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Over the past 50 years, a wealth of applications has resulted from researchers turning their attention to operations such as fire suppression, law enforcement and ambulance services. The 1970s might even be argued as the 'golden age' of this particular effort, producing many of the seminal works in fire station location planning, unit assignment and ambulance queuing models. Such efforts naturally continue through to the present, but with a focus shifting away from earlier contexts of established urban emergency service systems. Simultaneously, current evidence from the field suggests that far more work remains. In this paper, we review the operational research (OR) foundation in emergency response so far, highlighting the fact that most of what has been accomplished addresses the well-structured problems of emergency services. This, in turn, offers an explanation for some paradoxical challenges from the field: most of emergency response itself is semistructured, at best. While OR has traditionally focused on the management of an organization, emergency response ultimately requires the management of disorganization, suggesting an important OR growth area for the next 50 years.

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This study introduces one modeling methodology that describes a broad range of multiple stage production planning issues, including multiple limited resources with setup times and joint fixed cost relationships. An existing production system is modeled in this fashion, creating a new set of 1350 highly generalized benchmark problems. A computational study is conducted with the 1350 benchmark problems introduced in this paper and 2100 benchmark problems, with more restrictive assumptions, from the existing literature. The relative merits of a decomposition-based algorithm and a neighborhood search technique known as NIPPA, or the Non-sequential Incremental Part Period Algorithm, are assessed. NIPPA is generally the more successful of the two techniques, although there are specific instances in which the decomposition-based algorithm displayed a distinct advantage.

Purpose The purpose of this paper is to promote social network analysis (SNA) methodology within the humanitarian research community, surveying its current state of the art and demonstrating its utility in analyzing humanitarian operations. Design/methodology/approach A comprehensive survey of the related literature motivates a proposed agenda for interested researchers. Analysis of two humanitarian networks in Afghanistan demonstrates the use and utility of SNA, based on secondary data. In the second case study, the use of random graphs to detect network motifs is demonstrated using Monte Carlo simulation to create the benchmark null sets. Findings SNA is an adaptable and highly useful methodology in humanitarian research, quantifying patterns of community structure and collaboration among humanitarian organizations. Network motifs suggesting distinct affinity between particular agencies within humanitarian clusters are observed. Research limitations/implications The authors summarize common challenges of using SNA in humanitarian research and discuss ways to alleviate them. Practical implications Practitioners can use SNA as readily as researchers, to visualize existing networks, identify areas of concern and better communicate observations. Social implications By making SNA more accessible to a humanitarian research audience, the authors hope its ability to capture complex, dynamic relationships will advance understanding of effective humanitarian relief systems. Originality/value To the best of knowledge, it is the first study to conduct a systematic analysis of the application of SNA in empirical humanitarian research and outline a concrete SNA-based research agenda. This is also a currently rare instance of a humanitarian study using random graphs to assess observed SNA measures.