A review of discrete-time optimization models for tactical production planning

Abstract

This paper presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, setup times, parallel machines, overtime capacities and network-type multi-site configuration stand out in terms of extensions; (4) the most widely used modeling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modeling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.

Optimizing planned lead times for enhancing performance of MRP systems

“…Diaz-Maderonero et al (2014) survey other mathematical programming approaches that can be used in solving these types of problems. Our experimental results are summarized in Table 5.…”

Planned lead times optimization for multi-level assembly systems under uncertainties

“…Interested readers may refer to Van Kampen Tim et al (2010), where the cases in which the use of safety stocks and/or safety lead times could be advantageous are explained, or to Jansen and de Kok (2011) for further details on the importance of lead time anticipation. More generally, readers who are interested in supply planning models under uncertainty may turn to Aloulou et al (2014), Díaz-Madroñero et al (2014), Dolgui et al (2013), Ko et al (2010), Peidro et al (2009), Mula et al (2006) and Koh et al (2002).…”

“…So to decide the client delivery due date and the start times for supply chains, a model based on the probability distributions of the partner lead times is developed. This is a common approach in contracting and planning under uncertainty (Song et al 2000, Fiala 2005, Berlec et al 2008and Yoo et al 2015, which is commonly used owing to the complexity of the problem (Guide and Srivasta 2000, Koh et al 2002, Dolgui and Prodhon 2007, Damand et al 2013, Dolgui et al 2013and Díaz-Madroñero et al 2014.…”

Problema de dimensionamento de lotes em uma indústria de móveis seriados

“…[2] considera o algoritmo de colônia de abelhas para solução o PDL em um sistema de produção multiestágio. Segundo [4], estudos que analisem modelos em planejamento da produção com dados reais envolvem a menor parte dos artigos publicados.…”