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1 Park, Szmerekovsky, Osmani, and Aslaam
An integrated multimodal transportation model for switchgrass-based bioethanol supply
chain with a case study based on North Dakota
Yong Shin Park1*, Joseph Szmerekovsky2, Atif Osmani3 and N. Muhammad Aslaam4
1First Author: Yong Shin Park (*Corresponding Author)
Affiliation: Transportation and Logistics Program, North Dakota State University
Address: 1320 Albrecht Blvd, Fargo, ND 58105, USA
Phone: +1 (701) 231-7767
Fax: +1 (701) 231-1945
Email: Yong.Park@ndsu.edu
2Second Author: Joseph Szmerekovsky
Affiliation: Department of Management and Marketing, North Dakota State University
Address: NDSU Department 2420, PO Box 6050, Fargo, ND 58108, USA
Phone: +1 (701) 231-8128
Fax: +1 (701) 231-7508
Email: joseph.szmerekovsky@ndsu.edu
3Third Author: Atif Osmani
Affiliation: Department of College of Business, Minnesota State University
Address: 1104 7th Ave South, Moorhead, MN 56563, USA
Phone: +1 (218) 477-2489
Fax: +1 (218) 447-2238
Email: atif.osmani@mnstate.edu
4 Fourth Author: N. Muhammad Aslaam
Affiliation: Transportation and Logistics Program, North Dakota State University
Address: 1320 Albrecht Blvd, Fargo, ND 58105, USA
Phone: +1 (701) 231-7767
Fax: +1 (701) 231-1945
Email: nmuhammadaslaam.moha@ndsu.edu
CONTENTS Number of Words
Abstract 129
Text 3506
Reference 1135
Figure and Table Figure: 4, Table: 5, Total 9 (250 * 9 = 2250)
Total 7020
2 Park, Szmerekovsky, Osmani, and Aslaam
ABSTRACT
This study formulates a mixed integer linear programming (MILP) model that integrates
multimodal transport into the switchgrass-based bioethanol supply chain (MTSBSC). The two
transport modes are truck and rail. The objective of this study is to minimize the total cost for
cultivation/harvesting, infrastructure, storage process, bioethanol production, and transportation.
Strategic decisions, including the number and location of intermodal facilities and biorefineries,
and tactical decisions, such as amount of biomass shipped, processed, and converted into
bioethanol are validated using the state of North Dakota as a case study. It was found that
multimodal transport scenario is more cost effective than single mode of transport (truck), which
results in cheaper bioethanol cost. A sensitivity analysis was conducted to demonstrate the impact
of key factors on MTSBSC decision and bioethanol cost.
3 Park, Szmerekovsky, Osmani, and Aslaam
INTRODUCTION
Due to worldwide global warming, energy security, multiple societal issues, and increasing oil
demand, there has been great interest in the development of cellulosic biofuel using renewable
biomass feedstock from wood, forest residues, and agricultural residues, which are supreme
alternatives for transportation fuel. According to the U.S. Energy Information Administration
(EIA), in 2015, the United States (U.S) consumed about 7.08 billion barrels of petroleum products
(i.e., an average of about 20 million barrels per day), which account for 21% of worldwide
consumption (1). The transportation industry is the most dominant sector for nation’s petroleum
consumption accounting for 56% of total U.S. fuel use (2). Bioethanol is one type of cellulosic
biofuel, and corn is the major source of current bioethanol as a first generation renewable resource
in U.S. However, there is much debate about first generation biofuel associated with global food
security due to biofuel production directly from food crops (3). As an alternative, lignocellulosic
biomass feedstock is a promising source for producing bioethanol. Switchgrass is one type of
lignocellulosic biomass which is regarded as one of the best second generation renewable energy
resource (4). Many researchers have made great effort on lignocellulosic biomass based bioethanol
supply chain design with a primary focus on minimizing the total system cost by prescribing a
strategic (i.e. location of biomass storage and size of new refinery) and tactical (i.e. amount of
biomass shipped and processed) supply chain plan (5-10). Some other studies developed a model
that maximizes profit (11,12) and/or minimizes risk associated with biomass supply chain
investment (9). A number of studies have extended previous models by considering a multi-period
model to deal with spatial and temporal dimension for long term strategic plan for biomass supply
chain (14,15). Multiple types of biomass feedstock were addressed for forest (12), urban waste
(12), and other agricultural biomass (13,17,18). Recent studies have contributed to sustainability
issues to investigate the environmental impact and regulation (19-23).
A typical biofuel supply chain plan should simultaneously consider the determination of
location of feedstock area, harvesting method, storage, biorefineries, transport of biomass and
biofuel, and biofuel production (17). Making financially optimal decisions is a key strategy in
biomass supply chain. Locating storages close to biorefineries will reduce unit transportation costs,
but might increase the transportation costs if the storages are far away from the harvesting
/collecting area. Biomass can be directly shipped to a preprocessing plant or sent to an intermodal
hub or storage facility from the harvesting/collecting area. Storage serves as a warehouse to store
biomass and manage inventories. Intermodal hubs also play an important role in consolidating
freight load of multiple mode of transportation (i.e. truck, rail, and ship) in supply chain networks
(18). Each transportation mode will impact supply chain costs as well (3). The truck is known to
be the most economical mode for short haul shipment and rail car is the cheaper mode for long
haul shipment. Rail car can handle more tons of cargo at a lower cost than truck, which is also
more energy efficient transportation mode (19). Multimodal transport, which is a combination of
at least two different modes of transport offers more flexibility, is cheaper, and more efficient
transportation mode. It enhances commercial viability, and should be integrated into cellulosic
biofuel supply chain design (20). However, existing literature related to cellulosic bioethanol
supply chain design, assumes the truck is the only transport mode, although the multimodal
transportation option is very attractive for geographic dispersion of demand and supply chain of
biofuel (21).
To the best of authors knowledge, only limited number of studies are found in the literature
for the application of multimodal transport into bioethanol supply chain design (8, 17, 18, 20, 23,
29, 27, 24). This study is motivated by Ekşioǧlu et al.(25), which addressed the impact of
4 Park, Szmerekovsky, Osmani, and Aslaam
intermodal facilities for decision support system (DSS) for corn-based biofuel supply chain design.
This study also determines the minimum cost of biofuel delivery with different levels of production
capacity and transportation cost. However, lack of investigation of biomass storage location when
it is integrated with intermodal facility on biofuel supply chain, is missing part from previous study.
Also, there is limited work that integrate multimodal transport into switchgrass-based bioethanol
supply chains (MTSBSC). William et al. (26) examined the cost comparison between truck and
rail transport mode for downstream switchgrass-based bioethanol supply chain throughout the
United States. Other works including Zhu and Yaq (27), You et al. (15), An et al. (28) and Zhang
et al. (3), considered only truck transport mode in switchgrass-based bioethanol supply chain as a
whole.
Based on the commonly identified supply chain aspects from reviewed literatures, this
paper proposes a MILP (Mixed-integer linear programming) that will investigate the cost effective
MTSBSC. The goal of this study is to minimize total system cost including, marginal rental cost,
cultivation cost, harvesting cost, infrastructure capital cost, transportation cost across the entire
supply chain over one year planning horizon time period. Proposed supply chain structure of
MTSBSC model are shown in Fig 1. Switchgrass biomass is harvested and transported to storage
located at intermodal facility directly by trucks. The switchgrass biomass stored at truck yard is
shipped to biorefineries by truck and biomass stored at rail yard is transported by rail. Then,
bioethanol produced from biorefineries is delivered to demand zone via truck.
FIGURE 1 Switchgrass-based multimodal bioethanol supply chain structure.
Key features of this study includes: (1) two transport modes using both truck and rail will
demonstrate the applicability of the model for case of North Dakota throughout the entire
switchgrass-based bioethanol supply chain from feedstock to end user; (2) Prior literatures lacks
the investigation of switchgrass biomass storage at intermodal facility. Either round bales or
5 Park, Szmerekovsky, Osmani, and Aslaam
square bales stored under tarp storage system will be built nearby rail spurs or along state highway
at the intermodal facility, which have not been addressed in any other study except a study by
Zhang et al. (17), their study placed forest wood storage at nearby rail spurs along with Class A
highways to alleviate the impact of the spring breakup period on truck flow, which is also
applicable to our case study.
PROBLEM STATEMENT AND MODEL FORMULATION
The main objective of this paper is to build a MTSBSC model that aids the design and operation
management of the bioethanol supply chain network. The MTSBSC design problem consists of
locating a set of intermodal hubs, selecting suitable biorefineries among existing location, and to
determining the route of biomass and bioethanol flows.
Two sets of decision, such as strategic and tactical decision, are made simultaneously. The
strategic decisions are mainly on the location of intermodal storage, number of intermodal storage,
biorefinery location, harvesting area assigned to particular intermodal storage or to biorefineries,
and storage that are assigned to a particular biorefineries. Tactical decisions are amount of biomass
harvested and shipped through the multimodal supply chain network, biomass stored, and amount
of bioethanol produced.
The objective of this study is to seek a minimum cost-strategy of the total switchgrass-
based bioethanol supply chain that integrates both truck and rail transportation modes by
determining various supply chain logistics decision variables. Before describing the model in
detail, notations of subscript indices, input parameters, and decision variables used in model
formulation are presented in the table 1.
The objective function Eqs. (1) - (13) minimizes the annual total supply chain cost
including switchgrass marginal rental cost 𝐶𝑟𝑒𝑛𝑡, cultivation cost 𝐶𝑐𝑢𝑙𝑡, harvesting cost 𝐶ℎ𝑎𝑟𝑣,
storage cost at intermodal facility 𝐶𝑠𝑡𝑜𝑟, switchgrass and bioethanol transportation cost 𝐶𝑡𝑟𝑎𝑛𝑠,
bioethanol production cost 𝐶𝑝𝑟𝑜𝑑, intermodal facility capital investment cost 𝐶𝑖𝑛𝑡𝑐𝑎𝑝, biorefinery
capital investment cost 𝐶𝑏𝑟𝑐𝑎𝑝. Transportation cost 𝐶𝑡𝑟𝑎𝑛𝑠 in Eqs (9) – (13), consists of four
terms: transport cost from switchgrass harvesting area to biorefinery 𝑐𝑡𝑟𝑎𝑛𝑠,𝑠𝑏, transport cost from
switchgrass harvesting area to intermodal storage 𝑐𝑡𝑟𝑎𝑛𝑠,𝑠𝑖, transport cost from intermodal storage
to biorefinery 𝑐𝑡𝑟𝑎𝑛𝑠,𝑖𝑏 , and transport cost from biorefinery to demand zone 𝑐𝑡𝑟𝑎𝑛𝑠,𝑏𝑑. In particular,
Eqs. (12) indicates both truck and rail transportation mode used in model formulation. All
variables except binary variables are non-negative continuous.
Minimize 𝐶𝑟𝑒𝑛𝑡 + 𝐶𝑐𝑢𝑙𝑡 + 𝐶ℎ𝑎𝑟𝑣 + 𝐶𝑠𝑡𝑜𝑟 + 𝐶𝑡𝑟𝑎𝑛𝑠 + 𝐶𝑝𝑟𝑜𝑑 + 𝐶𝑖𝑛𝑡𝑐𝑎𝑝 + 𝐶𝑏𝑟𝑐𝑎𝑝 (1)
𝐶𝑟𝑒𝑛𝑡 = ∑ ∑ 𝑐𝑖 × 𝑄𝑖𝑡
𝑖𝜖𝐼𝑡𝜖𝑇
(2)
𝐶𝑐𝑢𝑙𝑡 = ∑ ∑ 𝑣𝑖 × 𝑄𝑖𝑡
𝑖𝜖𝐼𝑡𝜖𝑇
(3)
𝐶ℎ𝑎𝑟𝑣 = ∑ ∑ ℎ𝑖 × 𝑄𝑖𝑡
𝑖𝜖𝐼𝑡𝜖𝑇
(4)
6 Park, Szmerekovsky, Osmani, and Aslaam
𝐶𝑠𝑡𝑜𝑟 = ∑ ∑ 𝑐𝑖𝑠𝑡𝑜𝑟 × 𝑄𝑗𝑡
𝑖𝜖𝐼𝑡𝜖𝑇
(5)
𝐶𝑝𝑟𝑜𝑑 = ∑ ∑ 𝑐𝑏𝑝 × 𝑄𝑘𝑡
𝑖𝜖𝐼𝑡𝜖𝑇
(6)
𝐶𝑖𝑛𝑡𝑐𝑎𝑝 = ∑ ∑ 𝑓𝑖𝑐 × 𝑋𝑗
𝑗𝜖𝐽
(7)
𝑡𝜖𝑇
𝐶𝑖𝑛𝑡𝑐𝑎𝑝 = ∑ ∑ 𝑓𝑏𝑐 × 𝑌𝑘
𝑗𝜖𝐽𝑡𝜖𝑇
(8)
𝐶𝑡𝑟𝑎𝑛𝑠 = 𝑐𝑡𝑟𝑎𝑛𝑠,𝑠𝑏 + 𝑐𝑡𝑟𝑎𝑛𝑠,𝑠𝑖 + 𝑐𝑡𝑟𝑎𝑛𝑠,𝑖𝑏 + 𝑐𝑡𝑟𝑎𝑛𝑠,𝑏𝑑 (9)
𝑐𝑡𝑟𝑎𝑛𝑠,𝑠𝑏 = ∑ ∑ ∑
𝑘𝜖𝐾
( 𝑐𝑡𝑟𝑢𝑐𝑘,𝑚𝑐
𝑖𝜖𝐼𝑡𝜖𝑇
× 𝑑𝑠𝑏 + 𝑐𝑡𝑟𝑢𝑐𝑘,𝑙𝑢) × 𝑄𝑠𝑏𝑡 (10)
𝑐𝑡𝑟𝑎𝑛𝑠,𝑠𝑖 = ∑ ∑ ∑
𝑗𝜖𝐽
( 𝑐𝑡𝑟𝑢𝑐𝑘,𝑚𝑐
𝑖𝜖𝐼𝑡𝜖𝑇
× 𝑑𝑠𝑖 + 𝑐𝑡𝑟𝑢𝑐𝑘,𝑙𝑢) × 𝑄𝑠𝑖𝑡 (11)
𝑐𝑡𝑟𝑎𝑛𝑠,𝑖𝑏 = ∑ ∑ ∑ ∑
𝑚𝜖𝑀
𝑘𝜖𝐾
{( 𝑐𝑡𝑟𝑢𝑐𝑘,𝑚𝑐
𝑗𝜖𝐽𝑡𝜖𝑇
× 𝑑𝑖𝑏 + 𝑐𝑡𝑟𝑢𝑐𝑘,𝑙𝑢) × 𝑄𝑖𝑏𝑚𝑡}
+{( 𝑐𝑟𝑎𝑖𝑙,𝑚𝑐 × 𝑑𝑖𝑏 + 𝑐𝑟𝑎𝑖𝑙,𝑓𝑐 ) × 𝑄𝑖𝑏𝑚𝑡} (12)
𝑐𝑡𝑟𝑎𝑛𝑠,𝑏𝑑 = ∑ ∑ ∑
𝑒𝜖𝐸
( 𝑐𝑡𝑟𝑢𝑐𝑘,𝑚𝑐
𝑘𝜖𝑘𝑡𝜖𝑇
× 𝑑𝑠𝑖 + 𝑐𝑡𝑟𝑢𝑐𝑘,𝑙𝑢,𝑏) × 𝑄𝑏𝑑𝑡 (13)
The model constrains are presented in Eqs. (14) - (20). Constraint (14) assures that amount
of switchgrass harvested at area i does not exceed the marginal land availability. Constraint (15)
is the feedstock flow conservation constraint that amount of biomass transported from harvesting
area to intermodal storage and refinery is same as what is actually available in feedstock area
during time period t. Constraint (16) impose a flow conservation on intermodal storage. Constraint
(17) is a logic constraint, stating that there is no flow through intermodal storages unless one is
open. Constraint (18) is a flow conservation constraint for refineries. Constraint (19) ensure that a
maximum of one biorefinery can be chosen at each location. Constraint (20) is another logic
constraint that there is no biofuel production unless one is open. Constraint (21) ensure that during
7 Park, Szmerekovsky, Osmani, and Aslaam
any time period t, the volume of bioethanol from biorefineries to each demand zone must be greater
or equal to the biofuel requirement for each demand zone.
𝑄𝑖𝑡
≤ 𝑎𝑖𝑡 ∀i ∈ I, ∀t ∈ T (14)
𝑄𝑖𝑡 = ∑ 𝑄𝑠𝑖𝑡 +
𝑖𝜖𝐼
∑ 𝑄𝑠𝑏𝑡
𝑗𝜖𝐽
(15)
∑ 𝑄𝑠𝑖𝑡 +
𝑖𝜖𝐼
(1 − 𝛿) × 𝑆𝑗,𝑡−1 = 𝑆𝑗𝑡 + ∑ ∑
𝑚𝜖𝑀
𝑄𝑖𝑏𝑚𝑡
𝑘𝜖𝐾
∀j ∈ J, ∀k ∈ K, ∀t ∈ T (16)
𝑆𝑗𝑡 ≤ ∑
𝑗𝜖𝐽
𝑝𝑗 × 𝑋𝑗 ∀j ∈ J, ∀t ∈ T (17)
∑ ∑ ∑
𝑚𝜖𝑀
( 𝑄𝑠𝑏𝑡 + 𝑄𝑖𝑏𝑚𝑡) × 𝜃 = ∑ ∑
𝑒𝜖𝐸
𝑄𝑏𝑑𝑡
𝑘𝜖𝐾
𝑗𝜖𝐽𝑘𝜖𝐾
∀j ∈ J, ∀k ∈ K, ∀m ∈ M, ∀t ∈ T (18)
∑
𝑝𝜖𝑃
𝑌𝑘𝑝 ≤ 1 (19)
𝑆𝑘𝑡 ≤ ∑
𝑘𝜖𝐾
𝑏𝑘 × 𝑌𝑘 ∀k ∈ K, ∀t ∈ T (20)
∑ 𝑄𝑘𝑡 ≥
𝑘𝜖𝐾
𝑑𝑡 ∀e ∈ E, ∀t ∈ T (21)
8 Park, Szmerekovsky, Osmani, and Aslaam
TABLE 1 Notations Used in Model Development
Symbol Description Symbol Description
Indices/sets 𝒉𝒊 Harvesting cost of switchgrass ($/ha)
i Switchgrass supply points 𝒄𝒊𝒔𝒕𝒐𝒓 Unit storage cost at storage yard at
intermodal facilities ($/ton)
j Intermodal facility locations 𝒄𝒃𝒓𝒔𝒕𝒐𝒓 Unit storage cost at biorefineries ($/ton)
k Biorefinery locations 𝒄𝒃𝒑 Bioethanol production cost at refineries
($/gallon)
q Capacity level of biorefineries 𝒄𝒕𝒓𝒖𝒄𝒌,𝒍𝒖 Truck loading and unloading cost ($/ton)
e Bioethanol demand points 𝒄𝒕𝒓𝒖𝒄𝒌,𝒎𝒄 Truck variable mileage cost ($/ton-mile)
m Transport mode 𝒄𝒓𝒂𝒊𝒍,𝒇𝒄 Rail fixed cost ($)
t Modeling horizon of 1 year with time periods 𝒄𝒓𝒂𝒊𝒍,𝒎𝒄 Rail variable mileage cost ($/ton-mile)
Input parameters used in model development 𝒄𝒕𝒓𝒖𝒄𝒌,𝒍𝒖 Truck loading and unloading cost ($/ton)
𝑪𝒓𝒆𝒏𝒕 Marginal land rental cost ($) 𝒅𝒔𝒊 Transport distance from supply area to
intermodal facilities (mile)
𝑪𝒄𝒖𝒍𝒕 Biomass cultivation cost ($) 𝒅𝒔𝒃 Transport distance from supply area to
biorefineries (mile)
𝑪𝒉𝒂𝒓𝒗 Biomass harvesting cost ($) 𝒅𝒊𝒃 Transport distance from intermodal facilities
to biorefineries (mile)
𝑪𝒕𝒓𝒂𝒏𝒔 Biomass transport cost ($) 𝒅𝒃𝒅 Transport distance from biorefineries to
demand points (mile)
𝑪𝒊𝒏𝒕𝒄𝒂𝒑 Intermodal facility capital cost ($) 𝜹 Biomass deterioration rate (%)
𝜽 Bioethanol conversion rate (gallon/ton)
𝑪𝒃𝒓𝒄𝒂𝒑 Biorefinery capital cost ($) 𝒅𝒕 Biofuel demand in period t (gallon)
𝑪𝒔𝒕𝒐𝒓 Biomass storage cost ($) Decision variable used in model development
𝑪𝒑𝒓𝒐𝒅 Biofuel production cost ($) 𝑿𝒋 = 1 if an intermodal facility is opened at
location j; 0 otherwise (Binary)
𝒂𝒊 Maximum marginal biomass availability (ton) 𝒀𝒌𝒑 = 1 if a biorefinery is opened at location k
with capacity level p; 0 otherwise (Binary)
𝒄𝒕𝒓𝒂𝒏𝒔,𝒔𝒃 Transport cost of biomass from supply area to
biorefineries ($/ton-mile) 𝑸𝒊𝒕 The quantity of biomass harvested at supply
area i (ton)
𝒄𝒕𝒓𝒂𝒏𝒔,𝒔𝒊 Transport cost of biomass from supply area to
intermodal facility ($/ton-mile) 𝑺𝒋𝒕 The quantity of biomass stored at intermodal
facility (ton)
𝒄𝒕𝒓𝒂𝒏𝒔,𝒊𝒃 Transport cost of biomass from intermodal facilities
to biorefineries ($/ton-mile)
𝒄𝒕𝒓𝒂𝒏𝒔,𝒃𝒅 Transport cost of biofuel from biorefineries to
demand points ($/ton-mile) 𝑺𝒌𝒕 The quantity of biomass stored at biorefinery
(ton)
𝒑𝒋 Storage capacity (ton) 𝑸𝒔𝒊𝒕 The quantity of biomass transported from
supply area to intermodal facility (ton)
𝒇𝒊𝒄 Annualized intermodal facility fixed capital cost ($) 𝑸𝒔𝒃𝒕 The quantity of biomass transported from
supply area to biorefinery (ton)
𝒃𝒌 Biorefinery capacity (gallon) 𝑸𝒊𝒃𝒎𝒕 The quantity of biomass transported from
intermodal facility to biorefinery by transport
mode m during time t (ton)
𝒇𝒃𝒄 Annualized biorefinery fixed capital cost ($) 𝑸𝒃𝒅𝒕 The quantity of biofuel transported from
biorefinery to demand point (gallon)
𝒄𝒊 Annual rental cost of marginal land in i ($/ha) 𝑸𝒌𝒕 The quantity of biofuel produced at
biorefinery (gallon)
𝒗𝒊 Cultivation cost of switchgrass ($/ha)
9 Park, Szmerekovsky, Osmani, and Aslaam
CASE STUDY
The model proposed above is applied to a case study of switchgrass-based bioethanol supply chain
in North Dakota in order to validate our model, and in response to state policy that promotes the
use of multimodal transportation in delivering switchgrass-based alternative transportation fuel.
North Dakota is an ideally suited region for commercial cultivation of switchgrass with a lot of
potential for use of switchgrass-based bioethanol in the future (3).
FIGURE 2 Intermodal storage, biorefinery candidates in North Dakota. (Note: Intermodal
facility #1: Fairmount, #2: Williston, #3: Tioga, #4: Minot, #5: Bowbells, #6: Devils lake, #7: Grand forks, #8:
Dickinson, #9: Bismark, #10: Carrington, #11: Valley city, #12: Casselton, #13: Fargo, #14: Hankinson, and #15:
Enderlin; Refinery candidates ‘R’ stand for Red Trail Energy, ‘B’ for Blue Flint Ethanol, ‘D’ for Dakota Spirit, ‘T’
for Tharaldson Ethanol, and ‘G’ for Guardian Hankinson)
Harvesting area
All 53 counties in North Dakota are considered to be in support of switchgrass. Switchgrass yield
rate is assumed to be a linear function of the North Dakota annual rainfall, which can be used to
estimate the amount of switchgrass supplied from supply zone (3). Two types of bale including
10 Park, Szmerekovsky, Osmani, and Aslaam
square and round are considered. Harvesting areas of switchgrass are defined using county
boundaries in ArcGIS platform. To integrate feedstock data onto transportation network data, it
is assumed that the centroid of each county’s polygon is feedstock supply area, which is auto-
generated and identified in ArcGIS map. The associated feedstock parameters including marginal
rental cost (varies by county) (29), cultivation cost ($85.0/ton) (30), harvesting cost (round bale
=$48.2/ha, square bale =$27.9/ha) (31), marginal land availability for each county (29) are
collected.
Intermodal storage
There is only one intermodal facility used for freight transportation in North Dakota. With
increasing agricultural demand and oil delivery, more intermodal option may enhance traffic and
customer service for agricultural and energy industry. Fifteen intermodal facility candidates (#1
~ #15; including existing intermodal facility at Minot) were selected based on North Dakota
strategic freight analysis report from Upper Great Plains Transportation Institute (UGPTI) (32).
Bale storages with tarp system are located at yard where both railway and highway are available
using ArcGIS. The capacity of storage is set as 125,000 tons regardless of locations (17). The
storage cost is set at $21.7/ton, which includes any expense incurred to maintain inventory and
storage (31). Dry matter loss for both types of bale is assumed to be 2% (33). Fixed intermodal
facility capital investment cost is set at $470,597 (32).
Biorefinery
Currently, five corn-based biorefineries including Blue Flint Ethanol (65 MGY), Dakota Spirit (70
MGY), Guardian Hankinson (132 MGY), Red Trail Energy (50 MGY), and Tharaldson Ethanol
(153 MGY) are available in North Dakota, which are presented in Fig 2. It is assumed that with
advanced biofuel conversion technology, multiple types of feedstock could be converted to
bioethanol at refineries. Therefore, these five biorefineries are used as switch-grass based
bioethanol production candidates in this study. A conversion factor of 85 gallons of bioethanol per
ton of biomass is used (34). The capital cost of biorefinery consists of fixed and variable capital
cost (10). Each biorefinery has a different fixed cost and the variable cost is proportional to size
of refinery (12). To determine the fixed capital cost for each biorefinery, cost scaling factor of 1.6
was multiplied by the size of biorefinery (35). Therefore, a medium level of annualized fixed
capital cost is interpolated. The fixed capital cost is $27 million for 65 MGY biorefinery, $28
million for 70 MGY biorefinery, $42.8 million for 132 MGY biorefinery, $22 million for 50 MGY
biorefinery, and $46.8 million for 153 MGY biorefinery. The variable cost is 0.64/gallon
regardless of the fixed capital cost variation (36).
Transportation data
The multimodal transportation network is presented in Fig 2. In this study, transportation networks
including local, rural, urban roads and highways, and railways are considered. It is assumed that
the centroid of each harvesting area is the origin of biomass supply chain. The longitudes and
latitudes of intermodal facility and biorefinery are identified in ArcGIS. The shortest path based
on Dijkstra’s algorithm from origins to destinations are calculated using the OD cost matrix
application in ArcGIS network analysis. In terms of cost associated with truck and railway,
loading/unloading cost of truck is $5/ton (20) and the variable mileage cost is $0.1/ton-mile for
round bale and $0.12/ton-mile for square bale (31). Rail variable mileage cost is $0.02/ton-mile
(26) and fixed cost of rail is $ 6.54/ton (17).
11 Park, Szmerekovsky, Osmani, and Aslaam
Bioethanol demand
Cities that provide E85 ethanol are considered to be demand centers, and 18 cities were chosen in
this study. Fig.2 shows the geographic distribution of these eighteen cities. The total annual
bioethanol demand is set at 30 MGY, according to official portal for North Dakota State
Government (37).
RESULTS AND DISCUSSION
The optimal system results and comparison with single mode
The minimum cost strategy to integrate multimodal transportation model into switchgrass-based
supply chain suggests that four intermodal facilities (#3, 4, 6 and 14) are required and two
biorefineries (D and R) should be selected. The optimized total cost for the supply chain is $237
million. The total system cost breakdown is presented in Fig 3. It is found that cultivation cost has
the highest contribution, accounting for 36.56% of total cost, followed by production cost,
accounting for 19.48%. The optimal assignment and its flow pattern of biomass to intermodal
storages and biorefineries are analyzed in Table 2. From intermodal storage to biorefinery, only
rail transport mode is used because rail haulage cost is cheaper for long distance shipment. Truck
is used from harvesting area to intermodal facility and from biorefineries to demand center,
because it is the only possible mode for some segments which originate from harvesting area or
end at markets (20).
In order to compare multimodal solutions and single mode solutions, the model was re-run
by eliminating rail transport mode from the model. The comparison made in Table 3 shows that
single mode solution is about $76 million more expensive than multimodal solution. The
multimodal solution indicates that the optimal bioethanol delivered cost is $1.904/gallon, which is
cheaper than single model solution ($2.663/gallon).
FIGURE 3 Total cost breakdown for switchgrass-based multimodal bioethanol supply
chain.
12 Park, Szmerekovsky, Osmani, and Aslaam
TABLE 2 Optimal Assignment of Biomass Flow to Intermodal Storages and Biorefineries
Counties assigned to intermodal storages Note
Tioga (#3) Burke, Divide, Mountrail, Williams
Minot (#4) Bottineau, McHenry, Pierce, Renville, Rolette, Sheridan, Ward
Devils Lake (#6) Cavalier, Pembina, Ramsey, Towner, Walsh, Rolette
Hankinson (#14) Richland, Sargent
N/A
Counties assigned to biorefineries
D Barnes, Cass, Dickey, Eddy, Foster, Grand Forks, Griggs, Kidder,
LaMoure, Logan, McIntosh, Nelson, Ransom, Steele, Stutsman, Traill, Wells
RAdams, Billings, Bowman, Burleigh, Dunn, Emmons, Golden Valley, Grant,
Hettinger, McKenzie, McLean, Mercer, Morton, Oliver, Sioux, Slope, Stark
N/A
Intermodal Storage assigned to biorefineries
D Hankinson (#14)
R Tioga (#3), Minot (#4), Devils Lake (#6), Hankinson (#14)
Rail is the only mode
to ship biomass from
intermodal storage to
biorefinery
TABLE 3 Cost Comparison for Single mode and Multi-mode
Cost breakdown Single mode Multi-mode
Transportation cost $ 107,592,261.18 $ 82,601,443.23
Bioethanol delivered cost $ 2.663 $ 1.904
Total supply chain cost $ 313,716,741.38 $ 237,253,908.70
Sensitivity analysis
This section discusses results from several sensitivity analyses and analyzed the factors that are
significant to the switchgrass-based multimodal bioethanol supply chain. Sensitivity analysis for
key inputs include conversion rate of switchgrass feedstock to bioethanol, biomass feedstock
availability, different levels of bioethanol demand, and all the unit cost factors - marginal rental
cost, cultivation cost, harvesting cost, transportation cost, storage cost, bioethanol production cost,
and capital investment cost.
Influence of biomass availability and conversion rate on bioethanol cost and location
As a baseline case, the conversion rate is 85 gallons/ton and 13 million tons are available from 53
counties in North Dakota (3). It is assumed that the conversion rate decreases from 85 gallons/ton
to 55 gallons/ton in increments of 5 gallons/ton, and the availability of switchgrass increases by a
total of 5% from current volume of availability. Table 4 presents the change of bioethanol delivered
cost and location decision of intermodal facility and biorefineries by biomass availability and
conversion rate. The highest bioethanol cost is $2.977/gallon with the baseline biomass
availability and the conversion rate of 55 gallons/ton, and the lowest bioethanol cost is $1.850 with
5% increase in biomass availability and conversion rate of 80 gallons/ton. From the results, it was
13 Park, Szmerekovsky, Osmani, and Aslaam
found that bioethanol cost increases dramatically at the lowest biomass availability and at the
lowest conversion rate, which is because the long haulage shipment occurs at low biomass
availability (17). Intermodal storage location and biorefineries location change over different
biomass availabilities and conversion rates. As can be seen from the results, intermodal storage
location #3 and #6 show up as optimal candidate locations in most case scenarios in addition to
#4, which is currently operating in North Dakota. A higher biomass availability and more
conversion rate result in more requirements for intermodal facility to be opened. In terms of
biorefinery location, most of the case scenarios show that biorefineries ‘D’ and ‘R’ are candidate
sites which should increase their capacity level to handle multiple types of feedstock to convert
biomass into bioethanol and minimize total cost.
TABLE 4 Bioethanol Cost and Location Decision by Biomass Availability and Conversion
Rate
Conversion rate (gallon/ton)
Biomass availability (%) Bioethanol delivered cost ($/gallon)
Baseline 80 75 70 65 60 55
Baseline 1.904 2.016 2.352 2.346 2.520 2.524 2.951
1% 1.885 2.001 2.373 2.355 2.540 2.712 2.931
2% 1.863 1.987 2.384 2.339 2.489 2.729 2.912
3% 1.862 1.972 2.100 2.359 2.505 2.674 2.977
4% 1.863 1.958 2.040 2.353 2.465 2.664 2.873
5% 1.856 1.850 1.970 2.388 2.448 2.637 2.855
Biomass availability (%) Intermodal facility location
Baseline 80 75 70 65 60 55
Baseline 3,4,6,14 3,4,6 3,4,6 3,4,6 3,4,6 3,6,8 3,6,8
1% 3,4,6,14 3,4,6 3,4,6 3,4,6 3,4,8 3,4,6,8 3,6,8
2% 3,4,6,10,15 3,4,6 3,4,6 3,4,6 3,4,6 3,4,6 3,4,6
3% 3,4,6,14 4,6,7 3,4,6 3,4,6 3,4,6 3,4,6,8 3,4,6
4% 3,4,6,10,11,14 3,4,6,14 3,4,6,14 3,4,6 3,4,6 3,4,6 3,6
5% 3,4,6,14 3,4,6,14 3,4,6 3,4,6 3,4,6 3,4,6 3,6,8
Biomass availability (%) Refinery location
Baseline 80 75 70 65 60 55
Baseline D,R D,R D,R D,R D,R B,T B,T
1% D,R D,R D,R D,R D,R B,T B,T
2% D,R D,R D,R D,R D,R D,R B,T
3% D,R D,R D,R D,R D,R B,T D,R
4% D,R D,R D,R D,R D,R D,R B,T
5% D,R D,R D,R D,R D,R D,R B,T
Bioethanol delivered cost change over different bioethanol demand and conversion rate
In addition to the bioethanol delivered cost change with different scenario analyses of biomass
availability and conversion rates, the bioethanol delivered cost with different annual level of
14 Park, Szmerekovsky, Osmani, and Aslaam
bioethanol demand (MGY) versus conversion rate (gallons/ton) are investigated. It is assumed that
bioethanol demand increases from current annual level of demand of 30MGY up to 45MGY (a
total of 50% increase). Fig 4 presents the resulting bioethanol delivered cost changes by bioethanol
demand and conversion rate. When bioethanol demand is fixed, the bioethanol delivered cost
increases with the increase in conversion rate (Fig 4. a). When the conversion rate remains the
same, the delivered cost of bioethanol also increases, meaning that higher bioethanol delivered
cost occurs with the increasing demand of bioethanol and decreasing conversion rate (Fig 4.b).
The experimental results from Tables 4 and 5, and Fig 4 indicates that relationship between
both biomass availability and conversion rate, and bioethanol demand and conversion rate are
major factors affecting the bioethanol delivered cost. Higher biomass availability means that the
intermodal storage and biorefinery would be supplied from harvesting areas that are close by,
therefore incurs lower shipment cost, resulting in lower unit cost of bioethanol (17). Lower
conversion rate with higher demand implies higher bioethanol production cost, which would
increase transport cost and unit cost of bioethanol.
FIGURE 4 Bioethanol delivered cost by bioethanol demand change and conversion rate.
Influence of different unit cost factors on bioethanol cost
In order to find the most influential unit cost factors on bioethanol delivered cost, each unit cost
was increased or decreased by 10% for sensitivity analysis to investigate the overall switchgrass-
based bioethanol multimodal supply chain system, as presented in Table 5. The results show that
bioethanol cost is not dependent on rental cost, cultivation cost, or harvesting cost. The most
influential unit cost factor is truck transportation cost for biomass, which accounts for 1.42%
increase and 5.62% decrease of optimal value of bioethanol cost ($1.904/gallon). The second most
influential factor is rail transportation cost, accounting for 0.95% increase and 3.53% decrease.
Capital investment cost and bioethanol production cost are third and fourth influential factors.
15 Park, Szmerekovsky, Osmani, and Aslaam
TABLE 5 Sensitivity Analysis for Delivered Bioethanol Cost
Unit cost factor (10% increase or decrease) Bioethanol cost ($/gallon) Percentage change (%)
Rental cost +10% 0 0
- 10% 0 0
Cultivation cost +10% 0 0
- 10% 0 0
Harvesting cost +10% 0 0
- 10% 0 0
Truck transportation cost +10% 1.931 1.42%
- 10% 1.797 -5.62%
Rail transportation cost +10% 1.922 0.95%
- 10% 1.837 -3.52%
Storage cost +10% 1.899 -0.26%
- 10% 1.894 -0.53%
Production cost +10% 1.917 0.68%
- 10% 1.906 0.11%
Capital cost +10% 1.920 0.84%
- 10% 1.841 -3.31%
SUMMARY AND CONCLUSION
This study presented a Mixed-integer linear programming (MILP) model for integrating
multimodal transport (truck and rail) into switchgrass-based bioethanol supply chain design
(MTSBSC). The model was applied to case study of North Dakota. This research demonstrates
how proposed model can be adopted to make strategic and tactical decision for bioethanol supply
chain. Experimental results indicate that multimodal solution is more cost effective than single
mode solution in terms of total system cost and bioethanol delivered cost. Also, there is interaction
between bioethanol conversion rate and biomass availability as well as conversion rate and
bioethanol demand in supply chain decision for biorefinery and intermodal storage. Higher
biomass availability results in lower unit cost of bioethanol. On the other hand, higher demand of
bioethanol increases bioethanol cost. From the sensitivity analysis, transportation costs are the
most influential factor on bioethanol delivered cost followed by capital investment and production
cost. The storage cost shows no impact on bioethanol cost. This impact should be identified in
future research by considering biomass inventory change over time. This study optimizes
MTSBSC using single objective of economic performance. The current study can be extended by
considering multiple objectives that incorporates environmental impact of MTSBSC.
16 Park, Szmerekovsky, Osmani, and Aslaam
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