Shop floor lot-sizing and scheduling with a two-stage stochastic programming model considering uncertain demand and workforce efficiency

Abstract

Efficient and flexible production planning is necessary for the manufacturing industry to stay competitive in today's global market. Shop floor lot-sizing and scheduling is one of the most challenging and rewarding subjects for the management. In this study, a two-stage stochastic programming model is proposed to solve a single-machine, multi-product shop floor lot-sizing and scheduling problem. Two sources of uncertainties are considered simultaneously: product demand from the market, and workforce efficiency, which is the major contribution of this study. The workforce efficiency affects the system productivity, and we propose different distributions to model its uncertainty with insufficient information.The model aims to determine optimal lot sizes and the production sequence that minimizes expected total system costs over the planning horizon, including setup, inventory, and production costs. A case study is performed on a supply chain producing brake equipment in the automotive industry. The numerical results illustrate the usefulness of the stochastic model under volatile environment, and the solution quality is analyzed. AbstractEfficient and flexible production planning is necessary for the manufacturing industry to stay competitive in today's global market. Shop floor lot-sizing and scheduling is one of the most challenging and rewarding subjects for the management. In this study, a two-stage stochastic programming model is proposed to solve a single-machine, multi-product shop floor lot-sizing and scheduling problem. Two sources of uncertainties are considered simultaneously: product demand from the market, and workforce efficiency, which is the major contribution of this study.The workforce efficiency affects the system productivity, and we propose different distributions to model its uncertainty with insufficient information. The model aims to determine optimal lot sizes and the production sequence that minimizes expected total system costs over the planning horizon, including setup, inventory, and production costs. A case study is performed on a supply chain producing brake equipment in the automotive industry. The numerical results illustrate the usefulness of the stochastic model under volatile environment, and the solution quality is analyzed. Highlights• A two-stage stochastic programming model for lot-sizing and scheduling is proposed.• Two uncertain factors, product demand and workforce efficiency are considered.• The numerical results of optimal production sequence and quantities are illustrated.• The significance of stochastic model is presented, and solution quality is analyzed.

Multi-objective permutation and non-permutation flow shop scheduling problems with no-wait: a systematic literature review

“…From them, release/due-date based objective functions represent only the 4%, lot-sizing 5%, and costing 4% of the publications, advising that these essential objective functions are under-presented, needing more profound attention. This fact has been underlined in particular in [1,45,66].…”

Stochastic Model For Setpoint of a Rolling Mill: An Application In The Soybean Oil Production

“…The results showed that the approaches were able to obtain solutions compatible with the reality of the production process under study. In [20][21][22], uncertainties are addressed in the production process with stochastic models outperforming deterministic ones. The optimal value of the optimization model was properly approximated when using an enough number of scenarios in the stochastic approach.…”

Modelagem Estocástica Aplicada a Problemas Industriais: Estudos de Casos Reais

“…O tratamento da incerteza em processos produtivos é um desafio que tem sido tratado na literatura através da proposição de modelos e métodos estocásticos HU, 2016;LI;HU, 2017). Todavia, o presente trabalho busca endereçar tal desafio através de uma abordagem que permite alocar o risco incorrido no processo de tomada de decisão para planejamento e sequenciamento da produção.…”

“…Como mencionado, os autores em (TANG; CHE; LIU, 2012; ALEM; MORABITO, 2015) abordam técnicas de modelagem estocástica para tratar o processo de produção em indústrias considerando contextos reais. Vários autores na literatura, tais como (HU; HU, 2016;LI;HU, 2017;ALEM et al, 2018), abordam incertezas no processo de produção. Todavia, até onde foi pesquisado na condução deste projeto, não há na literatura estudos mais aprofundados com tratamentos de incertezas em ambientes que lidam com o processo de preparação da soja para extração de óleo.…”

“…Portanto, estamos usando ideias introduzidas em (BLACKMORE; ONO; WILLIAMS, 2011) e (ARANTES et al, 2019) para transformar um modelo linear a partir de um modelo não-linear. A técnica de programação estocástica de two-stage, que geralmente é relatada na literatura para resolver problemas similares HU, 2016;LI;HU, 2017;ALEM et al, 2018), é utilizada para comparar os resultados obtidos aplicando CCRA. Além disso, a simulação Monte Carlo é aplicada para validar o nível de risco incorrido após a aplicação de ambas as técnicas, seguindo uma validação semelhante descrita em (BLACKMORE; ONO; WILLIAMS, 2011).…”