Optimal configuration of polygeneration plants under process failure, supply chain uncertainties, and emissions policies.

The COVID-19 pandemic exacerbated the erratic demand, supply, and prices of energy. It is unlikely that these effects would subside post-pandemic, especially with the pre-existing climate change crisis that also needs to be addressed. Emissions policies aimed at mitigating climate change place economic pressures on already disrupted energy systems, which could worsen energy insecurity. Configuring disrupted energy systems to build robustness to supply chain-related uncertainties and economic pressures of emissions policies are desired to simultaneously address these problems. To this end, this study introduces a robust mixed-integer linear program that simultaneously incorporates the abovementioned needs for configuring energy production systems. The proposed model is tested through a demonstrative case study that deals with a biomass-based polygeneration plant design problem. The scenario analysis and sensitivity test on the model concerning the case under consideration yields the following results: (1) setting ambitious target profits reduces the probability of the resulting plant configuration to achieving the set targets in the presence of supply chain-related uncertainties, while conservative targets promote the opposite; (2) the inoperability of the plant's process units reduces the robustness of optimal process configurations, and drastic configurations may be required to achieve targets despite the inoperability of process units; (3) a hybrid cap-and-trade and emissions tax policy yields approximately similar implications to the robustness of the resulting optimal plant configurations compared to a pure cap-and-trade policy, but the rate of decrease in robustness with respect to the initial emissions cap is lesser in the hybrid policy than in the pure cap-and-trade policy.

Synthesis of strategies in post-COVID-19 public sector supply chains under an intuitionistic fuzzy environment.

Entities in public sector supply chains (SCs) often operate independently despite having interdependent objectives. Such a fragmented operational design poses several problems magnified by the presence of necessary public health measures fueled by COVID-19. This work contributes to the domain literature by introducing an overarching framework for synthesizing strategies in public sector SCs. The underlying component is the translation of information from the upstream to the downstream entities of the SCs, which is carried out by a Kano-enhanced quality function deployment. The proposed framework introduces intuitionistic fuzzy (IF) decision maps with the aid of the full consistency method to incorporate inherent interrelationships among strategies in the translation agenda. Under an IF environment that better captures judgment uncertainties, an actual case study of a multi-level public sector SC motivated by a government-funded project under the COVID-19 pandemic is demonstrated in this work. Findings of the case suggest that the government prioritizes meeting all project objectives. This requirement is reflected in the downstream SC. The project planning entity focuses on creating an overarching plan of operations, material request entity on complying with government procurement protocols, and maintaining public health and safety in operations for the procurement entity. Results show the effective synthesis of strategies across the SC, ensuring SC integration and collaboration. The case study demonstrates that maintaining public health and safety is a significant component of post-COVID-19 public sector SCs. Several practical insights on the synthesis of public sector SC strategies are also provided in this work.

On K-Means Clustering with IVIF Datasets for Post-COVID-19 Recovery Efforts

The recovery efforts of the tourism and hospitality sector are compromised by the emergence of COVID-19 variants that can escape vaccines. Thus, maintaining non-pharmaceutical measures amidst massive vaccine rollouts is still relevant. The previous works which categorize tourist sites and restaurants according to the perceived degree of tourists’ and customers’ exposure to COVID-19 are deemed relevant for sectoral recovery. Due to the subjectivity of predetermining categories, along with the failure of capturing vagueness and uncertainty in the evaluation process, this work explores the use k-means clustering with dataset values expressed as interval-valued intuitionistic fuzzy sets. In addition, the proposed method allows for the incorporation of criteria (or attribute) weights into the dataset, often not considered in traditional k-means clustering but relevant in clustering problems with attributes having varying priorities. Two previously reported case studies were analyzed to demonstrate the proposed approach, and comparative and sensitivity analyses were performed. Results show that the priorities of the criteria in evaluating tourist sites remain the same. However, in evaluating restaurants, customers put emphasis on the physical characteristics of the restaurants. The proposed approach assigns 12, 15, and eight sites to the “low exposure”, “moderate exposure”, and “high exposure” cluster, respectively, each with distinct characteristics. On the other hand, 16 restaurants are assigned “low exposure”, 16 to “moderate exposure”, and eight to “high exposure” clusters, also with distinct characteristics. The characteristics described in the clusters offer meaningful insights for sectoral recovery efforts. Findings also show that the proposed approach is robust to small parameter changes. Although idiosyncrasies exist in the results of both case studies, considering the characteristics of the resulting clusters, tourists or customers could evaluate any tourist site or restaurant according to their perceived exposure to COVID-19.