During the past few years, transportation of agricultural products is increasingly becoming a crucial problem in supply chain logistics. In this paper, we present a new mathematical formulation and two solution approaches for an intermodal transportation problem. The proposed bi-objective model is applied to the transportation of agricultural products from Morocco to Europe to minimise both the transportation cost either in the form of uni-modal or intermodal, as well as the maximal overtime to delivery products. The first solution approach is based on a non-dominated sorting genetic algorithm improved by a local search heuristic and the second one is the GRASP algorithm (Greedy Randomised Adaptive Search Procedure) with iterated local search heuristics. They are tested on theoretical and real case benchmark instances and compared with the standard NSGA-II. Results are analysed and the efficiency of algorithms is discussed using some performance metrics.

An existing robust three dimensional finite element based on the strain approach is presented. This element is implemented, for the first time in the commercial computer code ABAQUS, by using the subroutine (UEL), for the static and dynamic analysis of isotropic plates, whatever thin or thick. It is Baptised SBH8 (Strain Based Hexahedral with 8 nodes) and has the advantage to overcome the problems involved in numerical locking, when the thickness of the plate tends towards the smallest values. The implementation is justified by the capacities broader than offers this code, especially, in the free frequencies computation. The results obtained by the present element are better than those given by elements used by ABAQUS code and the other elements found in the literature, having the same number of nodes.

The Indonesian government currently focuses on high infrastructure development to prepare for connectivity between one city and another. The massive construction of the new government capital is also called the archipelago's capital. This condition impacts companies supporting construction projects, which are growing rapidly. This growth is supported by corporate governance and the cash-holding industry, which supports infrastructure projects for the company's financial performance. This research aims to determine the role of cash holding and corporate governance (board skill, independent director, managerial ownership) on the financial performance of infrastructure-supporting manufacturing companies in Indonesia. The sample used in this research was 83 manufacturing companies listed on the Indonesia Stock Exchange in the 2010-2020 period. This research uses panel data regression as a data analysis method. The results of this research show that managerial ownership significantly impacts increasing cash holding by maintaining the optimization of cash and cash equivalents. The company's managerial ownership cannot impact board skills because many companies still need to accommodate share ownership by management and board-skill-educated doctors. This condition also causes board size skills not to impact increasing cash holding in the company. Board skills have yet to be able to impact financial performance significantly. Managerial ownership and cash holding impact increasing return on assets, thereby increasing financial performance for manufacturing companies that support infrastructure projects. The independent director, as a variable moderator, still needs to be capable of moderating managerial ownership, board skill, and cash holding to improve financial performance. This condition is caused by data from several companies still needing more independent directors. Research provides a practical contribution to the owner as soon as possible, recruiting board members with doctoral education as competent board skills and making independent directors have a vital role in improving financial performance. The theoretical contribution to research is to enrich the theory of corporate governance and financial performance.

The editors of Engineering Solid Mechanics retract this article [1] due to severe similarity between the paper and the one published in [2].

This paper proposes a metaheuristic algorithm to solve the Multi-Depot Vehicle Routing Problem with a Heterogeneous Fleet (MDHFVRP). The problem consists of determining the customers and the vehicles to be assigned to each used depot and the routes to be performed to fulfill the demands of a set of customers. The objective is to minimize the sum of the fixed cost associated with the used vehicles and of the variable traveling costs related to the performed routes. The proposed approach is based on a modified genetic algorithm, which generates an initial population with heuristic solutions obtained from the well-known (LKH) heuristic algorithm for the TSP together with the solution of a mathematical model for the shortest path problem. In addition, two recombination methods and a mutation operator are considered. Computational experiments on benchmark instances show that the proposed algorithm can obtain high-quality solutions within short computing times.

To effectively manage the challenges associated with meeting schedules, costs, and quality requirements in construction projects, it is crucial to address potential risk factors. Despite extensive research on risk management in construction, a significant gap persists concerning the uniformity of risk factors and their effects across various construction project types, geographical locations, and cultural contexts. This research presents a structured three-step approach to risk management, covering risk identification, analysis, and response. The methodology is applied to both the General Construction (GC) and Banking Construction (BC) sectors in Egypt, involving industry professionals through surveys. Quantitative analysis using the Relative Importance Index (RII) and Fuzzy-set theory gauges the influence of each risk factor, while Spearman Ranked Correlation tests differentiate risk profiles between sectors. A comparative analysis highlights Egypt-specific risk factors versus regional or global factors. Key risk factors with high centrality scores are identified, and optimal risk reduction strategies are selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The study reveals significant sector-specific risk differences and highlights the need for tailored risk management strategies. Key contributions include identifying vital risk factors, comparing them globally and regionally, and proposing effective mitigation strategies to enhance project timelines, costs, and quality.

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 study investigates the impact of different leadership styles on project success, specifically examining the mediating role of team collaboration. This study utilized a diverse sample of 202 respondents representing five distinct national contexts: Jordan, Saudi Arabia, China, Australia, and the United Arab Emirates. PLS-SEM were employed to analyze the relationships between transformational leadership, transactional leadership, laissez-faire leadership, team collaboration, and project success. Our findings reveal that both transformational and transactional leadership styles are positively associated with project success. Conversely, laissez-faire leadership demonstrated a negative relationship with team collaboration but did not directly predict project success. Importantly, team collaboration was found to mediate the relationship between both transformational leadership and project success, as well as between transactional leadership and project success. However, the hypothesized mediating role of team collaboration between laissez-faire leadership and project success was not supported. These findings underscore the critical role of leadership style and team collaboration in achieving project success, offering valuable insights for organizations and project managers seeking to optimize leadership practices and foster collaborative work environments.

Particle swarm optimization has been established to be one of the efficient algorithms for finding solutions for continuous optimization problems. The discretized form of particle swarm optimization, known as the discrete particle swarm optimization is an efficient tool for solving combinatorial optimization problems and other problems involving discrete variables. In this paper, a revised version of the discrete particle swarm optimization algorithm is proposed for solving Quadratic Assignment Problems (QAP). Instead of using the general velocity and position update procedures in particle swarm optimization algorithms, four different possible positions are found out for each particle and the best among them is accepted as the updated position. The algorithm is applied to solve some benchmark instances of QAP taken from QAP Library and the results show minute deviations from best-known solutions.

This study proposes a simheuristic that hybridizes NSGA-II with Monte Carlo simulation to address a stochastic flexible flow shop problem featuring stochastic machine breakdowns. In real-world scenarios, machine breakdowns frequently occur, resulting in negative impacts such as time loss, late deliveries, decreased productivity, and order accumulation. Therefore, this study considers the times between failures and times to repair as stochastic parameters. Multiple objectives are concurrently addressed, including expected makespan, expected tardy jobs, and the standard deviation of tardy jobs. A mathematical model was formulated for the deterministic version of the problem and separately solved for the minimization of tardy jobs and the minimization of makespan in small instances. Subsequently, the proposed simheuristic was executed for both small and large instances. The results demonstrate that the NSGA-II simheuristic enhances outcomes across all objective functions compared to the simulation of optimal solutions provided by the mathematical models in small instances, yielding average GAPs of -16.64%, -21.87%, and -53.33% for expected tardy jobs, expected makespan, and standard deviation of tardy jobs, respectively. Furthermore, the simheuristic outperforms the simulation of solutions given by seven dispatching rules, showcasing average improvements of 48.01%, 48.18%, and 95.63% for the same objectives, respectively.