A novel search space reduction optimization algorithm

Mahesh, Aeidapu; Sushnigdha, Gangireddy

doi:10.1007/s00500-021-05838-7

Cited by 20 publications

(6 citation statements)

References 25 publications

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“…On algorithm efficiency, results obtained indicate that the modified solution space reduction strategy applied here leads to an improved search efficiency. This outcome is consistent with the findings of Mahesh and Sushnigdha [46].…”

supporting

confidence: 93%

“…to be large enough to allow for sufficient exploration of solution space, but not too high as to increase the computational expense significantly During the search for a global optimum solution, the DES simulation is run multiple times within the SA algorithm as determined by the simulation parameters specified, as shown in Table 2. All parameters shown are critical to the efficiency of the search and the quality of solutions [45,46]. Due to the need to balance efficiency with performance between the DES and SA modules, the researchers set the number of iterations per temperature step at 50 runs.…”

Section: Set Parameters For Simulated Annealingmentioning

confidence: 99%

“…Solution Space Search Strategy: accordingly, the researchers adopt a solution space reduction (SSR) strategy that progressively delimits a smaller and smaller active search space within the global solution space as the system cools down to a stable entropy value. The chosen strategy is based on a number of variations that have been demonstrated to improve the efficiency of the search and accuracy of results [46,49]. A form of dynamic SSR, called hull form optimisation, works by using data mining to reduce the range and number of variables continuously, only enabling subsequent optimisation steps in ranges where high performance is indicated, thereby reducing calculations and improving efficiency and accuracy [49].…”

Section: Set Parameters For Simulated Annealingmentioning

confidence: 99%

“…In the present study, however, authors only implement range reduction but maintain all variables since they are of equal interest in healthcare maintenance optimisation. They further build on the work of Mahesh & Sushnigdha [46] to formulate a solution search method that negates the need first to check if a candidate solution violates boundary limits, thus gaining a small improvement in efficiency. Figure 6 indicates how this logic is implemented.…”

Section: Set Parameters For Simulated Annealingmentioning

confidence: 99%

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Optimising Maintenance Workflows in Healthcare Facilities: A Multi-Scenario Discrete Event Simulation and Simulation Annealing Approach

2023

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Healthcare systems in low-resource settings need effective methods for managing their scant resources, especially people and equipment. Digital technologies may provide means for circumventing the constraints hindering low-income economies from improving their healthcare services. Although analytical and simulation techniques, such as queuing theory and discrete event simulation, have already been successfully applied in addressing various optimisation problems across different operational contexts, the literature reveals that their application in optimisation of healthcare maintenance systems remains relatively unexplored. This study considers the problem of maintenance workflow optimisation with respect to labour, equipment availability and cost. The study aims to provide objective means for forecasting resource demand, given a set of task requests with varying priorities and queue characteristics that flow from multiple queues, and in parallel, into the same maintenance process for resolution. The paper presents how discrete event simulation is adopted in combination with simulated annealing to develop a decision-support tool that helps healthcare asset managers leverage operational performance data to project future asset-performance trends objectively, and thereby determine appropriate interventions for optimal performance. The study demonstrates that healthcare facilities can achieve efficiency in a cost-effective manner through tool-generated maintenance strategies, and that any future changes can be expeditiously re-evaluated and addressed.

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supporting

confidence: 93%

Section: Set Parameters For Simulated Annealingmentioning

confidence: 99%

Section: Set Parameters For Simulated Annealingmentioning

confidence: 99%

Section: Set Parameters For Simulated Annealingmentioning

confidence: 99%

See 2 more Smart Citations

Optimising Maintenance Workflows in Healthcare Facilities: A Multi-Scenario Discrete Event Simulation and Simulation Annealing Approach

2023

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“…The inner radius of the vessel in pressure vessel design problem 𝑆 A control parameter for the size of DG unit 𝑇 A control parameter for the type of DG unit 𝑡 𝑤 The height of the bar in welded beam design problem 𝑇 1 A control parameter for the tap position of a voltage regulator in the first phase 𝑇 2 A control parameter for tap position of a voltage regulator in second phase 𝑇 3 A Using population-based algorithms has several advantages compared to single-based algorithms. Evaluation of the search agent's position based on individual and/or social statements in the populationbased methods reduces the chance of being stuck in local optimum solutions.…”

mentioning

confidence: 99%