As a result of liberalization and the globalization of

international trade, factors of production and consumer

products come from destinations around the world, and

therefore the interdependence of Supply Chains between

suppliers and wholesalers is increasing day by day.

The effectiveness of Supply Chains has also become very

important for proper competition in international markets that

have emerged with the removal of trade barriers between

countries. In this study, it is aimed to reveal the characteristics,

processes, functions and development of Supply Chains and

Supply Chain Management. Important definitions and

theoretical analyzes of both Supply Chain and Supply Chain

management are explained, transportation, which takes an

important function of the supply chain, is discussed under a

separate title, and information about the costs in the supply

chain and their management are given.

Optimization of the supply chain includes the number and

capacity of production sites, factories, distribution centers,

transportation vehicles, warehouse locations and facilities.

Structuring the supply chain is a strategic and long-term

decision, so it has a significant impact over time. It largely

defines more operational aspects such as means of transport,

its features and capabilities. In addition, the industrial ecology

approach to achieve sustainable development includes supply

chains.

The analysis of the supply chain, which includes all its

actors, must meet requirements such as meeting customer

demands under constraints such as the delivery capacity of

suppliers, the production capacity of factories, and the storage

capacity of distribution centers. This requires complex

processing of a large number of variables, which requires

resorting to optimization to achieve optimal results, both to

speed up the process and to ensure sufficient precision.

Suppliers, production facilities, distribution facilities,

storage facilities, collection and recovery facilities are the

members that make up the supply chain, and the supply chain

is a dynamic process that includes the continuous flow of

materials, funds and information across many functional areas

within and between these members.

Entering a market of large wholesalers and supermarket

chains has increased competition at the expense of small

farmers. About 30-35% of total food produced is wasted each

year due to inadequate infrastructure and ineffective supply

chains. Globalization has also led to a collapse in biodiversity

and ecosystems, obesity and increased food poverty, and the

impossibility of consumers knowing enough about food

source and quality.

However, consumers today are increasingly aware of the

negative impacts of a globalized food system and are eager to

reconnect directly with farmers, support local communities,

consume healthy food. In addition, global food demand is

projected to increase by 50% by 2030, leading to increased

demand for resources for production and transportation.

Planners, stakeholders and researchers wonder if we will

have enough healthy food in the future, and at what cost. For

this reason, companies, especially in food supply chains, need

to be faster, cheaper and more flexible than their competitors

in order to meet customer expectations, as well as apply

sustainable paradigms. When all these conditions are taken

into consideration, both producers and farmers will benefit

economically by purchasing the raw materials directly from

the farmers and eliminating them from the intermediaries in

the food supply chain. Thanks to the fresh and natural raw

materials taken directly from the farmer, the harms of the

products to human health will be reduced. By collecting back

the used products from the end customer, the harm of wastes

to the environment will be prevented and economic benefit

will be provided by recycling the products.

In this research, an optimization has been carried out for

the transporting in the supply chain, which aims to change

route because of Russia and Ukraine war. The proposed model

to define the optimal configuration allows decision making

from raw material suppliers to possible distribution centers to

the customer. This optimization was applied on the supply

chain of a company selling ready-cooked food. Our study

Cost Minimization by Lineer Shipping Transport Integration into the

Supply Chain and Supplier Selection in a New Production Facility

AHMET KARAKAYA, MEHTAP DURSUN, NAZLI GOKER

Galatasaray University, Industrial Engineering Department, Research and Application Center, Ortakoy,

Besiktas, Istanbul, TURKEY

Abstract: As a result of liberalization and the globalization of international trade, factors of production and

consumer products come from destinations around the world, and therefore the interdependence of supply

chains between suppliers and wholesalers is increasing day by day. In this research, an optimization is carried

out for the transportation in the supply chain, which aims to change route because of Russia and Ukraine war.

The method is applied to the supply chain of a company that sells ready meals. This study aims to increase the

competitive structure in the new market by reducing the cost of a fast food company that does not carry out

maritime transport in its supply chain.

Keywords: cost minimization, lineer shipping, supplier selection, supply chain management

Received: August 9, 2021. Revised: May 15, 2022. Accepted: June 12, 2022. Published: August 2, 2022.

1. Introduction

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proposes to go further by interweaving for the several

parameters which is possible suppliers and transportation

costs.

Govindan et al. [1] proposed a multi-objective

optimization model by integrating sustainability into decision-

making about distribution in a perishable supply chain

network. Sustainable supply chain network design and a time

windowed two-stage location orientation problem are used to

optimize economic and environmental objectives in a

perishable food supply chain. Soysal et al. [2] developed a

multi-objective linear programming model to minimize the

total logistics cost and to minimize the total amount of

greenhouse gas emissions from transportation operations in a

general cattle logistics network problem.

To reduce post-harvest loss (PHL) in supply chain

networks, Nourbakhsh et al. [3] proposed a mathematical

model that identifies optimum logistics for grain transport and

infrastructure investment by identifying optimal locations for

new pre-processing plants and optimizing road/rail capacity

expansion. Bortolini et al. [4] proposed a three-target supply

chain network to tackle the tactical optimization of fresh food

distribution networks, taking into account operating cost,

carbon footprint and delivery time objectives. A real case

study of the distribution of fresh fruit and vegetables from a

number of Italian producers to several European retailers was

used to validate the applicability.

Allaoui et al. [5] made stakeholder selection using a hybrid

multi-criteria decision-making method based on the

Analytical Hierarchy Process (AHP) method and the Ordinary

Weighted Average (OWA) aggregation method in the first

stage. developed a mathematical model. The feasibility and

efficiency of the model is demonstrated by the case of an agri-

food company.

Rohmer et al. [6] developed a new network design that

addresses sustainability issues in the context of the global

supply chain. He illustrated trade-offs between alternative

production and consumption scenarios, as well as conflicting

goals, through a nutritional case study. Darestani and

Hemmati [7] proposed a supply chain network model for

perishable goods while considering the uncertainties

associated with spoilage. The proposed model includes two

sub-objectives, minimizing total grid costs and minimizing

greenhouse gas emissions. General weighting method and

Torabi-Hassini method were used to solve the dual-objective

model.

Pourmohammadi et al. [8] developed a mixed integer

linear mathematical model for wheat supply chain redesign

and planning in Iran, which takes into account the differences

between long-term and short-term storage facilities and wheat

quality. Mohammadi et al. [9] proposed a multi-purpose

model to design the supply chain in the processed food

industry with products. Among the objectives of the model are

profit maximization as an economic index, carbon dioxide

emissions in the manufacturing sector and maximizing the

number of jobs created as an environmental and social index

by the wastewater treatment index.

Jouzdani and Govindan [10] developed a multi-objective

mathematical model, taking into account the "Triple

Bottomline Approach" of sustainability, to optimize cost,

energy consumption and traffic congestion in the supply chain

of perishable food products. Product lifetime uncertainty is

modeled as a Weibull random variable and it is assumed that

food perishability is affected by the use of car refrigerators,

which is considered a decision variable. The study concluded

that an economic compromise of 15% can increase the

sustainability of the supply chain network design by 150%.

Nimo is an active and developing company that has been

in the Ukrainian market since 1942. In the Food and Ready

Meal sector, Nimo works with companies that it exports to 7

countries, raw material suppliers from 3 countries, and about

30 different store chains where its products are sold in the

domestic market.

It processes raw materials it has supplied from its

suppliers. The company has a large store for wholesale

plasters, chains of Ukrainian stores selling its own products. It

has a warehouse to store the products it produces in Ukraine,

a processing in facility, raw material and semi-raw material

warehouses. It supplies raw materials for its products mainly

from Russia. It also imports 55% of the final product to

Russia. The company wants to develop its market network

since 2013. With the Russia-Ukraine crisis, the company

wants to expand to Europe and plans to establish a production

facility in Europe. The work contents planning a route and

choosing a supplier with the data we receive from the

company. Because Nimo will open a new production facility

in Europa and distribution will be provided from there.

The company plans to establish a new production facility

in Albania. For this reason, it has started to search for new

suppliers and markets. It receives its products and supply raw

materials by land transport. However, it wants to be

competitive in the European market and to have a place in the

market share with competitive prices with using sea

transportation.

Interaction with counterparties in the supply network on

behalf of the company is established as follows: A report is

created on the success of the supplier in certain positions and

based on this it is decided whether to reorder a particular

product from a supplier. If a positive decision has been made,

the purchasing department creates an order, which is then sent

to the suppliers. According to the information they have given

us, there is no problem regarding the possible suppliers and

their countries in terms of quality and shelf life. For this

reason, with the information and cost information given in this

study, a choice will be made in terms of planning a route and

supplier in sea transportation.

A study will be made for a container routing and empty

container routing with the given cost coefficients. The

suppliers used by the company with land transportation are as

follows.

a) Romania

b) Ukraine

c) Poland

The company related to the above suppliers has done a

study and sent us the cost of 50.000 over the coefficients. If

the result is less than 50,000 in our study, it has been reported

that working with LS reduces the cost and a decision will be

made in this direction. While calculating the cost coefficients,

they evaluated the raw material price and the data received

2. Literature Rewiev

3. Case Presentation and Problem

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from LS companies within the scope of confidentiality and

gave them to us by establishing this numerical connection

between them.

Our work will take place in the following 2 stages. Stages

are;

a) Testing possible supplier routes between containers

loaded with raw materials and determining their costs (Row

material)

b) Determining cost of containers loaded with final

products

Notations are presented in Table 1 to facilitate the presence

of notation marks.

TABLE I. NOTATIONS

Parameters

csij

The cost of sending an empty container of type s

over the service network from node i to node j

(i,j)

ckij

The cost of sending a unit of commodity k over

the service network from node i to node j (i,j)

The demand quantity for commodity k

uij

The vessel capacity over the service network

from node i to node j (i,j)

Sufficiently large non-negative number

Number of nodes in service network

Decision Variables

Ɣsij

Amount of empty container flow of type s over

the service network from node i to node j (i,j)

ξk

Amount of artificial flow for commodity k

xkij

Dual variable associated with constraints

Sets

Set of service arcs in Graph G

Set of service nodes in Graph G

Set of destination nodes (Commodity or Empty

container) in Graph G

Set of origin nodes (Commodity or Empty

container) in Graph G

Set of commodities sent over the service network

in Graph G

Set of empty container types i.e., foldable

containers folded as s=1, s=2 or s=3 containers

(Only empty containers

Miscellaneous

A node of service network in Graph G

A commodity

Origin node of commodity K

Destination node of commodity K

G = (N,A)

Graph representing the liner shipping network

The mathematical formulation of Laden and foldable

Empty Container routing Problem (LECP) can be given as

follows.

There is no need for cold chain application for raw

materials. For this reason, the containers we use can be used

as laden and foldable containers. The model was used in the

same form for supplier selection.

1st possible 3 supplier countries are as follows;

a) Spain

b) Tunisia

c) Belgium

The information required for the model is shown in Tables

2-4.

TABLE II. ARCS AND COST COEFFICIENT FOR 1ST SCENARIO

Origin (Node i)

Destination (node j)

Cost Coefficient

Belgium

Spain

Tunisia

Albania

Belgium

Tunisia

Albania

Belgium

TABLE III. COMMODITIES ORIGINS AND DESTINATIONS FOR 1ST

SCENARIO

Commodity

Origin (node i)

Destination (node j)

Raw Material 1

Belgium

Albania

Raw Material 2

Spain

Albania

Raw Material 3

Tunisia

Albania

TABLE IV. COUNTRIES AND CONTAINER DEMANDS FOR 1ST

SCENARIO

Country

Container Demands

Belgium

200

Spain

200

Tunisia

200

Albania

600

Using given information above, the optimal solution of

objective function is 13.950.

2nd possible 3 supplier countries are as follows;

a) Portugal

4. Mathematical Model 5. Application

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b) Morocco

c) Italy

The information required for the model is shown in tables

5-7.

TABLE V. ARCS AND COST COEFFICIENT FOR 2ND SCENARIO

Origin (Node i)

Destination (node j)

Cost Coefficient

Portugal

Morocco

Italy

Albania

Morocco

Albania

Albenia

Portugal

TABLE VI. COUNTRIES AND CONTAINER DEMANDS FOR 2ND

SCENARIO

Commodity

Origin (node i)

Destination (node

Raw Material 1

Portugal

Albania

Raw Material 2

Morocco

Albania

Raw Material 3

Italy

Albania

TABLE VII. COUNTRIES AND CONTAINER DEMANDS FOR 2ND

SCENARIO

Country

Container Demands

Portugal

200

Morocco

200

Italy

200

Albania

600

Using given information above, the optimal solution of

objective function is 11.950.

3rd possible 3 supplier countries are as follows;

a) Denmark

b) Portugal

c) Tunisia

The information required for the model is shown in tables

8-10.

TABLE VIII. ARCS AND COST COEFFICIENT FOR 3RD SCENARIO

Origin (Node i)

Destination (node j)

Cost Coefficient

Denmark

Portugal

Tunisia

Albania

Denmark

TABLE IX. COUNTRIES AND CONTAINER DEMANDS FOR 3RD

SCENARIO

Commodity

Origin (node i)

Destination (node j)

Raw Material 1

Denmark

Albania

Raw Material 2

Portugal

Albania

Raw Material 3

Tunisia

Albania

TABLE X. COUNTRIES AND CONTAINER DEMANDS FOR 3RD

SCENARIO

Country

Container Demands

Denmark

200

Portugal

200

Tunisia

200

Albania

600

Using given information above, the optimal solution of

objective function is 15.750.

Based on the above optimizations, the lowest cost route is

the route in scenario 2. Row material 1 supply from Portugal,

row material 2 supply from Morocco, Row material 3 supply

from Italy. This selection is give us 25% cost saving according

to worst scenario.

As a result of the processes we have done above, the

supplier country has been selected, as well as the routing of

possible routes for the market. The results we found are as

follows.

1st possible 3 supplier countries and cost coefficient are as

follows;

a) Spain

b) Tunisia

c) Belgium

We found 13.950 cost coefficient using Gams for LECP.

2nd possible 3 supplier countries and cost coefficient are

as follows;

a) Portugal

b) Morocco

c) Italy

We found 11.950 cost coefficient using Gams for LECP.

3rd possible 3 supplier countries and cost coefficient are

as follows;

a) Denmark

b) Portugal

c) Tunisia

We found 15.750 cost coefficient using Gams for LECP.

The supplier countries we chose in the light of the

information above are Portugal, Morocco, Italy. This choice

gave us approximately 25% cost savings over the worst-case

scenario. Later, when we did this study for the market, we

found 26.950 cost coefficient.

When the choices and transportation we described above

are used, a total cost is 38.900 cost coefficient. The company

informed that if land transportation is used, there will be

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50.000 cost coefficient. In this direction, we have achieved

approximately 22.2% cost savings with our work.

Supply chain is the general expression of the system of

producing and delivering a product or service from the very

beginning of the procurement of raw materials to the final

delivery of the product or service to the end consumers. The

supply chain is a set of processes that encompasses all aspects

of the manufacturing process, including the activities involved

at each stage, the information transmitted, the natural

resources converted into useful products, human resources,

and other components that go into the finished product or

service. Businesses have to optimize their supply chains in

order to maintain their cost and market advantage by

delivering quality products and services to the end consumer

as soon as possible and at the desired time. The increase in

global trade at this level has been an inevitable result of

minimizing transportation costs in order to reduce the cost for

exporting countries. Increasing fuel costs in the global have

put great pressure on the cold chain. For this reason, maritime

transportation has become the number one point for cost

reduction. Containerization of frozen commodities is steadily

progressing, while bulk reefers retain a significant market

share, particularly for certain commodity flows. With the

spread of containerization, the competition between frozen

bulk and container becomes increasingly intense. More

academicians are focusing on this area, while business leaders

are looking for practical tools to help them with their daily

operations and decision-making.

Increasing fuel prices in the world have caused a great cost

increase in supply chains. For this reason, the use of more

economical transportation lines has gradually increased.

Along with rising fuel prices, the amount of fuel used is

constantly harming our planet. In addition to cost

optimization, this study was also carried out in terms of

sustainable engineering and green engineering concepts. A

green planet is the greatest gift we can give to future

generations.

This research has been financially supported by

Galatasaray University Research Fund FBA-2021-1050.

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References

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