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In this paper, a new responsive-green-cold vaccine supply chain network during the COVID-19 pandemic is developed for the first time. According to the proposed network, a new multi-objective, multi-period, multi-echelon mathematical model for the distribution-allocation-location problem is designed. Another important novelty in this paper is that it considers an Internet-of-Things application in the COVID-19 condition in the suggested model to enhance the accuracy, speed, and justice of vaccine injection with existing priorities. Waste management, environmental effects, coverage demand, and delivery time of COVID-19 vaccine simultaneously are therefore considered for the first time. The LP-metric method and meta-heuristic algorithms called Gray Wolf Optimization (GWO), and Variable Neighborhood Search (VNS) algorithms are then used to solve the developed model. The other significant contribution, based on two presented meta-heuristic algorithms, is a new heuristic method called modified GWO (MGWO), and is developed for the first time to solve the model. Therefore, a set of test problems in different sizes is provided. Hence, to evaluate the proposed algorithms, assessment metrics including (1) percentage of domination, (2) the number of Pareto solutions, (3) data envelopment analysis, and (4) diversification metrics and the performance of the convergence are considered. Moreover, the Taguchi method is used to tune the algorithm's parameters. Accordingly, to illustrate the efficiency of the model developed, a real case study in Iran is suggested. Finally, the results of this research show MGO offers higher quality and better performance than other proposed algorithms based on assessment metrics, computational time, and convergence. [ABSTRACT FROM AUTHOR]

Nowadays, the amounts of infectious medical waste (IMW) have surged considerably so waste management has become a critical emergency in many developing countries. However, most large medical waste generation centers (MWGC) are equipped with treatment facilities, small MWGC faces the waste management problem. It reveals the significance of having a proper program for small health centers. This is an indisputable difficulty that governments bordered because it imposes great costs on societies, also the environmental problems caused by improper treatment are irreparable. To attend to all the essential aspects of the problem, this paper recommended a location-routing model with four objective functions to minimize the total costs, environmental pollution, the risk imposed on the population around disposal sites, and the total violation from the expected arrival time. Considering a multi-period problem with a maximum acceptable delay plays a key role to connect the assumptions to the real-world problem. In addition, for solving mathematical models based on case studies, the role of uncertainty is undeniable. The demand for dental waste treatment is not definite and is changed based on the different conditions thus fuzzy chance-constrained programming is proposed for this problem to tackle the uncertainty. The revised multi-choice goal programming method is considered to solve the model and a real case study for dental clinics in Babol city of Iran is investigated to illustrate the validation of the proposed model. The results indicate that the solution method can create a balance between four objective functions. Finally, sensitivity analyses are performed for some parameters to analyze the behavior of the objective functions. [ABSTRACT FROM AUTHOR]

Planning and scheduling of operating rooms (ORs) is important for hospitals to improve efficiency and achieve high quality of service. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. In this paper, surgical case scheduling problem with uncertain duration of surgeries in multi resource environment is investigated. We present a two-stage stochastic mixed-integer programming model, named SOS, with the objective of total ORs idle time and overtime. Also, in this paper a two-step approach is proposed for solving the model based on the L-shaped algorithm. Proposing the model in a multi resources environment with considering real-life limitations in academic hospitals and developing an approach for solving this stochastic model efficiently form the main contributions of this paper. The model is evaluated through several numerical experiments based on real data from Hasheminejad Kidney Center (HKC) in Iran. The solutions of SOS are compared with the deterministic solutions in several real instances. Numerical results show that SOS enjoys a better performance in 97% of the cases. Furthermore, the results of comparing with actual schedules applied in HKC reveal a notable reduction of OR idle time and over time which illustrate the efficiency of the proposed model in practice. [ABSTRACT FROM AUTHOR]

This paper introduces a novel two-phase framework for designing a proactive–reactive scheduling model in the multi-mode resource-constrained project scheduling problem under disruptions. The proactive phase involves constructing a resilient baseline scheduling model using a mixed-integer linear programming model. This phase contributes to a multi-objective model that minimizes the project completion time and total project cost while maximizing resilience criteria. In this context, resilience refers to allocating float time to project activities to protect their start and finish times against future disruptions as much as possible. The reactive phase involves a bi-objective mathematical model that mitigates the impact of disruptions through preempt-repeat, preempt-resume, and activity-crashing strategies. Real-world projects involve many uncertain parameters that can negatively affect the optimization of rescheduling problems if overlooked. Therefore, for the first time, a scenario-based robust optimization approach is proposed to cope with the uncertainty of the reactive phase. Additionally, a novel hybrid multi-objective method based on goal programming is introduced to solve the proposed multi-objective model. Finally, to demonstrate the capability of the proposed approach, an oil and gas project in Iran is regarded as a real case study. The results indicate that the negative impact of disruptions on the makespan and total cost of the project can be largely mitigated by considering resilience criteria in the proactive phase and preempt-repeat, preempt-resume, and activity-crashing strategies in the reactive phase. [ABSTRACT FROM AUTHOR]

The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids. [ABSTRACT FROM AUTHOR]

Assessing the efficiency of a supply chain (SC) is of great importance for managers and policy makers. For this aim, we propose a network data envelopment analysis (NDEA) model to reflect the internal structure of networks in efficiency evaluation. For many of the real-world performance evaluation problems, data of inputs and outputs are available, and their ratio conveys important messages to managers. However, conventional data envelopment analysis (DEA) models are no longer able to deal with ratio data. This paper aims to extend the NDEA models with the ratio data (NDEA-R) to evaluate the performance of SCs. Therefore, given the internal structure of a supply chain, relationships among different divisions of an SC are determined under two assumptions of free-links and fixed-links. Applicability of the proposed models is illustrated by evaluating supply chain of 19 hospitals in Iran over 6 months. By performing sensitivity analysis, we find out that the overall efficiency score of decision-making units (DMUs) under the fixed link assumption is greater than or equal to the overall efficiency of DMUs under free link assumption. Our proposed model overcomes the underestimation of efficiency and pseudo-inefficiency scores. [ABSTRACT FROM AUTHOR]

Transportation sector with the consumption of 25% of energy play a major role in Iranian economy. This sector produces 27% of total undesirable greenhouse gases in Iran which has directly harmful effects on the environment. Hence, performance assessment of energy efficiency of transportation sector is one of the most important issues for policy makers. In this paper, energy efficiency of transportation sector of 20 provinces in Iran is evaluated based on data envelopment analysis (DEA)—cooperative game approach. First, selected inputs and outputs are categorized into energy and non-energy inputs and desirable and undesirable outputs. Then, classical DEA model is applied to evaluate and rank the provinces. Since, the classical DEA model can't distinguish between efficient provinces, so, this paper ranks the provinces based on combination of cross-efficiency DEA and cooperative game approaches. In the cooperative game theory, each province is considered as a player and the suitable characteristic function is defined for players. Finally, by calculating the Shapley value for each player, the final ranks of transportation sectors in provinces are concluded. The results indicate that some smaller provinces have better energy efficiency in transportation sector in comparison with big provinces. [ABSTRACT FROM AUTHOR]

This paper considers a real case problem of supply chain network design inspired from a wheat distribution network in Iran. It generates a network with capacity acquisition and fleet management. The problem first is formulated as a mixed integer linear programming model. Then, a logic-based Benders decomposition algorithm is appropriately developed as the solution methodology. In the presented algorithm, the problem is decomposed into two models of master and subproblem. The master problem is improved by means of the preprocessing and valid inequalities. Moreover, three Benders cuts, one optimality and two feasibility cuts, are developed for the algorithm. The general and relative performance of the model and algorithm is experimentally evaluated. The wheat distribution system of Iran is considered here as the case study of this research. The model is developed based on Iran's wheat distribution system. All the results show that the algorithm significantly outperforms the mathematical model of the case study. For example, the algorithm solves 95% of the tested instances to optimality, yet the model solves 29%. [ABSTRACT FROM AUTHOR]

As a service-oriented supply chain, blood supply chain management deals with a human-to-human network involved with a number of direct challenges such as irregularity in both blood supply and demand as well as multiplicity of blood products, their respective lifetimes and perishability. Besides, facing risks originating from natural disasters or man-made incidents such as labor strikes, economic problems, and so on, and from uncertainty embedded in the input data is also a real highlight in designing and managing a blood supply chain network. This paper addresses an enhanced perspective incorporating a two-phase preemptive policy by which the disruption risk is diminished through a hybrid technique using the fuzzy analytic hierarchy process and grey rational analysis for determining supplementary blood facilities, to cooperate in production process and decrease interruptions. Furthermore, a p-robust formulation is presented to control the network reliability under disruption scenarios at minimum cost. To protect the network against the uncertainty, a consolidated approach based on a recently developed fuzzy measure is extended. We also examine the validity and practicality of the proposed model and its solution perspective along with the reliability of the network by a real case of Iran. It is worth nothing that the problems in the case of disasters in which the impact of the two risk streams will be felt most keenly, could benefit from the proposed procedure advantages since it develops a proactive and controlling approach which can be extensively applicable in disastrous situation. [ABSTRACT FROM AUTHOR]

Every year natural and man-made disasters cause considerable human and economic losses. It is essential to prepare for different relief operations to prevent and reduce these losses. In this paper, we propose an integrated evacuation and distribution logistic system to obtain simultaneous routing and scheduling of vehicles to evacuate people from affected areas to shelters and provide them with necessary relief commodities. We assume that shelters and vehicles have limited capacity and the demand of each affected area and distribution center could be fulfilled by more than one vehicle (split delivery). The proposed problem is formulated as a Mixed-Integer Linear Programming model with the objective of minimization of the sum of arrival times of the vehicles at affected areas, shelters, and distribution centers. We also propose a Memetic Algorithm (MA) to solve this integrated model on large-scale problems efficiently after tuning the MA parameters using the Taguchi method. The proposed model and algorithm are used to solve a case study in Tehran, the capital of Iran. The evaluation of the results shows the effectiveness of the proposed disaster relief logistic system in minimizing the total waiting time of evacuees and delivery time of supplies. The results also show that the number of relief vehicles and capacity of shelters can considerably affect the total relief time in disaster relief operations. [ABSTRACT FROM AUTHOR]

Timely, effective delivery of relief resources to the sufferers in a natural disaster is quite crucial. All governments especially those having high risk of natural disasters must have a comprehensive and implementable plan to suitably manage humanitarian issues after a natural disaster occurs. They should construct sufficient facilities and prepare and store enough relief items (e.g., tents, medical packages, and meals) to be able to quickly respond to necessary needs of injured and homeless people; otherwise, it might lead to humanitarian crises with high numbers of casualties. In this study, we extend the mathematical two-stage stochastic optimization model proposed by Mete and Zabinsky (Int J Prod Econ 126:76-84, 2010) simultaneously considering two phases: pre- and post-disaster. All decisions pertinent to the pre-disaster phase, such as the locations of warehouse and pre-positioned relief items, should be taken in the first stage; however, other decisions pertinent to the post-disaster phase, such as the allocations of warehouses to the demanding points are taken in the second stage. In this study, we consider bi-directional relations between warehouses, which can increase the flexibility of the constructed network to handle the needs of injured people in a shorter time interval. Furthermore, whereas the relief process should be implemented within a specific time interval (e.g., 3 days), our extended optimization model is constructed for a multi-period and multi-product situation. We construct the extended model based on a professional report prepared by the Japanese International Cooperation Agency in a study of the seismic micro zoning of Tehran on April 13, 1999. The respective data in this report has also been updated based on the reports of Tehran Municipality to be matched with the current situation of Tehran. The results of our extended multi-period model for the respective real case study in this paper verify suitable responses and better services to the affected areas compared to what are provided through the respective single-period optimization model. [ABSTRACT FROM AUTHOR]

Unpredictable natural and man-made disasters highlight importance of humanitarian supply chain (HSC) to serve people and affected areas. The main challenges of applying effective relief operations are creating "swift trust" and "coordination" between aid organizations. Implementation of Industry 4.0 facilitates coordination and swift trust within HSC performance. The study intends to assess the readiness status of swift trust and coordination between stakeholders as well as to recommend the most suitable Industry 4.0 tools for improving relief operations. Firstly, a comprehensive set of critical success factors for implementing Industry 4.0 tools are introduced. The factors are categorized into limited groups using Exploratory Factor Analysis. In the next step, hierarchy fuzzy expert system is designed for assessing the readiness status of swift trust and coordination as well as to suggest the most suitable Industry 4.0 tool for enhancing HSC performance within given case study. The framework was applied for three aid organizations to address the pandemic disease in Iran. The outcome denotes that the organization has the highest readiness in logistic and transparency while information quality received the lowest readiness value. In addition to that, the organization should invest on the development of Industry 4.0 enablers including "Internet of Things and Big Data Analytics". The study extends organizational information process theory within HSC for reaching competitive advantage by information processing. The study suggests theoretical and practical implications by introducing a comprehensive set of critical success factors for implementation of Industry 4.0 and providing practical advice for enhancing HSC performance. [ABSTRACT FROM AUTHOR]

Construction of new dams in undeveloped transboundary basins causes two serious disputes between the stakeholders: conflicts over more water interest and over the new dams' locations. Hence, water development planning of these basins needs to be done in conjunction with the examination of stakeholders' new water shares. This study extends the model presented in Roozbahani et al. (Water Resour Manag 31:4539–4556, 2017) to be multi-objective and applies the methodology outlined in Roozbahni et al. (Ann Oper Res 229:657–676, 2015a) to solve the model. The proposed three steps approach determines the equitable allocation of the surface water of an undeveloped transboundary basin while determining optimal number, locations and capacities of new dams. The first step utilizes a mixed-integer-multi-objective model to outline the water shares of stakeholders, as well as optimal dam locations for a given number of dams. Using a sensitivity analysis, the second step pinpoints the required number of dams. The role of third step is the exploration of the dams' lowest possible capacities. Environmentally, our approach takes the entire watershed's water requirements into account. We have applied the proposed approach to the Sefidrud Basin, a transboundary basin located in Iran. The results of the approach show that, to significantly improve the security of the Sefidrud Basin's water supply, three new dams would be optimal. [ABSTRACT FROM AUTHOR]

In a natural disaster such as an earthquake, very often due to the extensive number of severe injuries, demands for blood units sharply increase in emergency hospitals. Regarding such a problem, we propose a new robust two-stage multi-period stochastic model for the blood supply network design with the consideration of a possible natural disaster. The demand for blood units from different types and their derivatives including plasma and platelets are uncertain variables. As a novel contribution, the possibility of transfusion of one blood type as well as its derivatives to other types based on the medical requirements is considered in the optimization model. The pertinent network consists of three layers including the donated areas, the collection blood centers, and the transfusion blood center, which is usually a governmental organization. The model is also constructed for considering a likely earthquake in Tehran (the capital of Islamic Republic of Iran) using a professional report prepared in the year 1999 and also updated in a next research work. The scenarios for the demands of blood units and their derivatives are generated based on these reports. The mathematical model is implemented and assessed in a proper way using the simulation method. [ABSTRACT FROM AUTHOR]

With the severe outbreak of the novel coronavirus (COVID-19), researchers are motivated to develop efficient methods to face related issues. The present study aims to design a resilient health system to offer medical services to COVID-19 patients and prevent further disease outbreaks by social distancing, resiliency, cost, and commuting distance as decisive factors. It incorporated three novel resiliency measures (i.e., health facility criticality, patient dissatisfaction level, and dispersion of suspicious people) to promote the designed health network against potential infectious disease threats. Also, it introduced a novel hybrid uncertainty programming to resolve a mixed degree of the inherent uncertainty in the multi-objective problem, and it adopted an interactive fuzzy approach to address it. The actual data obtained from a case study in Tehran province in Iran proved the strong performance of the presented model. The findings show that the optimum use of medical centers' potential and the corresponding decisions result in a more resilient health system and cost reduction. A further outbreak of the COVID-19 pandemic is also prevented by shortening the commuting distance for patients and avoiding the increasing congestion in the medical centers. Also, the managerial insights show that establishing and evenly distributing camps and quarantine stations within the community and designing an efficient network for patients with different symptoms result in the optimum use of the potential capacity of medical centers and a decrease in the rate of bed shortage in the hospitals. Another insight drawn is that an efficient allocation of the suspect and definite cases to the nearest screening and care centers makes it possible to prevent the disease carriers from commuting within the community and increase the coronavirus transmission rate. [ABSTRACT FROM AUTHOR]

COVID-19 is a highly prevalent disease that has led to numerous predicaments for healthcare systems worldwide. Owing to the significant influx of patients and limited resources of health services, there have been several limitations associated with patients' hospitalization. These limitations can cause an increment in the COVID-19-related mortality due to the lack of appropriate medical services. They can also elevate the risk of infection in the rest of the population. The present study aims to investigate a two-phase approach to designing a supply chain network for hospitalizing patients in the existing and temporary hospitals, efficiently distributing medications and medical items needed by patients, and managing the waste created in hospitals. Since the number of future patients is uncertain, in the first phase, trained Artificial Neural Networks with historical data forecast the number of patients in future periods and generate scenarios. Through the use of the K-Means method, these scenarios are reduced. In the second phase, a multi-objective, multi-period, data-driven two-stage stochastic programming is developed using the acquired scenarios in the previous phase concerning the uncertainty and disruption in facilities. The objectives of the proposed model include maximizing the minimum allocation-to-demand ratio, minimizing the total risk of disease spread, and minimizing the total transportation time. Furthermore, a real case study is investigated in Tehran, the capital of Iran. The results showed that the areas with the highest population density and no facilities near them have been selected for the location of temporary facilities. Among temporary facilities, temporary hospitals can allocate up to 2.6% of the total demand, which puts pressure on the existing hospitals to be removed. Furthermore, the results indicated that the allocation-to-demand ratio can remain at an ideal level when disruptions occur by considering temporary facilities. Our analyses focus on: (1) Examining demand forecasting error and generated scenarios in the first phase, (2) exploring the impact of demand parameters on the allocation-to-demand ratio, total time and total risk, (3) investigating the strategy of utilizing temporary hospitals to address sudden changes in demand, (4) evaluating the effect of disruption to facilities on the supply chain network. [ABSTRACT FROM AUTHOR]

These two main objectives of this study are to present a theoretical model to explain how business intelligence capabilities influence the company's supply chain sustainability and to examine the relationships among different BI and CSCS dimensions. This study was conducted with the use of a standard BI questionnaire along with the United Nations CSCS questionnaire among 234 Iranian pharmaceutical companies, from which 188 were also surveyed. Smart pls3 and partial least squares methods were used for validity as well as reliability evaluation of the measurement model. According to the findings, BI significantly affects the sustainability of the pharmaceutical supply chain and some of its dimensions, including vision, scope, and internal aspects, thereby the hypothesis indicating the effect of BI on these dimensions was accepted. However, there was an insignificantly positive relationship between BI and the other dimensions of CSCS, including expectation, engagement, and goals; hence, the hypothesis indicating the effect of BI on these dimensions was rejected. If the policy of the board is to implement supply chain sustainability, BI can have a greater impact on the company. Otherwise, BI may be implemented with not much effect though it can be indirectly beneficial to these companies. No studies have been performed on direct examination of the relationship of BI and CSCS and their various dimensions with the use of an extensive survey among Iran's pharmaceutical companies. Also, this study reveals some facts about the sustainability of the pharmaceutical supply chain, BI, and relevant issues as significant obstacles against a sustainable supply chain and BI. This article also supports the UN questionnaire on supply chain sustainability and adopts it in the surveys. Furthermore, various social networks such as Facebook, Twitter, and Instagram were compared, and it was concluded that the data required for the pharmaceutical industry was more accessible from Twitter, in comparison to the other social networks. [ABSTRACT FROM AUTHOR]

As a powerful tool for decision analysis, fifteen distance measures are incorporated into the technique for order preference by similarity to ideal solution (TOPSIS) to carry out a knowledge-driven approach in mineral exploration, whereby a multi-criterion decision making problem is solved for spatial data analysis. The kernel of the original version of the TOPSIS method as an integral part of the analysis will be defined on Euclidean distance. This research investigates to define the kernel on each of 15 distance measures from four general families of functions including L1, intersection, inner product and fidelity. To check the performance of each distance measure, the North Narbaghi porphyry copper mine in Iran was chosen. The significance of kernel substitution lies in the improvement of synthesized evidence maps in comparison to the Euclidean-based TOPSIS method in this study. [ABSTRACT FROM AUTHOR]

In recent years, attention to blood supply chain in disaster circumstances has significantly increased. Disasters, especially earthquakes, have adverse consequences such as destruction, loss of human lives, and undermining the effectiveness of health services. This research considers a six-echelon blood supply chain which consists of donors, blood collection centers (permanent and temporary), regional blood centers, local blood centers, regional hospitals, and local hospitals. For the first time, we considered that helicopters could carry blood from regional hospitals to local hospitals and return injured people that cannot be treated in local hospitals to regional hospitals due to the limited capacity. In addition to the above, different transportations with limited capacities regarded, where the optimal number of required transportations equipment determined after the solution process. This research aims to avoid the worst consequences of a disaster using a neural-learning process to gain from past experiences to meet new challenges. For this aim, this article considers three objective functions that are minimizing total transportation time and cost while minimizing unfulfilled demand. The model implemented based on a real-world case study from the most recent earthquake in the Iran–Iraq border which named the deadliest earthquake of 2017. Based on our results, we learned how to design an efficient blood supply chain that can fulfill hospitals blood demand quickly with the lowest cost using simulation and optimization processes. Moreover, we performed in-depth analyses and provided essential managerial insights at last. [ABSTRACT FROM AUTHOR]

This research proposes a new tri-objective mathematical model for designing blood supply chain network in emergency situations. The mathematical model aims to minimize total supply chain costs and transportation time between facilities while maximizing total testing reliability of the donated blood in the laboratories. The model considers five echelons including blood donor groups, blood collection facilities, laboratories, blood centers and hospitals. Different transportation means with variant speed and capacity are considered in the model to carry the blood between facilities. Since, most of the main parameters of the mathematical model are tainted with uncertainty in real-world applications, two robust possibilistic flexible chance constraint programming (RPFCCP) and possibilistic flexible chance constraint programming models are developed to provide risk-averse and robust solutions to the decision makers. In addition, the application of the proposed multi-objective mathematical model is investigated in a real-world case study using real data on Iran's capital, Tehran, which is considered to be a potential place for a destructive earthquake. Using different realizations, the applicability and efficiency of the models are investigated in the case study. The results indicated that the RPFCCP model is able to handle uncertainty in the parameters of the objective function and constraints more efficiently and is able to provide robust and risk-averse solutions for the problem which are resistant to different scenarios. [ABSTRACT FROM AUTHOR]

This research proposes a new multi-objective mathematical model to design efficient and effective blood supply chain network in earthquakes. For the first time in this field of knowledge, the devastating impact of earthquake destruction radius is considered on blood supply chain network based on its magnitude. Two different transportation means, with variant speed and capacity, are employed to carry the blood from blood collection centers to blood centers. However, the number of available conveyors is limited in each site. To solve the proposed multi-objective mixed integer linear programming model, five multi-objective decision making methods as well as the lexicographic weighted Tchebycheff method are utilized to provide the decision maker with Pareto optimal solutions. Further, the application of the proposed multi-objective mathematical model is investigated in a real-world case study using data from the latest earthquakes in one of the recent activated faults of Iran's capital, Tehran, which is considered to be a potential place for a severe earthquake. Using different solution approaches, various Pareto optimal solutions are obtained for the case study. The results indicated that the proposed mathematical model is able to design the most cost and time efficient blood supply chain in a severe earthquake. At the end, sensitivity analyses are performed to explore the effects of any changes in main parameters of the multi-objective mathematical model on the objective functions value to demonstrate the most critical parameter. [ABSTRACT FROM AUTHOR]