The sustainability of service and manufacturing operations rely heavily on the availability of equipment and assets. High availability of assets can be achieved with effective maintenance strategies. In this direction, we study a multi-skilled workforce planning problem to establish a resilient maintenance service network for high-value assets. We improve the efficiency of the maintenance network by optimising the workforce capacity in repair shops and achieving workforce heterogeneity by cross-training. As a solution strategy, we develop a two-stage iterative heuristic algorithm. At the first stage, the set of all feasible cross-training policies is effectively and systematically searched via a state-of-art multi-thread simulated annealing (MTSA) metaheuristic to find a policy(ies) that achieves the minimum cost. Further, the developed MTSA algorithm is enhanced with the multi-neighbourhood feature to escape from local optimality and implemented via parallel programming techniques. In the second stage, workforce capacity and spare parts inventory levels are optimised for the cross-training policy found at the first stage by a queuing approximation and a greedy heuristic. The MTSA obtains the lowest cost in 91 cases out of 128 compared to genetic algorithm (GA), variable neighbourhood search (VNS), an improved single-thread simulated annealing algorithm (SA) and integer programming-based clustering (IPBC) algorithms.

This study presents a novel approach to assess countries’ relative logistics performance differentials and identifies the underlying drivers using a data envelopment analysis (DEA) approach. The findings offer strategic insights and provide nations with valuable guidance in navigating the dynamic landscape of global logistics competitiveness. Recognizing the pivotal role of logistics in fostering economic activity and contributing to sustainability, we utilize the Logistics Performance Index (LPI) within the DEA model to gauge efficiencies at the country level and scrutinize influencing factors. Our findings indicate that international shipment practices in the last decade, as well as tracking and tracing recently, no longer contribute to the competitiveness of the countries, despite their potential for improvement. The overall change in logistics efficiency during the last decade remained relatively marginal, primarily driven by the frontier-shift effect. However, notable disparities among the country performance groups, particularly in adopting technological advancements, are manifested in the frontier-shift effect. Countries with lower logistics performance should prioritize addressing structural challenges related to customs, infrastructure, quality of logistics services, and timeliness. Medium-performance countries, while benefiting from a focus on customs, tracking and tracing, and timeliness in the early 2010s, are advised to align their efforts with high-performance countries by emphasizing managerial aspects in recent times.

The predictability of wind energy is crucial due to the uncertain and intermittent features of wind energy. This study proposes wind speed forecasting models, which employ time series clustering approaches and deep learning methods. The deep learning (LSTM) model utilizes the preprocessed data as input and returns data features. The Dirichlet mixture model and dynamic time-warping method cluster the time-series data features and then deep learning in forecasting. Particularly, the Dirichlet mixture model and dynamic warping method cluster the time-series data features. Next, the deep learning models use the entire (global) and clustered (local) data to capture the long-term and short-term patterns, respectively. Furthermore, an ensemble model is obtained by integrating the global model and local model results to exploit the advantages of both models. Our models are tested on four different wind data obtained from locations in Turkey with different wind regimes and geographical aspects. The numerical results indicate that the proposed ensemble models achieve the best accuracy compared to the deep learning method (LSTM). The results imply that the feature clustering approach accommodates a promising framework in forecasting.