This study presents a Reinforcement Learning (RL)-assisted Genetic Algorithm (GA) framework for the Multi-Objective Capacitated Vehicle Routing Problem with Time Windows (MOCVRPTW). In this problem, a set of homogeneous vehicles depart from a depot, visit all customers exactly once, and return back to the depot. The routes of the vehicles are constructed by considering three objectives: minimizing the total travel time, minimizing the number of vehicles, and maximizing the satisfaction obtained from the customers who are visited within their time windows. We propose using an NSGA-II-based approach that is assisted by Q-learning-based operator selection methods for this problem. Unlike traditional GAs that use fixed operators, the proposed approach enables learning-based selection of each operator (crossover and mutation) considering the current performance of solutions. We make tests with five different Q-learning-based operator selection strategies and compare their results to using fixed or randomly selected operators by nonparametric statistical methods. The results show that all Q-learning-based operator selection strategies outperform the fixed-operator approach, whereas the random selection strategy is outperformed by four. In addition, when the best operator for each state of solutions is found considering all solution approaches and used in NSGA-II throughout the algorithm, it consistently results in the best performance among all. Overall, the results demonstrate that the proposed RL-supported GA framework provides a competitive alternative in terms of Pareto-front solution quality for MOCVRPTW, and learning-based operator selection can be an effective mechanism for adaptively controlling the evolutionary search process.

This study investigates the mixed-model two-sided U-type assembly line balancing (MMTsUtALB) problem and proposes a hybrid solution approach based on Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). Unlike existing studies on two-sided U-type assembly line balancing (TsUtALB), the mixed-model structure is explicitly considered. In the proposed approach, GA is used to explore the solution space through evolutionary operators, while VNS is applied as a local improvement procedure to refine promising solutions and improve convergence. A problem-oriented priority rule–based encoding method is adopted to represent solutions, which are transformed into feasible two-sided U-type assembly line configurations using a decoding-based task assignment procedure. This structure allows the algorithm to balance diversification and intensification during the search process. The effectiveness of the GA–VNS hybrid algorithm is evaluated using benchmark test problems. Since this study represents the first attempt to solve the MMTsUtALB problem, the obtained results are compared with closely related mixed-model two-sided assembly line balancing studies. Computational results show that GA–VNS achieves competitive performance, particularly for larger instances, by reducing the number of stations and positions with lower computational times than SA and PSO.

Industrial Control Systems (ICS) are critical infrastructure components that manage essential services including power grids, water treatment facilities, and manufacturing processes. The Modbus TCP protocol, widely deployed in these systems, lacks inherent authentication mechanisms, rendering it vulnerable to replay, man-in-the-middle, and impersonation attacks. Existing authentication solutions based on public-key cryptography impose significant computational overhead on resource-constrained Programmable Logic Controllers (PLCs). In this paper, we propose a lightweight bidirectional authentication scheme for Modbus TCP utilizing enhanced Chebyshev chaotic maps with binary-exponentiation-based acceleration. We formally analyze the protocol in the Dolev-Yao model using ProVerif and evaluate its computational and communication costs on representative industrial computing platforms. The results show a total authentication cost of approximately 3.11 ms, including 1.17 ms on the slave side, with a communication overhead of 1280 bits across four messages. Based on our literature review, the contribution of this work is not the use of chaotic maps per se, but their adaptation to the Modbus TCP setting through a Modbus-oriented credential structure, low slave-side computational burden, and a deployment path compatible with existing protocol stacks. Direct validation on commercial PLC hardware remains an important next step.

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 addresses the daily scheduling and routing problem for home healthcare workers while incorporating lunch break requirements. The Home Healthcare Scheduling and Routing Problem is analysed alongside its common constraints, including patient and caregiver time windows, caregiver qualifications, and mandated breaks. To address this, four different variants of an effective Adaptive Large Neighbourhood Search (ALNS) algorithm were developed to provide high-quality solutions. The algorithms demonstrate significant efficiency, solving 30-patient instances optimally within an average of 12 seconds. For scenarios involving 100 patients, they maintained robust performance with a slight increase in computational time of about 54 seconds. Results indicate operational efficiency improvements of up to 36% through optimized travel routes and patient visitation schedules. To translate these findings into practice, a decision support system, the Home Healthcare Decision Support System (HHDSS), was designed to assist administrators by automating the complex task of scheduling and routing of caregivers. Tested using realistic patient data generated from Turkey, the system effectively allocates healthcare resources and improves responsiveness. Overall, the proposed framework shows strong potential as a valuable practical tool for improving the responsiveness and efficiency of home healthcare logistics.

This study addresses the drone delivery problem with a unique focus on minimizing total customer waiting times, considering the heterogeneous nature of drones and the setup times required for loading customer demands. Unlike traditional routing problems that prioritize cost and route optimization, this research emphasizes timely deliveries, which are critical in both commercial and humanitarian applications. The study introduces two mathematical models and four versions of the coot optimization algorithm, including three modified variants and one classical version. These algorithms incorporate new movement mechanisms, enhanced leader selection strategies, and adaptations of the regenerating strategy to efficiently solve the drone delivery problem. Computational experiments reveal that one modified coot optimization algorithm significantly outperforms the classical version, offering valuable insights into both coot optimization literature and the drone delivery problem. By emphasizing the importance of timely deliveries, this research provides effective solution strategies applicable to both commercial and humanitarian contexts.

This study investigates how effectively European Union Member States transform circular economy (CE) practices into social well-being, addressing a critical but underexplored dimension of circular transition research. While existing CE assessments primarily emphasize material flows, recycling performance, and resource productivity, far less is known about how circularity contributes to consumer-relevant and inclusive social outcomes. Using an output-oriented Data Envelopment Analysis (DEA) framework under variable returns to scale, the study evaluates the social efficiency of CE across 27 EU countries, drawing on indicators of circular material use, material intensity, international recycling flows, self-perceived health, social inclusion, and real income. The results reveal substantial heterogeneity in social efficiency, showing that higher levels of circular activity do not automatically translate into stronger well-being outcomes for consumers. Countries such as Denmark, Finland, Estonia, Ireland, Luxembourg, and Spain achieve full efficiency, indicating effective alignment between circularity practices, welfare structures, and socially inclusive outcomes. Conversely, several countries operate under decreasing returns to scale, suggesting that CE initiatives may dilute social effects and generate uneven distributional outcomes when implemented beyond their optimal capacity. Overall, the findings highlight the importance of policy coherence, institutional capability, and consumer-oriented governance in ensuring that circular economy strategies deliver inclusive and socially sustainable benefits.

Studies in the literature on flexible job-shop scheduling problems (FJSP) generally assume that one worker is assigned to each machine and that processing times are constant. However, in some industries, multiple workers with cooperation can process complex operations faster than one worker. If the possibility of completing jobs in a shorter time with worker cooperation is not taken into account, the opportunity to create more effective schedules may not be taken advantage of. Therefore, it is essential to consider the flexibility of collaboration between employees. However, to increase labor efficiency in businesses, jobs are also expected to be done with the minimum number of workers possible. This study considers the FJSP with both machine and number of workers dependent processing times. The objectives are minimizing the total tardiness and the total number of workers. A bi-objective mathematical model and an NSGA-II algorithm for large-sized problems have been proposed. The performance of the proposed solution approaches is demonstrated by using randomly generated test problems. For each problem, the most successful Pareto solution among the obtained solutions by the mathematical model and the NSGA-II algorithm was determined using the TOPSIS method. Furthermore, the effect of the total number of workers on the total tardiness is examined. The performance of proposed solution approaches, and when the worker number increases, the total tardiness of jobs can be reduced by an average of 75.88%, have been shown through comprehensive experimental studies.

This study addresses the minimization of total tardiness in a hybrid flow shop scheduling problem with sequence-dependent setup times and blocking constraints. Each production stage includes multiple machines, and there are no buffers between the stages. The setup time required to process a job depends on the previously processed job. Two mixed-integer linear programming models are developed to formulate the problem. Moreover, an iterative local search algorithm and hybrid genetic algorithms are proposed to have quality solutions with minimal computational efforts. Several computational tests are conducted to tune the heuristic parameters for better performance. Computational experiments are carried out to evaluate the performance of solution methodologies in terms of quality and time. The results indicate that while mixed-integer programming models can solve small-size problem instances, they are not capable of solving large-sized instances. However, the proposed heuristic algorithms find quality solutions for all instances in a very short time.

In recent years, various issues such as industrial waste and emissions of greenhouse gases have led to serious environmental pollution. Industrial managers nowadays need to regard cutting carbon emissions as one of their principal responsibilities in relation to the environment, as industry is a major source of carbon emissions. Two prominent regulatory approaches to reducing carbon emissions from operations are the carbon tax and the cap-and-trade system. The existing literature on inventory studies has often considered the market-expanding effects of greening efforts. Nevertheless, a number of additional factors exert influence on greening efforts, with the cost reduction effect representing a critical one. This paper develops an inventory system in which each time a lot of items is received, a proportion of items are found to be of imperfect quality; to identify these, the retailer carries out a 100% inspection of goods received. Following this inspection, the saleable items are added to the inventory in the warehouse in batches of equal size, rather than one by one, and the retailer allows backordering to meet demand. Carbon emissions are incurred at every stage, including ordering, purchasing, repairing, transporting, and holding, so advanced green technology is employed to reduce them. Imperfect products can be sold to a second-hand market or sent to a repair shop. The model discussed in this paper calculates, for both options, the most cost-effective lot size for orders, shortage quantity, scale of green investment and number of batches.