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The adverse interactions between disruptions can increase the supply chain's vulnerability. Accordingly, establishing supply chain resilience to deal with disruptions and employing business continuity planning to preserve risk management achievements is of considerable importance. The aforementioned idea is discussed in this study.

This study proposes a multi-objective optimization model for employing business continuity management and organizational resilience in a supply chain for responding to multiple interrelated disruptions. The improved augmented e-constraint and the scenario-based robust optimization methods are adopted for multi-objective programming and dealing with uncertainty, respectively. A case study of the automotive battery manufacturing industry is also considered to ensure real-world conformity of the model.

The results indicate that interactions between disruptions remarkably increase the supply chain's vulnerability. Choosing a higher fortification level for the supply chain and foreign suppliers reduces disruption impacts on resources and improves the supply chain's resilience and business continuity. Facilities dispersion, fortification of facilities, lateral transshipment, order deferral policy, dynamic capacity planning and direct transportation of products to markets are the most efficient resilience strategies in the under-study industry.

Applying resource allocation planning and portfolio selection to adopt preventive and reactive resilience strategies simultaneously to manage multiple interrelated disruptions in a real-world automotive battery manufacturing industry, maintaining the long-term achievements of supply chain resilience using business continuity management and dynamic capacity planning are the main contributions of the presented paper.

In this study, the authors investigate a different routing and scheduling problem in the field of home health care (HHC) management system. The purpose of this paper is to route and schedule the workday of health workers, assign the patients to suitable health workers, make accurate decisions to minimize costs, provide timely services and, in general, enhance the efficiency of HHC centers.

A mixed-integer linear programming model is developed to assign health workers to patients. The model considers health professionals with different skills, namely nurses and physicians. Additionally, three groups of patients are considered: patients who need a nurse, patients who need a physician and patients who need both. In the third group, the nurse must be present at the patient’s home following the physician’s visit in order to perform the required tasks.

The results of this study show a reduction in costs which results from the fewer health workers employed and dispatched in comparison with traditional approaches. With the help of our solution approach and model, HHC centers may not only successfully reduce their costs but also manage to meet their patients’ demands by assigning suitable nurses and physicians.

Previous studies have often focused on problems involving only one group of health professionals and rarely address problems involving multiple groups. The authors consider this a shortcoming, because in many cases, patients should be visited several times and by various health professionals.

The purpose of this paper is to estimate energy efficiency of 132 countries from 2007 to 2014 according to their performance, categorizing the nations into similar groups.

Data envelopment analysis model based on Goal Programming and then K-Means clustering algorithm are used to determine the efficiency and clustering the nations based on their efficiency performances.

The results of the study reveal that developing low-income countries could lead to high energy-efficiency scores, and countries with different development and income levels can become efficient in the field of energy consumption. Following the nations during a seven-year period also indicates that the changes in energy-related indicators such as renewable energy consumption and energy productivity are the main drivers to move a country between clusters.

The present study aimed to investigate whether similar nations with similar energy efficiency level in a cluster are similar in their development and income level, and changing the energy consumption pattern during the seven-year period could move the countries from a cluster to another one.