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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.20 No.4 pp.596-603
DOI : https://doi.org/10.7232/iems.2021.20.4.596

# An Integrated Multi-Objective Approach to Managing Supply Risks in a Flexible Supply Chain

School of Accounting, Jiujiang University, Jiangxi, China
Kuban State Agrarian University Named after I.T. Trubilin, Krasnodar, Russian Federation
Dentistry Department, Kut University College, Kut, Wasit, College of Technical Engineering, The Islamic University, Iraq
Al-Nisour University College/Iraq, Iraq
Department of Education, Common Stations Academy, London, United Kingdom
August 10, 2021 September 12, 2021 September 18, 2021

## ABSTRACT

Nowadays, it is necessary to paying attention to the opportunities and threats in the field of industry and trade, and evaluate the ability of industries and companies in dealing with uncertainties and existing risks, and it is very important to manage supply chain risk. The main purpose of this study is to be careful against risky suppliers and reducing the injury rate in the event of a disruption. Therefore, in this regard, a multi-stage mixed integer programming model with a proactive approach has been used; that in the first stage, the model reports the amount of supply from suppliers without considering the risk criterion, and at the same time, it seeks to optimal state of minimization the supply chain costs (including purchase cost, shipping, maintenance, supplier selection and return goods). In the second stage, after the suppliers which supplying the parts, have been identified, the model seeks to minimize the identified risks of suppliers under different scenarios. In the third stage, the model tries to achieve an optimal state of supplying the parts from less risky suppliers. In the continuation of this study, an integrated multi-objective programming model has been designed, which will be solved by the epsilon constraint method, and the best output will be reported from the Pareto’s optimal set of answers; Finally the results of the model will be compared in two multi-stage and integrated multiobjective modes and the correctness of the performance is confirmed.

## 1. INTRODUCTION

Risk management is one of the most important topics in the supply chain. In fact, the academic communities and beneficiaries in the field have widely recognized the importance of risk management and the integrated method in the supply chain to deal with complexities and uncertainties. Enterprises seek to manage risks or classify unexpected disruptions and change their function in an unclear business environment (Munir et al., 2020). Supply chain risk management primarily aims to decrease the impact of these risks through the development of methods and models to identify, evaluate and decrease supply chain risks (Kirilmaz and Erol, 2017). In general, supply chain risk is a potential phenomenon that prevents the natural flow of materials and information in the chain, thereby causing interruptions in the chain (Wu et al., 2006). A wide range of risks in the supply chain may have adverse impacts on the supply chain performance. The close relationship among the supply chain members affects the entire supply chain, disrupting its performance (Christopher et al., 2003). As such, an organization must use proper strategies to manage and control risks in the supply chain. On the other hand, the supplier selection issue is an important and strategic decision in the supply chain, and choosing a proper set of suppliers is extremely important and crucial for the success of different industries.

The risk management process has three levels of risk identification, risk assessment, and risk reduction (Wagner and Bode, 2009;Tang, 2006;Kleindorfer and Saad, 2005). However, all assessments are based on the anticipation of an unknown future and must always be reviewed and updated since the process is a dynamic one. Therefore, the risk control and monitoring stage has also been added to the supply chain risk management process. In some studies, supply chain risk management has been carried out in five stages of risk identification, measurement, assessment, reduction, and control and monitoring (Kirilmaz and Erol, 2017). Risk assessment is the process of comparing the results of risk analysis with risk criteria to determine whether the risk is acceptable / tolerable or not. However, risk criteria are based on organizational objectives and can be extracted from standards, laws and policies, and other requirements (Hosseini and Ivanov, 2020). Risk reduction strategies can be divided into two reactive and preventive groups. In a reactive approach, no action is performed before a risky event. However, some measures are taken afterwards to decrease the effect and possibility of risk. On the other hand, risk reduction is planned before occurrence in the preventive approach. In addition, this method may include the implementation of programs to decrease the possibility and effect of risky events in the future (Kleindorfer and Saad, 2005).

In a recent study, Wang and Jie (2020) presented a framework for managing pharmaceutical supply chain uncertainty and risk in the petrochemical industry. Munir et al. (2020) combined supply chain risk management and supply chain performance improvement. These researchers used empirical instruments to decrease supply chain risks. In addition, they assessed supply chain risks, for which expenditure goals (e.g., minimization of storage costs, minimization of supplier selection costs, minimization of cost (penalty) of returned goods, and vehicle capacity), which have been overlooked until now, were considered in the present study. In addition, studies related to supply chain risk management were reviewed and selected based on risk identification, measurement, assessment, and reduction. In this regard, given the dispersion of supply risks in the research literature, which often does not conform to the environmental conditions of the supply chain studied in this study (due to domestic conditions), attempts were made to identify and select risks related to the supply of domestic suppliers. Afterwards, the supply risks were measured by supply chain network experts at Sapco and the issue of reducing the risk of the entire supply chain was put on the agenda. In the next step, the present study aimed to propose a multi-objective integrated model due to a lack of a previous simultaneous assessment of objectives such as cost reduction, risk reduction, and supply from suppliers. It is worth noting that the current research includes a preventive approach since, contrary to the reactive approach, this method accepts risk probability and properly manages risk before its occurrence by analyzing the events.

## 2. PROBLEM DEFINITION

In this section, the proposed model is described. Fig. 3 shows the framework of our approach. First, the manufacturer identifies potential suppliers and defines appropriate criteria. Then, decision makers evaluate suppliers by proposed fuzzy model. The results of this phase are the weights (importance) of suppliers based on qualitative metrics. In the next phase, the closed loop supply chain (CLSC) network is formulated as multi-objective mixedinteger linear programming model. In this stage, the related variables (strategic and tactical decision variables) are calculated.

The supply chain studied in this research was a single- product three-level supply chain with rings consisting of seven suppliers (Modern, New Union Auto Parts Co., Jamsaz, Seraj Noor Toos, Maadco, Fanavaran Parto Alvand, and Niknam), five manufacturers (Crouse, Sarv Sanat Sepahan, Mehrkam Pars, Mehrkhah, and Mehrsaz Gostar Pars) and three customers (Iran Khodro in Tehran, Khorasan and Tabriz sites), where car bumper manufacturers purchase fog lamps from suppliers and produce the end product (i.e., the set of bumper) and present it to its customers (Iran Khodro in Tehran, Khorasan and Tabriz sites).

### 2.1 The First Model

Indexes

• i: index of the potential supplier

• j: index of the manufacturer (assembly center) with potential disassembly line

• k: index of customer

• l: index of the piece

• m: index of product

Parameters

• FOi : cost of supplier selection

• FDmj : fixed cost of launching a disassembly line for the m-th product in the j-th production center

• TClig : cost of transportation of each l piece from the i-th supplier to the j-th manufacturer

• TCmjk : cost of transportation of each m-th product unit from the j-th manufacturer to the k-th customer

• $T C m k i '$ : cost of transportation of each m-th product unit from the k-th customer to the disassembly center of the j-th manufacturer

• Plij : cost of purchasing each l-th piece of product from the i-th supplier to the j-th manufacturer

• CPmj : cost of production (assembly) of each m-th product unit in the j-th production center

• CDmj : cost of disassembly of each m-th product unit in the disassembly line of the j-th production center

• CapLli : supply capacity of the l-th piece by the i-th supplier

• CapMmj : production capacity of the m-th product in the j-th production center

• CapDmj : disassembly capacity of the m-th product in the disassembly line of the j-th production center

• Nlm : number of the l-th piece in each m-th product unit

• αl : percentage of returned l-th piece (faulty)

• βm : rate of returned m-th product (faulty)

• ηmj : rate of returned m-th product (faulty) recyclable at the disassembly line of the j-th production center

• Flj : the price of penalty for disposal of each returned l-th piece (faulty) in the j-th production center

• Dlj : number of demands for the l-th piece by the j-th manufacturer

• Dmk : number of demands for the m-th product by the k-th customer

• Smjk : sales price per each m-th product unit by the j-th manufacturer to the k-th customer

• Hlj : cost of maintaining each unit of intact l-th piece in the j-th production center

• CapVij : capacity of the transportation vehicle from the i-th supplier to the j-th producer

• CapVjk : capacity of the transportation vehicle from the j-th manufacturer to the k-th customer

• $C a p V k j '$ : capacity of the transportation vehicle from the k-th customer to the disassembly line of the j-th production center

• Rlj : safety stock (SS) of the l-th piece in the j-th production center

Decision Variables

• Qlij : number of the L-th piece delivered from the i-th supplier to the j-th manufacturer

• Qmj : number of the m-th product manufactured in the j-th production center

• Qmjk : number of the m-th product delivered from the j-th manufacturer to the k-th customer

• QRlj : total number of returned l-th piece (faulty) in the j-th production center

• QRmkj : number of returned the m-th product from the k-th customer to the disassembly line of the j-th production center

• Qlj : total number of intact l-th piece stored in the j-th production center

• NVij : number of transportation vehicles from the ith supplier to the j-th manufacturer

• NVjk : number of transportation vehicles from the jth manufacturer to the k-th customer

• $N V k j '$ : number of transportation vehicles from the kth customer to the disassembly line of the jth manufacturer

• Xi : binary variable for selection or lack of selection of the i-th supplier

• Ymi : 1, if the disassembly line of the m-th product is established in the j-th production center; otherwise, 0.

(1)

(2)

(3)

(4)

(5)

$Q l j ≥ ∑ m N l m . Q m j ∀ l , j$
(6)

$Q m j . N m l = ∑ k Q m k j ∀ m , j$
(7)

(8)

(9)

$∑ j Q l i j ≤ C a p L i j . X i ∀ l , i$
(10)

$∑ k Q m j k ≤ C a p M m j ∀ m , j$
(11)

$∑ k Q R m k j ≤ C a p D m j . Y m j ∀ m , j$
(12)

$∑ l Q l i j ≤ C a p V i j . N V i j ∀ m , j$
(13)

$∑ m Q m j k ≤ C a p V j k . N V j k ∀ m , j$
(14)

$∑ m Q R m k j ≤ C a p V k j ' . N V k j ' ∀ m , j$
(15)

(16)

$Q l i j , Q l j , Q m j , Q R l j , Q m k j , Q R m j k ≥ 0 N V i j , N V j k , N V k j ' ≥ 0 , I n t e g e r$
(17)

The objective function (1) minimizes the total costs of the proposed network, which include costs of ordering parts from a potential supplier (potential supplier selection), cost of launching a potential disassembly line in the production center, cost of purchasing pieces from the supplier, cost of production of each product unit in the production center, cost of disassembly of faulty products in the potential disassembly line of the production center, cost of storage of each unit of intact pieces in the warehouse of the manufacturer, cost of disposal of faulty pieces by the manufacturer, cost of transportation of pieces between the supplier and manufacturer, cost of transportation of products between the manufacturer and the customer, and cost of transportation of faulty products between the customer and manufacturer. Furthermore, revenues from the sale of products were deducted from expenses. Constraints (2) determine the total number of faulty pieces in the production center while constraints (3) show the number of faulty products returned by customers and the total number of intact pieces in the manufacturer’s warehouse. Constraints (5) demonstrate that the number of intact pieces should not be less than safety stock (SS) so that ordering could be made before going out of stock. Constraints (6) ensure that the number of manufactured products should not exceed the total number of intact pieces based on the number of pieces used. Constraints (7) show that the number of manufactured products should be equal to the number of products delivered to the customer. Constraints (8) show the number of pieces delivered from the supplier to the manufacturer, and constraints (9) determine the number of products delivered from the production center to the demand point (customer). Constraints (10-12) demonstrate the supplier capacity limit, production capacity limit, and disassembly line, respectively. In addition, constraints (13-15) show the vehicle capacity limitation for transportation from the supplier to the manufacturer, from the manufacturer to the customer, and from the customer to the production center’s disassembly line. Finally, constraints (16) demonstrate binary variables, whereas constraints (17) show the condition for variables to be positive and correct.

### 2.2 The Second Model: Minimization of Risks of Suppliers under Different Scenarios

At this stage, the risks identified for this research, including quality problems, shortage of raw materials, rising prices of raw materials, supplier capacity risk, failure of machinery and equipment, IT system failure, accidental risks (e.g., fire), risk of industrial actions (e.g., strikes and sanctions), failure of the transportation system, failure of management policies, exchange restrictions (e.g., increased customs duties) and inflation rate were selected. In addition, they were inquired and measured by supply chain experts based on a questionnaire using the method the FMEA method. Afterwards, a scenario-based mathematical programming model was presented (under three scenarios of good, moderate and bad, with an occurrence possibility of 0.2, 0.6, and 0.2) to minimize the total level of supply risks.

Indexes

• q: index of types of risk based on Table 2-3 (example: lq= the risk of problems is qualitative)

• s: index of scenario

Parameters

• ps: the probability of occurrence of the s-th scenario

• λiqs : level of q-type risk for the i-th supplier in the s-th scenario

• NS: minimum required number of suppliers

• Riskis : total risk related to the i-th supplier in the sth scenario

• XLib: number of transferable products between high-risk and low-risk suppliers (output of the third model)

• Qlij: number of l-th pieces delivered from the supplier to the j-th manufacturer (output of the first model)

Decision Variable

• Xi: binary variable for selection or lack of selection of the i-th supplier

(18)

$∑ i X i ≥ N S$
(19)

$R i s k i s = ∑ q λ i q s . X i ∀ i , s$
(20)

$R i s k i s ≥ 0$
(21)

$X L i b ≤ ∑ l Q l i j . X i ∀ i , b$
(22)

$X i ∈ B i n a r y$
(23)

$X L i b , Q l i j ≥ 0$
(24)

The objective function (18) minimizes the total risk level, and constraints (19) express the minimum number of required suppliers. Constraints (20) calculate the total risk level related to each supplier in each scenario. Constraints (21) show the range of changes in the variable. Constraints (22) claims that the transfer of parts between two high and low-risk suppliers occurs when that supplier is selected and the part is delivered to the manufacturer based on the risk supplier. Constraints (23) demonstrate binary variables while constraints (24) show the condition for variables to be positive and correct.

### 2.3 The Third Model: Maximization of Supply from the Lower-risk Suppliers

In this section, the supply method from the revised suppliers was used, and suppliers with relatively lower costs and less risk were applied. The model primarily aims to maximize the flow of products from a high-risk supplier to a low-risk supplier. Therefore, the parameters of the decision variables in the objective function are the positive differences between the normalized risk value of the suppliers. The objective function value presents no value. However, since the objective function attempts to maximize, it establishes the condition for transfer from a high-risk supplier to a low-risk supplier.

Indexes

• i: supplier

• a: higher-risk suppliers compared to the i-th supplier

• b: lower-risk suppliers compared to the i-th supplier

Parameters

• RNij : positive difference in the value of normalized risks between suppliers

• Qi : allowed transferable amount from the i-th supplier

• Ci : capacity of the i-th supplier

• Qlij : number of the l-th piece from the i-th supplier to the j-th manufacturer (output of the first model)

Decision Variables

• XLib : the number of transferable products between high-risk and low-risk suppliers

• QNi : remaining product in the i-th supplier

• Capn : remaining capacity in the i-th supplier

(25)

$∑ b X L i b ≤ Q i ∀ i i ≠ b$
(26)

$∑ a X L a i − ∑ b X L i b ≤ C i ∀ i i ≠ b ≠ a$
(27)

(28)

$X L i b ≥ 0 i ≠ b$
(29)

$R G T = ∑ i ∑ j ( R i s k i − R i s k b ) ∀ i i ≠ b$
(30)

$Q N i = ( Q l i j − ( R G T × Q l i j ) ) ∀ i = n$
(31)

$C a p n = ( C a p L l i − Q l i j )$
(32)

The objective function (25) aims to maximize the transfer of products from a risky supplier to a low-risk supplier. Constraints (26) show that the number of transferable products from the i-th supplier to the j-th supplier should not exceed the allowed number of transferable products.

Constraints (27) declare that the number of transferable products from the k-th higher-risk supplier (compared to the i-th supplier) to the j-th lower risk supplier (compared to the i-th supplier) should be smaller than the remaining capacity of the i-th supplier. Formula (28) calculates the risk value, and constraints (29) show the type of X decision variable. Constraints (30) demonstrate the relative value of the total risk based on the low-risk supplier. Constraints (31) show the remaining products in the i-th supplier, and constraints (32) show the remaining capacity of the supplier.

### 2.4 Multi-objective Integrated Model

In this section, we integrated the multi-stage model of the previous stage and examined it as a multi-objective model. Therefore, the integrated model included three objective functions of stages 1, 2 and 3 with all the constraints of the three stages.

## 3. RESULTS

### 3.1 Report of Multistage Model Results

This section reports and analyzes the results according to the input values in Table 1.

### 3.2 Comparison of Results of Supply from Suppliers (Risk-Free/Risky)

In this section, the two modes of without considering risk criterion and with considering risk criterion were compared in ordering mode.

After considering the risk criterion, the amount of supply from some suppliers was changed, which indicated the proper functioning of the program and modeling. In both cases, the amount of demand from manufacturers, which was a total of 1134,000 units, was met.

### 3.3 Multi-objective Integrated Model Results

In the proposed Epsilon constraint method, we first calculated the pay-off table, which included the best and worst responses of each objective function, for all three objective functions. In the end, we reached a 3*3 pay-off table for the model.

After obtaining the pay-off table, one of the objective functions was selected as the main objective function, and the range of changes in the other two objective func-tions (i.e., the difference between the best and worst responses) was calculated in the mentioned table, and 21 Pareto fronts were obtained. Afterwards, the best Pareto front was selected by the decisionmaker, which is shown in Table 4. In addition, the responses were compared to the optimal response of the multi-objective model. Since the main purpose of this study was to be cautious about higher-risk suppliers, and supply occurred from lowerrisk suppliers, and the model simultaneously sought to minimize supply chain costs, the integrated multiobjective model reduced supply chain risk (second objective function) again while maintaining the values of objective functions one and three with little change compared to the multi-stage model. It is worth noting that it is very unlikely that an optimal solution was found that could simultaneously optimize all the objective functions defined in the problem. In most cases, the defined objective functions are in conflict with each other in the multiobjective optimization problem.

## 4. CONCLUSION

The approach presented in the current research can help senior managers of companies achieve competitive advantages. This is mainly due to the fact that, as observed, the amount of supply by the supplier was determined in addition to optimally minimizing the costs of purchasing, transportation, storage, supplier selection, and returned product (penalty). Afterwards, we identified supply chain risks and evaluated and measured them through the FMEA method following receiving experts’ opinions. Following that, risks were minimized in a scenario- based model, and the amount of supply by the supplier was modeled by considering the risk criterion. In the end, the mentioned multi-stage model (three stages) was integrated and solved as a multi-objective model using the Epsilon constraint method. According to the results, the multi-objective model yielded better results, compared to the multi-stage model, which confirmed the proper performance of the former. Given the wide range of materials discussed and the tools used in this research, the following are proposed for future studies: developing a multi- product model or a model with more levels (distributors ring, customers ring), considering periodic and timebased risks and risk of products, considering the cause of return, supply chain resilience and stability, applying the proposed method to different industries to evaluate their performance, and developing large-scale problems using accurate, heuristic and meta-heuristic methods.

## Figure

Proposed closed supply chain model optimization.

## Table

Values of objective function in three stages

Generation method of model’s parameters

Generation method of model’s parameters

Comparison of the effect of objective functions on each other in a multi-objective model

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