In the context of the industrial revolution 4.0 that is firmly taking place globally, the digital transformation process is considered a revolution that changes the operating and business model. In Vietnam, logistics is one of eight areas that need to be prioritized in the national conversion program to 2025, the orientation towards 2030 by the Prime Minister under Decision No. 749/QĐ-TTg. Digital transformation is an essential solution that helps businesses improve their competitiveness, increase labor productivity, sustainably develop businesses, and integrate with the global economy. This study analyzes the influencing factors of digital transformation and the situation in Vietnam’s logistics enterprises. This paper used a qualitative research method carried out through direct interviews with 20 digital transformation experts in the field of logistics about the current situation, adjusting models and scales, and discussing research results. Quantitative research was conducted online through 258 survey questionnaires of logistics enterprises in the country. The authors performed descriptive statistics, tested the scale, analyzed EFA using SPSS software, and tested the research hypotheses. Research results indicate that five factors—managers, digital transformation human resources, information technology, investment cost, and support services for digital transformation—affect the digital conversion activity in logistics enterprises. Afterward, the research team proposed solutions to promote this operation in Vietnam’s logistics enterprises, contributing to implementing critical tasks of the government’s digital transformation.

This paper defines and introduces the formulation of the Real-RCPSP (Real-Resource-Constrained Project Scheduling Problem), a new variant of the MS-RCPSP (Multiskill Resource-Constrained Project Scheduling Problem). Real-RCPSP is an optimization problem that has been attracting widespread interest from the research community in recent years. Real-RCPSP has become a critical issue in many fields such as resource allocation to perform tasks in Edge Computing or arranging robots at industrial production lines at factories and IoT systems. Compared to the MS-RCPSP, the Real-RCPSP is supplemented with assumptions about the execution time of the task, so it is more realistic. The previous algorithms for solving the MS-RCPSP have only been verified on simulation data, so their results are not completely convincing. In addition, those algorithms are designed only to solve the MS-RCPSP, so they are not completely suitable for solving the new Real-RCPSP. Inspired by the Cuckoo Search approach, this literature proposes an evolutionary algorithm that uses the function Reallocate for fast convergence to the global extremum. In order to verify the proposed algorithm, the experiments were conducted on two datasets: (i) the iMOPSE simulation dataset that previous studies had used and (ii) the actual TNG dataset collected from the textile company TNG. Experimental results on the iMOPSE simulation dataset show that the proposed algorithm achieves better solution quality than the existing algorithms, while the experimental results on the TNG dataset have proved that the proposed algorithm decreases the execution time of current production lines at the TNG company.

The paper proposed a new algorithm to solve the Multiskill Resource-Constrained Project Scheduling Problem (MS-RCPSP), a combinational optimization problem proved in NP-Hard classification, so it cannot get an optimal solution in polynomial time. The NP-Hard problems can be solved using metaheuristic methods to evolve the population over many generations, thereby finding approximate solutions. However, most metaheuristics have a weakness that can be dropping into local extreme after a number of evolution generations. The new algorithm proposed in this paper will resolve that by detecting local extremes and escaping that by moving the population to new space. That is executed using the Migration technique combined with the Particle Swarm Optimization (PSO) method. The new algorithm is called M-PSO. The experiments were conducted with the iMOPSE benchmark dataset and showed that the M-PSO was more practical than the early algorithms.

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is an NP-Hard problem that involves scheduling activities while accounting for resource and technical constraints. This paper aims to present a novel hybrid algorithm called MEMINV, which combines the Memetic algorithm with the Inverse method to tackle the MS-RCPSP problem. The proposed algorithm utilizes the inverse method to identify local extremes and then relocates the population to explore new solution spaces for further evolution. The MEMINV algorithm is evaluated on the iMOPSE benchmark dataset, and the results demonstrate that it outperforms. The solution of the MS-RCPSP problem using the MEMINV algorithm is a schedule that can be used for intelligent production planning in various industrial production fields instead of manual planning.

Applying IoT systems in industrial production allows data collection directly from production lines and factories. These data are aggregated, analyzed, and converted into reports to support manufacturers. Business managers can quickly and easily grasp the situation, making timely and effective management decisions. In industrial sewing, IoT applications collect production data from sewing lines, especially from industrial sewing machines, and transmit that data to cloud-based systems. This allows businesses to analyze production situations, thereby improving management capacity. This article explores the implementation of IoT applications at industrial sewing enterprises, focusing on data collection during the production process and proposing a data structure to integrate this information into the company’s MIS system enterprise. In addition, the research also considers applying the Real-RCPSP problem to support businesses in planning automatic production operations.

In this paper, an optimization approach in solving Multi-Skill Resource Constrained Project Scheduling Problem (MS-RCPSP) has been presented. We proposed an evolutionary algorithm, which called CSM, based on Cuckoo Search algorithm. Proposed algorithm not only ensures fast convergence but also avoid program getting trapped in local extremum. The experimental results show that CSM is superior to its predecessor algorithms. Moreover, the deviation of CSM’s solution from the best one is very small.