Synchronization of scarce resources for a parallel machine lotsizing problem

• Autor(es): Christian Almeder ; ernardo Sobrinho Simões de Almada Lobo
• Data: 2011
• Identificador Persistente: http://hdl.handle.net/10216/56802
• Origem: Repositório Aberto da Universidade do Porto
• Assunto(s): Ciências tecnológicas; Engenharia; Engenharia industrial; Engenharia de produção industrial
• Tipo de Documento: Artigo
• Idioma: Inglês

Descrição

In this paper we present a novel approach to tackle the synchronization of a secondary resource in lotsizing and scheduling problems. This kind of problem occurs in different manufacturing processes (e.g. wafer testing in the semiconductor industry, production and bottling of soft drinks). We consider a scenario of parallel unrelated machines which have to be equipped with a tool or need a special kind of resource for processing. Our approach allows tracing the assignment of these secondary resources across different machines and synchronizing their usage independently of time periods. We present extensions of the general lotsizing and scheduling problem and of the capacitated lotsizing problem. We prove that the latter model is a special case of the first one, but it performs computationally much better. In this paper we present a novel approach to tackle the synchronization of a secondary resource in lotsizing and scheduling problems. This kind of problem occurs in different manufacturing processes (e.g. wafer testing in the semiconductor industry, production and bottling of soft drinks). We consider a scenario of parallel unrelated machines which have to be equipped with a tool or need a special kind of resource for processing. Our approach allows tracing the assignment of these secondary resources across different machines and synchronizing their usage independently of time periods. We present extensions of the general lotsizing and scheduling problem and of the capacitated lotsizing problem. We prove that the latter model is a special case of the first one, but it performs computationally much better.