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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.

The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers and storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on maximizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. This entails the need to adopt an effective scheduling algorithm in order to minimize task completion time (makespan). Although workflow scheduling has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this paper, we propose the LPSO, a novel algorithm for workflow scheduling problem that is based on the Particle Swarm Optimization method. Our proposed algorithm not only ensures a fast convergence but also prevents getting trapped in local extrema. We ran realistic scenarios using CloudSim and found that LPSO is superior to previously proposed algorithms and noticed that the deviation between the solution found by LPSO and the optimal solution is negligible.

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.

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