This paper addressed an important variant of two-dimensional cutting stock problem. The objective was not only to minimize trim loss, as in traditional cutting stock problems, but rather to minimize the number of machine setups. This additional objective is crucial for the life of the machines and affects both the time and the cost of cutting operations. Since cutting stock problems are well known to be NP-hard, we proposed an approximate method to solve this problem in a reasonable time. This approach differs from the previous works by generating a front with many interesting solutions. By this way, the decision maker or production manager can choose the best one from the set based on other additional constraints. This approach combined a genetic algorithm with a linear programming model to estimate the optimal Pareto front of these two objectives. The effectiveness of this approach was evaluated through a set of instances collected from the literature. The experimental results for different-size problems show that this algorithm provides Pareto fronts very near to the optimal ones.

The lively field of assembly line configuration and adjustment often have a significant impact on the performance of manufacturing systems. In this context, assembly line balancing problems (ALBPs) are widely cited in the literature. An ALBP consists of distributing the total product manufacturing workload among the stations along the manufacturing line. Previous research has focused on developing effective and fast solution methods for solving simple assembly line balancing problems (SALBP) and their various extensions. Each extension is motivated by several real-life applications and the need for solving precise practical problems. In this article, another interesting extension of SALBP (named in this work ‘Task Restrictions Assembly Line Balancing Problem’ of type 2 (TRALBP-2)) is focused on. In this situation, the number of stations is known and the objective is to minimise cycle time where both precedence and zoning constraints between tasks must be satisfied. For the resolution of such problem, an innovative hybrid genetic algorithm (HGA) scheme hybridised with a local search procedure is implemented. This genetic algorithm consists of a new representation scheme and a special genetic operator. The effectiveness of the proposed HGA is evaluated through various sets of instances which are (1) theoretically and randomly generated, (2) collected from the literature and (3) based on a real case study of an automotive cable manufacturer. Comparison of the proposed HGA results with CPLEX software for the TRALBP-2 demonstrates that, in a reasonable time, the proposed HGA generates consistent solutions that are very close to their optimal ones. Therefore, the proposed HGA approach is very effective and competitive.

This study investigates date palm fiber (DPF) and sheep wool hybrid polyester composites with fiber loadings of 0%, 10%, 20%, and 30% by weight, fabricated by compression molding, to develop a sustainable and reliable material system. Experimental data from prior work were modeled using Weibull analysis for reliability evaluation and response surface methodology (RSM) for multi-objective optimization. Weibull statistics fitted a two-parameter distribution to tensile strength and fracture toughness, extracting shape (η) and scale ($\beta$) parameters to quantify variability and failure probability. The analysis showed that 20% hybrid content achieved the highest scale values ($\beta$ = 28.85 MPa for tensile strength and $\beta$ = 15.03 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ for fracture toughness) and comparatively low scatter (η = 10.39 and 9.2, respectively), indicating superior reliability. RSM quadratic models were developed for tensile strength, fracture toughness, thermal conductivity, acoustic attenuation, and water absorption, and were combined using desirability functions. The RSM optimization was found at 18.97% fiber content with a desirability index of 0.673, predicting 25.89 MPa tensile strength, 14.23 $\mathrm{M}\mathrm{P}\mathrm{a}\sqrt{\mathrm{m}}$ fracture toughness, 0.08 W/m·K thermal conductivity, 20.49 dB acoustic attenuation, and 5.11% water absorption. Overlaying Weibull cumulative distribution functions with RSM desirability surfaces linked probabilistic reliability zones (90–95% survival) to the deterministic optimization peak. This integration establishes a unified framework for designing natural fiber composites by embedding reliability into multi-property optimization.