ABSTRACT
This study develops a hybrid model to investigate the factors affecting transnational e-commerce supply chain resilience (TNSCRE) by integrating the Entropy Weight Method (EWM), Simple Additive Weighting (SAW), and Interpretive Structural Modeling (ISM). The study identifies 36 critical factors categorized under supply chain adaptability, supply chain efficiency, and supply chain evolution, and five criteria are used to rank these factors. The EWM is used to calculate the relative weights of the criteria, and the SAW method is used to rank the factors based on their weighted scores. The ISM is then used to evaluate the interrelationships among the key factors. The research highlights the significance of several factors, such as the speed of supply chain disruption recovery, interactive collaboration, and response time to supply chain disruption. Sensitivity analysis was performed to assess the robustness of the findings. Finally, a SWOT analysis is conducted to develop a strategic action plan for addressing these significant factors. The study provides a comprehensive understanding of the factors that impact TNSCRE from the perspective of multiple stakeholders. The findings can help e-commerce business owners improve their existing supply chain resilience and achieve sustainable growth in the context of globalization.
ABSTRACT
In real-world construction sites, On-Site Workshops (OSW) are installed to accelerate construction activities and facilitate the material handling process. These temporary OSWs are cost-effective, leading to decreasing the material handling cost and project makespan, which indicates their important role as a part of a construction project. However, considering the OSW, which has not been addressed in the project scheduling problems, requires the construction site to have a space capacity constraint while considering the workshop size, availability level, and other project-related constraints. In the present work, by considering the OSWs, a real construction project scheduling problem is studied as a Multi-Mode On-Site Workshop Investment Problem with Tardiness (MOSWIPT) while finding the installation/dismantling time of the OSWs. Two new (linear) mathematical programming models are proposed for MOSWIPT. Next, due to the NP-hardness of the problem, an enhanced Genetic Algorithm (GA)-based metaheuristic with efficient problem-specific improvement rules as local search and effective crossover and mutation operators is proposed. Computational experiments show that the proposed method has solved most of the instances of the addressed problem to optimality and outperformed the existing metaheuristics, e.g., Simulated Annealing (SA) and Particle Swarm Optimization (PSO). Finally, conclusions and suggestions for future studies are stated.
