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

A Simulation Optimization Approach to Inventory Optimization in Supply Chain Networks

International audienc

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

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

A Multi-objective Optimization Model for Robust Skip-Stop Scheduling with Earliness and Tardiness Penalties

Inefficient transport systems impose extra travel time for travelers, cause dissatisfaction and reduce service levels. In this study, the demand-oriented train scheduling problem is addressed using a robust skip-stop method under uncertain arrival rates during peak hours. This paper presents alternative mathematical models, including a two-stage scenario-based stochastic programming model and two robust optimization models, to minimize the total travel time of passengers and their waiting time at stations. The modeling framework accounts for the design and implementation of robust skip-stop schedules with earliness and tardiness penalties. As a case study, each of the developed models is implemented on line No. 5 of the Tehran metro, and the results are compared. To validate the skip-stop schedules, the values of the stochastic solution and the expected value of perfect information are calculated. In addition, a sensitivity analysis is conducted to test the performance of the model under different scenarios. According to the obtained results, having perfect information can reduce up to 16% of the value of the weighted objective function. The proposed skip-stop method has been shown to save about 5% in total travel time and 49% in weighted objective function, which is a summation of travel times and waiting times as against regular all-stop service. The value of stochastic solutions is about 21% of the value of the weighted objective function, which shows that the stochastic model demonstrates better performance than the deterministic model

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

Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window

International audienceThis study addresses a many-to-many hub location-routing problem where the best-found locations of hubs and the bestfound tours for each hub are determined with simultaneous pickup and delivery within the hard time window. To find practical solutions, the hubs and transportation fleet have constrained capacity, in which every node can be serviced by multiple allocations with the hard time window and limited tour length. First, a bi-objective optimization model is proposed to balance travel costs among different routes and to minimize the total sum of fixed costs of locating hubs, the costs of handling, traveling, assigning, and transportation costs. The problem is then solved using an augmented e-constraint technique for small to medium size instances of the problem. Due to the NP-hardness nature of the problem, the proposed multi-objective optimization model is solved by a multi-objective imperialist competitive algorithm (MOICA). To show the superior performance of the MOICA, the solutions are compared with those obtained by the non-dominated sorting genetic algorithm (NSGA-II). For the large-scale problem instances, the comparative results indicate that the MOICA can indeed provide better Pareto optimal solutions compared to NSGA-II for the large-scale problem instances

Data-Driven Multi-Criteria Assessment Framework for Analyzing the Reliability of Bus Services

International audienceIntelligent systems have been extensively used to improve the reliability of transport services as a result of technological advances. Despite the technical and methodological achievements, public transportation companies are still facing excessive challenges in assessing the performance and reliability of the system. This study establishes a data-driven multi-criteria decision-making model for prioritizing bus routes that illustrates both operator and consumer views on bus routes. The multi-criteria fuzzy outranking process is handled by ELECTRE III and Condorcet methods. The developed model utilizes alternative indices of bus travel-time reliability to fully capture the uncertain nature of the input data. The reliability assessment framework is based on automatic vehicle location (AVL) data which works as an effective evaluation system for enhanced service reliability on different routes network-wide. Using this model, bus transport companies can set a benchmark and a reliable ranking system for their bus routes. This hybrid prioritization framework is used for characterizing and enhancing transport network efficiency. The effectiveness of the model is examined by quantifying the reliability of eight bus routes controlled by the Qazvin public transportation system, in Iran. A wide range of AVL data sources is employed within an in-depth statistical analysis based on both user and operator preferences. According to the concordance matrix results, line 18 has been found to be superior to other bus routes, and the possibility of identifying less efficient bus routes has been fulfilled