Hizmet Tesis Yeri

Hizmet Tesis Yeri Planlaması Rekabete dayalı konumlandırma: Çoklu konum ya da başlangıç konumu giriş engeli olabilir. Talep yönetimi: Talebin kalitesi, miktarı ve zamanlamasını kontrol yeteneği. Esneklik: gelecekteki ekonomik değişimler ve portfolio etkisi için konum kararları planlama. Odaklanma: Pekçok konumda belirlenmiş aynı hizmeti sunarak geliştirilebilir. Flexibility: of a location is a measure of the degree to which the service can react to changing economic situations. Competitive positioning: refers to methods by which the firm can establish itself relative to its competitors.

Hizmet Tesis Yeri Konularını Sınıflandırma Coğrafik Yapı Ağ Düzlem Dikdoğrusal Kuşuçuşu Tesis Sayısı Tekli Çoklu Hizmet kapasitesi Hizmet düzeyi Hizmet verilecek bölge Eniyileme Kriteri Kamu sektörü Özel sektör

Stratejik Faktörler Competitive Clustering Rekabete Dayalı Kümelenme (Rakipler Arasında) (Örn: Otomobil Bayileri, Moteller) Yaygın Pazarlama (Aynı Firma) (Örn: Dondurma Sağlayıcıları) Pazarlama Aracıları (Örn: Kredi Kartları, HMO) Hareketin Yerini Tutan İletişim (Örn: Ofise Uzaktan Bağlanarak Çalışma, e-Ticaret) Ön Ofisi Arkadan Ayırma (Örn: ATM, ayakkabı tamiri) Hizmet Yerine İnternetin Etkisi (Örn: Amazon.com, eBay, FedEx) Breaking the Rules Competitive Clustering When firms in the same business locate close to each other so that customers can compare more easily, ex., car dealership, motels Saturation Marketing When a company locates its units close to each other to squeeze out the competition, reduce advertising needs, increase customer awareness, better inventory management.; example Au Bon Pain, Risk of cannibalizing your sales Marketing Intermediaries Services cannot be inventoried and are intangible, cannot be transported, thus limiting the geographic area of service By using marketing intermediaries you can expand geographical coverage; e.g. banks extend credit worldwide through Credit Cards, HMO Substitution of Communication for Travel Technology interphase has provided communication possibilities that expand the coverage of service, example. Nurse practitioners can use communication with a distant hospital to provide health care without transporting the patient Working out of home or providing service at customer ‘s location , example, telebanking Impact of the Internet on Service Location E-commerce – website becomes the virtual location of these firms Location of the warehouse is a concern because you have to ship the products, e.g. Amazon.com, eBay, FedEx

Stratejik Yer Faktörleri Ön Ofis Arka Ofis Dış Müşteri (tüketici) Hareket müşteriye doğru mu yoksa müşteri tesise mi hareket ediyor? Elektronik araçlar fiziksel hareketin yerini alabilir mi? Yer girişe bir engel mi? Hizmet kişiye mi yoksa mülke mi yapılıyor? Yardımcı-yer gerekli mi? İletişim nasıl yapılacak? İç (çalışan) İş gücü bulma durumu? Self-servis kioskları bir alternatif mi? Ölçek ekonomileri mümkün mü? Personel evden çalışabilir mi? Offshoring bir seçenek mi? Offshoring: bir şirketin maliyetlerini azaltmak amacıyla üretimin bazı aşamalarını ülke dışında gerçekleştirmesi

Yer Seçim Faktörleri 1. Erişim: 4. Park etme: Otoyol giriş ve çıkış bağlantı Sokak dışında park etme uygunluğu yollarına yakın 5. Genişleme: Toplu taşıma yoluyla hizmet edilir Genişleme için odalar 2. Görünürlük: 6. Çevre: Cadde üstünde bulunma Yakın çevre hizmeti Etraftaki koşuşturma tamamlamalıdır. İşaret yerleştirme 7. Rekabet: 3. Trafik: Rakiplerin konumu Sokakta olası trafik yoğunluğu 8. Hükümet: Potansiyel satın alma dürtüsü göstergesi İmar kısıtlamaları Bir engel olabilecek trafik sıkışıklığı Vergiler (Örn: itfaiye istasyonu) 9. İşgücü: Uygun becerideki mevcut işgücü

Coğrafik Gösterim Yer seçenekleri ve hareket mesafesi bir düzlemde(düz yüzey) ya da bir ağ üzerinde temsil edilebilir. Yerler arasındaki mesafeler iki yolla ölçülür: Öklit/Kuşuçuşu Uzaklık Dikdoğrusal Uzaklık

Coğrafik Gösterim Düzlem Üzerinde Yer Gösterimi Y Varış yeri j Yj Öklit Başlangıç i Yi Dik doğrusal X Xi Xj

Eniyileme Kriteri Yeri eniyileme kriteri özel ve kamu mülkiyetine göre farklılık gösterir. Özel sektörde tesis yeri kararı ya maliyet enküçükleme ya da kar enbüyükleme yoluyla yönetilir. Kamu tesislerinin yer seçiminde kararlar tümüyle toplumun ihtiyaçlarına göre yürütülür.

Eniyileme Kriterinin Etkisi 1. Maximize Utilization (City C: elderly find distance a barrier) 2. Minimize Distance per Capita (City B: centrally located) 3. Minimize Distance per Visit (City A: many frequent users) City A 3 * 2 -10 -5 5 10 15 * -15 -10 -5 5 10 15 20 25 * City C City B 1 Kullanımı enbüyükleme Maximize the total number of visits to the health care center The center should be located in city C, because it contains a large number of elderly people for whom distance is a strong deterrent(engelleyici) Minimize distance per capita(kişi başına) Minimize the average distance per capita to the closest center City B should be selected, because it is centrally located between the two large cities Minimize distance per visit Minimize the average per-visit travel distance to the nearest center City A should be selected as it has the largest population and has the most mobile and frequent users of health care.

Estimation of Geographic Demand Define the Target Population To forecast demand, we need to define target population. We use past records to project future demand Select a Unit of Area To forecast , we need to define geographic units based on 2 factors Unit must be large enough to contain a sample size required for estimating demand We should not have so many geographic units, that we cannot do calculations using our computing power. Estimate Geographic Demand Regression analysis Map Geographic Demand on a three dimensional map to provide a visual representation of the geographic distribution Alan birimi

Tesis Yeri Seçim Teknikleri Single Facility Metropolitan Metric Euclidian Metric Center of Gravity Locating a Retail Outlet Multiple Facilities Location Set Covering Problem Maximal Covering Location Problem

1.Tek Tesis a.Dikdoğrusal Metrik Locating a single facility on a plane to minimize the weighted travel distances by means of the metropolitan metric is straightforward using the cross median approach.The objective is 𝑍= 𝑖=1 𝑛 𝑊𝑖{ 𝑋𝑖−𝑋𝑠 +|𝑌𝑖−𝑌𝑠|} Where Wi=weight attached to the i-th point(e.g.,population) Xi,Yi=coordinates of the i-th demand point Xs,Ys=coordinates of the service facility n=number of demand points served

EXAMPLE 9.1 (page 260) COPYING SERVICE A copying service has decided to open an office in the central business district of a city. The manager has identified 4 office buildings. Weights are attached to each point and represent potential demand per month in hundreds of orders. The manager would like to determine a central location that will minimize the total distance per month that customers travel to the copying service.

A metropolitan metric is appropriate for this question A metropolitan metric is appropriate for this question. A site located by the cross-median approach will be used to solve this problem. The median is calculated using this equation: Median= 𝑖=1 𝑛 𝑊𝑖/2

Solution: İ Xi Wi ∑Wi 1 7 2 8 3 11 4 5 16 İ Yi Wi ∑Wi 4 1 5 2 7 12 3 13 16 A(2,2) İ Xi Wi ∑Wi 4 5 3 8 1 9 2 7 16 İ Yi Wi ∑Wi 3 5 2 1 4 7 11 16 B(3,2)

Office Distance Weight Total 1 1 x 7 = 7 2 1 = 3 4 x 3 = 12 4 3 x 5 = 15 Office Distance Weight Total 1 2 x 7 = 14 2 1 = 3 3 x 3 = 9 4 5 = 10 35 35 The total weighted travel distance calculated for point A and point B is equal. Thus, any location will be acceptable.

1.Single Facility b.Euclidian Metric Changing the geographic structure to the straight-line distance between points complicates the location problem. The objective now becomes minimize 𝑍= 𝑖=1 𝑛 𝑊 𝑖 𝑋 𝑖 − 𝑋 𝑠 2 + 𝑌 𝑖 − 𝑌 𝑠 2 1 2

2.Center of Gravity An intuitive but incorrect approach to solving the single location problem is the use of the center of gravity formulas shown below: 𝑋 𝑐𝑔 = 𝑖=1 𝑛 𝑤 𝑖 𝑥 𝑖 𝑖=1 𝑛 𝑤 𝑖 𝑌 𝑐𝑔 = 𝑖=1 𝑛 𝑤 𝑖 𝑦 𝑖 𝑖=1 𝑛 𝑤 𝑖

Example: Copying Service The trial «center of gravity» values for Xs and Ys are calculated. 𝑋 𝑐𝑔 = 𝑖=1 𝑛 𝑤 𝑖 𝑥 𝑖 𝑖=1 𝑛 𝑤 𝑖 = 7 1 +1 2 +3 3 +5(4) 16 =2.375 𝑌 𝑐𝑔 = 𝑖=1 𝑛 𝑤 𝑖 𝑦 𝑖 𝑖=1 𝑛 𝑤 𝑖 = 7 2 +1 3 +3 5 +5(1) 16 =2.3125

Huff Retail Location Model First, a gravity analogy is used to estimate attractiveness of store j for customers in area i. Aij= Attraction to store j for customers in area i Sj = Size of the store (e.g. square feet) Tij= Travel time from area i to store j lambda = Parameter reflecting propensity to travel Attraction: çekim Feet kare Hareket eğilimi/isteğini gösteren lambda

Huff Retail Location Model Second, to account for competitors we calculate the probability that customers from area i will visit a particular store j. Rakipleri hesaba katarak

Huff Retail Location Model Third, annual customer expenditures for item k at store j can now be calculated. Pij = Probability customers from area i travel to store j Ci = Number of customers in area i (e.g. census track) Bik = Annual budget for product k for customers in area i m = Number of customer areas in the market region

Huff Retail Location Model Fourth, market share of product k purchased at store j can now be calculated.

Example: Copying Service Assumptions: the copying service has been established at (2,2) each customer order represents an expenditure of approximately $10. λ=2 We wish to open a competing store at location (3,2) but with twice the capacity of the existing copy center. How much market share would be expect to capture?

Travel distances (using metropolitan metric) Site (j) Customer Location (i) 1 2 3 4 Proposed (3,2) Existing (2,2)

Attraction Site (j) Customer Location (i) 1 2 3 4 Proposed (S1=2) 0.5 0.2222 0.500 Existing (S2=1) 1.0 0.0625 0.111 TOTAL 1.5 0.2847 0.611 1/12 1/12 1/42 1/32

Probability Site (j) Customer Location (i) 1 2 3 4 Proposed 0.3333 0.7805 0.8184 Existing 0.6667 0.2195 0.1816 TOTAL 0.0625/ 0.2847 0.111 / 0.611 1/1.5 1/1.5

Monthly Expenditures Site (j) Customer Location (i) 1 2 3 4 Proposed 2333 (0.3333 x 7000) 333 (0.3333 x 1000) 2340 4100 Existing 4667 667 660 900 TOTALS $7000 (700x$10) $1000 (100x$10) $3000 (300x$10) $5000 (500x$10)

Market Share Site (j) Customer Location (i) Monthly Total Market Share 1 2 3 4 Proposed 2333 333 2340 4100 $9106 57 % Existing 4667 667 660 900 6894 43 % TOTALS $7000 $1000 $3000 $5000 $16000 1.00

LOCATION SET COVERING PROBLEM Find the minimum number and location of facilities that will serve all demand points within some specified maximal service distance.

Example: Rural Medical Clinics A state of health is concerned about the lack of medical care in rural areas, and a group of nine communities has been selected for a pilot program in which medical clinics will be opened to serve primary healt care needs. It is hoped that every community will be within 30 miles of at least one clinic. The planers would like to determine the number of clinics that are required and their locations.Any community can serve as a potential clinic site except for community6, because facilities are unavailable there. Figure shows a network identifying the cities as numbered circles; lines drawn between sites shown the travel distances in miles.

Travel Network for a Rural Area 2 9 40 40 30 20 7 20 30 35 1 30 30 8 20 30 3 6 25 10 4 15 15 5

Solution: Range of service for potantial sites Community Set of Communities Served from Site Potential Sites That Could Serve the Community 1 1,2,3,4 2 1,2,3 (1,2,3)+ 3 1,2,3,4,5 4 1,3,4,5,6,7 1,3,4,5,7 5 3,4,5,6 (3,4,5)+ 6 4,5,6,7,8 4,5,7,8 7 4,6,7,8 (4,7,8)+ 8 6,7,8,9 7,8,9 9 8,9 (8,9)+

4.Multiple Facilities b.Maximal Covering Location Problem A variation of the location set covering problem is maximal covering. This problem is based on a very appealing objective: maximizing the population covered within desired service distance. En büyük kapsayan yer problemi Appealing: çekici Desired arzu edilen

EXAMPLE: EMS Location Planning (Rardin) A city was divided into service districts needing EMS (emergency medical services) services, and vehicle stations selected from a list of alternatives so that as much of the population as possible would experience a quick response to calls for help.

The city is divided into 20 service districts that we wish to serve from some combination of the 10 indicated possibilities for EMS stations. Each station can provide service to all adjacent districts. For example, station 2 could service districts 1,2, and 7. Adjacent: komşu

Service district and candidate locations for EMS example

Minimum cover EMS model The most obvious approach to modeling EMS example is to minimize the number of locations needed to cover all districts. The following set covering model results.

Maximum Coverage EMS model (Rardin) In the EMS case, as many other real instances, the straightforward covering model proves inadequate because it calls for too many sites. Suppose that we have funds for only 4 EMS locations. How can we find the collection of 4 that minimizes coverage insufficiency? For this version of the model we need estimates of the demand or importance of covering each service district.

Assume that the following values have been estimated by EMS staff:

We introduce extra decision variables to model uncovered districts i.

QUADRATIC ASSIGNMENT PROBLEM

Example: Mall Layout (Rardin) There are 4 possible locations for stores in a shopping mall.