Deprivation Costs Research Articles

Memory ability declined due to sleep deprivation

This passage focuses on whether memory ability will decline due to sleep deprivation. As the pressure on people's lives increases, people tend to sleep less to finish more work. The sacrifice of sleep time for the work product is worth it. So this paragraph will summarize this issue by analyzing articles, and the conclusion is lack of sleep will cause irreversible damage to the brain. So, people should recognize that they probably cannot afford the cost of sleep deprivation.

A humanitarian cold supply chain distribution model with equity consideration: The case of COVID-19 vaccine distribution in the European Union

This research develops a humanitarian cold supply chain model with equity consideration for COVID-19 vaccine distribution during a pandemic, considering deprivation cost and an important social concept named equity. The proposed comprehensive plan minimizes all incurred costs, including transportation costs, shortage costs, deprivation costs, and holding costs, while aiming at eliminating infection and mortality rates. The proposed three-echelon supply chain model includes suppliers, distributors, and affected regions (ARs), as destinations. We apply the proposed model to the actual vaccine distribution data during the COVID-19 outbreak in Europe. A mixed integer programming (MIP) model is developed to minimize the costs and satisfy the demand goals in the vaccine distribution plan. A sensitivity analysis demonstrates how total and deprivation costs affect each other, helping the managers establish a trade-off between them. The results show that appropriate supply chain planning can minimize logistics and social costs. The proposed model can help policymakers, and decision-makers better understand the importance of equity and implement a fair distribution of vaccines, considering the deprivation cost as a social cost.

Questioning the implication of the utility-maximization assumption for the estimation of deprivation cost functions after disasters

Deprivation cost functions (DCFs) allow for the quantification of human suffering after disasters strike. Commonly, DCF estimation methods assume that affected individuals aim to maximize their wellbeing while making rational decisions. However, after disasters, people are often under stress and pressure, possibly traumatized, and they can adopt behaviors that are neither compensatory nor utility based. This paper questions the use of Random Utility Maximization (RUM) to estimate DCFs and compares its results with a Random Regret Minimization (RRM) approach, and a combined method that considers both Regret- and Utility-based decision rules. DCFs for multiple supplies are estimated using stated preference data from two case studies: Colombia and Ecuador. The results suggest that deprivation cost estimations yield significantly different valuations depending on the heuristic used to explain choice behavior, with the DCFs estimated using RUM having higher cost values for a time window shorter than 48 h. This suggests that for longer time windows, RRM is the approach that should be used. Moreover, results show that deprivation costs are context-dependent, and should not be transferred directly. Finally, it is shown that DCFs for multiple commodities can be added separately in the same objective function when planning relief distribution operations. This research is the first attempt to consider different choice heuristics for estimating deprivation cost functions, and it is the first to compare data from multiple locations. The implications of these findings provide disaster managers and planners with new challenges and research directions to improve relief distribution plans.

Multi-Objective Modeling of Relief Items Distribution Network Design Problem in Disaster Relief Logistics Considering Transportation System and CO2 Emission

The present study aims to propose a multi-objective mixed integer mathematical programming model for designing a relief items distribution network in sustainable disaster relief logistics. The first objective function minimizes the total network costs. Which are divided into two parts: 1- relief costs including (transportation costs, inventory costs and fixed costs of facilities) 2- social costs (deprivation cost). The second objective function minimizes the amount of pollution generated by the network. Considering the related literature review, this is the first study that to propose a robust fuzzy optimization approach for relief items distribution network design problem considering environmental (CO2 emission), social (deprivation cost) and economic impacts under reliability and uncertainty. Then, the multi-objective model was solved using the multi-choice goal programming. To indicate the validity of the proposed model, a case study was evaluated based on real data (2019 flood in Sari city, Mazandaran Province). Using the proposed model, decision-makers and managers are able to make strategic and tactical decisions with the least cost and time, and in relief planning can enhance the structure of distribution networks and inventory and reduce victims’ dissatisfaction.

Fair allocation of personal protective equipment to health centers during early phases of a pandemic

We consider the problem of allocating personal protective equipment, namely surgical and respiratory masks, to health centers under extremely limited supply. We formulate a multi-objective multi-period non-linear resource allocation model for this problem with the objectives of minimizing the number of infected health workers, the number of infected patients and minimizing a deprivation cost function defined over shortages. We solve the resulting problem using the ε-constraint algorithm so as to obtain the exact Pareto set. We also develop a customized genetic algorithm to obtain an approximate Pareto frontier in reasonable time for larger instances. We provide a comparative analysis of the exact and heuristic methods under various scenarios and give insights on how the suggested allocations outperform the ones obtained through a set of rule-of-thumb policies, policies that are implemented owing to their simplicity and ease-of-implementation. Our comparative analysis shows that as the circumstances get worse, the trade-off between the deprivation cost and the ratio of infections deepens and that the proposed heuristic algorithm gives very close solutions to the exact Pareto frontier, especially under pessimistic scenarios. We also observed that while some rule-of-thumb policies such as a last-in-first-receives type policy work well in terms of deprivation costs in optimistic scenarios, others like split policies perform well in terms of number of infections under neutral or pessimistic settings. While favoring one of the objectives, these policies typically fail to provide good solutions in terms of the other objective; hence if such policies are to be implemented the choice would depend on the problem characteristics and the priorities of the policy makers. Overall, the solutions obtained by the proposed methods imply that more complicated distribution schemes that are not induced by these policies would be needed for best results.

On the Emergency Water Distribution Problem: Optimizing Vehicle Routing Decisions with Deprivation Costs Considerations

Disaster relief operations are characterized by the need for a fast and cost-efficient response to provide the affected population with the basic supplies required for survival. The emergency water distribution problem arises when the disaster affects the water supply system. Thus, distributing water by trucks is a quick solution to respond to the disaster in a humanitarian context. The problem that is studied in this paper aims to optimize the routing decisions for the water distribution trucks that minimize the sum of operational costs plus the population deprivation cost. The deprivation cost is associated with the economic valuation of human suffering by lack of access to water. We propose a mathematical formulation for this problem based on mixed-integer non-linear programming and a metaheuristic algorithm to compute solutions. Experiments are performed inspired on a real setting of a disaster preparation plan for several municipalities in Cundinamarca (Colombia) to test the capability of the algorithm. Our results show that including the deprivation costs in the objective function significantly modifies the search space of the problem when compared to a classical VRP. Further, the study provides insights on the impact of the fleet size on the operations’ expected performance indicators such as the last delivery times. In fact, we determine the number of vehicles that are required to complete the water distribution operation in less than 48-hours.

Designing a New Framework Agreement in Humanitarian Logistics Based on Deprivation Cost Functions

Designing a New Framework Agreement in Humanitarian Logistics Based on Deprivation Cost Functions

A novel scenario-based robust bi-objective optimization model for humanitarian logistics network under risk of disruptions

Humanitarian aid in disasters is critical to saving lives and alleviating human suffering. This paper presents a novel scenario-based robust bi-objective optimization model that integrates medical facility location, casualty transportation, and relief commodity allocation considering triage. The proposed model aims to minimize the total deprivation cost of casualties due to the delayed access to medical services and the total operation cost. Following a set of disruption scenarios, the scenario-based robust approach is applied to protect solutions against the risk of disruptions in temporary medical centers. Considering the uncertain number of casualties under each scenario, the robust method which denotes the uncertainty as interval data is adopted. We utilize the ε-constraint method to deal with the bi-objective model. Additionally, we consider real case studies of the Wenchuan Earthquake to validate the proposed model. Several numerical experiments are conducted to examine the effects of uncertainties and capacities of medical facilities on the main objective value. The performance of considering the uncertainty and facility disruption is also discussed.

Estimating the societal impact of water infrastructure disruptions: A novel model incorporating individuals’ activity choices

• Developing a model to quantitatively estimate the societal impacts of water service suspension. • Proposing an individual activity estimation model mainly driven by prioritizing activities with the higher suffering level. • Establishing a suffering level function of disrupted activities by combining tolerance level and deprivation cost function. • Capturing the tolerance level variations of individuals with Monte Carlo sampling. The well-being of society can be severely impacted by infrastructure disruptions. This study proposes a novel mathematical model to estimate the societal impact of water disruption quantitatively from two aspects: the percentage of people who can perform certain water-related activities and the percentage of people intolerant to disrupted activities. The model begins by incorporating the tolerance level (TL) to establish a suffering level function of the disrupted activity. Then, from the individual's perspective, an activity estimation model is developed to predict an individual's activity choices when water is limited due to infrastructure disruptions, and this model is mainly driven by prioritizing activities with the maximum suffering level. To quantify the societal impact in regions, a Monte Carlo simulation is adopted to generate simulated residents with randomly sampled TL following lognormal or Weibull distributions, and the activity estimation model is conducted for each simulated resident; consequently, societal impacts can be aggregated and derived. Additionally, an illustrative case study of Osaka and sensitivity analyses are performed; the results validate the model's effectiveness and applicability. The proposed model provides insightful information to support emergency management and can be integrated with infrastructure resilience models to better build human-centric sustainable and resilient cities.

Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network

Continuous power supply in an integrated electric system supplied by solar energy and battery storage can be optimally maintained with the use of diesel generators. This article discusses the optimum setting-point for isolated wind, photo-voltaic, diesel, and battery storage electric grid systems. Optimal energy supply for hybrid grid systems means that the load is sufficient for 24 h. This study aims to integrate the battery deprivation costs and the fuel price feature in the optimization model for the hybrid grid. In order to count charge–discharge cycles and measure battery deprivation, the genetic algorithm concept is utilized. To solve the target function, an ANN-based algorithm with genetic coefficients can also be used to optimize the power management system. In the objective function, a weight factor is proposed. Specific weight factor values are considered for simulation studies. On the algorithm actions, charging status, and its implications for the optimized expense of the hybrid grid, the weight factor effect is measured.

The impact of metro-based underground logistics system on city logistics performance under COVID-19 epidemic: A case study of Wuhan, China

The global outbreak of COVID-19 has further exposed deficiencies in city logistics based on human and ground roads, such as poor emergency response capacity and high risk of infection during transportation. Metro-based underground logistics system (M-ULS) may be an innovative approach to deal with this city-level disaster due to its efficient operation, contactless and driverless characteristics. However, the market evolution process and the quantitative calculation framework of comprehensive benefits after the application of M-ULS are still unclear, which has become a problem of mutual restriction with the extensive application of M-ULS. This paper attempts to use the system dynamics method, based on the real-world simulation, to analyze the quantitative relationship between the M-ULS implementation and the city logistics performance under epidemic outbreaks. Wuhan city in China was selected as the empirical background, and five simulation scenarios were set under different implementation strategies of M-ULS in response to the epidemic. Six variables were selected to measure city logistics performance and M-ULS operation status, including demand fill-rate, unit delivery time, total deprivation cost, total transportation cost, total number of susceptible people, and utilization rate of M-ULS. The results show that M-ULS is effective in improving the performance of city logistics and responding to the epidemic. The delivery time and transportation cost have a strong impact on the market share of M-ULS. Finally, a set of incentive policies was designed to promote the adoption of M-ULS. The findings not only provide a method for evaluating the overall performance of M-ULS, but also provide a unique perspective for promoting the implementation of M-ULS and responding to the transportation challenges brought by the epidemic.

Including decision-makers preferences in the cost-deprivation trade-off during disaster interventions

PurposeIn this contribution, the purpose of this study is to extend the established social cost concept of humanitarian logistics into a preference-based bi-objective approach. The novel concept offers an efficient, robust and transparent way to consider the decision-maker’s preference. In principle, the proposed method applies to any multi-objective decision and is especially suitable for decisions with conflicting objectives and asymmetric impact.Design/methodology/approachThe authors bypass the shortcomings of the traditional approach by introducing a normalized weighted sum approach. Within this approach, logistics and deprivation costs are normalized with the help of Nadir and Utopia points. The weighting factor represents the preference of a decision-maker toward emphasizing the reduction of one cost component. The authors apply the approach to a case study for hypothetical water contamination in the city of Berlin, in which authorities select distribution center (DiC) locations to supply water to beneficiaries.FindingsThe results of the case study highlight that the decisions generated by the approach are more consistent with the decision-makers preferences while enabling higher efficiency gains. Furthermore, it is possible to identify robust solutions, i.e. DiCs opened in each scenario. These locations can be the focal point of interest during disaster preparedness. Moreover, the introduced approach increases the transparency of the decision by highlighting the cost-deprivation trade-off, together with the Pareto-front.Practical implicationsFor practical users, such as disaster control and civil protection authorities, this approach provides a transparent focus on the trade-off of their decision objectives. The case study highlights that it proves to be a powerful concept for multi-objective decisions in the domain of humanitarian logistics and for collaborative decision-making.Originality/valueTo the best of the knowledge, the present study is the first to include preferences in the cost-deprivation trade-off. Moreover, it highlights the promising option to use a weighted-sum approach to understand the decisions affected by this trade-off better and thereby, increase the transparency and quality of decision-making in disasters.

Optimizing the response for Arctic mass rescue events

We propose and study a model that optimizes the response to a mass rescue event in Arctic Alaska. The model contains dynamic logistics decisions for a large-scale maritime evacuation with the objectives of minimizing the impact of the event on the evacuees and the average evacuation time. Our proposed optimization model considers two interacting networks - the network that moves evacuees from the location of the event out of the Arctic (e.g., a large city in Alaska such as Anchorage) and the logistics network that moves relief materials to evacuees during the operations. We model the concept of deprivation costs by incorporating priority levels capturing the severeness of evacuees’ current medical situation and the period indicating the amount of time an evacuee has not received key relief resources. Our model is capable of understanding the best possible response given the current locations of response resources and is used to assess the effectiveness of an intuitive heuristic that mimics emergency response decision-making.

A multi-objective stochastic programming model to configure a sustainable humanitarian logistics considering deprivation cost and patient severity

As the occurrence of disasters has increased frequently and has resulted in growing concern about their adverse effects on the environment, Sustainable Humanitarian Logistics (SHL) has received great attention recently. SHL aims to reduce disaster damages in an environmentally-friendly manner in the shortest possible time. The terms including ‘environmentally-friendly’ and ‘shortest possible time’ refer to the environmental and social aspects of sustainability. This research proposes a stochastic multi-objective mixed-integer programming model to configure an SHL network during the response phase. Having compared to the research literature, this is the first study that considers economic, social, and environmental aspects of sustainability by incorporating relief cost, deprivation cost, and carbon emissions, respectively. Then, the improved multi-choice goal programming approach is applied to solve the proposed multi-objective model. To indicate the validity of the proposed model, an earthquake that occurred in a region of Kermanshah, Iran, in 2017 is investigated as a real case study. Finally, sensitivity analysis is performed and several managerial and theoretical insights are provided. The results show that exerting environmental issues in humanitarian logistics does not necessarily increase the relief costs, but can be in contrast with the social aspect. Furthermore, a minor increase in the budget of the preparedness phase drastically decreases the response costs.

A possibilistic mathematical programming model to control the flow of relief commodities in humanitarian supply chains

In emergency situations, disaster relief organizations are faced with the difficult decision of how to allocate scarce resources in an efficient manner in order to provide the best possible relief action. This paper aims to provide an analytical model that will help relief organizations in reducing human suffering following a disaster while maintaining an acceptable level of cost efficiency. A mathematical model is introduced to optimize the relief distribution problem which considers the social cost —the total sum of logistics and deprivation costs. The fuzzy nature of the deprivation cost function is addressed with possibilistic mixed integer programming with fuzzy objectives to reflect variation in deprivation costs perceptions. The model is solved using the Rolling Horizon method in a sequence of iterations. In each iteration, part of the planning horizon is modeled in detail and the rest of the time horizon is represented in an aggregated manner. The model is tested both empirically and on a case study of internal displacement in northwest Syria. Computational results showed that considering the demographic structure in affected areas and reflecting it to the deprivation cost function helped to reach better prioritization in distribution of commodities. The rolling horizon methodology is also found to be efficient in solving large scale instances and in capturing the dynamic changes in demand and supply parameters.

Pre-Positioning Facility Location and Resource Allocation in Humanitarian Relief Operations Considering Deprivation Costs

Alleviating human sufferings during and in the aftermath of disasters is one of the most important goals in humanitarian relief logistics. The lack of relief commodities, especially life-saving items, is a life-threatening loss to victims and must be considered when making emergency supply allocation and transportation decisions, even in the pre-disaster prepositioning phase. This paper proposes a scenario-based stochastic program that integrates the decisions of prepositioning facility locations, quantities of stocked emergency supplies, and service allocations in each scenario in the same modeling framework. The estimation of victims’ losses for waiting for emergency supplies is measured in the typical deprivation cost function and treated as one of the main bases of decision making, besides traditional transportation costs, in determining the service allocation strategies in each scenario. Specifically, a case study with data from the hurricane threat in the Gulf Coast area of the US was conducted to demonstrate the application of this model and the significance of considering victims’ welfare loss in humanitarian relief logistics. Some interesting managerial insights were also drawn from a series of numerical experiments and sensitivity analyses.

Reinforcement learning approach for resource allocation in humanitarian logistics

When a disaster strikes, it is important to allocate limited disaster relief resources to those in need. This paper considers the allocation of resources in humanitarian logistics using three critical performance indicators: efficiency, effectiveness and equity. Three separate costs are considered to represent these metrics, namely, the accessibility-based delivery cost, the starting state-based deprivation cost, and the terminal penalty cost. A mixed-integer nonlinear programming model with multiple objectives and multiple periods is proposed. A Q-learning algorithm, a type of reinforcement learning method, is developed to address the complex optimization problem. The principles of the proposed algorithm, including the learning agent and its actions, the environment and its states, and reward functions, are presented in detail. The parameter settings of the proposed algorithm are also discussed in the experimental section. In addition, the solution quality of the proposed algorithm is compared with that of the exact dynamic programming method and a heuristic algorithm. The experimental results show that the efficiency of the algorithm is better than that of the dynamic programming method and the accuracy of the algorithm is higher than that of the heuristic algorithm. Moreover, the Q-learning algorithm provides close to or even optimal solutions to the resource allocation problem by adjusting the value of the training episode K in practical applications.

An applied approach to multi-criteria humanitarian supply chain planning for pandemic response

PurposeWhen a large-scale outbreak such as the COVID-19 pandemic happens, organizations that are responsible for delivering relief may face a lack of both provisions and human resources. Governments are the primary source for the humanitarian supplies required during such a crisis; however, coordination with humanitarian NGOs in handling such pandemics is a vital form of public-private partnership (PPP). Aid organizations have to consider not only the total degree of demand satisfaction in such cases but also the obligation that relief goods such as medicine and foods should be distributed as equitably as possible within the affected areas (AAs).Design/methodology/approachGiven the challenges of acquiring real data associated with procuring relief items during the COVID-19 outbreak, a comprehensive simulation-based plan is used to generate 243 small, medium and large-sized problems with uncertain demand, and these problems are solved to optimality using GAMS. Finally, post-optimality analyses are conducted, and some useful managerial insights are presented.FindingsThe results imply that given a reasonable measure of deprivation costs, it can be important for managers to focus less on the logistical costs of delivering resources and more on the value associated with quickly and effectively reducing the overall suffering of the affected individuals. It is also important for managers to recognize that even though deprivation costs and transportation costs are both increasing as the time horizon increases, the actual growth rate of the deprivation costs decreases over time.Originality/valueIn this paper, a novel mathematical model is presented to minimize the total costs of delivering humanitarian aid for pandemic relief. With a focus on sustainability of operations, the model incorporates total transportation and delivery costs, the cost of utilizing the transportation fleet (transportation mode cost), and equity and deprivation costs. Taking social costs such as deprivation and equity costs into account, in addition to other important classic cost terms, enables managers to organize the best possible response when such outbreaks happen.

A Novel Hybrid Algorithm for Multiobjective Location-Allocation Problem in Emergency Logistics.

Increased frequency of disasters keeps reminding us of the importance of effective resource distribution in postdisaster. To reduce the suffering of victims, this paper focuses on how to establish an effective emergency logistics system. We first propose a multiobjective optimization model in which the location and allocation decisions are made for a three-level logistics network. Three objectives, deprivation costs, unsatisfied demand costs, and logistics cost, are adopted in the proposed optimization model. Several cardinality and flow balance constraints are considered simultaneously. Then, we design a novel effective IFA-GA algorithm by combining the firefly algorithm and genetic algorithm to solve this complex model effectively. Furthermore, three schemes are proposed to improve the effectiveness of the IFA-GA algorithm. Finally, the numerical results provide several insights on the theory and practice of relief distribution, which also illustrate the validity of the proposed solution algorithm.

Exponential Rank Differential Evolution Algorithm for Disaster Emergency Vehicle Path Planning

When disasters happen, victims suffer from lots of miseries. Vehicles are used to carry relief supplies for these victims. However, these vehicles are subject to threats as roads and bridges are often destroyed by these disasters. To dispatch vehicles safely and minimize the misery of victims, a disaster emergency vehicle path planning model is proposed by introducing the deprivation cost function and two constraints. One constraint is the risk of the route, and the other is the angle of the vehicle. A differential evolution algorithm based on the exponential selection is developed to optimize the model. The proposed algorithm uses the exponential ranking method, making individuals have exponential probability as parents enter into the mutation stage. The approach improves the exploitation capacity while maintain the exploration ability of the algorithm. Three disaster scenarios in three-dimension are used to do simulations. The experimental results have verified that our proposed model can deal well with the vehicle path planning problem in disaster emergency management. The proposed algorithm has acquired superior performances compared with differential evolution variant algorithms.