Operating room (OR) surgery scheduling is a challenging combinatorial optimization problem that determines the operation start time of every surgery to be performed in different surgical groups, as well as the resources assigned to each surgery over a schedule period. One of the main challenges in health care systems is to deliver the highest quality of care at the lowest cost. In real-life situations, there is significant uncertainty in several of the activities involved in the delivery of surgical care, including the duration of the surgical procedures. This paper tackles the operating room surgery scheduling problem with uncertain surgery durations, where uncertainty in surgery durations is represented by means of fuzzy numbers. The problem can be considered as a Fuzzy Flexible Job-shop Scheduling Problem (FFJSP) due to similarities between operating room surgery scheduling with uncertain surgery durations and a multi-resource constraint flexible job-shop scheduling problem with uncertain processing times. This research handles both the advanced and allocation scheduling problems simultaneously and provides an Ant Colony Optimization (ACO) metaheuristic algorithm which utilized a two-level ant graph to integrate sequencing jobs and allocating resources at the same time. To assess the performance of the proposed method, a computational study on five test surgery cases is presented, considering both deterministic and fuzzy surgery durations to enhance the significance of the study. The results of this experiment demonstrated the effectiveness of the proposed metaheuristic algorithm.

Internet of Things (IoT) as a new technological revolution has been proposed recently wherein the things are connected over the Internet. Because of the inherent characteristics of IoT for storage of data at untrusted and heterogeneous hosts, data replication across large geographic distances for efficient data management is unavoidable. The selection of appropriate replication things in the IoT, which reduces response time and cost is one of the most important issues of data management. Since this problem is an NP‐hard problem, classic approaches are not efficient to solve this issue, and evolutionary algorithm such as ant colony optimisation (ACO) and genetic algorithm (GA) seems to be very useful. This study offers a method based on a combination of ACO and a GA to solve this problem. In the proposed method, the ACO has been used to create diversity, and afterwards, the GA is performed to provide a full search over the search space. The obtained results have shown the better performance of the proposed method in comparison with ACO, the High‐QoS First‐Replication (HQFR), and the Response Time‐based Replica Management algorithms with regard to waiting time. In addition, the obtained results have revealed the better performance of the hybrid method in comparison with HQFR and the Dynamic Cost‐aware Re‐replication and Re‐balancing Strategy.

Purpose: BPR is “the central reconsideration and thorough restructuring of business procedures to enhance the critical and contemporary aspects of performance like the expense, quality, service, and speed”. Also, as it's a key factor for guaranteeing businesses' achievement, however, the profound discussion about the BPR is very rare as far as we know. We need more studies regarding the subject due to the absence of BPR works in the logistics industry. Hence, this study investigates the Systematic Literature Review (SLR) of BPR for logistics companies, leading the managers and writers active in BPR, and making them aware of the present, past and future trends in this discipline.Design/methodology/approach: As BPR is a necessary foundation for ensuring enterprise systems' success, this study will systematically investigate the BPR in logistics. The proposed BPR in logistics research classification framework is based on a comprehensive literature review, which concentrates on peer-reviewed journal papers published until 2019. A total of 22 academic sources have been retrieved and analyzed in terms of research purpose and nature, the employed method, theoretical approach and analysis level.Findings: The findings of this paper showed that BPR companies outperform the non-BPR ones regarding information computing, technology uses, organizational architecture, coordination and all key logistics procedures. The results can motivate non-BPR logistics organizations to reassess the feasibility of these plans.Research limitations/implications: This paper provides an overview of BPR to young academics. Also, it identifies some distinct research gaps that could be worth studying. However, this paper may be restricted by choice of dimensions and the selection of relevant articles. In turn, researchers need to become more innovative in terms of their research techniques when examining BPR implementation.Practical implications: This paper guides researchers and practitioners to insight into published research work and their findings. The findings are valuable to logistics firms in an emerging market, as logistics resources may affect logistics service costs and quality. Also, it discusses indications for future research in BPR. It emphasizes the need to bridge the lacuna between BPR theory and evidence-based practice. Furthermore, it provides a better understanding of BPR implementation, which can be applied toward overcoming operational difficulties during the implementation process.Originality/value: This paper fulfills an identified need for a comprehensive classification framework of BPR in logistics studies. We can consider it as the first-evaluated methodically gathered workaround BPR in logistics. It essentially provides both academics and practitioners with a conceptual map of existing BPR in logistics research and points out future research opportunities.