ENVIRONMENT
4. A MIP Model
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PTEs’ choices of relocation destinations are expected to have funda- mental impacts on HKP development as they serve as the key cargo source.
If PTEs relocate to Asian lower cost countries or near major markets, as represented by India and Mexico respectively in the example, HKP will be in a very unfavorable position. If PTEs relocate within China, their choices among Guangdong, Pan-PRD and Inland will cause different implications on HKP development. The routing patterns of cargo are influenced by several key factors, including port connectivity, transportation modes, transport distance, shipping cost and port competition. Generally speaking, better connectivity, shorter transport distance and lower shipping cost attract more cargo. In terms of transportation modes, barging incurs substantially lower cost than land transportation modes, and thus is preferred as long as water- ways are available. HKP possesses geographical advantage for cargoes from its west via barging (Lin, 2008). If the Hong Kong-Zhuhai-Macau Bridge is completed in the next few years as planned, HKP will extend its advantage to cargoes from its west via trucking. Though the port of Gaolan in Zhuhai is nearer to cargoes from the west side, it is far less established and may not be able to develop into a competitive mainline port. However, for cargoes from its north and east, the nearest seaports are well-established Guangzhou and Shenzhen ports respectively. For both waterway and land transportation, these two ports are in more convenient locations than HKP. In addition, high THCs, high trucking cost and the low efficiency of border-crossing have been causing cargo diversion from HKP to Guangzhou and Shenzhen ports (Seabrookeet al., 2003; Cullinaneet al., 2004).
Currently, it is unclear which relocation destination will attract most PTEs. As different relocation trends cause varying effects on HKP develop- ment, it is essential to understand which trend would become dominant and which factor plays a most influential role. To answer this research question, scenario analysis is conducted in the following three sections through MIP modeling and sensitivity analysis.
Transportation costs are assumed to be in liner correlation with oil prices.
Raw materials are assumed to be equally priced and available at all candidate locations. The PTE maintains a same net profit margin no matter where it may relocates. The model is formulated by using the parameters and variables as defined below. After presentation of the model formulation, model components are described and discussed.
Parameters
M Markets{1, 2, . . . m, . . . M} F Facilities{1, 2, . . . f, . . . F}
Dm Yearly demand for the marketmsupplied by the PTE DFCf Base domestic freight cost per unit from facilityf to port IFCfm Base international freight cost per unit from facilityf to
marketm
RMCf Raw material cost per unit of production at facilityf CRCf Unit capacity retaining cost at facilityf
RLCf Unit capacity relocation cost at facilityf DLCf Hourly direct labor cost at facilityf
Hrf Man hour per unit of production at facilityf
OP Oil price
OPB Base oil price
ef Exchange rate to USD from the currency used for facilityf Ctaxf Corporate income tax rate at facilityf
VATf Realized VAT rate at facilityf with consideration of VAT rebate
NPATf Net profit margin after tax at facilityf IDMf Days of inventory accountable to facilityf
IDTfm Days of inventory in transit from facilityf to marketm Tarifffm Tariff rate imposed by marketmfor the product imported
from facilityf
φ Correlation factor between freight costs and oil price µ Inventory holding cost coefficient
L A sufficiently large constant Decision variables and convenience variables
πf Binary value for facilityf (1 if open; 0 if not open) qfm Yearly production quantity at facilityf for marketm SPf Unit selling price from facilityf at EXW term MFGCf Unit manufacturing cost at facilityf
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80 A. Zhang and G. Q. Huang
DFCCf Domestic freight cost per unit from facilityf to port
IFCCfm International freight cost per unit from facilityf to marketm CTAXCf Corporate income tax per unit at facilityf
VATCf VAT per unit at facilityf Objective function
Minimize:
f
mqfmSPf(1 +IDTfmµ)
+
f
mqfm(DF CCf+IF CCfm+TarifffmSPfm) (1) Constraints
MF GCf = (CRCf+RLCf+DLCfHrf)ef
+RMCf(1 +IDMfµ) ∀f ∈F (2) DF CCf =DF Cf(1 +φ(OP/OP B−1))ef ∀f∈F (3) IF CCfm=IF Cfm(1 +φ(OP/OP B−1)) ∀f ∈F (4) CT AXCf= (SPf −MF GCf−V AT Cf)Ctaxf ∀f ∈F (5) V AT Cf = (SPf−RMCf)V ATf ∀f ∈F (6) SPf−MF GCf−V AT Cf−CT AXCf =NP ATfSPf ∀f ∈F (7)
f
qfm=Dm ∀m∈M (8)
m
qfm≥πf ∀f ∈F (9)
m
qfm≤πfL ∀f ∈F (10)
qfmis an integer∀f ∈F and m∈M (11) πf ∈ {0,1} ∀f ∈F (12)
All variables ≥0 (13)
The objective function (1) minimizes yearly LC in all international mar- kets. LC components include selling prices of the manufacturer, freight costs and tariff costs. Equations (2)–(6) are convenience constraints to simplify model formulations while defining individual cost components, including manufacturing cost, domestic freight cost, international freight cost, corpo- rate income tax and VAT tax. Equation (7) is a constraint on net profit margin after tax. Equation (8) is a demand constraint. Constraint (9) ensures that no facility is open without any production. Constraint (10) is a binary force constraint. Constraints (11)–(13) define the characteristics of variables.
5. Experimental Design