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Every day, small and big incidents happen all over the world, and given the human, financial and spiritual damage they cause, proper planning should be sought to deal with them so they can be appropriately managed in times of crisis. This study aims to examine humanitarian supply chain models.

A new model is developed to pursue the necessary relations in an optimal way that will minimize human, financial and moral losses. In this developed model, in order to optimize the problem and minimize the amount of human and financial losses, the following subjects have been applied: magnitude of the areas in which an accident may occur as obtained by multiple attribute decision-making methods, the distances between relief centers, the number of available rescuers, the number of rescuers required and the risk level of each patient which is determined using previous data and machine learning (ML) algorithms.

For this purpose, a case study in the east of Tehran has been conducted. According to the results obtained from the algorithms, problem modeling and case study, the accuracy of the proposed model is evaluated very well.

Obtaining each injured person's priority using ML techniques and each area's importance or risk level, besides developing a bi-objective mathematical model and using multiple attribute decision-making methods, make this study unique among very few studies that concern ML in the humanitarian supply chain. Moreover, the findings validate the results and the model's functionality very well.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

This study employs Stein Rokkan's methodological approach to analyse state formation in the Greater Middle East. It develops a conceptual framework distinguishing colonial, populist and democratic pacts, suitable for analysis of state formation and nation-building through to the present period. The framework relies on historical institutionalism. The methodology, however, is Rokkan's. The initial conceptual analysis also specifies differences between European and the Middle Eastern state formation processes. It is followed by a brief and selective discussion of historical preconditions. Next, the method of plotting singular cases into conceptual-typological maps is applied to 20 cases in the Greater Middle East (including Afghanistan, Iran and Turkey). For reasons of space, the empirical analysis is limited to the colonial period (1870s to the end of World War 1). Three typologies are combined into one conceptual-typological map of this period. The vertical left-hand axis provides a composite typology that clarifies cultural-territorial preconditions. The horizontal axis specifies transformations of the region's agrarian class structures since the mid-19th century reforms. The right-hand vertical axis provides a four-layered typology of processes of external intervention. A final section presents selected comparative case reconstructions. To the authors' knowledge, this is the first time such a Rokkan-style conceptual-typological map has been constructed for a non-European region.

Organizational resilience, defined by a firm’s speed in reaching a dynamic equilibrium after a shock and after the shocks are absorbed, and crisis management are critical in a global crisis. The concept of resilience is increasingly used in the economic press; nevertheless, few studies demonstrate empirically how firms became resilient and the lessons to be learned from it. Traditionally, the concept of resilience is approached as resistance in the face of a crisis. The authors go further by showing three-loop learning, which is part of a logic of innovation and regeneration. This study aims to examine how a business can regenerate itself by effectively managing the external threats and disruptions caused by a crisis. Also, this study deepens knowledge on learning process. The double-loop learning process is known in the literature as enabling firms to learn from unexpected events and react accordingly. The findings point out a third loop implying the co-invention of a new business model and a collective mindfulness of changes made.

Using longitudinal data, the authors investigate how the global crisis affects merger negotiations between two companies. This study analyzes the period of dialogue (negotiation) between the two entities with a view to carrying out a merger and then their withdrawal from the project during the pandemic, reshuffling the cards for each company. The negotiation period is not normally disclosed because of its highly confidential and strategic nature and it is therefore difficult for researchers to access merger operations at the negotiation stage. From this viewpoint, this case study was chosen because of the availability of generally inaccessible documentation.

This in-depth case study provides new insights on organizational resilience and the recovery capacity of a firm. The results underline four main triggers that a firm should develop in facing a major crisis: skills; credits; previous and historical relationships; and corporate culture. Recovery capacity depends on reactivity, flexibility, learning and regeneration. Finally, this study points out a three-loop learning experience that can be understood as a learning process in two steps to generate lasting and adaptive changes.

The limitations are those concerning a single case study.

This study highlights the ability to deal with unexpected events. First, this work identifies concrete items that can be perceived by managers as elements enabling a firm to develop resilience. Second, the results show main elements enabling this capacity as reactivity – both companies react quickly and effectively to disturbances to limit the impact on their performance; or flexibility – firms adapt their business model to deal with disruptions. Third, this work underlines a learning capacity process in three steps to recover capacity. This process stimulates creativity and innovation by the teams and stakeholders by placing them at the heart of the change.

This case provides a vivid illustration of firms’ adaptation to a rapidly evolving context because of a global crisis. Theoretical concepts and empirical findings from the literature are combined to present a single consistent picture.