Designing a sustainable multi-channel supply chain distribution network: A case study

Journal Pre-proof Designing a sustainable multi-channel supply chain distribution network: A case study Arezoo Vafaei, Saeed Yaghoubi, Javad Tajik, Farnaz Barzinpour PII:

S0959-6526(19)34498-1

DOI:

https://doi.org/10.1016/j.jclepro.2019.119628

Reference:

JCLP 119628

To appear in:

Journal of Cleaner Production

Received Date: 17 August 2019 Revised Date:

10 November 2019

Accepted Date: 8 December 2019

Please cite this article as: Vafaei A, Yaghoubi S, Tajik J, Barzinpour F, Designing a sustainable multichannel supply chain distribution network: A case study, Journal of Cleaner Production (2020), doi: https://doi.org/10.1016/j.jclepro.2019.119628. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

"CRediT Author Statement"

Arezoo Vafaei: Software, Validation, Investigation, Formal analysis, Resources, Data Curation, Writing - Original Draft Preparation. Saeed Yaghoubi: Conceptualization, Methodology, Formal analysis, Writing- Reviewing and Editing, Visualization, Supervision, Project administration. Javad Tajik: Software, Validation, Formal analysis, Data Curation, Writing- Reviewing and Editing. Farnaz Barzinpour: Formal analysis, Methodology, Visualization.

Designing a sustainable multi-channel supply chain distribution network: A case study a

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Arezoo Vafaeia, Saeed Yaghoubia,1, Javad Tajika, Farnaz Barzinpoura School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Corresponding author: Tel:+982173225053; Fax:+982173021537, E-mail address: [email protected]

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Designing a sustainable multi-channel supply chain distribution network: A case study Abstract The existence of numerous social, economic, and environmental problems and the need to improve the long-term performance in organizations have led the supply chain designs toward sustainable designs. On the other hand, due to the emersion of new technologies and the creation of new business models, the increase of competition level among retailers and the raise of customers expectations, the use of a multi-channel distribution system is nowadays growing. Therefore, the appropriate distribution channel according to the product type and the number of vehicles for the transportation of the products is a significant and important issue in retailing systems. In this regard, in this paper, a mixed-integer programming model for the sustainable distribution network design, considering multi-product, multi-echelon and multi-transportation mode especially third party logistics (3PL), is presented. The economic, environmental, and social objectives are mathematically formulated, where these objectives include: 1) minimizing transportation costs, purchasing vehicles and building warehouses as the first objective, 2) minimizing the amount of carbon dioxide released by transport vehicles and building warehouses, 3) maximizing the number of job opportunities. After linearization of the proposed model, it is solved using the goal programming technique by GAMS software. Finally, to evaluate the applicability of the proposed mathematical model, Digikala company is used as a case study. Keywords: Sustainable distribution network design, Retailing system, Multiple channels, Goal programming, Third party logistics (3PL). 1. Introduction Due to the increase in production rates and geographical distances between suppliers and customers, distribution operations have an important place in the supply chain performance. Therefore, the supplier would inevitably bring his products to the customer at long distances, through a person called the wholesaler or retailer. A proper facility location, selecting appropriate distribution channels, and choosing a suitable type of vehicle will have social, environmental, and economic impacts on the region of the construction site of the factory, in addition to the economic impact on the performance of the industrial unit. Studies related to finding the optimal location of warehouses, choosing the appropriate distribution channels, reducing the adverse environmental impacts and the social problems caused by the lack of job positions, not only have academic value but also have numerous economic benefits. To get the customer satisfaction, cost reduction, and adverse environmental impacts between the supplier and the customer, the present study intends to design a sustainable distribution network by employing a mixed-integer programming model with a multi-channel, multi-objective, singleperiod, multi-product, and multi-vehicle, multi-3PL problem and with the free shipping. Besides, in the proposed model, it is considered that if the customer’s order is higher than a specific price, free shipping is suggested by the retailer companies.

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One of the major challenges in the distribution network design is choosing the optimal distribution channel among multi-channel distribution networks. In these distribution channels, customers' orders can be received from any facility (suppliers, retailer warehouses). Furthermore, sustainability objectives are used such as reducing costs, reducing environmental impacts, and increasing the number of job opportunities. In order to improve the distribution network's performance, it can be referred to the research carried out by Cintron et al. (2010). As shown in Fig. 1, in the distribution channel 1 and 2, the distribution of the product is carried out by the company and in the distribution channel 3 and 4, the company assigns the product to a company of the third-party logistics.

Fig.1. Different distribution channels in the research of Cintron et al. (2010)

According to the above by considering the distribution network costs and environmental and social impacts, the present paper is seeking an optimal method for distributing the product from the supplier to the customer. In the multi-objective, single-period, multi-product, and multi-vehicle, multi-3pl model delivering products to customers is made up of several distribution channels. Also, with the investigation of different distribution channels and the choice of the appropriate vehicle types, the best distribution channel of each product is selected for each customer and it is presented as a mixed-integer programming model. Therefore, the objectives of this paper are minimizing the cost of establishing warehouse, the cost of buying the vehicles, the cost of transporting products, minimizing the amount of carbon dioxide released by the establishing warehouse and transporting different vehicles, and maximizing the number of job opportunities. The model of this paper is solved by CPLEX solver of the GAMS® optimization software, and in order to validate the proposed model, the data of Digikala company is used as a case study. In the supply chain structure of the proposed model, the products are sent to the customer in two ways: a. direct: from suppliers to customers and b. indirect: collecting from different suppliers, transferring to the retailer's warehouses, and then transferring them to customers. Furthermore, the product distribution is done in three ways: a. supplier distribution team, b. retailer distribution team, and c. third-party logistics. Designing an effective distribution network can significantly reduce the cost of transportation and increase the customer satisfaction. As a result, the design of the distribution network can be considered as a key factor for the company's profitability. At the macro level, due to the importance and significance of the distribution sector in the country's gross domestic product and 2

the macro objectives set in the macro plans in this regard, improving the efficiency of networks and distribution infrastructure is one of the important points for reorganizing of the supply chain in the country. The effects of this change will be on improving the country's economic indicators, especially GDP, the transition of goods, increasing the share of services in the economy, and strengthening the country's economic status in the region. As a result, one of the most important issues faced by managers is deciding the distribution system of the goods. This decision has direct effects on other marketing decisions such as pricing, advertising, packaging, etc. Our model has some advantages in comparison with other studies, five of which will be deeply explained in what follows: • • • • •

Multi-channel distribution networks are considered in the modeling. Therefore, the best distribution channel for a specific type of product is selected and this leads to the reduction of transportation cost. Considering different vehicles for using the retailer's distribution team as well as selecting the optimal type of vehicle. Modeling in order to consider free shipping. Studying and modeling the different aspects of sustainability (economic, environmental, and social) in the supply chain, in accordance with the real-world needs, which have a practical aspect. Finally, the data of Digikala company is used as a case study to demonstrate the applicability of the model.

Here are the questions of research of the paper: • • • •

What types of distribution channels are typically best suited for specific types of products? What is the optimal allocation of product from each warehouse to the customers in the supply chain? What will be the configuration of the supply chain network to compromise between economic, environmental, and social objectives? What are the environmental and social impacts of the supply chain and how are they calculated?

The rest of the paper is investigated as follows: The literature review is examined in section 2. The problem definition and mathematical model is expressed in section 3. The case study and sensivity analysis are mentioned in section 4 & 5, respectively. The conclution and future research of the paper are finally indicated in section 6. 2. Literature Review In this section, the topics we are discussing fall into three areas: network design, distribution systems, and sustainability. Geoffrion and Graves (1974) were among the first authors to consider middle distribution equipment in addition to the manufacturer's equipment. They proposed a mixed-integer linear programming model for the design of a multi-product distribution system with capacity constraints. In a study by Jayaraman (1998), the relationship between inventory management, facility locating, and transportation policy designation has been considered. So that it analyzes the 3

interdependence between the three domains and proposes an integrated model of mixed-integer programming for the design of the distribution network. Jayaraman and Ross (2003), have investigated PLOT design system (production, logistics, outsourcing, transportation). The system explores a range of distribution network design issues that include several product types, a central production facility, multiple distribution centers, temporary storage warehouses, and retailer (customer), which each of the units demands to different products. To solve this problem, a simulated annealing algorithm has been used. Alptekinoglu and Tang (2005) have studied a kind of retailer network, which consists of suppliers, warehouses, and stores based on the customer’s profiles. Distribution is carried out through two direct (from warehouses) and indirect (through stores) channels. Using the decomposition method, the problem is subdivided into problems which they are solved using a heuristic method. A research conducted by Amiri (2006) has addressed the issue of designing a distribution network in a supply chain including the location of facilities (factories and distribution depots) and yet determined the best strategy for distributing the product from the factory to the warehouse and from the warehouse to the customer. For this purpose, a composite integer programming model is presented in which the facility has multiple capacity levels and the optimal capacity of the facility is determined by the model. A heuristic method has been proposed to solve the problem. Ko et al. (2006) also have proposed a hybrid optimization/simulation methodology in order to design a distribution network for third-party logistics companies. In this study, a genetic algorithm is used to solve the model. On the other hand, Selim and Ozkarahan (2008) have designed a distribution network by means of fuzzy goal programming. The purpose of the model is to select the optimum number, location, and capacity levels of the factories and warehouses for delivering the products to the retailers with the lowest cost and maximum satisfaction level. In a study by Lee et al. (2007), for the design of a forward and reverse integrated distribution network in third-party logistics companies, an integer linear programming model is presented. Due to the complexity of the problem and a large number of variables and model constraints, heuristic solving methods including genetic algorithm and two greedy algorithms are proposed. Several studies have been carried out on the status and function of the network and the distribution channels of products. Hulthén (2007) has concluded that the factors such as the creation of networks and large distribution chains, the market analysis and optimal market segmentation could greatly reduce the cost of physical distribution of goods. In the paper by Lorentz et al. (2007), it is shown that the factors such as the use of modern technologies (including e-commerce), the creation of large distribution chains (such as retailers’ stores and proprietary stores), and advertising tailored to each market segments are the placeholders to improve the efficiency of distribution networks in these areas. Ross and Jayaraman (2008) have presented a new heuristic solution to locate temporary storage and distribution centers in the design of the supply chain network. They have proposed two heuristic methods in their study using a simulated annealing algorithm and Tabu Search. Akkerman et al. (2009) have presented a mixed-integer Programming model for the production and distribution of persistent ready-made meals. Cintron et al. (2010) have presented a multicriteria mixed-integer linear programming model for designing a distribution network. The purpose of this model is to take tactical decisions, or specifically, to design the flow of products 4

from the manufacturing plants to customers. For validate of the model, data from a consumer goods company is applied, which is used from two distribution centers located in the same area. Grant and Banomyong (2010) have conducted a study on the design of the distribution chain for consumer goods in Thailand and Japan. In their study, they have concluded that the skill and expertise of human resources, the availability of appropriate infrastructure for the distribution and transportation of goods, and the use of new technologies, had a great effect on improving efficiency and reducing the distribution costs of consumer goods are in these countries. Millet (2011) has examined the criteria for achieving a sustainable supply chain that simultaneously incorporates economic, social and environmental considerations. Sadjady and Davoudpour (2012) have proposed a mixed-integer linear programming model for the design of a dual-level supply chain network with multi-product delivery. Shu et al. (2013) have proposed a model for designing a logistical distribution network, including a supplier, a set of potential warehouses, and a set of crude vendors offer two different product categories. In a study by Mangiaracina et al. (2015), 126 articles from 1970 to 2013 have been studied. Bortolini et al. (2016) have presented a multi-objective linear programming model for a sustainable food distribution network. These objectives include minimizing operational costs, carbon effects, and delivery times. Zhang et al. (2016) have presented a mixed-integer linear programming model for designing a sustainable supply chain network with multiple channels. This model has economic, social, and environmental objectives. The first objective is to reduce the costs of setting up facilities and the cost of transporting products. The second objective is to maximize customer service and the third objective is to measure the environmental impact of the network in question, which is based on the operation of the facility and the transportation provided. Janatyan et al. (2018) have presented a multi-objective model to design a pharmaceutical distribution network according to the main concepts of sustainability. Yadav et al. (2018) have introduced an MCDSCN MILP model to integrate online giants with local distribution network retailers. Ouhimmou et al. (2019) have addressed the problem of designing a robust distribution network under demand uncertainty based on a real industrial case study in pulp and paper. Akgün and Erdal (2019) have studied the strategic-level ammunition distribution network design problem of the GCG (General Command of the Gendarmerie) where the purpose is to determine the number and locations of depots and the service assignments considering several factors. Samani et al. (2019) recently proposed a multi-objective mathematical model by incorporating both quantitative and qualitative factors for distribution of blood supply chain, considering a multilateral perspective in an uncertain environment. The different features of mathematical models in the literature are summarized in Table 1, in the design of supply chain distribution network from 2010 to 2019 and are compared to the proposed model in this paper. The review of the literature (Table 1) shows that the number of articles which have been considered transport mode selection, distribution channel selection and the three aspects of sustainability (economic, environmental, and social) at the same time in their mathematical model, is very limited. Also, there are no articles for free shipping model.

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Table 1. Comparison of literature of supply chain distribution network design with our model

Cintron et al. (2010) Shu et al. (2013) Azad & Davoudpour (2013) Hlyal et al. (2015) Bortilini et al. (2016) Deqqaq & Abouabdellah (2016) Zhang et al. (2016) Halat & Hafezalkotob (2017) Barzinpour & Taki (2018) Janatyan et al. (2018) Yadav et al. (2018) Validi et al. (2018) Guerrero Campanur et al. (2018) Mogale et al. (2019) Our study

✓

✓

✓ ✓

✓

✓ ✓

✓

✓

✓

✓

✓

✓

✓

✓

✓

✓ ✓

✓ ✓ ✓

✓

✓

✓ ✓

✓

✓ ✓

✓

✓ ✓

✓

✓ ✓

✓ ✓

✓ ✓ ✓

✓

✓

✓

Maximizing the number of job opportunities

✓

✓ ✓

Minimizing CO2 emission

✓

✓

✓

Minimizing cost

✓

✓

✓

Objectives

Social

Economic

✓

Environmental

sustainability

Free Shipping model

Distribution channel selection

Transport mode selection

Author

Multi-objective

Features

✓ ✓ ✓

✓ ✓ ✓

✓ ✓ ✓

✓

✓ ✓

✓ ✓

✓

3. Problem definition and mathematical formulation According to the introduction, reducing the costs of the distribution network, and the environmental and social impacts of the supplier and the customer is very important. Therefore, it is necessary to examine the distribution of the product from the supplier to the customer using a multi-objective, single-period, multi-product, and multimodality with multiple modes of transportation. Also, with the investigation of different distribution channels and the choice of the appropriate vehicle types, the best distribution channel of each product is selected for each customer. Therefore, designing a sustainable distribution network model with multiple channels is a fundamental issue of the present study, which has objectives such as minimizing transportation costs, purchasing vehicles, and building warehouses as the first objective, minimizing the amount of carbon dioxide released by transport vehicles and building warehouses as the second objective, and maximizing the number of job opportunities as the third objective. To achieve these objectives, mathematical modeling has been performed and then the obtained model is linearized and solved using a goal programming method. As shown in Fig. 2, in the supply chain structure of 6

the proposed model, the products are sent in two ways to the customer: a. direct: from suppliers to the customer and b. indirect: collecting from different suppliers and transferring to the warehouse of the retailer and then transferring it to the customer. The product distribution is also done in three ways: a. Supplier distribution team, b. Retailer distribution team, and c. Third party logistics. In this section, there is a sustainable design of single-periodic, multi-product, multi-echelon, multi-transportation mode, and considering multiple channels. Hence, a mixed-integer programming model has been formulated with multi objectives. In this issue, we identify the build of the warehouse in the potential locations, the possibility of assigning the product distribution to the third-party logistics company, or the retailer distribution team, and the type and number of vehicles for the transportation of the product. In Fig. 2, this concept is presented. In this sense, manufacturer/suppliers and warehouses are used as direct and indirect facilities for customers. The objectives of the proposed model are as follows: • •

The first objective is to reduce the cost of the retailer company, which is including the cost of building warehouses, purchasing the vehicles, and transporting products. The second objective is to minimize the amount of carbon dioxide released by transporting different vehicles and building warehouses. • The third objective is to increase employment by taking into account the number of job opportunities created by the construction of warehouses and transport of vehicles.

Fig. 2. Illustration of the proposed supply chain

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3.1. Assumptions The underlying assumptions of the problem are as follows: • • • • • • • • • •

Demand and customer location are definitive. The customer is the final consumer. There is no capacity restriction for the warehouses. The location of the suppliers is predefined. Products are defined and each supplier supplies one type of product. Locations and the customer demand are preset. The potential locations of the warehouses are determined. Any facility can obtain its products only from upstream facilities except for customers. Customers can buy their products from just one distribution channel. The customer demand should be met.

3.2. Notations Sets:

i j s b o e

r t Parameters: M bmax ′ ′ Ⅱ Ⅰ Ⅲ Ⅳ Ⅰ Ⅱ Ⅲ Ⅳ !" !"′

The set of suppliers (i = 1,2, ..., I) The set of customers (j = 1,2, ..., J) The set of supplier distribution team (s = 1,2, ..., S) The set of potential locations for warehouse (b = 1,2, ..., B) The set of third-party logistics companies from the supplier to the warehouse (o = 1,2, ..., O) The set of third-party logistics companies from the supplier/warehouse to the customer (e = 1,2, ..., E) The set of retailer distribution team from supplier to the customer/warehouse (r = 1,2, ..., R) The set of retailer distribution team from the warehouse to the customer (t = 1,2, ..., T) A large real number Maximum number of warehouses allowed to build The demand amount of customer j from supplier i Capacity of retailer distribution team r vehicle for supplier product i Capacity of retailer distribution team t vehicle for supplier product i Product price i The price level for free shipping Cost of purchasing vehicle retailer distribution team r Cost of purchasing vehicle retailer distribution team t Fixed cost of establishing a warehouse b Shipping cost of product flow from supplier i to customer j by 3pl e for free shipping if customer j purchases more than µ Shipping cost of product flow from supplier i to customer j by retailer distribution team t for free shipping if customer j purchases more than µ The cost of transporting product flow from warehouse b to customer j by the retail distribution team t for free shipping if customer j purchases more than µ The cost of transporting product flow from warehouse b to customer j by 3pl e for free shipping if customer j purchases more than µ Environmental impacts of the supplier distribution team s Environmental impacts of establishing potential warehouse b Environmental impacts of 3pl o Environmental impacts of 3pl e Carbon di-oxide emission factor of vehicle retailer distribution team r Carbon di-oxide emission factor of vehicle retailer distribution team t

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#$ Ⅰ #$ Ⅱ #$ Ⅲ #$ Ⅳ #$ Ⅴ #$ Ⅵ %′ % %′′

The number of job opportunities created by the supplier distribution team s The number of fixed job opportunities created by establishing potential warehouse b The number of job opportunities created by 3pl o The number of job opportunities created by 3pl e The number of job opportunities created by vehicle retailer distribution team r The number of job opportunities created by vehicle retailer distribution team t The distance between supplier i and warehouse b The distance between supplier i and customer j The distance between warehouse b and customer j

Continues Variables: &Ⅵ

&Ⅶ &Ⅷ' ( &Ⅲ

&Ⅳ &Ⅰ &Ⅱ &Ⅴ )!Ⅰ

)!Ⅶ

)!Ⅲ

The amount of product shipped from supplier i to warehouse b by supplier distribution team s The amount of product shipped from supplier i to warehouse b by retailer distribution team r The amount of product shipped from supplier i to warehouse b by 3pl o The amount of product shipped from warehouse b to customer j by retailer distribution team t The amount of product shipped from warehouse b to customer j by 3pl e The amount of product shipped from supplier i to customer j by retailer distribution team t The amount of product shipped from supplier i to customer j by 3pl e The amount of product shipped between supplier i, warehouse b and customer j The number of vehicles needed to transfer the product from supplier i to customer j by the retailer distribution team t The number of vehicles needed to deliver the product from supplier i to the warehouse b by the retailer distribution team r The number of vehicles needed to transfer the product from stock b to customer j by the retailer distribution team t

Binary Variables: *

+ ,

-Ⅲ

-Ⅳ .Ⅵ .Ⅶ

.Ⅷ

1 if the warehouse b is to be established, and 0 otherwise 1 if the order price of customer j is less than , and 0 if the order price of customer j is more than 1 if the supplier i is allocated to the customer j by 3pl e, and 0 otherwise 1 if the supplier i is allocated to the customer j by the retailer distribution team t, and 0 otherwise 1 if the warehouse b is allocated to the customer j by the retailer distribution team t, and 0 otherwise 1 if the warehouse b is allocated to the customer j by 3pl e, and 0 otherwise 1 if the supplier i is allocated to the warehouse b by the supplier distribution team s, and 0 otherwise 1 if the supplier i is allocated to the warehouse b by the retailer distribution team r, and 0 otherwise 1 if the supplier i is allocated to the warehouse b by 3pl o, and 0 otherwise

3.3. Objective functions The three-objective mathematical model presented in this model is in the form of mixed-integer programming, which is presented in the following:

9

!'/ 1 = 2

×

+222

+ 222

× )!Ⅶ

× ;2 2 2

!'/ 2 = 2

+ 222

Ⅱ ×

Ⅱ

Ⅲ

+ 365

× ;2 2 2

′ × )!Ⅰ

× &Ⅱ

+ 222 + 222

!>? 3 = 2 #$ Ⅱ ×

+ 365 × (1 − * )

× &Ⅲ

+ 222

+ 222

Ⅳ × % × &Ⅱ

+ 2 2 2 !"′ × %′′

+222

× &Ⅲ

Ⅰ × %′ × &Ⅵ

Ⅲ × %′ × &Ⅷ

× ;2 2 2 #$ Ⅳ × &Ⅱ

+ 2 2 2 #$ Ⅲ × &Ⅷ

Ⅳ

× &Ⅰ × &Ⅳ

(1) <

+ 2 2 2 !"′ × % × &Ⅰ

+ 222

Ⅳ × %′′ × &Ⅳ

(2)

+ 2 2 2 !" × %′ × &Ⅶ <

+ 2 2 2 #$ Ⅵ × )!Ⅰ

+ 2 2 2 #$ Ⅴ × )!Ⅶ

Ⅰ

′ × )!Ⅲ

+ 365

+ 2 2 2 #$ Ⅵ × )!Ⅲ

+ 2 2 2 #$ Ⅳ × &Ⅳ + 2 2 2 #$ Ⅰ × &Ⅵ

(3) <

The objective function (1) tries to minimize the total cost of the retailer company. The first part is related to the fixed cost of establishing the warehouse. The second, third and fourth parts belong to the fixed cost of purchasing vehicles and the fifth part is free shipping costs if the customer’s purchase is more than a certain amount. The objective function (2) minimizes the environmental impacts of the network. The first part refers to the amount of carbon dioxide emissions from the establishing of the warehouse. The second part is related to the amount of carbon dioxide emissions from vehicle transportation by the supplier distribution team, the retail distribution team, and the third-party logistics company. The objective function (3) is to maximize the number of job opportunities. The first part shows the number of job opportunities created by the establishment of the warehouse. The second, third and fourth parts relate to the number of job opportunities resulting from the selection of the retailer distribution team and the fifth part refers to the number of job opportunities arising from the transportation of the product by the third-party logistics company and the supplier distribution team.

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3.4. Constraints Flow and establishing of the warehouse constraints: &Ⅲ &Ⅳ &Ⅵ &Ⅶ &Ⅷ

2

≤

∀ ,,

×!

≤

∀ , ,C

×!

≤ ≤ ≤

∀', ,D ∀', ,E ∀', ,(

×! ×! ×!

= F>?

Single allocation constraint: 2+

+ 2,

(4)

+ 2 -Ⅲ

(5) (6) (7) (8) (9)

+ 2 -Ⅳ

Fulfillment of demand constraints: 2 &Ⅱ 2 &Ⅱ

+ 2 &Ⅰ ×

+ 2 &Ⅴ

+ 2 &Ⅰ

Capacity vehicles constraints: &Ⅰ &Ⅰ ≤ )!Ⅰ ≤ ′

&Ⅶ

&Ⅲ ′

≤

≤

)!Ⅶ

)!Ⅲ

≤

≤

′

&Ⅶ

&Ⅲ ′

×

+1

+1

+1

∀', ,

=1

≥

+ 2 &Ⅴ

×

∀',

+ 2 &Ⅶ

+ 2 &Ⅷ

(11) ≤

× * + ! × (1 − * )

∀', ,

∀',

(12)

(13)

∀', ,E

(14)

∀', , ,

(15)

Flow and allocation constraints: &Ⅱ ≤ + × ! ∀', ,C &Ⅰ ≤ , × ! ∀', , &Ⅲ ≤ - Ⅲ × ! ∀ ,, &Ⅳ ≤ -Ⅳ × ! ∀ , ,C &Ⅵ ≤ .Ⅵ × ! ∀', ,D &Ⅶ ≤ . Ⅶ × ! ∀', ,E &Ⅷ ≤ . Ⅷ × ! ∀', ,( Allocation and establishing of the warehouse constraints: -Ⅲ ≤ ∀ ,, -Ⅳ ≤ ∀ , ,C .Ⅵ ≤ ∀', ,D .Ⅶ ≤ ∀', ,E .Ⅷ ≤ ∀', ,( Flow balancing constraints: 2 &Ⅵ

(10)

= 2 &Ⅲ

11

(16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) + 2 &Ⅳ

∀', ,

(28)

&Ⅴ

=

H∑

&Ⅵ

+∑

&Ⅶ

+∑

&Ⅷ 2

+∑

&Ⅲ

+∑

, &Ⅶ , &Ⅷ , &Ⅲ , &Ⅳ , &Ⅰ , &Ⅱ , , )!Ⅰ , )!Ⅶ , )!Ⅲ ≥ 0 & '/ , * , + , , , - Ⅲ , - Ⅳ , .Ⅵ , .Ⅶ , .Ⅷ ∈ N0,1O

&Ⅵ &Ⅴ

&Ⅳ

J

∀', ,

(29) (30) (31)

Constraints (4-8) states that the product flow occurs between two tiers by the distribution teams when the warehouse has been established. The constraint (9) specifies the maximum number of permitted warehouses. The constraint (10) ensures that a distribution team is assigned to each customer from each tier. The constraint (11) represents the satisfaction of customer demand. The constraint (12) is for free shipping by the retailer distribution team when the order price of customer j is more than . Constraints (13), (14), and (15) indicate the number of vehicles required by the retailer distribution team, taking into account the capacity of the vehicle. Constraints (1622) suggest that the product flow occurs between two tiers when the shipment is allocated to distributed teams. Constraints (23-27) states that the product is allocated between the two tiers once the warehouse is established. Constraint (28) indicates flow balance. The constraint (29) shows the amount of product flow from the supplier to the customer by the warehouse. Constraints (30) and (31) show the range of variables. 3.5. Linearization of the proposed model In the fifth part of the first objective function, two decision variables are multiplied, which leads to the non-linearization of the model. For linearization of the model, the variables are defined as (32-35):

&1 &2 &3 &4

= H1 − * J × = H1 − * J × = H1 − * J × = H1 − * J ×

∀', ,C ∀', , ∀ ,, ∀ , ,C

&Ⅱ &Ⅰ &Ⅲ &Ⅳ

(32) (33) (34) (35)

The variables &1 , &2 , &3 , and &4 are positive integers, if * = 0, then they are equal to &Ⅱ , &Ⅰ , &Ⅲ , and &Ⅳ respectively, otherwise equal to zero. By applying the above changes, the fifth part of the first objective function becomes the relation (36):

222

&1 &1 &1 &2

Ⅱ

× &1

+ 222

+ 222

Ⅳ

Ⅰ

× &4

× &2

+ 222

Ⅲ

× &3

(36)

And the constraints (37-48) will be added to the model: ≤ Q! × H1 − * JR + &Ⅱ ≥ &Ⅱ

+ ! × QH1 − * J − 1R

≤ ! × H1 − * J

≤ Q! × H1 − * JR + &Ⅰ

∀', ,C

∀', ,C

∀', ,C

∀', ,

12

(37) (38) (39) (40)

&2

≥ &Ⅰ

&2

+ ! × QH1 − * J − 1R

≤ ! × H1 − * J

&3

≤ Q! × H1 − * JR + &Ⅲ

&3

≥ &Ⅲ

&3

+ ! × QH1 − * J − 1R

≤ ! × H1 − * J

&4

≤ Q! × H1 − * JR + &Ⅳ

&4

≥ &Ⅳ

&4

+ ! × QH1 − * J − 1R

≤ ! × H1 − * J

∀', ,

∀', ,

∀ ,,

(41) (42)

∀ ,,

(43)

∀ , ,C

(46)

∀ ,,

∀ , ,C ∀ , ,C

(44) (45) (47) (48)

3.6. Multi objective solution approach: Goal programming Goal programming is an optimization technique to solve problems with multiple objectives. This technique seeks to find optimal solutions, which is predefined goal values exist for one or more objectives and minimize deviations from these goal values. This methodology minimizes the sum of the positive and negative deviations (%ST (E %SU ) from each of the goals. For each goal, weights are given on a priority basis (VS ), indicating the importance of the objective function to other objectives. As stated above, the general model of goal programming will be as follows. !'/

W

2 VS (%ST + %SU )

(49)

SXY

ZS + %SU − %ST = ZS∗ ℎ = 1, … , ^ %ST . %SU = 0 ℎ = 1, … , ^

(50) (51)

To formulate a goal programming model, we must define the values of the goals for the objective functions. To obtain the values of the goals, the mathematical model of the problem is considered with each of the objective functions alone and the optimal value of each of the objective function is obtained. The objective function of the goal programming model is defined as (52).

!'/` = Va . %aT + V . % T + V . % T

(52)

In fact, in this model, we seek to minimize the total of these deviations.

%bT : Positive deviation (undesirable) of the economic objective function

%T : Positive deviation (undesirable) of the environmental objective function %dT : Positive deviation (undesirable) of the social objective function %ST . %SU = 0,

ℎ = e, C, D

(53)

4. Case study: Digikala company The expanding use of the Internet has led business activists to use this to introduce and sell goods. Setting up Internet stores is the most important way to use the Internet. Digikala is an online retailer platform. The company was originally dedicated only to the sale of digital and electronic products, but today there are various product groups such as cosmetics, clothing, home 13

appliances, kitchens, books and stationery, toys, art and culture, sports, entertainment, travel, supermarket, and a variety of car accessories and tools from a variety of brands. The products provided on the company's website are made in two ways by purchasing from reputable companies and official and legal importers and their offerings or the cooperation of some vendors with the company to supply their products online for sale. In Fig. 3, an overview of the supply chain of the Digikala company is provided. Digikala company has no sales and after-sales services at any point in the country other than the central unit located in Tehran, and all services are provided throughout the country only at this center. Since Digikala is an online store and one of its goals is to reduce unnecessary travel, it is not possible to deliver the order in the Digikala site. In Fig. 4, Digikala's network supply chain is seen for four of its products.

Fig. 3. Digikala company supply chain

4.1. Data collection The method for extracting the required data for the input parameters of the problem is presented in Table 2 As shown in Fig. 4, the network under study consists of three echelons. The first echelon is identified with suppliers. At this echelon, the retailer is contracted with the four different suppliers as shown in Table 3. Table 2. How to extract the data input parameters of the problem

Row 1

2

Input parameters of the problem Dimensions of the vehicle chamber • Supplier product price • Purchase rate for free shipping • Product shipping Costs by third-party logistics company • Product dimensions • Customer satisfaction of the product

Extracted data www.almascantin.ir • Headquarter experts and after-sales service of Digikala company • www.digikala.com Coefficient = Total population of Iran × Customer satisfaction of the product

3

4

Product demand

Product shipping cost by retailer distribution team

14

Demand of the province = Provincial population × Coefficient Distance traveled by vehicle × Vehicle fuel consumption × Price per liter of gasoline / diesel

5 6 7 8

Average emissions of carbon dioxide per kilometer by vehicles The amount of carbon dioxide emissions in retailer company distribution warehouses The number of job opportunities created by establishing potential warehouse Determine the distance between network components

Ameri and Zahed (2013) Rai et al. (2011) Hezarkhani (2017) www.bahesab.ir/map/distance

Furthermore, in the second echelon, a warehouse was constructed in Tehran and 3 candidate places are considered for establishing of the warehouse where the location of the warehouses is shown in Table 4. Table 3. Indicator number, location and supplier product i

Supplier indicator number i 1 2 3 4

Supplier location i Amin Hozur Crossing, Tehran Province 4 Baghe Bala street, Isfahan Province Basij Square, Zanjan Province Alborz Industrial Town, Qazvin Province

Supplier product i Steamer Suitcase Copper dish Washing machine

Table 4. Indicator number and candidate points for establishing warehouse b

Warehouse indicator number b 1 2 3 4

Warehouse location b Shad Abad Street, Tehran province Shokuhie Industrial Estate, Qom province Shiraz Industrial Estate, Fars province Fanavarihaye Bartar Industrial Estate, Razavi Khorasan province

Finally, the third echelon is related to customer, which is considered by 31 provinces as customer. Table 5 indicates the indicator number and province name for the customer. Table 5. Indicator number and province name for customer j

Customer indicator number i 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Customer j East Azerbaijan West Azerbaijan Ardabil Isfahan Alborz Ilam Bushehr Tehran Chahar Mahaal and Bakhtiari South Khorasan Razavi Khorasan North Khorasan Khuzestan Zanjan Semnan Sistan and Baluchistan

Customer indicator number i 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

15

Customer j Fars Qazvin Qom Kurdestan Kerman Kermanshah Kohgiluyeh and Boyer-Ahmad Golestan Gilan Lorestan Mazandaran Markazi Hormozgan Hamadan Yazd

Fig. 4. Digikala's supply chain for four products including steam, suitcase, copper dish and washing machine

4.2. Optimal amount of objective functions To formulate the goal programming model, one must define the values of the goals for the objective functions. To obtain the goal values, the mathematical model of the problem with each objective function is considered separately, and the optimal value of each function is obtained. The optimal value of each of the objective function is presented in the Table 6.

16

Table 6. Optimal amount of objective functions

Objective Economic ($) Environmental (gram/kilometer) Social (person)

Optimal amount 20417400000 23030400000 1128999000

5. Sensitivity Analysis In order to solve the model, using the data of Digikala, four constraints (54), (55), (56), and (57) are added to the model. &Ⅱ

=0

∀',j,e,

i=4,

e=1

(54)

&Ⅰ

=0

∀',j,t,

i=4,

t=3

(55)

The equation (54) prevents the flow of the product (washing machine) from the supplier i=4 (Qazvin) to the customer j, by the 3pl company e=1 (express mail). It is due to the point that the Digikala company has a contract for the transportation of heavy products with shipping.

The equation (55) prevents the flow of the product (washing machine) from the supplier i=4 (Qazvin) to the customer j, by the retailer distribution team t=3 (motorcycle), because the Digikala company has a contract for the transportation of heavy products with shipping and this product is not portable with the motorcycle. &Ⅴ

= f2 &Ⅵ

+ 2 &Ⅶ

+ 2 &Ⅷ

+ 2 &Ⅲ Xg

+ 2 &Ⅳ XY

h=0

∀', , ,

(56) i=4

The equation (56) prevents the flow of the product (washing machine) from the supplier i=4 (Qazvin) to the warehouse b and the customer j, by the 3pl company e=1 (express mail) and the retailer distribution team t=3 (motorcycle). the Digikala company has a contract for the transportation of heavy products with shipping and the product of the washing machine is not portable with a motorcycle. =1

∀ ,

=1

(57)

Due to the fact that Digikala company has a warehouse in the Tehran province, therefore Y = 1. Then, the decision making is done just for the construction of potential warehouses in Qom, Fars, and Khorasan Razavi.

5.1. Sensitivity analysis of parameter of cost of shipping In this section, changes to the objective function of the goal programming model and the percentage of deviations are measured relative to the parameter of the cost of shipping. The results of this measure are presented in Table 7 and Fig. 5.

17

Table 7. Effect of parameter of cost of shipping on objective function of goal programming model

Vehicle purchase cost 50% reduction 40% reduction 30% reduction 20% reduction 10% reduction Normal status 10% increase 20% increase 30% increase 40% increase 50% increase

Objective function of goal programming model 3.319 3.497 3.676 3.854 4.033 4.211 4.390 4.540 4.632 4.700 4.757

Economic objective function 0.537 0.716 0.894 1.073 1.251 49611700000 1.430 1.608 36732400000 1.033 28238600000 0.613 25326200000 0.494 23157100000 0.401

Environmental objective function Percentage deviation 1.781 1.781 1.781 1.781 1.781 64052000000 1.781 1.781 80765000000 2.507 92553100000 3.019 96885400000 3.207 100311000000 3.356

Social objective function 1.000 1.000 1.000 1.000 1.000 286 1.000 1.000 60 1.000 126 1.000 161 1.000 193 1.000

5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 -50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

Goal programming model

Economic objective function

Environmental objective function

Social objective function

50%

Fig.5. Effect of parameter of cost of shipping on objective function

Fig. 5 shows variations of the objective function of the goal programming model and the percentage of the deviation relative to the transportation parameter. By reducing the transportation cost parameter, only the value of the objective function of the goal programming model and deviations percentage of the economic objective function is reduced. These changes do not occur on the deviation percentage of environmental and social objective functions, which seems to be due to the lack of change in the model's response and model's decision making. By increasing the transportation cost parameter by 10%, the value of the objective function of the goal programming model and the deviation percentage of the economic objective function increase. But this change is not reflected in the deviation percentage of environmental and social functions. It is also noted that with an increase of 20 to 50 percent of the transportation cost parameter, the value of the objective function of the goal programming model and the deviation percentage of the environmental objective function will be significantly increased. However, the deviation percentage of the economic objective function decreases and there is no change in the deviation percentage of social function. 18

5.2. Effect of the costs on the objective function of the goal programming model In this section, the effect of the parameter of the costs of purchasing a vehicle, transporting and constructing a warehouse are measured on the objective function of the ideal model. 5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 -50% -40% -30% -20% -10%

0%

Vehicle purchase cost

10%

20%

30%

40%

50%

Shipping cost

Warehouse construction cost

Fig. 6. The effect of the cost of vehicle purchase, transportation and warehouse construction on the objective function

In Fig. 6, it can be seen that with the increase of the three parameters of the costs of purchasing a vehicle, transport, and warehouse construction, the trend of changes in the value of the objective function of the goal programming model is also incremental. Changes to the parameter of the cost of buying a vehicle make a slight effect on the objective function of the goal programming model and there is less sensitivity to this parameter. It is also seen that the model is more sensitive to changes in the cost of warehouse construction. 5.3. Sensitivity analysis of the parameter of the number of warehouses In this section, the sensitivity of the objective function of the goal programming model and the percentage of deviations is measured relative to the parameter of the number of warehouses. The results of this measure are presented in Table 8 and Fig. 7. Table 8. The effect of the parameter of the number of warehouses on objective function of goal programming model

Number of warehouses constructed 1 2 3 4

Objective function goal programming model 4.054 4.211 4.382 4.676

Economic objective function 46411700000 1.273 49611700000 1.430 53111700000 1.601 59111700000 1.895

Environmental objective function Percentage deviation 64052000000 1.781 64052000000 1.781 64052000000 1.781 64052000000 1.781

19

Social objective function 209 1.000 286 1.000 370 1.000 514 1.000

Provinces selected by model for warehouse construction Tehran Tehran - Fars Tehran - Qom - Fars Tehran - Qom - Fars Razavi Khorasan

5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0

1

2

3

4

Goal programming model

Economic objective function

Environmental objective function

Social objective function

Fig. 7. The effect of the parameter of the number of warehouses on the objective function

As it is shown in Fig. 7, if the number of constructed warehouses increases, the value of the objective function of the goal programming model and the percentage of the deviation of the economic objective function is increased and social and environmental objective functions are constant. However, these changes do not occur in the deviation percentage of environmental objective functions, which seems to be due to the lack of change in the model's response and model's decision making. 5.4. Sensitivity analysis of the parameter of demand In this section, sensitivity of the objective function of the goal programming model and percentage of deviations is measured relative to the demand parameter . The results of this measure are presented in Table 9 and Fig. 8. Fig.8 shows variations of the objective function of the goal programming model and the percentage of the deviation relative to the parameter of demand. By increasing the parameter of demand, the value of the objective function of the goal programming model and deviation percentage of the economic and environmental objective functions are increased. 7 6 5 4 3 2 1 0 -50% -40% -30% -20% -10%

0%

10%

20%

30%

40%

Goal programming model

Economic objective function

Environmental objective function

Social objective function

50%

Fig.8. The effect of the parameter of demand on the objective function

20

Table 9. The effect of the parameter of demand on the objective function of goal programming model

Demand 50% reduction 40% reduction 30% reduction 20% reduction 10% reduction Normal status 10% increase 20% increase 30% increase 40% increase 50% increase

Objective function goal programming model 1.880 2.417 2.895 3.279 3.745 4.211 4.677 5.144 5.610 6.076 6.542

Economic objective function 30405900000 0.489 34247000000 0.677 38088200000 0.865 41929400000 1.054 45770500000 1.242 49611700000 1.430 53452900000 1.618 57294100000 1.806 61135200000 1.994 64976400000 2.182 68817600000 2.371

Environmental objective function Percentage deviation 32026000000 0.391 38431200000 0.740 44836400000 1.030 51241600000 1.225 57646800000 1.503 64052000000 1.781 70457200000 2.059 76862400000 2.337 83267600000 2.616 89672800000 2.894 96078000000 3.172

Social objective function 242 1.000 251 1.000 260 1.000 269 1.000 277 1.000 286 1.000 295 1.000 304 1.000 313 1.000 322 1.000 331 1.000

In Fig. 8, Increasing demand will require more transportation to meet demand, leading to higher costs and higher gas emissions. The slope of the graphs also shows that environmental issues need more management and control. As a result, vehicles with less CO2 emissions can be used. 5.5. Sensitivity Analysis after Weighting the Objective Functions In the previous section, the objective function of the goal programming model is presented in the form (64):

!'/` = %aT + % T + % T

(64)

!'/` = Va . %aT + V . %T + V . % T

(65)

In this case, the objective functions have the same value. However, if the functions have different values, then the objective functions of the model can be represented as (65). If wb = wj = wd , then the relations (64) and (65) are identical. In Table 10, different weights are considered to sensitivity analysis of the objective functions. The numerical values of 0.8, 0.75, and 0.6 represent significant importance of objective functions, the numerical values of 0.3 and 0.2 show moderate importance of objective functions and the numerical value of 0.1 represents the low importance of objective functions. The analysis of Fig.9 and Table 10 indicates that with the increase in the weight of the economic objective function and reduction in weight of the social objective function, the amount of objective function of the goal programming model, the percentage of the deviation of the economic objective function and the 21

value of the social objective function decrease and the percentage of the deviation of the environmental objective function increases. 4 3.528 3.5 3 2.5 1.781

2 1.277

1.5

1.43 1.177 1

1

1 0.5 0.03 0 Goal programming model

Economic objective function

Case2: Wc=0.1, We=0.3, Ws=0.6

Environmental objective function

Social objective function

Case7: Wc=0.6, We=0.3, Ws=0.1

Fig.9. Comparing the modes 2 and 7 in weighting the objective functions and its effect on the percentage of deviations and the objective function of the goal programming model

Fig. 9 is presented for comparing the mode no. 2 with the mode no. 7 on weighting objective functions. Fig. 10 is presented for comparing the mode 3 with the mode 5 on weighting objective functions. 2

1.781

1.8 1.6

1.512

1.781

1.598 1.43

1.43

1.4 1.2

1

1

1 0.8 0.6 0.4 0.2 0 Goal programming model

Economic objective function

Case3: Wc=0.1, We=0.6, Ws=0.3

Environmental objective function

Social objective function

Case5: Wc=0.3, We=0.6, Ws=0.1

Fig. 10. Comparing the modes 3 and 5 in weighting the objective functions and its effect on the percentage of deviations and the objective function of the goal programming model

Comparison modes 3 and 5 in Fig.10 and Table 10 indicate that with the increase in weight of the economic objective function and reduction in the weight of the social objective function, the amount of objective function of the goal programming model is increased. However, it seems that because the model response is not changed, changes are not reflected in the deviation percentage of economic, environmental, and social objective functions. Fig. 11 is presented for comparison modes 4 and 6 on weighting objective functions.

22

Table 10. Weighing the objective functions and its effect on the percentage of deviations and the objective function of the goal programming model

Status

1

2

3

4

5

6

7

8

9

10

Va V V Va V V Va V V Va V V Va V V Va V V Va V V Va V V Va V V Va V V

Weight of objective functions

Objective function goal programming model

= 0.75 = 0.05 = 0.20 = 0.1 = 0.3 = 0.6 = 0.1 = 0.6 = 0.3 = 0.3 = 0.1 = 0.6 = 0.3 = 0.6 = 0.1 = 0.6 = 0.1 = 0.3 = 0.6 = 0.3 = 0.1 = 0.8 = 0.1 = 0.1 = 0.1 = 0.8 = 0.1 = 0.1 = 0.1 = 0.8

-

Economic objective function

Environmental Social objective objective function function Percentage deviation 20736900000 105140000000 266

0.390

0.016

3.565

1.000

-

49611700000

64052000000

286

1.277

1.430

1.781

1.000

-

49611700000

64052000000

286

1.512

1.430

1.781

1.000

-

20805400000

104873000000

256

0.961

0.019

3.554

1.000

-

49611700000

64052000000

286

1.598

1.430

1.781

1.000

-

20739300000

105114000000

262

0.666

0.016

3.564

1.000

-

21036800000

104282000000

244

1.177

0.030

3.528

1.000

-

20739300000

105114000000

262

0.469

0.016

3.564

1.000

-

49611700000

64052000000

286

1.668

1.430

1.781

1.000

-

49611700000

64052000000

286

1.121

1.430

1.781

1.000

4 3.554

3.564

3.5 3 2.5 2 1.5 1

0.961 1

1

0.666

0.5 0.019

0.016

0 Goal programming model

Economic objective function Environmental objective function

Case4: Wc=0.3, We=0.1, Ws=0.6

Social objective function

Case6: Wc=0.6, We=0.1, Ws=0.3

Fig. 11. Comparing the modes 4 and 6 in weighting the objective functions and its effect on the percentage of deviations and the objective function of the goal programming model

23

Comparison modes 4 and 6 in Fig.11 and Table 10 indicate that with the increase in the weight of the economic objective function and the reduction in the weight of the social objective function, the amount of objective function of the goal programming model, the percentage of the deviation of the economic objective function is decreased, the value of the social objective function is constant and percentage of the deviation of the environmental objective function increases. 6. Conclusions and future research In this research, a mix-integer programming model was developed for optimizing economic, environmental and social objectives, which these goals include minimizing transportation costs, purchasing a vehicle and building a warehouse, minimizing the amount of carbon dioxide released by transport vehicles and building warehouses and maximizing the number of job opportunities. The multi-objective model was solved using the goal programming technique and GAMS software. In the following, sensitivity analysis was performed on the main parameters of the model (number of warehouses, demand, vehicle purchase costs, transportation and warehouse construction) and the change in the objective function and the percentage of the deviations of the economic, environmental, and social objective functions were measured relative to those parameters. Finally, each of the objective functions was weighed according to a consultation with the logistics director of Digikala company, as well as the importance of economic, environmental and social considerations, and the results and sensitivity analysis were presented in the form of tables and charts. Computational results are reported based on real-world data analyses. The used instance was consisted of four suppliers, three warehouses, and 31 provinces, which meant that the integer linear programming could be solved optimally using CPLEX in a short CPU time. Solving the proposed model results in the selection of optimal distribution channel of Digikala products (including steamer, suitcase, copper dish and washing machine), the number of vehicles required, and the number of job opportunities created by the product transportation. The consequences of solving the model are concluded as: • •

•

•

If the cost of shipping increases by 10% in the next year, it will not have much effect on the total cost of the system, but if this cost increases by 50% in the next five years, the cost the system will dramatically increase. Thus, managing this cost is very significant. The variation in the cost of a vehicle's purchase cost has little effect on the objective function of the goal programming model, and there is less sensitivity to this parameter. This can encourage management to invest in the purchase of a vehicle. As a result, when demand increases, more vehicles are purchased to accelerate the delivery and customer satisfaction. If the Digikala company cares for economic considerations, the distribution of steamer product for 29 provinces by the third-party logistics company (shipping and express mail) and for the provinces of Tehran and Alborz by the distribution company t (pride vans), the distribution of suitcase product For 31 provinces, by the third-party logistics company e (shipping and express mail), copper dish product distribution for 30 provinces is provided by the third-party logistics company (shipping and express mail) and Qazvin province by the retailer distribution team t (pride van) and the distribution of the washing machine product for 31 provinces is done by the retailer's distribution team t (pride van). If the Digikala company is concerned with environmental or social considerations, distribution of products for 31 provinces will be done by the retailer distribution team t (pride van).

24

•

Digikala company main motivation for using this model was to reduce system costs. The distribution costs are reduced when the product reaches the customer directly from the supplier by third-party logistics and the retailer distribution team.

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: