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Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.

A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.

The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.

The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

This study aims to design a facility network for the weavers to do direct business in the Indian handloom industry by using tourists as potential customers.

Data from 4,001 weavers of the handloom industry and 82 tourist locations of Varanasi city were collected. This data was analysed using the k-mean and elbow methods to determine the locations and the optimal number of collection centres, selling stores and warehouses to provide opportunities for the weavers to do direct business.

The study's findings showed that a greater opportunity could be provided with four optimal collection centres and selling stores, along with two warehouses in the handloom industry of Varanasi city. These results provide valuable guidance for policymakers to plan the Varanasi handloom network of facilities efficiently and effectively to improve the conditions of weavers.

Determining the optimal locations is crucial for designing a facility network. The proposed network will aid the government and policymakers in comprehending and pinpointing potential sites to establish new facilities in the handloom industry in Varanasi, a city with tourism potential.

Medicine distribution logistics pattern in pharmaceutical supply chains is a hot topic, which aims to predict applicable and efficient medicine distribution patterns so that the medicine can be distributed effectively. This research aims to propose a new method, named density-distance method, that works based on Kth proximity using patient features (including age, gender, education, inherent diseases, systemic diseases and disorders); geographical features (city, state, population, density, land area) and supply chain features (destination and transportation system).

The proposed method of this research consists of two main phases where in the first phase, quantitative data analytics will be carried out to find out the significant factors and their impacts on medicine distribution. Then, in the next phase, a new Kth-proximity density-distance-based method is proposed to determine the best locations for the wholesalers while designing a supply chain.

The findings show that the proposed method can effectively design a supply chain network using realistic features. In addition, it is found that while the distance-density aggregate index is applied, the wholesalers' locations will be different compared to classic supply chain designs. The results show that age, public hygiene level and density are the most influential during designing new supply chains.

The outcomes of this research can be used in the medicine supply chains to predict appropriate medicine distribution logistics patterns.

In this research, the machine learning method based on the nearest neighbor has been used for the first time in the design of the supply chain network.