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On one side, the purpose of this paper is to numerically analyze the emergency order allocation mechanism and help managers to understand the relationship between the emergency coefficient, uncertainty and emergency cost in two‐echelon logistics service supply chain. On the other side, the purpose of this paper is to help managers understand how to deal with the problem of order allocation in the two‐echelon logistics service supply chain better in the case of emergency.

The paper presents a multi‐objective planning model for emergency order allocation and then uses numerical methods with LINGO 8.0 software to identify the model's properties. The application of the order allocation model is then presented by means of a case study.

With the augment of uncertainty, the general cost of logistics service integrator (LSI) is increasing, while the total satisfaction of all functional logistics service providers (FLSPs) is decreasing, as well as the capacity reliability; at the same time the emergency cost coefficient is closely correlative with the satisfaction and general penalty intensity of FLSPs; finally, the larger the emergency cost coefficient is, the more satisfaction of FLSPs, but the capacity reliability goes up first and down later.

Management should note that it is not better when emergency cost coefficient is bigger. The general satisfaction degree of FLSP increases with the augment of emergency cost coefficient, but there is an upper limit of the value, i.e. it will not increase indefinitely with the augment of emergency cost coefficient. This paper also has some limitations. The optional emergency cost coefficient only adopted a group of data to analyze while the trend of the reliability of logistics capacity needs to be further discussed. In addition, the algorithm of emergency order allocation model in the case of multi‐objective remains to be solved.

Under emergency conditions, LSIs can adopt this kind of model to manage their FLSPs to obtain the higher logistics performance. But LSIs should be careful selecting emergency cost coefficient. In accordance with different degrees of emergency logistics demand, LSIs can determine reasonable emergency cost coefficient, but not the bigger, the better, on the premise that LSIs acquire maximum capacity guarantee degree and overall satisfaction degree of FLSPs. FLSPs can make contract bargaining of reasonable emergency coefficient with LSIs to make both sides get the best returns and realize the benefit balance.

Many studies have emphasized the capacity allocation of manufactures, order allocation of manufacturing supply chain and scheduling model of emergency resources without monographic study of supply chain order allocation of logistics service. Because the satisfaction degree of FLSPs the cost of integrators needs to be considered in the process of order allocation, and the inventory cost of capacity does not exist, it is different from the issue of capacity allocation planning of manufacture supply chain. Meanwhile, the match of different kinds of logistics service capacity must be considered for the reason of the integrated feature of logistics service. Additionally, cost is not the most important decision objective because of the characteristics of demand uncertainty and weak economy. Accordingly, this paper considers these issues.

This paper proposes a two-level supply chain including suppliers and manufacturers. The purpose of this paper is to design a resilient fuzzy risk-averse supply portfolio selection approach with lead-time sensitive manufacturers under partial and complete supply facility disruption in addition to the operational risk of imprecise demand to minimize the mean-risk costs. This problem is analyzed for a risk-averse decision maker, and the authors use the conditional value-at-risk (CVaR) as a risk measure, which has particular applications in financial engineering.

The methodology of the current research includes two phases of conceptual model and mathematical model. In the conceptual model phase, a new supply portfolio selection problem is presented under disruption and operational risks for lead-time sensitive manufacturers and considers resilience strategies for risk-averse decision makers. In the mathematical model phase, the stages of risk-averse two-stage fuzzy-stochastic programming model are formulated according to the above conceptual model, which minimizes the mean-CVaR costs.

In this paper, several computational experiments were conducted with sensitivity analysis by GAMS (General algebraic modeling system) software to determine the efficiency and significance of the developed model. Results show that the sensitivity of manufacturers to the lead time as well as the occurrence of disruption and operational risks, significantly affect the structure of the supply portfolio selection; hence, manufacturers should be taken into account in the design of this problem.

The study proposes a new two-stage fuzzy-stochastic scenario-based mathematical programming model for the resilient supply portfolio selection for risk-averse decision-makers under disruption and operational risks. This model assumes that the manufacturers are sensitive to lead time, so the demand of manufacturers depends on the suppliers who provide them with services. To manage risks, this model also considers proactive (supplier fortification, pre-positioned emergency inventory) and reactive (revision of allocation decisions) resilience strategies.

The purpose of this study is to carry out the systematic literature review, bibliometric analysis and content analysis of extant literature of service supply chain (SSC).

Systematic literature review (SLR) technique was used for identifying the research papers. In the first step after reading titles, abstracts and keywords and, full-length articles wherever required, papers not related to SSC were removed. In second steps papers were read more critically and papers not related to SSC were removed. Finally on 502 papers bibliometric and content analysis was further carried out. Content analysis was based on the clusters formed by bibliographic coupling. Further, content analysis of the recent articles revealed the current research trends and research gaps.

This paper identified the six existing research diversifications in SSC as (1) logistics SSC, (2) model, framework and conceptual papers, (3) third-party logistics service providers, (4) articles from various perspective, (5) measurement of quality and performance on services and (6) impact of adoption of technology, cooperation and branding on logistics service providers. Further, six future research directions are also provided.

This research provides a clear view of the progression of publication, research diversification, research themes of six identified clusters, sub-themes of clusters and content analysis of each cluster. Content analysis of recent articles reveals the current research trend and future research directions.

This is a first of its kind of study which presents the diversification of research areas within SSC, bibliometric analysis, content analysis and provides actionable future research direction.

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.

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.

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.

The planning horizon and time variable have not been considered in the model, for example, the length of patients’ hospitalization at hospitals.

Presenting an effective strategy to respond to a mass casualty event (natural and man-made) is the main goal of the authors’ research.

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.

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.

The purpose of this paper is to present and apply a methodology that optimally assigns emergency response services (ERS) stations in Peloponnesus, Greece that was severely hit by wildfires in 2007, in an effort to describe the actual emergency response in this disaster and identify disaster management possibilities that can arise from the optimal allocation of the existing fire stations.

The methodology concerns the development of an objective function that aims to minimize maximum and average response times of ERS stations and the evaluation of developed scenarios. Simulated annealing is used for the minimization of the objective function, providing near-optimal solutions with low computation times for medium-scale networks.

The findings concern the comparison of average and maximum response times of ERS stations to hearths of fire, based on their actual and optimal allocation. They reveal an overall reduction in the average and maximum response time by 20 and 30 percent, respectively, for the entire region, while there is a reduction of 15 and 35 percent in the average and maximum response time for the locations affected by the 2007 wildfires.

The methodology is formulated as a facility location problem with unitary demand and unlimited capacity in the stations, which means that the allocation does not take into account simultaneous events.

The paper fulfills an identified need to apply innovative research solutions to actual case studies in order to identify existing gaps and future disaster management possibilities.

Every year volunteers play a crucial role in disaster responses around the world. Volunteer management is known to be more complex than managing a paid workforce, and this is only made worse by the uncertainty of rapidly changing conditions of crisis scenarios. The purpose of this paper is to address the critical problem of assigning tasks to volunteers and other renewable and non-renewable resources simultaneously, particularly under high-load conditions. These conditions are described by a significant mismatch between available volunteer resources and demands or by frequent changes in requirements.

Through a combination of literature reviews and interviews with managers from several major volunteer organizations, six key characteristics of crisis volunteer resource allocation problems are identified. These characteristics are then used to develop a general mixed integer programming framework for modeling these problems. Rather than relying on probabilistic resource or demand characterizations, this framework addresses the constantly changing conditions inherent to this class of problems through a dynamic resource reallocation-based approach that minimizes the undesirable impacts of changes while meeting the desired and changing objectives. The viability of this approach for solving problems of realistic size and scale is demonstrated through a large set of computational experiments.

Using a common commercial solver, optimal solutions to the allocation and reallocation problems were consistently obtained in short timespans for a wide variety of problems that have realistic sizes and characteristics.

The proposed approach has not been previously addressed in the literature and represents a computationally tractable method to allocate volunteer, renewable and non-renewable resources to tasks in highly volatile crisis scenarios without requiring probabilistic resource or demand characterizations.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

The problems and drawbacks of the Physical Distribution System (PDS) of a production company are described. A simulation model which takes into consideration the activities of the PDS and some part of the production system of the company are presented. Using this simulation model, experiments have been performed to find the effect of certain critical variables like service level, lead times and production rate on the total cost. The limitations of this study and potential areas of future work are assessed.

Based on the context of the Internet of Things (IoT), the territorial public emergency supplies will be networked, platform-based management, unified emergency dispatch. The problem of supplies dispatching in the “last kilometer” of emergency is solved, and the supplies needed in the disaster area are promptly delivered to the hands of the victims so that they can quickly be rescued after the disaster and to save valuable time for rapid rescue, which can greatly decrease casualties and property losses. This paper aims to discuss these issues.

By analyzing the shortage of existing emergency supplies dispatching research and taking all factors such as disaster area demand, social reserve, road conditions, mode of transport, loading limit, disaster area satisfaction rate and road capacity into consideration under the background of IoT, a variety of the territorial emergency supplies dispatching model with more rescue points, more affected areas are constructed. The objective function of the model is to aim in finding the shortest rescue time, giving the solution algorithm, and finally simulating the simulation case.

Based on the context of the IoT, the territorial public emergency supplies will be networked, platform-based management, unified emergency dispatch. Considering factors such as road conditions, modes of transport and road capacity, the authors construct a number of emergency rescue plans, multiple disaster scenarios and various emergency supplies dispatching models. The authors simulate the situation through simulation cases with the shortest time being the ultimate goal. The problem of supplies dispatching in the “last kilometer” of emergency is solved, and the supplies needed in the disaster area are promptly delivered to the hands of the victims so that they can quickly be rescued after the disaster and to save valuable time for rapid rescue, which can greatly decrease casualties and property losses.

This paper provides little research on the dispatch of emergency supplies. The problems of direct dispatch from the rescue point to the affected area and dispatch of supplies without relying on the arrival of emergency supplies at the rear are addressed. Therefore, this study does not focus on the arrival of emergency supplies at the rear but on direct dispatching issues during territorial public emergency supplies from the rescue point to the disaster point.