Facility Location Models

The facility location optimisation model is a critical aspect of strategic planning for a broad spectrum of public and private firms (Owen and Daskin, 1998). Facility location optimisation model problems derive their importance from two factors: their direct impact on the system‟s operating cost and the timeliness of response to the demand (Haghani, 1996). They are used to investigate where to physically locate a set of facilities (resources) so as to minimise the cost of satisfying some set of demands subject to some set of constraints (Hale and Moberg, 2003) where strategic planners are often challenged by difficult spatial resource allocation decisions (Owen and Daskin, 1998). Facility location models are mainly based on mixed integer programs with binary location variables associated with either evacuation operations, or stock pre-positioning, or stock pre-positioning and relief distribution (Caunhye et al. 2011). Meanwhile, location pre-positioning models are formulated by use of maximal covering location frameworks that locate facilities such that maximum demand discovered by a required amount of stock (Caunhye et al. 2011). The optimisation model has a well-developed theoretical background and it has developed actively since the formulation the classical Weber problem (1929) location theory. The facility location is viewed as a substantial body of knowledge with rich variety of models, methodologies and solution techniques that can be found in the literatures (Avella et al. 1998, and Francis et al. 1992). Most facility location optimisation models in emergency logistics combine the process of location with stock pre- positioning, evacuation or relief distribution (Caunhye et al. 2011). Facility location optimisation models are used in a wide variety of applications (Hale and Moberg, 2003) in commercial aspects, including:

1. Locating warehouses within a supply chain to minimise the average time to market; 2. Locating hazardous materials sites to minimise exposure to the public;

3. Locating railroad stations to minimise the variability of delivery schedules; 4. Locating automatic teller machines to best serve the bank‟s customers; and,

5. Locating a coastal search and rescue station to minimise the maximum response time to maritime accidents.

ReVelle et al. (1977), and Marianov and ReVelle (1995) discussed and reviewed the objective of emergency service facility location problem optimisation model with that of commercial facilities. The objective of facility location models for private sector problems is generally to

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minimise cost or maximise profit while the models addressing public and emergency services instead focus on user accessibility and response time.

2.7.3.1 Facility Location Problems and Applications

Studies of the facility location problem have tended to examine the emergency response. In an emergency, the primary objective is to save lives and, therefore, sending response units to the incident site at the earliest time has the highest priority (Dessouky et al. 2006). Murali et al. (2009) examined where to dispense medicine in an emergency given the restrictions of capacitated facilities and demand uncertainty. In their study they used location decisions made in advance and supplies which are pre-positioned. Most facility locations in the context of emergency services consider providing a single facility to cover a demand point (Church and ReVelle, 1974; Schilling et al. 1979). There has been some work on locating first responders for incidents. For example, Saccommano and Allen (1998) used a location model to determine sites for response-capable units (e.g. fire companies or police units) that could provide aid in case of spills of dangerous goods on a rural road network. Sathe and Miller-Hooks (2005) studied the relocation of first-response units (military and police forces) in order to maintain protection coverage to critical facilities under disaster conditions. However, locating first- response units is different from locating and sizing stocks of supplies where multiple commodities must be considered; for example, the commodities may have differing storage requirements and transportations costs (Rawls and Turnquist, 2010). The strategic decision choice for the optimal location and capacity of emergency clean-up equipment for oil spill response are also solved by optimisation location models (Iakovou et al. 1997; Psaraftis et al. 1986; Wilhelm and Srinivasa, 1996).

Pre-positioning of the facility is often used in military strategic operations because it helps ensure the timely support of forces during the initial phases of a military operation (King, 1991). The military tend to use pre-positioning strategies for the supply of equipment and ammunition to facilitate rapid and effective response to conflicts (Johnstone et al. 2004). In military situations, pre-positioning is defined as a “stockpiling of equipment and supplies at, or near the point of plane use (or point of debarkation)” (Department of the Air Force, 1981). Military approaches may or may not be applicable to peace-time emergencies. For example, Anderson (1998) uses available shipping assets to redistribute weapons based on a

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predetermined positioning plan for the Pacific Fleet. Sentlinger (2000) looked at the optimal weapons pre-positioning mix of U.S. Naval weapon stations with a focus on minimising demand shortfalls during a myriad of conflicts. Johnstone et al. (2004) minimise the overall response time by pre-positioning the equipment and ammunition to facilitate rapid and effective response to conflicts in military. Pre-positioning in military operation has its disadvantages. Pre-positioned stocks require duplicate equipment and supplies, as well as additional training and maintenance to maintain the material in operational condition (Johnstone et al. 2004). Pre-positioned assets are safer and easier to defend than land-based counterparts (Department of the Navy, 1998); therefore, the pre-positioned sites require security because the facility is not invulnerable to attack (Johnstone et al. 2004). In addition, fiscal constraints come into play as pre-positioning requires additional funding (King, 1991).

2.7.3.2 Humanitarian Relief Pre-Positioning Facility Location Problems

Although research on facility location problem is extensive, in terms of theory and applications these problems have not received much attention in the domain of humanitarian relief (Balcik and Beamon, 2008). The location and capacities of the resource providers are key components in managing response efforts after an event; however, relatively little research has been conducted on the topic on the topic of a priori planning for pre-positioning specific resources (Rawls and Turnquist, 2010). The importance of developing a strategic pre- positioning facility location was discussed by Adinofli et al. (2007). The ineffective and inefficient result of ad-hoc methods have been mentioned with regard to facility location and stocking decisions. The lack of a global stock positioning system has made it difficult to provide enough information for the research in their study. Balcik et al. (2010) discuss the role of pre-positioning warehouses in the aspect of the coordination practices in disaster relief. Gatigon et al. (2010) illustrated the implementation of a decentralised model at the international humanitarian organisation using the pre-positioning warehouse concept. Tatham and Kovács (2007) introduced the alternative strategy of quick response to satisfy a known requirement (i.e. flying supplies into an area to meet the needs of the beneficiaries) with the military „sea-basing‟ and „floating warehouse‟. In other words, a suitably sized ship is held at very short notice to transit to the relevant country with a cargo containing sufficient food and non-food items to meet the immediate needs of a significant number of beneficiaries. Balcik and Beamon (2008) studied the pre-positioning of facility location considering the response to

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the quick-onset disasters. The model considers pre-disaster and post-disaster budget restrictions but does not consider network reliability. Dekle et al. (2005) used set-covering location model to locate disaster recovery centres in Florida. Campbell and Jones (2011) examine the decision of where to preposition supplies in preparation for disaster and how much to stock at the warehouse considering the possibility of the warehouse being destroyed. Hale and Moberg (2005) propose the use of a decision process with set cover location model from the location science field to help establish a network of secure site locations. They suggest the optimal location with the balance of operational effectiveness and cost-efficiency by identifying the minimum number and possible location of off-site storage facilities. Rawls and Turnquist (2010) provide an emergency response pre-positioning strategy for disaster threats considering uncertainty in demand for the stocked supplies, as well as uncertainty regarding transportation network availability after the disaster event. Ukkusuri and Yushimoto (2008) developed a model for pre-positioning of supplies and location routing problem incorporating the reliability of the ground transportation network. Table 2.9 summarises the studies of the pre-positioning location models in humanitarian relief operations.

Table 2.9 Humanitarian pre-positioning facility location literature

Author(s) Contribution

Balcik and Beamon (2008)

 The number and the location of the distribution centre and stock held with affect directly

 As the number of distribution centres decreased, the capacity differences among the distribution centres increases

 Integrates facility location and inventory decisions, considers multiple item types and captures budgetary constraints and capacity restriction

Campbell and Jones (2011)

 Examine the decision of where to preposition supplies in preparation for disaster and how much to preposition at a location

 Cost model use to select the single best supply point location from a discrete set of choices and how it can be embedded within exiting location algorithms to choose multiple supply points considering the possibility of the facility being destroyed

Dekle et al. (2005)  Identity three idealised disaster recovery centre location requiring them to be within 20 miles to the residence

 Results provided significant improvements to the original location while maintaining acceptable travel distance

Hale and Moberg (2005)

 Propose secure site location using set cover location model

 Minimise the number and possible locations

 Consider location of external events which could prevent from accessing the site Rawls and Turnquist

(2009)

 Provides an emergency response pre-positioning strategy for disaster threats

 Considers uncertainty in demand for the stocked supplies as well as uncertainty regarding transportation network availability after an event

Soon (2007)  Models the problem of pre-positioning hurricane supplies for-profit driven supply chains

 Incorporates transport cost Ukkusuri and

Yushimoto (2008)

 Incorporate the reliability of the ground transportation network

 Maximise the probability that all the demand points can be served by a service location given fixed probabilities of link/node failure and a specified budget constraint

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The location problem literature which is listed in Table 2.9 mainly focuses on the finding a potential optimal location with optimisation models rather than focusing on finding the important attributes for the location of the pre-positioned warehouse in humanitarian relief sectors. In addition, it is difficult to establish preferences between factors by reference to an explicit set of objectives. In the literature there are a large number of facility location evaluation and selection models and reviews, some of which have concentrated on the selection of the suitable sites in the humanitarian warehouse exclusively. However, few studies have used Multi-Criteria Decision Making (MCDM) especially in Multi-Attribute Decision Making (MADM) methods, considering human judgements, tangible, intangible and multiple criteria. Although there are a limited number of publications evaluating the humanitarian pre-positioned warehouse in the literature (as briefly described above), the use of the Analytic Hierarchical Process (AHP) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is rare. The warehouse selection decision is a process during which multiple criteria must be considered (Gattorna et al. 1988). This is the most powerful motivation to consider the site selection problem in this present study.

2.8 Chapter Summary

This chapter has provided an overview of published literature relating to the research topic. The difference between the humanitarian relief logistics and commercial logistics has been discussed and several similar parallel aspects between the two have been identified. The involved actors in humanitarian relief logistics have also been discussed. The study of disaster relief management phases shows the there is no single stage that should be overlooked. The preparedness stage has been emphasised in many studies with various approaches to prevent the crucial impact due to the disaster. For humanitarian relief logistics, pre-purchase of stock has increased the need to establish the warehouse pre-positioning. The structure and the various locations of the pre-positioned warehouse are also presented. The importance of the pre-positioning warehouse strategy is currently increasing within the humanitarian organisations, as can be seen in the literature. The facility location model for humanitarian relief logistics is mainly researched using a mathematical approach. There is a gap in the literature in that few previous studies have applied a study of human opinion for the multi criteria decision-making process.

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CHAPTER 3

LITERATURE REVIEW II: AHP AND TOPSIS LOCATION