Cookies
Usamos cookies para melhorar nosso site e a sua experiência. Ao continuar a navegar no site, você aceita a nossa política de cookies. Ver mais
Fechar
  • Menu
Tópicos
de interesse
Detalhes

Detalhes

003
Publicações

2020

Cooperative coevolution of expressions for (r,Q) inventory management policies using genetic programming

Autores
Lopes, RL; Figueira, G; Amorim, P; Almada Lobo, B;

Publicação
International Journal of Production Research

Abstract
There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as (r,Q) policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. This work proposes a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than (Formula presented.), while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

2020

Production scheduling in the context of Industry 4.0: review and trends

Autores
Parente, M; Figueira, G; Amorim, P; Marques, A;

Publicação
International Journal of Production Research

Abstract

2019

Integration of Supplier Selection and Inventory Management under Supply Disruptions

Autores
Saputro, TE; Figueira, G; Almada Lobo, B;

Publicação
IFAC PAPERSONLINE

Abstract
Procurement plays an essential role in the supply of materials for the production of goods or products. The success of procurement management to fulfill demand with high service levels and on-time delivery relies on the suppliers' performance. Suppliers should be appropriately selected to source materials with the right quality, in the right quantity, at the right time, and for the right price. The scope of this problem, as well as other aspects such as the sourcing strategy, will depend on the type of items. Critical items, which represent high-profit impacts and high supply risks, should be approached comprehensively by considering all the main activities of the procurement process. This study focuses on a supplier selection problem integrated with inventory management under a multi-sourcing strategy, by taking into account stochastic demand and supply disruptions. This problem is approached by a simulation-optimization method, composed of discrete-event simulation and a genetic algorithm (GA). Finally, a numerical example is provided to illustrate the solution procedure.

2018

Designing new heuristics for the capacitated lot sizing problem by genetic programming

Autores
Hein, F; Almeder, C; Figueira, G; Almada Lobo, B;

Publicação
Computers and Operations Research

Abstract
This work addresses the well-known capacitated lot sizing problem (CLSP) which is proven to be an NP-hard optimization problem. Simple period-by-period heuristics are popular solution approaches due to the extremely low computational effort and their suitability for rolling planning horizons. The aim of this work is to apply genetic programming (GP) to automatically generate specialized heuristics specific to the instance class. Experiments show that we are able to obtain better solutions when using GP evolved lot sizing rules compared to state-of-the-art constructive heuristics. © 2018 Elsevier Ltd

2017

Decentralized Vs. Centralized Sequencing in a Complex Job-Shop Scheduling

Autores
Mehrsai, A; Figueira, G; Santos, N; Amorim, P; Almada Lobo, B;

Publicação
IFIP Advances in Information and Communication Technology

Abstract
Allocation of jobs to machines and subsequent sequencing each machine is known as job scheduling problem. Classically, both operations are done in a centralized and static/offline structure, considering some assumptions about the jobs and machining environment. Today, with the advent of Industry 4.0, the need to incorporate real-time data in the scheduling decision process is clear and facilitated. Recently, several studies have been conducted on the collection and application of distributed data in real-time of operations, e.g., job scheduling and control. In practice, pure distribution and decentralization is not yet fully realizable because of e.g., transformation complexity and classical resistance to change. This paper studies a combination of decentralized sequencing and central optimum allocation in a lithography job-shop problem. It compares the level of applicability of two decentralized algorithms against the central scheduling. The results show better relative performance of sequencing in stochastic cases. © IFIP International Federation for Information Processing 2017.

Teses
supervisionadas

2019

Exploring fish purchasing behaviour using data analytics

Autor
Rodrigo Teodoro Passos

Instituição
UP-FEUP

2019

A Machine Learning Approach to the Optimization of Inventory Management Policies

Autor
Álvaro Silva de Melo

Instituição
UP-FEUP

2016

Integration of supplier selection and production planning under uncertainty

Autor
Thomy Saputro

Instituição
UP-FEUP

2016

Production scheduling of the printing plant

Autor
Nicolau Santos

Instituição
UP-FCUP

2016

Bridging operations and marketing planning in the consumer packaged goods industry

Autor
Xavier António Reis Andrade

Instituição
UP-FEUP