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Minimization of the wood wastes for an industry of furnishing: a two dimensional cutting stock problem

Bouaine A., Lebbar M., Ha M. A.

Management and Production Engineering Review

2018

Vol. 9, No. 2

42--51

This paper presents the modeling and the resolution of a two dimensional cutting stock problem for a wooden industry. It is about a real problem of minimization of the wood wastes for an industry of furnishing. The raw material to be cut is a set of beams of various sizes. The purpose of the cut is to supply a list of orders characterized by a set of articles of various sizes. The problem is converted into an integer linear program where the decision variables are the numbers of beams to cut according to a set of feasible “patterns”. The designed solution is a heuristic in two stages: – Generation of the feasible patterns by various classic heuristics of the Bin-packing Problem. – Resolution of the integer linear program with the generated patterns as input variables. Moreover, based on this approach, the “Application Cutting Optimization” is developed to allow the immediate resolution of the problem and widening the stock management horizon. To end, a real case is studied to confirm the effectiveness of this approach.

Multi-Criteria 3-Dimension Bin Packing Problem

Kacprzak Ł., Rudy J., Żelazny D.

Research in Logistics & Production

2015

Vol. 5, No. 1

85--94

In this paper a multi-criteria approach to the 3-dimensions bin packing problem is considered. The chosen maximization criteria are the number and the total volume of the boxes loaded into the container. Existing solution representation and decoding method are applied to the problem. Next, two metaheuristic algorithms, namely simulated annealing and genetic algorithm are developed using the TOPSIS method for solution evaluation. Both algorithms are then used to obtain approximations of the Pareto front for a set of benchmarks from the literature. Despite the fact that both criteria work in favor of each other, we managed to obtain multiple solutions in many cases, proving that lesser number of boxes can lead to better utilization of the container volume and vice versa. We also observed, that the genetic algorithms performs slightly better in our test both in the terms of hyper-volume indicator and number of non-dominated solutions.