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Servicescape. Servicescape of supporting facility important because it influences the behavior of customer. Specifically, in self-service environment. e.g. Use of signs - Directions inside the IIT campus; Intuitive interfaces – Website designs.
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Servicescape • Servicescape of supporting facility important because it influences the behavior of customer. • Specifically, in self-service environment. e.g. Use of signs - Directions inside the IIT campus; Intuitive interfaces – Website designs. • Servicescape should also influences the interaction between customer and service provider. e.g. Layout of restaurants influences the type of service to expect from the waiter.
Environmental dimensions of servicescape • Ambient conditions: Background affects our five senses. e.g. tempo of music in departmental store. • Spatial layout and functionality: Create visual and functional landscape for delivery of service. e.g. layout of a fast food restaurant designed to facilitate self-service. • Signs, symbol and artifacts: Explicit and implicit signals that communicate acceptable norms of behavior. e.g. how would you feel if a restaurant has Picasso frame in the reception area?
Facility design • Direct correlation between service operations and facility design. • Design and layout represent the supporting facility component of service package. • Factors influencing facility design: Nature and objective of organization; land availability; flexibility; security; aesthetics; community and environment. • Community and environment: Design of service facility has the greatest important where it directly affects the society. e.g. A prison in a locality?
Facility design factors • Nature of organization: The core service offered should dictate the parameters of design. Appropriateness of design also important. e.g. Physician’s office should give patients privacy while undergoing medical check-up. Would you open an account in a bank which operates out of a tin-shade? • Land availability: Space constraints, zoning rules are a reality which a good design should accommodate. e.g. Franchise for Reid and Taylor in India should have certain minimum sq. feet area.
Facility design factors • Flexibility: Design should be dynamic to allow for future growth and changes in services. e.g. Parking lot for a restaurant. • Security: Airport design of today needs to consider space for passenger and luggage screening. • Aesthetics: Service providers delivering essentially same service could be perceived different because of aesthetics. e.g. Staff canteen and Tifanis?
Service facility location • Geographic representation Location problems are based on how geography is modeled. In 2D, location can be modeled as xy coordinate plane, or in a network. Two ways of measuring distances between two points with coordinates (xi, yi) and (xj, yj) Euclidean distance Metropolitan metric distance
Service facility location • Number of facilities Single facility location problem can be solved analytically with relative ease. Multi-facility location problems are difficult because of demand assignment at nodes. Also for some services, the type of facilities may vary. • Optimization criteria Objective function in the location problem different for different services. Depending on the owner of the facility, the objectives could be: Maximization of utility, profit, social benefit; Minimization of travel times, cost.
Single dimension single facility location problem • Suppose we wish to find location for a single bus stop that will serve all the boys-hostels from Cauvery to Mandakini. • Fix a arbitrary point on the road as origin.
Single facility location problem • Result says that the bus stop should be located at the median w.r.to demand density. • This method is called cross-median approach. • The result can be extended to 2D using either form the distance measurement (Euclidean or Metropolitan).
Single facility location problem • Since x and y coordinates are independent of each other. We can solve two sub-problems (one for each axis). • Thus the optimal location of the service facility will have: xs at the median value of wi ordered in x-direction ys at the median value of wi ordered in y-direction Because each (or both) of the coordinates may be unique or lie within a range, the optimal location may be at a point, on a line or within a region.
Example Median weight = (7+1+3+5)/2 = 8
Example: Solution • Non-unique optimal solution: (2,2) and (3,2). • Hence the line segment joining these two optimal solution is also an optimal location. Weighted distance of all the location from any of these optimal point will be same. Verify!
Single facility location by center of gravity method • An intuitive but non-optimal approach, because it does not minimize the travel distances from each location to the service facility. • Center of gravity solution could serve as a starting point for the previous, rather tedious, method.
Single facility location by center of gravity method • The optimal solution of the Euclidean method will always result in a point; and will rarely match the center of gravity solution. • Example of Euclidean method for location problem: FedEx
Locating a retail outlet • Objective: profit maximization • Countably finite number of alternative locations evaluated to find the most profitable site. • Method developed by David L. Huff. • Basic idea: Attractiveness of a facility is directly proportional to its size and inversely proportional to the distance.
Locating a retail outlet Shopping center may have the parameter value = 2; whereas for regular grocery store, this could be = 10.
Locating a retail outlet • The probability that the customers might be attracted to other stores is captured by calculating such probability: • Now we can calculate the total consumer expenditure for product class k at each facility j.
Example • From the previous discussion, assume that a facility is located at optimal point (2,2). Assume that we want to check profitability of location (3,2). Assume: • Ci= weight*100. • Each customer order represents an expenditure of approximately Rs 10; and λ = 2. • The new location at (3,2) is twice as big as the current one at (2,2). • Travel distances are given in the next table:
Example Proposed site will have higher market share than the current location. Hence, the proposed location is better.
Multiple facilities: Location set covering problem 2 9 20 40 40 30 1 30 7 20 35 20 8 30 30 3 15 30 4 25 6 15 Site within 30 miles could be served by a node. 15 5
Maximal covering problem • The “set covering problem” minimizes the number of facilities located under the constraint that all demand locations need to be served. • However, we have not considered budgetary constraints. • These budgetary constraints might not allow us to serve all demand locations. • In these cases, the problem is to locate the budgeted facilities to maximize the serviced demand. • These problems are called “maximal covering location problems.” • On the other hand, when the objective is to minimize the total weighted distance traveled from all demand centers to the opened facilities, the problem is called a p-median problem.
Maximal covering problem • In addition to maximizing the serviced population, additional constraints may be imposed within the maximal covering problem. • For example, for emergency facility locations, it may be desirable to locate facilities in order to maximize the population which can be served within five minutes while insuring the entire population can be served within ten minutes. • This secondary or reduced service time requirement is included by means of mandatory closeness constraints.
Maximal covering problem • Let there be J = 1,2,…n demand centers with known locations and demands pj. In addition, let there be I = 1,2,…m possible sites, where a maximum of K facilities may be opened. • Let the maximum allowable response time (or distance) be R, which is specified. • If the minimum # of facilities required to serve all the demand centers within the given response time (or distance) exceeds the allotted # (K), then not all demand centers may be served and we attempt to serve the maximum population. • Let tij be time (or distance) from ith site to jth demand center. • The problem formulation then becomes:
Maximal covering problem • For pj = 1 for all j’s, the problem is same as the set covering problem and will minimize the # of facilities while serving all demand centers. For demand distribution not uniform, the optimal set of facilities will serve maximum population. • Mandatory closeness constraints: The demand centers be located no more than T time units (or distance) from an open facility, where T > R. • We have to include these additional mandatory closeness constraints in our problem.
