Abstract
The definition of lot sizes represents one of the most important decisions in production planning. Lot-sizing turns into an increasingly complex set of decisions that requires efficient solution approaches, in response to the time-consuming exact methods (LP, MIP). This paper aims to propose a Tabu list-based algorithm (TLBA) as an alternative to the Generic Materials and Operations Planning (GMOP) model. The algorithm considers a multi-level, multi-item planning structure. It is initialized using a lot-for-lot (LxL) method and candidate solutions are evaluated through an iterative Material Requirements Planning (MRP) procedure. Three different sizes of test instances are defined and better results are obtained in the large and medium-size problems, with minimum average gaps close to 10.5%.
Highlights
The definition of lot sizes represents one of the most important decisions in production planning.Lot-sizing models aim to guarantee the fulfillment of the demand requirements, establishing a balance between holding and setup costs
An initial solution was calculated for every generated test instance, using a lot-for-lot method
The exact solutions were obtained through a branch-and-bound method using the CPLEX solver in GAMS
Summary
The definition of lot sizes represents one of the most important decisions in production planning.Lot-sizing models aim to guarantee the fulfillment of the demand requirements, establishing a balance between holding and setup costs. Complex assembly systems usually require wide and robust product structures, which may involve the use of alternate bills of materials and co-production settings. In these cases, the complexity of lot sizing decisions increases along with flexibility. Depending on the problem size and the number of considered constraints, the use of exact solution models can be inefficient in terms of computational times, especially for operational planning purposes [1,2]. Exact solution approaches have been widely used for the NP-hard Capacitated Lot Sizing Problem (CLSP), including cut-generation [3] and redefinition techniques [4], supported by mathematical approaches as Branch and Bound, Lagrangian Relaxation, and Wagner-Within.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
