of variables, designing a customised non-parametric frontier analysis solution that identifies the countries with the best practices and uses them as benchmarks for global efficient water usage. In particular, the proposed approach uses a non-radial Directional Distance Function (DDF) that estimates the inefficiency along the different dimensions, both desirable and undesirable, quantifying potential improvement and computing an efficiency score for each stage and for the whole system. Due to the heterogeneity of the sample, a metafrontier analysis has been carried out. The results indicate that there are significant differences between countries and regions in terms of water usage efficiency. The regions with highest efficiency include Australia, Western and Central Europe and Southern and Eastern Asia, while Central Asia has the lowest. Most of the inefficiency corresponds to the Gross Value Added dimension and the WW stage efficiency is generally higher than that of the WP stage.

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