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Material utilization optimization through area-minimized rectangle packing: a reliable and exact piecewise linearization method

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Abstract

Minimizing material consumption is a critical objective in numerous industries, driven by both economic and environmental concerns; particularly, the prices of several materials have fluctuated and increased over the past decade. For many industrial applications,

the area-minimized rectangle-packing problem (AMRPP) (or volume-minimized bin-packing problem) plays a pivotal role in achieving this by determining the smallest rectangular area required to contain a set of items, thereby directly reducing material waste. However, the inherent non-linear nature of the AMRPP objective function poses significant computational challenges to finding global optima. This study addresses this challenge by focusing on the logarithmic transformation of the AMRPP objective and introducing an exact piecewise linear function (PWLF) for logarithmic functions. This newly developed PWLF guarantees that the approximation error for the logarithmic terms is within a user-specified tolerance \(\epsilon \), performing consistently to provide a reliable and exact solution under tolerance for the function approximation itself. Numerical studies show that our proposed PWLF significantly reduces approximation errors compared to state-of-the-art algorithms—by up to 15% for the maximum error. The proposed linearized model also leads to a substantial reduction in total computation time for solving these packing problems—achieving up to a 21% improvement compared to the second-best approach in benchmark instances. This work underscores the practical value of exact PWL techniques in optimizing material utilization through more accurate and efficient solutions to AMRPP, contributing to both economic savings and environmental sustainability.

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References

  • Alinaghian, M., Zamanlou, K., & Sabbagh, M. S. (2017). A bi-objective mathematical model for two-dimensional loading time-dependent vehicle routing problem. Journal of the Operational Research Society, 68(11), 1422–1441.

    Article  Google Scholar 

  • Arbib, C., & Marinelli, F. (2007). An optimization model for trim loss minimization in an automotive glass plant. European Journal of Operational Research, 183(3), 1421–1432.

    Article  Google Scholar 

  • Bellman, R., & Roth, R. (1969). Curve fitting by segmented straight lines. Journal of the American Statistical Association, 64(327), 1079–1084.

    Article  Google Scholar 

  • Bertsimas, D., & Shioda, R. (2007). Classification and regression via integer optimization. Operations Research, 55(2), 252–271.

    Article  Google Scholar 

  • Cherri, A. C., Arenales, M. N., & Yanasse, H. H. (2009). The one-dimensional cutting stock problem with usable leftover-a heuristic approach. European Journal of Operational Research, 196(3), 897–908.

    Article  Google Scholar 

  • Codsi, J., Ngueveu, S. U., & Gendron, B. (2025). LinA: A faster approach to piecewise linear approximations using corridors and its application to mixed-integer optimization. Mathematical Programming Computation, 17(2), 265–306.

    Article  Google Scholar 

  • Do Nascimento, D. N., Cherri, A. C., & Oliveira, J. F. (2022). The two-dimensional cutting stock problem with usable leftovers: Mathematical modelling and heuristic approaches. Operational Research, 22(5), 5363–5403.

    Article  Google Scholar 

  • D’Ambrosio, C., & Lodi, A. (2013). Mixed integer nonlinear programming tools: An updated practical overview. Annals of Operations Research, 204, 301–320.

    Article  Google Scholar 

  • Ertel, J. E., & Fowlkes, E. B. (1976). Some algorithms for linear spline and piecewise multiple linear regression. Journal of the American Statistical Association, 71(355), 640–648.

    Article  Google Scholar 

  • Estrada-Moreno, A., Ferrer, A., Juan, A. A., Bagirov, A., & Panadero, J. (2020). A biased-randomised algorithm for the capacitated facility location problem with soft constraints. Journal of the Operational Research Society, 71(11), 1799–1815.

    Article  Google Scholar 

  • Hillier, F. S., & Lieberman, G. J. (2020). Introduction to Operations Research. McGraw-Hill.

    Google Scholar 

  • Huang, E., & Korf, R. E. (2010). Optimal rectangle packing on non-square benchmarks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 24, pp. 83–88).

  • Huang, E., & Korf, R. E. (2013). Optimal rectangle packing: An absolute placement approach. Journal of Artificial Intelligence Research, 46, 47–87.

    Article  Google Scholar 

  • Huang, Y.-H., & Hwang, F.-J. (2018). Global optimization for the three-dimensional open-dimension rectangular packing problem. Engineering Optimization, 50(10), 1789–1809.

    Article  Google Scholar 

  • Lee, C.-Y. (2019). Proactive marginal productivity analysis for production shutdown decision by DEA. Journal of the Operational Research Society, 70(7), 1065–1078.

    Article  Google Scholar 

  • Lee, C.-Y., & Charles, V. (2022). A robust capacity expansion integrating the perspectives of marginal productivity and capacity regret. European Journal of Operational Research, 296(2), 557–569.

    Article  Google Scholar 

  • Lee, C.-Y., & Wang, K. (2019). Nash marginal abatement cost estimation of air pollutant emissions using the stochastic semi-nonparametric frontier. European Journal of Operational Research, 273(1), 390–400.

    Article  Google Scholar 

  • Li, H.-L., Chang, C.-T., & Tsai, J.-F. (2002). Approximately global optimization for assortment problems using piecewise linearization techniques. European Journal of Operational Research, 140(3), 584–589.

    Article  Google Scholar 

  • Li, H.-L., Lu, H.-C., Huang, C.-H., & Hu, N.-Z. (2009). A superior representation method for piecewise linear functions. INFORMS Journal on Computing, 21(2), 314–321.

    Article  Google Scholar 

  • Li, H. L., & Yu, C. S. (1999). A global optimization method for nonconvex separable programming problems. European Journal of Operational Research, 117(2), 275–292.

    Article  Google Scholar 

  • Lin, C.-C. (2006). A genetic algorithm for solving the two-dimensional assortment problem. Computers & Industrial Engineering, 50(1–2), 175–184.

    Article  Google Scholar 

  • Lin, M.-H., Carlsson, J. G., Ge, D., Shi, J., & Tsai, J.-F. (2013). A review of piecewise linearization methods. Mathematical Problems in Engineering, 2013(1), 101376.

    Google Scholar 

  • Lundell, A., Westerlund, J., & Westerlund, T. (2009). Some transformation techniques with applications in global optimization. Journal of Global Optimization, 43, 391–405.

    Article  Google Scholar 

  • Lundell, A., & Westerlund, T. (2013). Refinement strategies for piecewise linear functions utilized by reformulation-based techniques for global optimization. In Computer aided chemical engineering (Vol. 32, pp. 529–534). Elsevier.

    Google Scholar 

  • Magnani, A., & Boyd, S. P. (2009). Convex piecewise-linear fitting. Optimization and Engineering, 10, 1–17.

    Article  Google Scholar 

  • Padberg, M. (2000). Approximating separable nonlinear functions via mixed zero-one programs. Operations Research Letters, 27(1), 1–5.

    Article  Google Scholar 

  • Quadri, D., & Soutil, E. (2015). Reformulation and solution approach for non-separable integer quadratic programs. Journal of the Operational Research Society, 66(8), 1270–1280.

    Article  Google Scholar 

  • Rebennack, S., & Kallrath, J. (2015). Continuous piecewise linear delta-approximations for univariate functions: Computing minimal breakpoint systems. Journal of Optimization Theory and Applications, 167(2), 617–643.

    Article  Google Scholar 

  • Rebennack, S., & Krasko, V. (2020). Piecewise linear function fitting via mixed-integer linear programming. INFORMS Journal on Computing, 32(2), 507–530.

    Article  Google Scholar 

  • Sierra-Paradinas, M., Soto-Sánchez, Ó., Alonso-Ayuso, A., Martín-Campo, F. J., & Gallego, M. (2021). An exact model for a slitting problem in the steel industry. European Journal of Operational Research, 295(1), 336–347.

    Article  Google Scholar 

  • Taha, H. A. (2017). Operations research: An introduction. Pearson Education India.

    Google Scholar 

  • Tian, T., Zhu, W., Zhu, Y., Liu, Q., & Wei, L. (2024). A two-phase constructive algorithm for the single container mix-loading problem. Annals of Operations Research, 332(1), 253–275.

    Article  Google Scholar 

  • Vielma, J. P., & Nemhauser, G. L. (2011). Modeling disjunctive constraints with a logarithmic number of binary variables and constraints. Mathematical Programming, 128, 49–72.

    Article  Google Scholar 

  • Wu, Y.-T., & Lee, C.-Y. (2024). Does marginal productivity of product mix matter? data envelopment analysis for marginal profit consistency in taiwan’s life insurance industry. Operations Research Forum, 5, 7.

    Article  Google Scholar 

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Acknowledgements

This research was funded by Ministry of Science and Technology (MOST111-2628-E-002-019-MY3), Taiwan.

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Correspondence to Chia-Yen Lee.

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Fang, YH., Lee, CY. Material utilization optimization through area-minimized rectangle packing: a reliable and exact piecewise linearization method. Ann Oper Res (2025). https://doi.org/10.1007/s10479-025-06995-w

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