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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]

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]

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]

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]