A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect

As of March 24, 2020, the Food and Drug Administration (FDA) authorized to bleed the newly recovered from Coronavirus Disease 2019 (COVID-19), i.e., the ones whose lives were at risk, separate Plasma from their blood and inject it to COVID-19 patients. In many cases, as observed the plasma antibodies have cured the disease. Therefore, a four-echelon supply chain has been designed in this study to locate the blood collection centers, to find out how the collection centers are allocated to the temporary or permanent plasma-processing facilities, how the temporary facilities are allocated to the permanent ones, along with determining the allocation of the temporary and permanent facilities to hospitals. A simulation approach has been employed to investigate the structure of COVID-19 outbreak and to simulate the quantity of plasma demand. The proposed bi-objective model has been solved in small and medium scales using -constraint method, Strength Pareto Evolutionary Algorithm II (SPEA-II), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi Objective Invasive Weed Optimization algorithm (MOIWO) approaches. One of the novelties of this research is to study the system dynamic structure of COVID-19’s prevalence so that to estimate the required plasma level by simulation. Besides, this paper has focused on blood substitutability which is becoming increasingly important for timely access to blood. Due to shorter computational time and higher solution quality, MOIWO is selected to solve the proposed model for a large-scale case study in Iran. The achieved results indicated that as the plasma demand increases, the amount of total system costs and flow time rise, too. The proposed simulation model has also been able to calculate the required plasma demand with 95% confidence interval.

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“…Of the benefits behind using the proposed meta-heuristic algorithms, we can point out the following [21] , [22] , [76] :…”

Section: Discussionmentioning

confidence: 99%

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

Shirazi

Kia

Ghasemi

2021

Applied Soft Computing

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“…They discovered that the objective functions are heavily dependent on the demand; thus, the management can evaluate the required resources by analyzing the demand fluctuations. In [ 44 ], the authors study the problem of allocation and scheduling rescue units during a natural disaster. They formulate a bi-objective mixed-integer linear programming model to simultaneously minimize the total time to complete all the rescue operations and the total delay time.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated fuzzy-grey relational analysis approach to portfolio optimization

2022

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This paper combines two approaches (Fuzzy set theory and Grey Relational Analysis) for modelling an investor’s imprecise linguistic expectations and the uncertain returns of assets. We propose a novel maximization-type risk measure capable of incorporating the investor’s individual preferences. The investor provides the expectations of what is considered the “ideal” return from the portfolio. We use Credibility theory to capture the investors’ subjective and imprecise expectations in a precise mathematical form. We construct a portfolio return sequence using the assets’ actual return data and an ideal sequence based on investors’ preferences. Subsequently, we calculate the Grey similitude and the closeness incidence degree between the two sequences. The closer the portfolio return is to the ideal return, the better. In this manner, we develop a new risk measure that can quantify an investor’s perception of risk. This measure is intuitive and easy to calculate. It does not involve estimating many parameters, something which would increase the estimation risk. We use a genetic algorithm to solve the resulting portfolio optimization model. We illustrate this method with two case studies: (i) a case study of 100 assets of the U.S. stock market’s NASDAQ-100 index and (ii) a case study of 50 assets of the Indian stock market’s NIFTY-50 index. We comprehensively analyze the model’s out-of-sample performance and discuss its implications. The portfolios obtained using the proposed approach exhibit healthy growth outside the in-sample period. We also compare the out-of-sample performance of the proposed model with several approaches in the literature to establish its superiority.

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“…For the distribution of emergency supplies, early scholars focused on maximizing the efficiency of supplies distribution. For example, Erfan et al [13] constructed a supplies allocation model to minimize the total completion time of rescue operations and the total delay of rescue operations. On this basis, scholars began to consider the impact of victims' psychology on the formulation of rescue plans and introduce the factor of fairness into the study of emergency supplies allocation.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Warehouse Location and Supplies Allocation for Emergency Rescue under Joint Government–Enterprise Cooperation Considering Disaster Victims’ Distress Perception

2021

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The location and allocation of emergency supplies are an important part of emergency rescue work. The existing emergency location and allocation process is inefficient, costly, and neglects the psychology of victims. To improve the emergency relief work and solve the current problems, this paper introduces the victims’ pain perception cost into the model, takes the lowest cost of the whole emergency rescue system as the goal, constructs a government–enterprise joint emergency material location–allocation model, and uses the simulated annealing algorithm to solve the model. This paper takes the 2008 Wenchuan earthquake in Sichuan Province as the background and verifies the validity and rationality of the model through a comparative analysis of case simulations. The results show that the model and algorithm can effectively solve the emergency supplies location–allocation problem considering the victims’ pain perception, reflecting the idea of human-centered sustainable development and providing support for building a sustainable emergency relief system.

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A robust bi-objective mathematical model for disaster rescue units allocation and scheduling with learning effect

Cited by 64 publications

References 46 publications

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

An integrated fuzzy-grey relational analysis approach to portfolio optimization

Optimization of Warehouse Location and Supplies Allocation for Emergency Rescue under Joint Government–Enterprise Cooperation Considering Disaster Victims’ Distress Perception

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