This paper addresses the Permutation Flow Shop Scheduling Problem with Heterogeneous Workers (PFSP-HW), an extension of the classical problem in which processing times depend not only on the job and machine, but also on the assigned worker. This variant better reflects practical environments where worker capabilities and proficiencies vary significantly. We propose a new Iterated Greedy (IG) heuristic adapted to handle worker heterogeneity. The IG heuristic combines destruction and reconstruction mechanisms with a local search procedure tailored for the problem. We develop two versions of the proposed algorithm and compare them with adapted state-of-the-art heuristics and metaheuristics from related problems. The algorithms were tested on a large benchmark set comprising 360 instances generated under various shop configurations. The suggested IG heuristics surpass current approaches in terms of solution quality and execution time, as determined by computational and statistical evaluations, making them reliable and efficient tools for solving the PFSP-HW.

This study proposes an adaptive local search heuristic to solve a real-world large-scale parallel machine scheduling problem with release dates and setup times, aiming to minimize total tardiness. The complexity of the problem stems from the need to synchronize machine availability, job release dates, and setup durations, which are crucial for meeting production deadlines and ensuring operational efficiency. Traditional optimization approaches often struggle to deliver timely solutions for large-scale industrial applications. Our heuristic method effectively explores the search space to identify schedules that significantly reduce total tardiness while adhering to the constraints of the production system. The approach was tested using real production data, and the results indicate that the heuristic consistently generated high-quality solutions within short computational times. The approach proved viable and efficient, offering a practical tool for improving scheduling performance and minimizing total tardiness in industries with similar operational constraints.

In this paper, we assess the performance of five metaheuristics for the single-machine under different time windows and sequence-dependent setup times, optimizing the total weighted earliness and tardiness: Iterated Greedy Algorithm (IGA), Artificial Bee Colony (ABC), Bat Algorithm (BA), Particle Swarm Optimization (PSO), and Fireworks Algorithm (FWA). Many real-world situations require delivery in a specific time interval, analogous to optimization problems with a time window in the Just-in-Time philosophy. Also, several practical situations require different time intervals to prepare the environment to process the activities depending on what was immediately done and what will be executed next, characterizing the sequence-dependent setup problem. These cases are common among operations handling materials of diverse colors, different temperatures, or high demands on sterilization requirements. Statistical results highlight the superiority of the FWA, with the best results in all the problem dimensions analyzed, especially in the larger-size instances, with only 1.23% average relative deviation against 61.18% of the known Iterated Greedy algorithm.

Sugarcane must be harvested at the time of maturity, known as the Period of Industrial Utilization (PIU). The PIU analysis is expensive and is performed in a laboratory by measuring an index defined as Total Recoverable Sugar (TRS). The fact is that harvesting is the most expensive stage in sugarcane production and decision-making in this segment depends on TRS level estimates. However, forecasting models aimed at replacing laboratory analyses do not meet the reality regarding the estimation of the TRS index. There is a great demand for tools capable of estimating the index and/or the factors that affect TRS. In this context, this article presents a case study whose objective is to apply statistical models to estimate the TRS index in the sugarcane production of a mill in the interior of São Paulo, Brazil. Variance Analysis (ANOVA) with one classification and Multiple Linear Regression (MLR) models are applied by using Minitab®. These models are based on covariates related to the TRS index to estimate the productivity of 48,151 plots from the 2016/2017 to the 2022/2023 harvests. It is shown that the adjusted models identify the most important covariates (5% significance level) that affect productivity and the TRS index. The accuracy is satisfactory for all covariates of the adjusted MLR model and for the coefficients that measure the proportion of data variability for productivity (76%) and the TRS index (55%). This article brings important contributions to the sugar and ethanol industries worldwide and in Brazil.

We approach an open dimension problem, in specific, a two-dimensional strip packing problem variation found in sheet metal laser cutting, where rectangular items must be cut from a metal sheet, aiming to increase the packing layout added value. Therefore, this research objective is to analyze the packing layout added value with raw material reuse and practical constraints found in real-life laser cutting operations. The Best Fit Decreasing Height heuristic was modified to reuse raw material and calculate the packing layout added value, being compared with three construction heuristics using a set of literature and generated instances. We show the modified best fit decreasing height heuristic obtained better results when compared to the selected heuristics, with a high sheet metal utilization by the original instance rectangles and efficient raw material reuse. Thus, for sheet metal laser cutting practical operations, the modified best fit decreasing height heuristic is suitable for generating good packing layouts, resulting in industrial benefits including cost savings, increased productivity, greater competitiveness, and sustainability. Approaching raw material reuse increased the packing layout added value in most solutions found, and should be considered in real-life laser cutting operations. However, prioritizing only raw material reuse is not ideal, since a high number of additional rectangles can cause manufacturing wastes including overproduction, stock, and extra processing.

The objective of this study is to propose an instrument for identifying the level of maturity of purchasing and supply management in relation to Strategic Sourcing. Based on an initial model, a maturity measurement instrument was applied to a case study company in a chemical industry company in Vale dos Sinos, in order to validate the tool. Based on this, a survey was carried out with the aim of capturing the perception of purchasing professionals on the topic. By sending a questionnaire to 250 professionals in the purchasing and supply management area, a return rate of 28% (70 respondents) was obtained from this sample. For the data obtained, structural equation modeling (SEM) was carried out using Smart PLS 3.0® software. With the modeling, a load of 0.428 was obtained for the suppliers construct, 0.555 for the inventory construct and 0.158 for the indicators construct. Overall, the satisfactory constructs for this sample positively impact 67.8% on the Strategic Sourcing construct. Therefore, for this specific sample, the constructs referring to suppliers, inventory and indicators jointly impact Strategic Sourcing. The research in question proposed a model for a better understanding of Strategic Sourcing structures and aims to contribute to practical and organizational contexts regarding the maturity of this topic among purchasing professionals. This understanding aims to provide readers with greater empirical and academic understanding of this emerging and important topic for business and company competitiveness.

There are few studies addressing the mediating effect of readiness in supply chains in developing countries. We use the theory of dynamic capabilities to investigate the impact of integration (INT) on readiness (REA) and on innovative product performance (IPP) and to examine the mediating effect of REA on INT and IPP. We conducted a survey with a sample of 213 supply chain (SC) managers of machinery and equipment for transport and lifting heavy loads in Brazil. To this end, we used structural equation modeling to test the hypotheses and PROCESS macro to confirm the indirect effect. The empirical results indicate a significant effect between INT and REA and REA and IPP. However, the indirect impact of REA was compromised. The literature review demonstrates that the microfoundations of dynamic capabilities (Sensing, Seizing and Reconfiguration) strengthen supply chain links, particularly between INT, REA and IPP. This article helps managers understand the functioning of SC routines and operations. To this end, they should develop strategies that strengthen SC ties with INT, REA and IPP to face future crises. This paper advances the findings on integration, readiness, and innovative product performance in an SC. The findings provide insightful implications for managers to improve their strategies. In doing so, we theorize how the microfoundations of dynamic capabilities support the efficiency of supply chain operations.

Offshore moorings play a key role in the stability of maritime structures such as floating oil production platforms. This study focuses on polyamide as a promising alternative for offshore moorings due to its mechanical characteristics and degradation resistance. Initially, characterization tests are carried out to determine the polyamide’s linear density, the rupture strength, and the linear tenacity. Following, impact tests are carried out on polyamide samples, using a methodology based on a free fall mass effect. The analysis of the results includes statistical data filtering and the parameterization of curves to correlate the number of impact cycles to the applied load. Furthermore, the effect of the accelerated hydrolysis on polyamide is investigated, subjecting the samples to an aging process in fresh water for 180 days at 65°C. The results are analyzed to evaluate the effect of the hydrolysis on the resistance to impact cycles. It was concluded that, in general, polyamide shows a promising ability for energy absorption and impact resistance, with the potential for its use in offshore moorings. However, it is necessary to consider the hydrolysis effect on the degradation of its mechanical properties over time. This study contributes to the advancement of knowledge about the performance of polyamide in marine environments, providing important insights for the design and maintenance of offshore mooring systems, and also contemplating an exploratory study for its material's behavior.

This paper aims at presenting a customer order scheduling environment in which the setup times are explicit and depend on the production sequence. The considered objective function is the total tardiness minimization. Since the variant under study is NP-hard, we propose a mixed-integer linear programming (MILP) model, an adaptation of the Order-Scheduling Modified Due-Date heuristic (OMDD) (referred to as Order-Scheduling Modified Due-Date Setup (OMMD-S)), an adaptation of the Framinan and Perez-Gonzalez heuristic (FP) (hereinafter referred to as Framinan and Perez-Gonzalez Setup (FP-S)), a matheuristic with Same Permutation in All Machines (SPAM), and the hybrid matheuristic SPAM-SJPO based on Job-Position Oscillation (JPO). The algorithms under comparison have been compared on an extensive benchmark of randomly generated test instances, considering two performance measures: Relative Deviation Index (RDI) and Success Rate (SR). For the small-size evaluated instances, the SPAM is the most efficient algorithm, presenting the better values of RDI and SR. For the large-size evaluated instances, the hybrid matheuristic SPAM-JPO and MILP model are the most efficient methods.

In this paper it is presented an improvement of the branch and bound algorithm for the permutation flow shop problem with blocking-in-process and setup times with the objective of minimizing the total flow time and tardiness, which is known to be NP-Hard when there are two or more machines involved. With that objective in mind, a new machine-based lower bound that exploits some structural properties of the problem. A database with 27 classes of problems, varying in number of jobs (n) and number of machines (m) was used to perform the computational experiments. Results show that the algorithm can deal with most of the problems with less than 20 jobs in less than one hour. Thus, the method proposed in this work can solve the scheduling of many applications in manufacturing environments with limited buffers and separated setup times.