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This study aims to design a proper supply chain network for the vaccine industry in Iran, which considers several features such as uncertainties in demands and cost, perishability of vaccines, wastages in storage, limited capacity and different priorities for demands.

This study presents a mixed-integer linear programming (MILP) model and using a robust counterpart approach for coping with uncertainties of model.

The presented robust model in comparison with the deterministic model has a better performance and is more reliable for network design of vaccine supply chain.

This study considers uncertainty in the network design of vaccine supply chain for the first time in the vaccine context It presents an MILP model where strategic decisions for each echelon and tactical decisions among different echelons of supply chain are determined. Further, it models the difference between high- and low-priority demands for vaccine.

This paper aims to propose an integrated supplier selection, order allocation, transportation planning model, along with investment planning for corporate social responsibility (CSR), over a given multi-period horizon under uncertainty. Furthermore, a customer’s behavior to pay more money for items with CSR attributes is considered in the total market demand.

The objective functions, i.e. social value of purchasing, total profit (TP), total delivery lead-time, total air pollution, total water pollution and total energy consumption with regard to a number of constraints are jointly considered in a multi-product system. It is worth noting that operational- and sustainable-related parameters are usually vague and imprecise in this area. Therefore, this paper develops a new fuzzy multi-objective optimization model to capture this inherent fuzziness in critical data.

Through the numerical examples in the textile industry, the application of the model and usefulness of solution procedures are carried out. The numerical results obtained from the proposed approach indicate the efficiency of the solution algorithm in different instances. Moreover, the authors observe that social investment of the buyer, to stimulate market demand, can affect the TP and also involve the total contribution of suppliers in social responsibility.

This research work concentrates on providing a procurement and inventory model through the lens of sustainability to enable textile supply chain managers and related industries to apply the approach to their inventory control and supply management. Totally, the proposed methodology could be applied by many fabric buyers of textile industry tackling purchasing issues and attempting to perfect understanding of social supply chains.

The purpose of this paper is to analyze the optimal reinsurance contract structure from the crop insurer's perspective.

A very powerful and flexible empirical‐based reinsurance model is used to analyze the optimal form of the reinsurance treaty. The reinsurance model is calibrated to unique data sets, including private reinsurance experience for Manitoba, and loss cost ratio (LCR) experience for all of Canada, under the assumption of the standard deviation premium principle and conditional tail expectation risk measure.

The Vasicek distribution is found to provide the best statistical fit for the Canadian LCR data, and the empirical reinsurance model stipulates that a layer reinsurance contract structure is optimal, which is consistent with market practice.

While the empirical reinsurance model is able to reproduce the optimal shape of the reinsurance treaty, the model produces some inconsistencies between the implied and observed attachment points. Future research will continue to explore the reinsurance model that will best recover the observed market practice.

Private reinsurance premiums can account for a significant portion of a crop insurer's budget, therefore, this study should be useful for crop insurance companies to achieve efficiencies and improve their risk management.

To the best of the authors' knowledge, this is the first paper to show how a crop insurance firm can optimally select a reinsurance contract structure that minimizes its total risk exposure, considering the total losses retained by the insurer, as well as the reinsurance premium paid to private reinsurers.

The cluster supply chain is widely used in the professional towns in China, and improves the competitiveness of small and medium enterprises through integrating the supply chain with the industrial cluster. The paper aims to discuss this issue.

This paper studies a cluster supply chain under vendor managed inventory (VMI) system, which includes vendors, third-party logistics (TPL) enterprises and retail enterprises, and aims to study the replenishment decisions and coordination contracts in the supply chain. The economic order quantity model is applied to analyze the influence of marginal transportation cost factor under two replenishment modes – direct delivery and milk-run delivery, in order to find out the optimal replenishment decisions corresponding to different marginal transportation cost factors. And then, the revenue sharing contract is used to identify the change of profits of enterprises in the supply chain before and after the coordination contract.

It is concluded that the marginal transportation cost factor is an important factor influencing the replenishment decision especially in milk-run delivery, and the introduction of the revenue sharing contract can improve the revenue in the supply chain.

This is the first study that explores the relationship between a single transport cost and a single transport batch of cluster supply chain in centralized VMI & TPL system. The conclusions of the study have certain theoretical significance for the decision making and coordination of cluster supply chain.

The strategic location of city logistics facilities may help to establish more efficient urban logistics systems, reduce social and environmental costs of urban freight transport, and improve urban traffic conditions. In addition, it may allow a number of shippers or freight carriers to jointly operate freight vehicles and terminals or information systems while allowing them to have the capability to provide higher levels of services to their customers. This paper considers the problem of selecting a location for a city logistics facility while considering linguistic factors. This paper identifies the important factors in the selection of a location for a city logistics facility by performing a case study which applies the analytic hierarchy process method on data from Chongqing, China. The optimal location in Chongqing is then determined by using the fuzzy synthetic evaluation method. The results of this paper are expected to help municipal governments select appropriate locations for city logistics facilities and quantify the advantages and disadvantages of alternative locations.

The purpose of this paper is to measure the financial risk and optimal capital structure of a corporation.

Irregular disjunctive programming problems arising in firm models and risk management can be solved by the techniques presented in the paper.

Parallel processing and mathematical modeling provide a fruitful basis for solving ultra-scale non-convex general disjunctive programming (GDP) problems, where the computational challenge in direct mixed-integer non-linear programming (MINLP) formulations or single processor algorithms would be insurmountable.

The test is limited to a single firm in an experimental setting. Repeating the test on large sample of firms in future research will indicate the general validity of Monte-Carlo-based VAR estimation.

The authors show that the risk surface of the firm can be approximated by integrated use of accounting logic, corporate finance, mathematical programming, stochastic simulation and parallel processing.

Parallel processing has potential to simplify large-scale MINLP and GDP problems with non-convex, multi-modal and discontinuous parameter generating functions and to solve them faster and more reliably than conventional approaches on single processors.

The purpose of this study is to discuss the principles and factors that influence the site selection of emergency medical facilities for public health emergencies. This paper discusses the selection of the best facilities from the available facilities, proposes the capacity of new facilities, presents a logistic regression model and establishes a site selection model for emergency medical facilities for public health emergencies in megacities.

Using Guangzhou City as the research object, seven alternative facility points and the points' capacities were preset. Nine demand points were determined, and two facility locations were selected using genetic algorithms (GAs) in MATLAB for programing simulation and operational analysis.

Comparing the results of the improved GA, the results show that the improved model has fewer evolutionary generations and a faster operation speed, and that the model outperforms the traditional P-center model. The GA provides a theoretical foundation for determining the construction location of emergency medical facilities in megacities in the event of a public health emergency.

First, in this case study, there is no scientific assessment of the establishment of the capacity of the facility point, but that is a subjective method based on the assumption of the capacity of the surrounding existing hospitals. Second, because this is a theoretical analysis, the model developed in this study does not consider the actual driving speed and driving distance, but the speed of the unified average driving distance and the driving distance to take the average of multiple distances.

The results show that the method increases the selection space of decision-makers, provides them with stable technical support, helps them quickly determine the location of emergency medical facilities to respond to disaster relief work and provides better action plans for decision makers.

The results show that the algorithm performs well, which verifies the applicability of this model. When the solution results of the improved GA are compared, the results show that the improved model has fewer evolutionary generations, faster operation speed and better model than the intermediate model GA. This model can more successfully find the optimal location decision scheme, making that more suitable for the location problem of megacities in the case of public health emergencies.

The research findings provide a theoretical and decision-making basis for the location of government emergency medical facilities, as well as guidance for enterprises constructing emergency medical facilities.