Scheduling and simulation of maritime traffic in congested waterways: an application to the Strait of Istanbul

2021 ◽  
pp. 1-17
Author(s):  
Şirin Özlem ◽  
İlhan Or ◽  
Yiğit Can Altan
Keyword(s):  
Simulation Model ◽  
Performance Measures ◽  
Scheduling Algorithm ◽  
Traffic Pattern ◽  
Expert Opinions ◽  
Maritime Traffic ◽  
Transit Traffic ◽  
Heuristic Scheduling ◽  
Interarrival Times

Abstract The aim of this study is to develop a simulation model which is capable of mimicking actual vessel arrival patterns and vessel entrance decisions (which are made based on expert opinions generally) on congested, narrow waterways. The model is tested on the transit traffic pattern in the Strait of Istanbul. Based on a heuristic scheduling algorithm, this model decides entrance times and vessel types on the strait. The model, with different policies for day and night traffic, is run for a period of seven years with 20 replications for each year. The performance measures of the model are: average interarrival times, number of vessels passed and entrance times for each successive vessel pair in both traffic directions. The model results are congruent with the actual results of performance measures. Therefore, it may be deduced that the proposed algorithm can be a guide for operators regarding scheduling decisions in congested, narrow waterways.

Placement of Digital Microfluidic Biochips via a New Evolutionary Algorithm

2021 ◽  
Vol 26 (6) ◽  
pp. 1-22
Author(s):  
Chen Jiang ◽  
Bo Yuan ◽  
Tsung-Yi Ho ◽  
Xin Yao
Keyword(s):  
Evolutionary Algorithm ◽  
Resource Constraints ◽  
Scheduling Algorithm ◽  
Search Space ◽  
Placement Problem ◽  
Heuristic Scheduling ◽  
Microfluidic Biochips ◽  
Feasible Solutions ◽  
Digital Microfluidic ◽  
Operation Scheduling

Digital microfluidic biochips (DMFBs) have been a revolutionary platform for automating and miniaturizing laboratory procedures with the advantages of flexibility and reconfigurability. The placement problem is one of the most challenging issues in the design automation of DMFBs. It contains three interacting NP-hard sub-problems: resource binding, operation scheduling, and module placement. Besides, during the optimization of placement, complex constraints must be satisfied to guarantee feasible solutions, such as precedence constraints, storage constraints, and resource constraints. In this article, a new placement method for DMFB is proposed based on an evolutionary algorithm with novel heuristic-based decoding strategies for both operation scheduling and module placement. Specifically, instead of using the previous list scheduler and path scheduler for decoding operation scheduling chromosomes, we introduce a new heuristic scheduling algorithm (called order scheduler) with fewer limitations on the search space for operation scheduling solutions. Besides, a new 3D placer that combines both scheduling and placement is proposed where the usage of the microfluidic array over time in the chip is recorded flexibly, which is able to represent more feasible solutions for module placement. Compared with the state-of-the-art placement methods (T-tree and 3D-DDM), the experimental results demonstrate the superiority of the proposed method based on several real-world bioassay benchmarks. The proposed method can find the optimal results with the minimum assay completion time for all test cases.

Simulation and ABC to Improve the Performance of Emergency Department

2012 ◽  
Vol 3 (3) ◽  
pp. 15-31
Author(s):  
Abbas Al-Refaie ◽  
Mohammed Shurrab ◽  
Ming-Hsien Li
Keyword(s):  
Emergency Department ◽  
Simulation Model ◽  
Performance Measures ◽  
Decision Makers ◽  
Activity Based Costing ◽  
Cost Information ◽  
Controllable Factors ◽  
Financial Practices

This study aims at developing an Activity Based Costing (ABC) for the Emergency Department (ED) in a Jordanian hospital to determine associated costs and clarifies the financial practices inside ED rooms. For this purpose, simulation model is built for patients at the current ED activities and determine the costs for each room in ED. The total costs (JD), number of served patients, and profits (JD) are 263562, 10256, and 146677, respectively. Improvement is suggested by studying three three-level controllable factors using simulation. It is noted that focusing on the lowest total costs, B-1 (adding two beds with one added nurse) and B-2 (introducing four beds with two added nurses) provide the two smallest total costs (JD) of 252568 and 253320, respectively. Whereas, for the largest number of served patients is achieved by A-1 (assigning one sharing doctor for Abdominal) and C-1 (adding another room for abdominal with new doctor) of equal number of served patients of 10466. In order to combine these two performance measures, the profit associated with each scenario is considered. It is found that factor A-1 provides the largest profit (= 150,390 JD). The results of this research may provide decision makers valuable cost information that helps them in improving ED performance.

scholarly journals Some performance measures for vacation models with a batch Markovian arrival process

1994 ◽  
Vol 7 (2) ◽  
pp. 111-124 ◽  
Author(s):  
Sadrac K. Matendo
Keyword(s):  
Performance Measures ◽  
Waiting Times ◽  
Distribution Functions ◽  
Markovian Arrival Process ◽  
Arrival Process ◽  
Service Discipline ◽  
Renewal Processes ◽  
Single Server ◽  
Batch Markovian Arrival Process ◽  
Interarrival Times

We consider a single server infinite capacity queueing system, where the arrival process is a batch Markovian arrival process (BMAP). Particular BMAPs are the batch Poisson arrival process, the Markovian arrival process (MAP), many batch arrival processes with correlated interarrival times and batch sizes, and superpositions of these processes. We note that the MAP includes phase-type (PH) renewal processes and non-renewal processes such as the Markov modulated Poisson process (MMPP).The server applies Kella's vacation scheme, i.e., a vacation policy where the decision of whether to take a new vacation or not, when the system is empty, depends on the number of vacations already taken in the current inactive phase. This exhaustive service discipline includes the single vacation T-policy, T(SV), and the multiple vacation T-policy, T(MV). The service times are i.i.d. random variables, independent of the interarrival times and the vacation durations. Some important performance measures such as the distribution functions and means of the virtual and the actual waiting times are given. Finally, a numerical example is presented.

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