A Simulation-Based Optimization Approach for Integrated Port Resource Allocation Problem

Todays, due to the rapid increase in shipping volumes, the container terminals are faced with the challenge to cope with these increasing demands. To handle this challenge, it is crucial to use flexible and efficient optimization approach in order to decrease operating cost. In this paper, a simulation-based optimization approach is proposed to construct a near-optimal berth allocation plan integrated with a plan for tug assignment and for resolution of the quay crane re-allocation problem. The research challenges involve dealing with the uncertainty in arrival times of vessels as well as tidal variations. The effectiveness of the proposed evolutionary algorithm is tested on RAJAEE Port as a real case. According to the simulation result, it can be concluded that the objective function value is affected significantly by the arrival disruptions. The result also demonstrates the effectiveness of the proposed simulation-based optimization approach. </span

Relationship Between Microbial Growth and Hydraulic Properties at the Sub-Pore Scale

Accumulation of microbial biomass and its influence on porous media flow were investigated under saturated flow conditions. Microfluidic experiments were performed with model organisms, and their accumulation was observed in the pore space and on the sub-pore scale. Time-lapse optical imaging revealed different modes of biomass accumulation through primary colonization, secondary growth, and filtration events, showing the formation of preferential flow pathways in the flooding domain as result of the increasing interstitial velocity. Navier–Stokes–Brinkmann flow simulations were performed on the segmented images—a digital-twin approach—considering locally accumulated biomass as impermeable or permeable based on optical biomass density. By comparing simulation results and the experimental responses, it was shown that accumulated biomass can be considered as a permeable medium. The average intra-biomass permeability was determined to be 500 ± 200 mD, which is more than a factor of 10 larger than previously assumed in modeling studies. These findings have substantial consequences: (1) a remaining interstitial permeability, as a result of the observed channel formation and the intra-biomass permeability, and (2) a potential advective nutrient supply, which can be considered more efficient than a purely diffusive supply. The second point may lead to higher metabolic activity and substrate conversion rates which is of particular interest for geobiotechnological applications.publishedVersio

A hybridisation of adaptive variable neighbourhood search and large neighbourhood search: Application to the vehicle routing problem

In this paper, an adaptive variable neighbourhood search (AVNS) algorithm that incorporates large neighbourhood search (LNS) as a diversification strategy is proposed and applied to the capacitated vehicle routing problem. The AVNS consists of two stages: a learning phase and a multi-level VNS with guided local search. The adaptive aspect is integrated in the local search where a set of highly successful local searches is selected based on the intelligent selection mechanism. In addition, the hybridisation of LNS with the AVNS enables the solution to escape from the local minimum effectively. To make the algorithm more competitive in terms of the computing time, a simple and flexible data structure and a neighbourhood reduction scheme are embedded. Finally, we adapt a new local search move and an effective removal strategy for the LNS. The proposed AVNS was tested on the benchmark data sets from the literature and produced very competitive results

A Simulation Optimization Approach to Inventory Optimization in Supply Chain Networks

International audienc

Scenario Assessment for COVID-19 Outbreak in Iran: A Hybrid Simulation–Optimization Model for Healthcare Capacity Allocation

International audienceToday, all countries globally, including Iran, face the challenge of spreading the COVID-19 disease. Given the limited capacity of the health care system and the risk of a recurrence of demand for testing and intensive care units (ICU), some countries may again experience fluctuating behaviors or the return of viral pandemics. Various reasons are resulting in this emerging virus in the world. Apart from the tendency to cause severe disease with a higher death rate than previous coronavirus diseases, it can spread so quickly. This study aimed to reach an optimal capacity allocation in the health care centers by intervening in government decisions and public holidays in Iran on disease control. This research is the first to analyze and develop a healthcare capacity allocation strategy by considering the mutual effects of disease outbreaks and government actions as decision aiding tools via a hybrid simulation–optimization framework. Also, we calibrate the developed hybrid simulation–optimization model for Iran on the improved SEIR framework to generate reliable outputs. The simulation results show that it is necessary to allocate some part of the health capacity to the mild symptomatic patients

Optimizing sepsis care through heuristics methods in process mining: A trajectory analysis

Process mining can help acquire insightful knowledge and heighten the system’s performance. In this study, we surveyed the trajectories of 1050 sepsis patients in a regional hospital in the Netherlands from the registration to the discharge phase. Based on this real-world case study, the event log comprises events and activities related to the emergency ward, admission to hospital wards, and discharge enriched with data from lab experiments and triage checklists. At first, We aim to discover this process through Heuristics Miner (HM) and Inductive Miner (IM) methods that can deal with noise and can be used to express the main behavior recorded in an event log. Then, we analyze a systematic process model based on organizational information and knowledge. Besides, we address conformance checking given medical guidelines for these patients and monitor the related flows on the systematic process model. The results show that HM and IM are inadequate in identifying the relevant process. However, using a systematic process model based on expert knowledge and organizational information resulted in an average fitness of 97.8%, a simplicity of 77.7%, and a generalization of 80.2%. The analyses demonstrate that process mining can shed light on the patient flow in the hospital and inspect the day-to-day clinical performance versus medical guidelines. Also, the process models obtained by the HM and IM methods cannot provide a concrete comprehension of the process structure for stakeholders compared to the systematic process model. The implications of our findings include the potential for process mining to improve the quality of healthcare services, optimize resource allocation, and reduce costs. Our study also highlights the importance of considering expert knowledge and organizational information in developing effective process models

Cost Overrun Risk Assessment and Prediction in Construction Projects: A Bayesian Network Classifier Approach

Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship between cost overrun risks during the risk assessment process is one of the primary reasons construction projects go over budget. Conversely, recent studies have failed to account for potential interrelationships between risk factors in their machine learning (ML) models. Additionally, the presented ML models are not interpretable. Thus, this study contributes to the entire ML process using a Bayesian network (BN) classifier model by considering the possible interactions between predictors, which are cost overrun risks, to predict cost overrun and assess cost overrun risks. Furthermore, this study compared the BN classifier model’s performance accuracy to that of the Naive Bayes (NB) and decision tree (DT) models to determine the effect of considering possible correlations between cost overrun risks on prediction accuracy. Moreover, the most critical risks and their relationships are identified by interpreting the learned BN model. The results indicated that the 18 BN models demonstrated an average prediction accuracy of 78.86%, significantly higher than the NB and DT. The present study identified the most significant risks as an increase in the cost of materials, lack of knowledge and experience among human resources, and inflation

Application of Multi-Criteria Decision-Making Methods for Human Resource’s Task Scheduling in Multi-Sided Platforms

International audienc