The number of types of wire welding that circulate on the market and the development of welding technology, especially related to current welding, create a lack of information related to the quality of welding results for several types of materials. This can cause the strength of the welding connection to not be maximized. Therefore, this study aims to optimize the use of three types of wire welding and three current welding on the strength of the welding connection in two types of material testing using the response surface methodology. Box-Behnken, coupled with the RSM and the desirability function, was used to optimize the strength of the welding connection of the wire types (RD-46, LB-52, and RB-26), and current welding (100, 130, and 160 A) against two types of material testing (IWF-150 and ASTM-A517-G70). The strength of the welding connection observed in response included the tensile strength, Charpy impact-absorbed energy, hardness values in the welding metal and the hardness values in the main metal. Optimization of the strength of the welding connection in this study recommends the application of wire types RB-26, current welding of 100 A, and ASTM-A517-G70 material testing with the highest desirability value of 71.6%. Optimization of tensile strength, Charpy impact-absorbed energy, hardness values in welding metal, and hardness values in main metal by applying this parameter are 575.64 MPa, 110.69 J, 216.75 (HV10) and 126.6 (HV10), respectively. The results proved that an appropriate welding connection strength could be achieved using wire welding types and current welding in material testing.

During the past two decades, there has been an increase on maritime freight traffic particularly in container flow. Thus, the Berth Allocation Problem (BAP) can be considered among the primary optimization problems encountered in port terminals. In this paper, we address the Dynamic Berth Allocation Problem (DBAP) in a conventional layout terminal which differs from the popular discrete layout terminal in that each berth can serve multiple vessels simultaneously if their total length is equal or less than the berth length. Then, a Modified Sailfish Optimizer (MSFO) meta-heuristic based on hunting sailfish behavior is developed as an alternative for solving this problem. Finally, computational experiments and comparisons are presented to show the efficiency of our method against other methods presented in the literature in one hand. We also discuss the productivity of a container terminal based on different scenarios which can happen.

In this study, several series of experiments on turning process of AISI 12L14 free cutting steel characterized by its self-lubrication and the high percentage of lead in its composition were performed to rate the influence of cutting conditions (Vc, f and ap) on the machining performance such as surface roughness, cutting force, cutting power and material removal rate. A computer generated optimal design of experiment based on the I-optimality criteria along with analysis of variance was created to study the characterizations in turning of this steel, and desirability function was utilized for the optimization. The global optimization, combined high surface quality and productivity with low cutting power consumption, gave 12 optimal setting points provided high desirability values. The obtained correlation for surface roughness, cutting force, material removal rate and cutting power were 99.4%, 95.5%, 99.7% and 94.3%, respectively.

Manufacturing, in general, creates a finished good from a set of simpler supplied parts. Supplied parts are installed into higher assemblies, higher assemblies move into even higher assemblies, and eventually this terminates at the finished good. Delays or variation during the manufacturing process ripple all the way to the finished good, possibly from different branches of the build and possibly magnifying any individual effect. There is extensive literature regarding Lean Manufacturing and it provides strategies and business philosophy to deal with variation, however it offers little in the way of quantitative analysis on the effects of that variation upon the whole. Digital Twins and discrete event simulations can and have been used to model the impact of variation in its totality. Various papers on Digital Twins have explored how to model manufacturing, but very little on generalized behavior. (i.e. How schedule slips at the subassemblies impacts the delivery dates / quantities at the finished good level). This paper explores the analytical quantitative effects of input/sales variation through the manufacturing cycle and the resultant effect on the finished good manufacturing schedule/cycle. We demonstrate that even small random variations/interruptions propagate up the build chain, get reduced in magnitude and end up producing predictable reductions in the average build rate of the final product. Additionally, it is shown that the more supplied parts that comprise a finished good the greater the expected reduction in average build rate.

With increase in differentiation and decreasing batch size of products, feeding the assembly line at regular intervals is considered to be a critical problem in today’s manufacturing sector. Yet no clear solution has been developed for this problem; therefore, the main focus of this research is to discuss the different aspects of line feeding, the latest trend in literature, and to propose an innovative method to support solving the problem. A discrete event simulation model is developed and a mathematical model based on particle swarm optimization is used to support the simulation. The hybrid model is finally applied to practical situations. Results show how different settings of kanban influence the performance of the assembly line feeding system. The biggest novelty item is certainly the recognition of the trade-off between kanban size and number of kanban and the importance of investigating its behaviour during the design of the system.

The Resource-Constrained Project Scheduling Problem (RCPSP) remains a critical area of study in project management, focusing on optimizing project schedules under constraints such as limited resources and task interdependencies. This review synthesizes advancements from 2016 to 2024, encompassing problem variants, optimization techniques, objectives, and real-world applications. Key developments include the evolution of hybrid metaheuristics, multi-objective optimization approaches, and the integration of stochastic models to enhance robustness against uncertainties. Furthermore, the application of machine learning and sustainability-driven models has expanded the practical scope of RCPSP in dynamic and complex environments. Challenges such as scalability, uncertainty management, and the need for practical implementations are addressed, with future directions emphasizing AI integration, decentralized scheduling, and real-time adaptive solutions. This study provides a comprehensive perspective on RCPSP, bridging theoretical research with practical implications for diverse industries.

The agricultural sector is directly affected by climate variables. The presence of climate variability causes a considerable risk to agricultural productivities. Thus, risk management is an alternative to reduce risks, including optimizing the allocation of farmland and choosing crop insurance for a specific planting date. The purpose of this study is to investigate the agricultural risk management through risk measure of climate variability using the Conditional Value-at-Risk (CVaR) in rice production. This paper investigated several possible considerations of agricultural insurance premiums based on losses climate index. We concluded that the climate index insurance policy is the best choice that farmers can choose for each planting date, the higher the significance value considered, the more the value of Value-at-Risk and Conditional Value-at-Risk.

This paper aims to develop a maintenance optimization model to maintain a high level of efficiency and reliability of the machinery. The methodological approach is based on preventive maintenance through the partial or total replacement of critical components. Although an intermediate intervention control, the focus is on a particular machine that has stopped several times, reducing its operational availability and resulting in a high cost of non-production. This study uses a Weibull model to analyze and optimize the correct maintenance process of the machinery considered. The failure data are then analyzed and scheduled. The final purpose is to standardize the operators' intervention procedures to reduce the time for the same interventions.

This study employs mathematical modeling to explore the effects of overtime option, rework, and discontinuous end-item issuing policy on the economic manufacturing quantity (EMQ) model. Conventional EMQ model assumed that all products fabricated are of good quality and are issued under continuous policy. In real world, however, nonconforming items are randomly produced, due to diverse unexpected factors in fabrication process. When finished items are to be distributed to outside locations, discontinuous multi-shipment policy is often used rather than continuous rule. In addition, with the intention of increasing short-term capacity or shortening replenishment cycle length to smooth the production planning, adopting overtime option can be an effective strategy. To cope with the aforementioned features in real production systems, this study incorporates overtime option, rework, and multi-shipment policy into the EMQ model and explores their joint effects on optimal lot size and number of shipments, and on other relevant system parameters. Mathematical modeling and Hessian matrix equations enable us to derive the optimal policies to the problem. Through the use of numerical example, the applicability of research result is exhibited and a variety of significant effects of these features on the proposed system are revealed.

This paper deals with retrial queues of the |MQ/M/c/∞|- type in which the rate of input flow depends on the number of sources of repeated calls and each call has only one retrial attempt. That is, if a call fails to enter the server facility at the retrial attempt, then it leaves the system without service. The existence conditions of stationary regime and the vector-matrix of the stationary probabilities of the service process are represented. This representation uses an approximation of the initial model by means of the truncated one and the directs passage to the limit. For these systems a threshold strategy for the rate of input flow is used. The multi-criterion optimization problem for finding optimal strategy of control is considered. The quality functionals of the optimization problem are represented through the stationary probabilities.