Multicriteria sorting method based on global and local search for supplier segmentation

[EN] The aim of this research is to develop a robust multicriteria method to classify suppliers into ordered cate-gories and its validation in real contexts. The proposed technique is based on a property of net flows of thePROMETHEE method and uses global and local search concepts, which are common in the optimisationfield. The results obtained are compared to those from the most cited sorting algorithm, and an empiricalvalidation and sensitivity analysis is performed using real supplier evaluation data. Furthermore, it does notrequire additional information from decision-makers as other sorting algorithms do for assigning incompa-rable or indifferent alternatives to groups. An extension of the silhouette concept from data mining is alsocontributed to measure the quality of ordered classes. Both contributions are easy to apply and integrate intodecision support systems for automated decisions in the supply chain management. Finally, this practicalapproach is also useful to classify customers and any type of alternatives or actions into ordered categories,which have an increasing number of real applications.Barrera, F.; Segura Maroto, M.; Maroto Álvarez, MC. (2023). Multicriteria sorting method based on global and local search for supplier segmentation. International Transactions in Operational Research. 1-27. https://doi.org/10.1111/itor.1328812

Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method

[EN] For companies, customer segmentation plays a key role in improving supply chain management by implementing appropriate marketing strategies. The objectives of this research are to design and validate a multicriteria model to support decision making for customer segmentation in a business to business context. First, the model based on the transactional customer behaviour is extended by a hierarchy with three main criteria: Recency, Frequency and Monetary (RFM), customer collaboration and growth rates. Customer collaboration includes quota compliance, variety of products and customer commitment to sustainability (reverse logistics and shared information). Second, the Global Local Net Flow Sorting (GLNF sorting) algorithm is implemented and validated using real company data to classify 8,157 customers of a multinational healthcare company. Third, the SILS quality indicator has been implemented and validated to assess the quality of preference-ordered customer groups and its parameters have been adapted for contexts with thousands of alternatives. The results are also compared with an alternative model based on data mining (K-means). The multicriteria system proposed allows to segment thousands of customers in ordered categories by preferences according to company strategies. The segments generated are more homogeneous, robust and understandable by managers than those from alternative methods. These advantages represent a relevant contribution to automating supply chain management while providing detailed analysis tools for decision making.Barrera, F.; Segura Maroto, M.; Maroto Álvarez, MC. (2024). Multiple Criteria Decision Support System for Customer Segmentation using a Sorting Outranking Method. Expert Systems with Applications. 238:1-17. https://doi.org/10.1016/j.eswa.2023.12231011723

Multicriteria sorting method based on global and local search for supplier segmentation

The aim of this research is to develop a robust multicriteria method to classify suppliers into ordered categories and its validation in real contexts. The proposed technique is based on a property of net flows of the PROMETHEE method and uses global and local search concepts, which are common in the optimisation field. The results obtained are compared to those from the most cited sorting algorithm, and an empirical validation and sensitivity analysis is performed using real supplier evaluation data. Furthermore, it does not require additional information from decision-makers as other sorting algorithms do for assigning incomparable or indifferent alternatives to groups. An extension of the silhouette concept from data mining is also contributed to measure the quality of ordered classes. Both contributions are easy to apply and integrate into decision support systems for automated decisions in the supply chain management. Finally, this practical approach is also useful to classify customers and any type of alternatives or actions into ordered categories, which have an increasing number of real applications

Resúmenes de la XIV Reunión del Grupo Español de Decisión Multicriterio

Esta reunión es un foro en el que académicos e investigadores pueden intercambiar ideas y experiencias en el campo del multicriteri

Resúmenes de la XV Reunión del Grupo Español de Decisión Multicriterio

Publicación de los resúmenes de las conferencias plenarias y comunicaciones orales que tuvieron lugar en la XV reunión del Grupo Español de Decisión Multicriterio celebrado en Oviedo del 4 al 6 de abril de 2024

PPSTOW: An End-to-End Deep Reinforcement Learning Model for Master Stowage Planning on Container Vessels

Efficient supply chains are vital for both the worldwide economy and environmental sustainability. Container shipping plays a key role in this, known for being an eco-friendly mode of transport. Liner shipping companies are actively working to improve operational efficiency through stowage planning. Due to many combinatorial aspects, some of which are NP-hard, stowage planning is a challenging problem in its representative form. Even though stowage planning can be decomposed into master and slot planning, the subproblems remain challenging. As a result, we are searching for scalable algorithms to solve the stowage planning problem.In this work, we propose Proximal Policy optimization for master STOWage planning (PPSTOW), a deep reinforcement learning approach to address master planning with focus on global problem objectives and constraints. The experiments show the effectiveness of PPSTOW, as the framework efficiently finds near-optimal solutions for simulated problem instances with realistic vessel sizes and practical planning horizons. In the future, we aim to refine the representativeness of our approach by integrating revenue management, as well as local problem objectives and constraints

PPSTOW: An End-to-End Deep Reinforcement Learning Model for Master Stowage Planning on Container Vessels

Efficient supply chains are vital for both the worldwide economy and environmental sustainability. Container shipping plays a key role in this, known for being an eco-friendly mode of transport. Liner shipping companies are actively working to improve operational efficiency through stowage planning. Due to many combinatorial aspects, some of which are NP-hard, stowage planning is a challenging problem in its representative form. Even though stowage planning can be decomposed into master and slot planning, the subproblems remain challenging. As a result, we are searching for scalable algorithms to solve the stowage planning problem.In this work, we propose Proximal Policy optimization for master STOWage planning (PPSTOW), a deep reinforcement learning approach to address master planning with focus on global problem objectives and constraints. The experiments show the effectiveness of PPSTOW, as the framework efficiently finds near-optimal solutions for simulated problem instances with realistic vessel sizes and practical planning horizons. In the future, we aim to refine the representativeness of our approach by integrating revenue management, as well as local problem objectives and constraints