Differences between commercial SCs and Humanitarian SCs are: the commercial one is set up and remains unfixed for long time, whereas the latter is a temporary structure that is set up following incidents. The metrics in humanitarian SC may not be computed as cost. Uncertainty is inherent in design of SC.
In order to keep a record of these events, the Centre for Research on the Epidemology of Disasters (CRED) has developed the International Disaster Database (EM-DAT) containing information on the occurrence and impact of more than 18,000 disasters since 1900
Some useful conjunctive points: The primary difference between commercial and humanitarian supply chains lie in their goal and shape of the demand.
Although, the humanitarian logistics and disaster management are technically different, but the former becomes very challenging (and interesting to study) in disasters. Ways to categorize our work: 1. Think about pre- and post-disaster activities. We may want to assign separate sections to each and explain the OR-related papers. The bad news is, I expect to see, many models are both pre- and post-disaster, and we may not find enough papers to fill each section.
2. We can think of four steps as (1) Preparedness, (2) Response, (3) Mitigation, (4) Recovery, and separate OR-works based on their applicability to each of those four steps.
I think these information may be useful: mitigation phase : long-term effort to prevent occurrence of a disaster or to palliate effect of the disaster. also there is different terminologies in describing levels related to a disaster, e.g., preparation phase and pre-disaster phase and strategic level of planning, as well as response phase and post-disaster phase and operational phase are conveying the same notions.
Some famous OR journals are: 1) Operations Research, 2) Management Science, 3) Discrete Optimization, 4) Discrete Mathematics, 5) INFORMS Journal on Computing (All INFORMS published journals), 6) European Jnrl. of OR, 7) OR Society, 8) OR Spectrum, 9) Intl Journal of Production Econ, 10) Intl Journal of Prod Research, 11) Transportation Science, 12)Computers and IE, 13) Computers and OR, 14) OR Letters, 15) Optimization Letters,
As stated by the United Nations Office for Disaster Risk Reduction (UNISDR), the term “natural disaster” does not exist(citation not found: UNISDR website). Instead, disasters follow natural hazards, which are the direct result of various incidents such as floods, storms, droughts, earthquakes, and so on. The same reference indicates that about a thousand of natural incidents resulting in loss of life have happened solely in 2014. The overall loss cost is estimated to be beyond 1,500m USD. As a matter of fact, the global community has come into an agreement that a universal framework must be adopted by governments for the purpose of disaster risk reduction. (citation not found: UNISDR)
Aiming for mitigating the effects of disasters, the disaster risk reduction comprises several actions and efforts varying from wise managing the land and the environment to building and fortifying structures with the aim of protecting them against the impacts of disasters, the inevitable fact is that such incidents are part of human being life on the earth. Hence, the importance of preparedness for such events is of significant importance. To support various preparedness actions, we need to be equipped with a comprehensive set of tools that allow us optimal design and development of a comprehensive solution. This refers to all actions before the incident happens (such as establishing the warning systems, food and first-aid reservoirs, evacuation plans etc.) and after the incident happens (such as supply of relief-items from the local and/or global resources, relief-item distribution regimes, etc.)
The concept of supply chains (SC) exists more than a century and the related rich body of research works comprise supply chain management (SCM). As defined by Simchi-Levi et al. (citation not found: Simchi-Levi), SCM is the set of approaches that guarantees the on-time production and distribution of merchandises by integrating suppliers, manufacturers, warehouses, and stores, which aim to minimize the system-wide costs and satisfy the required service levels. For years, the SCM researchers have studied commercial (business) SCs, i.e., the ones that involve the cooperation of various agents with the aim of producing profits. In contrast, the term “humanitarian logistics” and its related challenges, until relatively recently, have not attracted serious consideration by academic community at large (citation not found: ChristopherTatham).
Despite a number of similarities, the humanitarian logistics is differentiated from business SCM through several key aspects. First, while the satisfaction of the customers must ultimately lead to more profit for the agents involved in the business SCM, the humanitarian logistics is about the customers’ cost reduction (cost of life and property loss and injuries). Second, several decisions in business SCs are made for the long-run, whereas the decisions in the humanitarian logistics are temporary and subject to change according the type and the location of the disaster. Third, the time-sensitivity of making optimal decisions is significantly more pronounced in humanitarian logistics; often the time-window to take actions is very tight and the whole system (human being, structures, etc.) may incur a significant amount of costs if the decision-making process takes considerable amount of time. There are some other aspects that have been pointed out in the literature. For example, Christopher and Tatham (citation not found: ChristopherTatham) mention the absence of clarity in identifying the customers and their needs when studying humanitarian logistics.
The concept of uncertainty has very-well studied within the SCM framework. In particular, the inherent uncertainties in the demand and the lead time has been the focal points of several studies. While being similar, the uncertainty poses a more serious challenge in humanitarian logistics compared to business SCs.
The opportunity to learn from the realization of uncertainties is far richer in business SCs. First, business SCs are more prevalently employed in the real-world. This allows gathering more data about the uncertainty, which allow better prediction about the outcomes. Besides, there is more opportunities to learn about uncertainty and its impacts through benchmarking from similar business SCs. As a result, more samples are available, which permits better understanding about the random outcomes of the uncertainties.
There are more uncertain parameters that are to be considered when studying humanitarian logistics compared to business logistics. For example, the location of warehouses, the availability of the distribution networks, and the physical locations of the demand points (e.g., affected people) are naturally uncertain. In contrast, for example, the location/allocation decision in commercial SCs remain unchanged often for longer period of time.
The outcomes of the uncertainty can have very catastrophic impacts on human lives. While the errors and wrong decisions due to the randomness in business SCs are measured in monetary values, the worst-case outcomes of natural disasters may be loss of lives. As a result, the models that immune the preparedness actions against the worst outcomes may seem more appropriate compared to the ones that are built upon the expected costs.
Hence, it is crucial to devote independent efforts to study the uncertainty and its impacts in humanitarian logistics.
“Mathematical programming”, “Probability and Statistics”, and “Simulation” are a few of the widely-used approaches to study humanitarian logistics problems. Among the aforementioned approaches, mathematical programming has attracted the most attention of OR community over the last decade (citation not found: RahaAkhavanORModelsSurvery). In particular, several optimization problems under uncertainty have been studied that arise in response planning, preparedness planning, mitigation planning, etc. The reader is advised to refer to for further details in this regard.
Several approaches to incorporate the uncertainty within optimization problems have been proposed since the realization of the need adopt more realistic mathematical programs. In particular, the majority of the efforts have been devoted to one of the two most prominent paradigms: 1) stochastic programs, and 2) robust optimization problems. While the latter emphasizes on the importance of imm