Mahsa Pouraliakbarimamaghani, Mohammad Mohammadi and Abolfazl Mirzazadeh
When designing an optimization model for use in a mass casualty event response, it is common to encounter the heavy and considerable demand of injured patients and inadequate…
Abstract
Purpose
When designing an optimization model for use in a mass casualty event response, it is common to encounter the heavy and considerable demand of injured patients and inadequate resources and personnel to provide patients with care. The purpose of this study is to create a model that is more practical in the real world. So the concept of “predicting the resource and personnel shortages” has been used in this research. Their model helps to predict the resource and personnel shortages during a mass casualty event. In this paper, to deal with the shortages, some temporary emergency operation centers near the hospitals have been created, and extra patients have been allocated to the operation center nearest to the hospitals with the purpose of improving the performance of the hospitals, reducing congestion in the hospitals and considering the welfare of the applicants.
Design/methodology/approach
The authors research will focus on where to locate health-care facilities and how to allocate the patients to multiple hospitals to take into view that in some cases of emergency situations, the patients may exceed the resource and personnel capacity of hospitals to provide conventional standards of care.
Findings
In view of the fact that the problem is high degree of complexity, two multi-objective meta-heuristic algorithms, including non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA), were proposed to solve the model where their performances were compared in terms of four multi-objective metrics including maximum spread index (MSI), spacing (S), number of Pareto solution (NPS) and CPU run-time values. For comparison purpose, paired t-test was used. The results of 15 numerical examples showed that there is no significant difference based on MSI, S and NPS metrics, and NRGA significantly works better than NSGA-II in terms of CPU time, and the technique for the order of preference by similarity to ideal solution results showed that NRGA is a better procedure than NSGA-II.
Research limitations/implications
The planning horizon and time variable have not been considered in the model, for example, the length of patients’ hospitalization at hospitals.
Practical implications
Presenting an effective strategy to respond to a mass casualty event (natural and man-made) is the main goal of the authors’ research.
Social implications
This paper strategy is used in all of the health-care centers, such as hospitals, clinics and emergency centers when dealing with disasters and encountering with the heavy and considerable demands of injured patients and inadequate resources and personnel to provide patients with care.
Originality/value
This paper attempts to shed light onto the formulation and the solution of a three-objective optimization model. The first part of the objective function attempts to maximize the covered population of injured patients, the second objective minimizes the distance between hospitals and temporary emergency operation centers and the third objective minimizes the distance between the warehouses and temporary centers.
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H. Dagdougui, E. Garbolino, O. Paladino and R. Sacile
The purpose of this paper is the definition and the implementation of a simplified mathematical model to estimate the hazard and the risk related to the use of high‐pressurized…
Abstract
Purpose
The purpose of this paper is the definition and the implementation of a simplified mathematical model to estimate the hazard and the risk related to the use of high‐pressurized hydrogen pipeline.
Design/methodology/approach
This study aims to investigate the effects of different hydrogen operations conditions and to tackle with different release or failure scenarios. Based on the combination of empirical relations and analytical models, this paper sets the basis for suitable models for consequence analysis in terms of estimating fire length and of predicting its thermal radiation. The results are compared either with experimental data available in the literature, thus by setting the same operations and failure conditions, or with other conventional gaseous fuel currently used.
Findings
The findings show that the release rate increasingly varies according to the supply pressure. Regarding the effect of the hole diameter, it hugely affects the amount of hydrogen escaping from the leak, up to a value of approximately 0.3 m, after which the release rate remains fixed at a maximum of 43 Kg/s. For failure consequences related to jet flame, the leak dimension has a strength impact on the flame length.
Originality/value
This paper represents a helpful engineering tool, to establish the safety requirements that are related to define adequate safety buffer zones for the hydrogen pipeline in order to ensure safety to people, as well the environment.
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Athakorn Kengpol, Sopida Tuammee and Markku Tuominen
The purpose of this paper is to develop a framework for route selection in multimodal transportation which can reduce cost, lead time, risk and CO2 emission in multimodal…
Abstract
Purpose
The purpose of this paper is to develop a framework for route selection in multimodal transportation which can reduce cost, lead time, risk and CO2 emission in multimodal transportation systems.
Design/methodology/approach
This research proposes the development of a framework for route selection in multimodal transportation that includes a six-phase framework to select an optimal multimodal transportation route. The first phase is to collect the data of each route and select the origin and destination. The second phase is to calculate time and cost of each route by using a multimodal transport cost-model. In the third phase, the CO2 emissions are calculated based upon the 2006 guidelines of Intergovernmental Panel on Climate Change. The fourth phase proposes an integrated quantitative risk assessment, analytic hierarchy process (AHP) and data envelopment analysis methodology to evaluate the multimodal transportation risk. The fifth phase is to prioritize criteria by using the AHP which can be used in the objective function. The final phase is to calculate the optimal route by using the zero-one goal programming.
Findings
The aims of the model are to minimize transportation costs, transportation time, risk and CO2 emission.
Practical implications
The approach has been tested on a realistic multimodal transportation service, originating from Bangkok in Thailand to a destination at Da Nang port in Vietnam. The results have shown that the approach can provide guidance in choosing the lowest cost route in accordance with other criteria, and to minimize the CO2 emission effectively.
Originality/value
The contribution of this research lies in the development of a new decision support approach that is flexible and applicable to logistics service providers, in selecting multimodal transportation route under the multi-criteria in term of cost, time, risk and importantly the environmental impact.
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Angela Maria Tomasoni, Emmanuel Garbolino, Massimo Rovatti and Roberto Sacile
This paper seeks to tackle the complex problem of integrating real‐time data information about the tracking of a hazardous material (hazmat) vehicle with classical risk evaluation…
Abstract
Purpose
This paper seeks to tackle the complex problem of integrating real‐time data information about the tracking of a hazardous material (hazmat) vehicle with classical risk evaluation methodologies in order to describe possible accident scenarios. The application deals with the transport of hydrocarbon dangerous goods, where the accident consequences may involve the population exposed along the infrastructure used for transportation.
Design/methodology/approach
The approach taken consists of three phases. First, the acquisition of real‐time data about the travel and the carried hazmat; second, the evaluation of the risk area; and finally, a Geographic Information System (GIS) are taken into account.
Findings
The findings of this analysis constitute the methodological basis to implement a decision support system as regards hazmat transport risk analysis, also in real time, with important evaluations for planning criteria. Using TIP (Transport Integrated Platform), the data collection is received in real time and the scenario construction and visualization may represent a user‐friendly tool for prompt risk evaluation.
Research limitations/implications
The information displayed by the GIS interface is easy to use, and gives prompt information about the accident consequences.
Originality/value
In terms of the total impact from the hazmat transport system to the whole environment (humans, goods, infrastructures, services and natural elements), the paper focuses on the importance of creating a historical real‐time database implemented from a real time information, that represents a standard set of information necessary to define an accident scenarios, for hazmat transport.
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W.M. Wang, J.W. Wang, A.V. Barenji, Zhi Li and Eric Tsui
The purpose of this paper is to propose an automated machine learning (AutoML) and multi-agent system approach to improve overall product delivery satisfaction under limited…
Abstract
Purpose
The purpose of this paper is to propose an automated machine learning (AutoML) and multi-agent system approach to improve overall product delivery satisfaction under limited resources.
Design/methodology/approach
An AutoML method is purposed to model delivery satisfaction of individual customer, and a heuristic method and multi-agent system are proposed to improve overall satisfaction under limited processing capability. A series of simulation experiments have been conducted to illustrate the effectiveness of the proposed methodology.
Findings
The simulated results show that the proposed method can effectively improve overall delivery satisfaction, especially when the demand of customer orders is highly fluctuating and when the customer satisfaction models are highly diversified.
Practical implications
The proposed framework provides a more dynamic and continuously improving way to model delivery satisfaction of individual customer, thereby supports companies to provide personalized services and develop scalable and flexible business at a lower cost, and ultimately improves the overall quality, efficiency and effectiveness of delivery services.
Originality/value
The proposed methodology utilizes AutoML and multi-agent system to model individual customer delivery satisfaction and improve the overall satisfaction. It can cooperate with the existing delivery resource planning methods to further improve customer delivery satisfaction. The authors propose an AutoML approach to model individual customer delivery satisfaction, which enables continuous update and improvements. The authors propose multi-agent system and a heuristic method to improve overall delivery satisfaction. The numerical results show that the proposed method can improve overall delivery satisfaction with limited processing capability.
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Despite substantial investment over recent years in telehealth there appears to be little consensus regarding what a successful implementation should achieve. However, defining…
Abstract
Despite substantial investment over recent years in telehealth there appears to be little consensus regarding what a successful implementation should achieve. However, defining success is often controversial and complex due to differing views from the large number of stakeholders involved, the local environment where telehealth is deployed and the scope, or size, of any planned initiative. Nevertheless, a number of generic measures are proposed in this paper which then provides a framework for the measurement of success. The local context can then be applied to determine the exact emphasis on specific measures, but it is proposed that all of the measures should be included in the holistic measurement of success. Having considered what constitutes success, attention is then given to how success should be quantified. Robust evaluation is fundamental and there is much debate as to whether the ‘gold standard’ randomised control trial (RCT) is the most appropriate methodology for telehealth. If the intervention, technology and system, can be maintained in a stable state then the RCT may well provide the most authoritative evidence for decision‐makers. However, ensuring such stability, in what is still a novel combination of technology and service, is difficult and consequently other approaches may be more appropriate when stability is unlikely to be maintained.
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Ali Ahmed, John Olsen and John Page
The overarching objective of this research is to integrate the Lean Six Sigma (LSS) framework with computer simulation to improve the production efficiency of a light-emitting…
Abstract
Purpose
The overarching objective of this research is to integrate the Lean Six Sigma (LSS) framework with computer simulation to improve the production efficiency of a light-emitting diode (LED) manufacturing factory.
Design/methodology/approach
Recently, the idea of taking advantage of the benefits of Six Sigma and simulation models together has led both industry and the academy towards further investigation and implementation of these methodologies. From this perspective, the present research will illustrate the effectiveness of using LSS methodology in a real factory environment by using the combination of three simulation methods which are system dynamics (SD), discrete-event simulation (DES) and agent-based (AB) modelling.
Findings
The hybrid simulation method applied in this research was found to accurately mimic and model the existing real factory environment. The define, measure, analyse, control and improve (DMAIC)-based improvements showed that the applied method is able to improve machine utilization rates while balancing the workload. Moreover, queue lengths for several stations were shortened, and the average processing time was decreased by around 50%. Also, a weekly production increase of 25% was achieved while lowering the cost per unit by around 8%.
Research limitations/implications
While the case study used was for a LED manufacturing system, the proposed framework could be implemented for any other existing production system. The research also meticulously presents the steps carried out for the development of the multi-method simulation model to allow readers to replicate the model and tailor it for their own case studies and projects. The hybrid model enables managers to navigate the trade-off decisions they often face when choosing advanced production output ahead of continuous improvement practices. The adoption of methodologies outlined in this paper would attain improvements in terms of queue lengths, utilization, reduced costs and improved quality and efficiency of a real, small factory. The findings suggest improvements and create awareness among practitioners for the utilization of quality tools that will provide direct benefits to their companies. Although the multi-method simulation is effective, a limitation of the current study is the lack of micro details within each station. Furthermore, the results are all based on one specific case study which is not enough to suggest and generalized findings.
Originality/value
This research combines the use of the three main hybrid simulation paradigms (SD, DES and AB) in a unified framework DMAIC methodology. Choosing the right models in DMAIC is important, challenging and urgently necessary. Also, this paper shows empirical evidence on its effectiveness.
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Ali Ahmed, John Page and John Olsen
This paper aims to compare the prognostic and visualisation capabilities of all the three simulation paradigms to identify their suitability and rigor in eliminating weaknesses…
Abstract
Purpose
This paper aims to compare the prognostic and visualisation capabilities of all the three simulation paradigms to identify their suitability and rigor in eliminating weaknesses and bottlenecks in a Lean Six Sigma (LSS) project.
Design/methodology/approach
The paper uses an light-emitting diode (LED) factory as a business case to show the differences and advantages of using three different simulation techniques to solve a manufacturing problem.
Findings
Even though this is only one business case that shows how system dynamics (SD) can be effective in a Six Sigma manufacturing project, more examples are needed to validate this hypothesis within Six Sigma and Lean manufacturing fields. Even though, discrete-events (DE) and agent-based (AB) models are both equally well suited to develop the manufacturing processes and the choice of what to use may be arbitrarily dependent on the available software or the preference of the modeller, hybrid models seem to become extremely powerful. Therefore, more hybrid models need to be constructed within LSS (especially when a flowchart and a SIPOC ((Suppliers, Inputs, Process, Outputs and Customers) table are combined to develop a hybrid model) to achieve the most accurate results with accurate representation of reality.
Originality/value
There are three commonly used simulation techniques, DE, AB and SD, but choosing the right simulation methodology has often been a challenge.
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Jaber Valizadeh and Peyman Mozafari
Production of waste has been increased exponentially due to world industrialization and urban and machine life expansion. On the other hand, the outbreak of the COVID-19…
Abstract
Purpose
Production of waste has been increased exponentially due to world industrialization and urban and machine life expansion. On the other hand, the outbreak of the COVID-19 coronavirus quickly became a global crisis. This crisis has added a large amount of waste to urban waste. The purpose of this study is to create cooperation between municipal waste collector contractors.
Design/methodology/approach
Thus, a mathematical model is proposed under uncertain conditions, which includes the volume of municipal waste and infectious waste including personal protective equipment and used equipment for patients. To reduce total costs, the results are evaluated with four cooperative game theory methods such as Shapley value, t value, core center and least core. Ultimately, the saved cost by cooperation in each coalition is allocated fairly among the contractors. Finally, a comparison was made between the solution methods based on the value of the objective function and the solution time.
Findings
The results indicate that the proposed cooperative method increases cost savings and reduces the fine of residual waste. Therefore, it can be mentioned that this kind of cooperation would finally result in more incentives for contractors to form larger coalitions. Genetic algorithms were used to solve the large-scale model.
Originality/value
The proposed model boosts the current understanding of waste management in the COVID-19 pandemic. The paper adds additional value by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.