This paper provides a comprehensive review of Appointment Scheduling (AS) in healthcare service while we propose appointment scheduling problems and various applications and solution approaches in healthcare systems. For this purpose, more than 150 scientific papers are critically reviewed. The literature and the articles are categorized based on several problem specifications, i.e., the flow of patients, patient preferences, and random arrival time and service. Several methods have been proposed to shorten the patient waiting time resulting in the shortest idle times in healthcare centers. Among existing modeling such as simulation models, mathematical optimization techniques, Markov chain, and artificial intelligence are the most practical approaches to optimizing or improving patient satisfaction in healthcare centers. In this study, various criteria are selected for structuring the recent literature dealing with outpatient scheduling problems at the strategic, tactical, or operational levels. Based on the review papers, some new overviews, problem settings, and hybrid modeling approaches are highlighted.

For the first time, the critical worldwide problem of prioritizing zero-emission last-mile delivery (LMD) solutions for sustainable city logistics is addressed and solved in this study. It not only aims to help city logistics companies sustainably decarbonize urban freight distribution but also provide valuable practical guidelines. To evaluate zero-emission LMD solutions, this study presents a novel multi-criteria group decision-making methodology with dual hesitant fuzzy (DHF) sets. Firstly, we propose some improved operations on DHF elements and investigate their vital properties. Secondly, based on these operations, we develop DHF improved weighted averaging operator to overcome the drawbacks of the existing operators on DHF sets. Thirdly, for measuring the weights of criteria, a new model called the cross-entropy-based optimization model (CEBOM) is developed. Fourthly, for the rational aggregation of the preferences, we formulate a new method namely score-based double normalized measurement alternatives and ranking according to the compromise solution (SDNMARCOS). The proposed DNMARCOS method couples the linear and vector normalization techniques. It is composed of the complete compensatory model and the incomplete compensatory model. Thus, SDNMARCOS is more robust compared to the available state-of-the-art approaches. To exhibit the applicability of the proposed DHF-CEBOM-SDNMARCOS methodology in real-world settings, a case study for one of the largest Austrian logistics companies in Serbia is provided. The research findings show that electric light commercial vehicles are the best LMD solution. Also, it is recommended to consider electric cargo bikes as a viable mid-term solution. The superiority of the introduced methodology is demonstrated through the comparative investigation.