Abstract

We introduce a novel variant of cutting production planning problems named Integrated Cutting and Packing Heterogeneous Precast Beams Multiperiod Production Planning (ICP-HPBMPP). We propose an integer linear programming model for the ICP-HPBMPP, as well as a lower bound for its optimal objective function value, which is empirically shown to be closer to the optimal solution value than the bound obtained from the linear relaxation of the model. We also propose a genetic algorithm approach for the ICP-HPBMPP as an alternative solution method. We discuss computational experiments and propose a parameterization for the genetic algorithm using D-optimal experimental design. We observe good performance of the exact approach when solving small-sized instances, although there are difficulties in finding optimal solutions for medium and large-sized problems, or even in finding feasible solutions for large instances. On the other hand, the genetic algorithm is shown to typically find good-quality solutions for large-sized instances within short computing times.

Highlights

  • Nowadays, concrete precast production is increasingly trending in constructions sites

  • We refer to this problem as the Integrated Cutting and Packing Heterogeneous Precast Beams Multiperiod Production Planning Problem (ICP-HPBMPP)

  • The mathematical model we propose is based on the model by [2], which deals with the cutting stock/leftover problem, and on the model by [1] for the HPBMPP

Read more

Summary

Introduction

Concrete precast production is increasingly trending in constructions sites. We consider the integration into a single production planning problem of the cutting process of bars, or of overlapping bars, which must be packed in the molds for the production of a given demand of beams. We refer to this problem as the Integrated Cutting and Packing Heterogeneous Precast Beams Multiperiod Production Planning Problem (ICP-HPBMPP). The production cost with an optimized process will be lower, which may lead to a reduction of the final product’s price, increasing competitiveness It is argued in [1] that the HPBMPP is NP-hard since it includes, as a particular case, the classical onedimensional cutting stock problem.

Literature review
Problem statement
Integer linear programming model
NP-hardness
Objective function lower bound
Packing patterns generation
Cutting patterns generation
Overlapping patterns
Genetic algorithm for the ICP-HPBMPP
Solution representation
Result
Fitness function and selection operator
Crossover operators
Mutation operator
Infeasible solution fixing
Population restart
Algorithm description
Computational experiments
Test instances generation
Computational experiments with the mathematical model
Experimental results and discussion
Method
Final remarks
Full Text
Published version (Free)

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 call