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However, excluding these flows would prevent them from taking advantage of value added services that could be provided in the hub. This also implies that, for other flows, it would always be better to use routes that go through a hub. This does not correspond to reality, as it cuts out the use of other route options, which could end up being more profitable or guaranteeing a determined service level.
This may even artificially overload the hub's usage, negatively impacting constructive aspects related to facilities' capacity and service dimensioning, leading ultimately to unnecessary investments. Representing the routes through direct connections between origins and hubs, and between hubs and destinations makes it difficult to observe the real distribution of flows.
This hinders the analysis from an infrastructure use perspective, as well as the evaluation of flow changes resulting from the implementation of a logistics hub. The use of HLP models itself actually leads us to believe that this may be a reason why the impact of a hub on networks and infrastructure planning is a subject that still requires further research. The representation of other existing connections also would allow the use of different routes between origins and destination. In this case, the resulting graph would also include transit nodes, resembling transshipment models.
Hence, an expanded representation would remain compatible to this class of problems. Yet it should be noted that the simplifications made are closely related to the complexity of solving HLP problems with larger graphs, once this is a NP-hard problem, as well as to the availability of models and solving techniques that would enable us to find a viable solution in a timely manner. There are some applications where the classic HLP model would be well suited.
Logistics hubs have been long dedicated to air transportation or postal services Allaz, In these cases, transportation via hub is mandatory and direct connections do not make much sense, either with regards to the transportation mode used or to the characteristics of the service performed.
However, this might lead authors to disregard features that are common to other scenarios. When locating a logistics hub, it may also be interesting to evaluate the available infrastructure and the need to build new links in order to improve the transportation performance. However, the design of large-scale networks with a variety of connections and logistics hubs is still a challenge, especially if we want to include this in the location model. Clearly, a free network design where many new links could be established would add great complexity to the model and be in conflict with the investment capacity of a region.
In the same direction, sets of new projects could be formulated, simplifying the model to test pre-defined network topologies. Since neither of the two types of models alone allows us to tackle all matters related to locating a logistics hub, an analytical modelling approach seems to be more suitable, combining features of both multi-criteria and single-criterion models. A multi-criteria model, taking into account strategic matters of hub positioning and regional competitiveness, could be adopted initially to define a location site, or a list of them.
The results would then be used as an input for a more generic network flow model; i. With this, a network could be represented in greater detail, allowing for the choice between different routes and assessment of the hubbing effect on the flow's distribution. This network flow model could take into account microeconomic perspectives for decision making, addressing transportation cost reduction and other issues related to the benefits that could obtained by hub users.
Thus, we would be able to combine both perspectives in one approach for solving the problem. Regarding the solutions techniques, we did not find a preference in the literature; nevertheless, we were able to identify some adoption patterns. There is a correlation between the models formulated and the solution techniques adopted: the degree of complexity used to represent the problem defines, in a certain way, the tools implemented.
Facility Location: Models, Methods and Applications | SpringerLink
Besides, the fact that MCDM tools are able to handle many, and sometimes conflicting, variables may also explain the preference for this kind of method when solvingmulti-criteria models. The choice of solution methods for single-criterion models, however, seems to be directly related to models' characteristics and the amount of time available to find a solution.
A more detailednetwork and an increase in the volume and variety of product flows add computational challenges, due to the greater number of connections and constraints to be considered. This is closely related to the combinatorial nature of these problems. The adoption of heuristics is related to the amount of time available to find a solution: they tend to achieve it in faster computational times. On the other hand, if we want to add uncertainty, stochastic or robust optimization could be good choices of tools. The growing importance of logistics hubs as an element of transportation networks fosters the study and definition of their features, as well as the development of knowledge on how to deal with such structures.
To shed light in this area, this paper presented a literature review on logistics hubs location. We surveyed models and solution techniques available, and assessed their applicability within this context. This work differs from others in the field of location science by evaluating an application area instead of a class of models or methods. It facilitates a better understanding of requirements and of how to solve this type of location problem. We identified two categories of models which are used in logistics hubs location.
Multi-criteria models enable the consideration of a broad range of criteria, both quantitative and qualitative, which makes them more suitable for representing such strategic decisions. However, they provide information only about location sites, and do not allow the assessing of the distribution of flows and their impact on the network infrastructure. Single-criterion models, on the other hand, tend be similar to the HLP and deliver results related not only to hub location, but also to flow allocation.
Because of these features they might seem, at first glance, more suitable and complete. Yet they adopt network simplifications that do not correspond to a correct representation of the transport system and the connections between origins, hubs, and destinations. It is also noteworthy that the papers surveyed adopt mainly two different research approaches, which are directly related to the type of models and solution techniques employed.
While the multi-criterion category follows an empirical design approach, where the goal is to create models that better represent the existing relationships in real world problems, the single-criterion one has an axiomatic perspective, where the primary interest is to understand the modeling process, explain its characteristics, find an optimal solution, and compare the performance of different solution techniques.
This contrast of approaches is emphasized by the different perspectives taken by each category: macroeconomic versus microeconomic. Perhaps a better way to address logistics hub location would be by considering aspects of both categories - a two stage analytical approach through the combination of different features. First, a multi-criteria analysis could be used to define a location site or a ranked list of sites, taking into account political, legal, environmental, and market aspects, among others.
This would furthermore enable the assessment of issues related to network design, by testing different sets of infrastructure projects and evaluating their impact on an integrated transportation networks considering logistics hubs.
Solving tools could be chosen respectively. There is, indeed, a stated need for a more refined representation of transportation networks. In this context, issues related to environmental impact, proximity to transportation modes, traffic, congestion, and volume of flow handled at the hub still require further investigation. In addition, the impacts of a new hub on the network should be further explored Farahani et al. Since all of this adds to the complexity of models, the search for new solution algorithms and improvements in computational power also find room in the logistics hub location context.
We thank to the anonymous referees for their valuables comments to improve this review. History of Air Cargo and Airmail from the 18th Century. London: Christopher Foyle Publishing. Network hub location problems: The state of the art.
Unit 7 Discussion
European Journal of Operational Research, 1 : Multimodal hub location and hub network design. Omega, 40 6 : Hub locations in urban multimodal networks. European Transport - Trasporti Europei, Twenty-five years of hub location research. Transportation Science, 46 2 : Applying an integrated fuzzy MCDM method to select hub location for global shipping carrier-based logistics service providers. Location model of specialized terminals for soybean exports in Brazil. Pesquisa Operacional, 31 1 : Efficient algorithms for the uncapacitated single allocationp-hub median problem.
Location Science, 4 3 : Hub location problems: A review of models, classification, solution techniques, and applications. Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34 7 : Location selection of Chinese modern railway logistics center based on DEA-Bi-level programming model. Advanced Materials Research, Optimal location for centers in a network. Transportation Science, 3 4 : Systematic location of the public logistic centres in Czech Republic. Transport, 26 4 : Foundations of Location Analysis. In Foundations of Location Analysis, Springer US.
In Perspectives on Operations Research, pp. Logistics centers location. Transport, 21 1 : Location Science, 6 : Locating the competitive relation of global logistics hub using quantitative SWOT analytical method. Quality and Quantity, 43 1 : Expert Systems with Applications, 38 6 : Study on logistics center site selection of jilin province. Journal of Software, 7 8 : Transportation Science, 43 2 : Facility location and supply chain management - A review. European Journal of Operational Research, 2 : The Location of Interacting Hub Facilities.
A mistake in location is not easily overcome. Business success often is being in the right place at the right time. For a service operation such as a restaurant, hotel, or retail store, being in the right place usually means in a location that is convenient and easily accessible to customers.
Location decisions for services tend to be an important part of the overall market strategy for the delivery of their products or services to customers. However, a business cannot simply survey the demographic characteristics of a geographic area and build a facility at the location with the greatest potential for customer traffic; other factors, particularly financial considerations, must be part of the location decision. Obviously, a site on Fifth Avenue in New York City would be attractive for a McDonalds restaurant, but can enough hamburgers and french fries be sold to pay the rent?
In this case, the answer is yes.
Location decisions are usually made more frequently for service operations than manufacturing facilities. Facilities for service-related businesses tend to be smaller and less costly, although a hospital, or hotel can require a huge investment and be very large. Services depend on a certain degree of market saturation; the location is actually part of their product. Where to locate a manufacturing facility is also important, but for different reasons, not the least of which is the very high expense of building a plant or factory.
Although the primary location criteria for a service-related business is usually access to customers, a different set of criteria is important for a manufacturing facility. These include the nature of the labor force, and labor costs, proximity to suppliers and markets, distribution and transportation costs, energy availability and cost, the community infrastructure of roads, sewers, and utilities, quality of life in a community, and government regulations and taxes.
When the site selection process is initiated, the pool of potential locations for a manufacturing or service facility is, literally, global. In todays international marketplace, countries around the. The site selection process is one of gradually and methodically narrowing down the pool of alternatives until the final location is determined.
In the following discussion, we identify some of the factors that companies consider when determining the country, region, community, and site at which to locate a facility. Trade agreements between countries have reduced trade barriers around the world and created new markets like the European Community EC , Eastern Europe, and Asia. Foreign firms have also begun to locate in the United States to be closer to their customers.
For both U. Relatively slow overseas transportation requires multinational companies to maintain large, costly inventories to serve their foreign customers in a timely manner. This drives up supply chain costs and makes it economical for companies to relocate closer to their markets. While foreign markets offer great opportunities, the problems with locating in a foreign country can be substantial, making site location a very important part of supply chain design.
For example, although China offers an extremely attractive market because of its huge population, growing economy, and cheap labor force, it has an inefficient transportation and distribution system, and numerous government regulations. Markets in Russia and the former Soviet states are attractive; however they can also be risky since the free market economy is still new to these states. Lack of familiarity with standard business practices and corruption can threaten success for foreign companies. Some of the factors that multinational firms must consider when locating in a foreign country include the following:.
Government stability Government regulations Political and economic systems Economic stability and growth Exchange rates Culture Climate Export and import regulations, duties, and tariffs. Raw material availability Number and proximity of suppliers Transportation and distribution systems Labor force cost and education Available technology Commercial travel Technical expertise Cross-border trade regulations Group trade agreements. Industry migrated to the sunbelt areas, the Southeast and Southwest, during the s and s, where labor was cheaper and not unionized , the climate was better, and the economy was growing.
However, in the late s, there was a perceptible shift in new plants and plant expansion back to the nations agricultural heartland. The North Central region, consisting of Illinois, Michigan, and Ohio, attracted new and expanded facilities as did the South Atlantic region. Certain states are successful in attracting new manufacturing facilities for a variety of reasons.
It has a good base of skilled and educated labor, a large mass of industry that spawns other businesses, and it has established good incentive programs to attract new businesses. Ohio also benefits from a number of towns and cities with populations less than 50, that have a rich agricultural heritage. The residents of these communities have a strong work ethic and are self-reliant and neighborly. These communities typically have quality health services; low crime rates; solid infrastructures of roads, water and sewer systems; open spaces to expand; and quality education.
Ohio attracts manufacturing facilities because of good transportation, skilled labor with a strong work ethic, incentive programs, and quality social services. Laborcost, availability, work ethic, conflict, and skillis important in a companys location decision. Labor is one of the most important factors in a location decision, including the cost of labor, availability, work ethic, the presence of organized labor and labor conflict, and skill and educational level. Traditionally, labor costs have been lower and organized labor has been less visible across the South and Southwest.
While labor conflict is anathema to many companies, in some cases labor unions have assisted in attracting new plants or in keeping existing plants from relocating by making attractive concessions. The proximity of suppliers and markets are important location factors.
Manufacturing companies need to be close to materials, and service companies like fast-food restaurants, retail stores, groceries, and service stations need to be close to customers and distribution centers. Transportation costs can be significant if frequent deliveries over long distances are required.
The closeness of suppliers can determine the amount of inventory a company must keep on hand and how quickly it can serve its own customers. Uncertainty in delivery schedules from suppliers can require excessive inventories. It is important for service-related businesses to be located near their customers. Many businesses simply look for a high volume of customer traffic as the main determinant of location, regardless of the competition.
An interstate highway exit onto a major thoroughfare always has a number of competing service stations and fast-food restaurants. Shopping malls are an example of a location in which a critical mass of customer traffic is sought to support a variety of similar and dissimilar businesses. Another important factor, infrastructure, is the collection of physical support systems of a location, including the roads, water and sewer, and utilities.
If a community does not have a good infrastructure, it must make improvements if it hopes to attract new business facilities. From a companys perspective, an inadequate infrastructure will add to its supply chain costs and inhibit its customer service. Business climate Community services Incentive packages Government regulations Environmental regulations Raw material availability Commercial travel Climate Infrastructure e.
Besides physical and societal characteristics, local incentives have increasingly become a major important factor in attracting companies to specific locations. Incentive packages typically include job tax credits, relaxed government regulations, job training, road and sewage infrastructure improvements, and sometimes just plain cash. These incentives plus the advantages of a superior location can significantly reduce a companys supply chain costs while helping it achieve its strategic goal for customer service.
States and communities cannot afford to overlook incentives if they hope to attract new companies and jobs. However, they must make sure that the amount of their investment in incentive packages and the costs they incur for infrastructure improvements are balanced against the number of new jobs developed and the expansion of the economy the new plant will provide.
Incentives are a good public investment unless they bankrupt the locality. While some small communities are successful in attracting new businesses, they are left with little remaining tax base to pay for the infrastructure improvements needed to support the increased population drawn by job. Thus, states and communities, much like businesses, need a strategy for economic development that weighs the costs versus the benefits of attracting companies. A GIS is a computerized system for storing, managing, creating, analyzing, integrating, and digitally displaying geographic i.
A GIS is both a database system as well as a set of operations for working with and analyzing this data. As a tool specifically used for site selection, it allows the user to interactively search and analyze the type of data and information i. Frequently a GIS used for site selection will incorporate quantitative models like the ones presented later in this chapter and text to help analyze the data. Figure S7. Each layer or spatial map in this diagram contains information about one characteristic or attribute of the location begin analyzed. A number of approximation algorithms have been developed for the facility location problem and many of its variants.
If we assume distances between clients and sites are undirected and satisfy the triangle inequality, we are talking about a metric facility location MFL problem. The minimax facility location problem seeks a location which minimizes the maximum distance to the sites, where the distance from one point to the sites is the distance from the point to its nearest site. Its study traced at least to the year of It has been proved that exact solution of k -center problem is NP hard.
The error level in the approximation algorithm is measured as an approximation factor, which is defined as the ratio between the approximation and the optimum. It's proved that the k -center problem approximation is NP hard when approximation factor is less than 1. There exist algorithms to produce exact solutions to this problem.
For the hardness of the problem, it's impractical to get an exact solution or precise approximation. The approximation is referred to as the farthest-point clustering FPC algorithm, or farthest-first traversal. It is easy to see that this algorithm runs in linear time. As approximation with factor less than 2 is proved to be NP hard, FPC was regarded as the best approximation one can find.
The maxmin facility location or obnoxious facility location problem seeks a location which maximizes the minimum distance to the sites. In the case of the Euclidean metric, it is known as the largest empty sphere problem. Facility location problems are often solved as integer programs. Further suppose that each facility has a maximum output.
The remainder of this section follows .