Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.20 No.2 pp.339-348
DOI : https://doi.org/10.7232/iems.2021.20.2.339

Investigating the Impact of Customer Service Priority on Optimizing the Location Problem of Hierarchical Allocation of Crowded Facilities in the Framework of Queue Systems

Kirill Yu. Kurilov*
Togliatti State University, Russia
*Corresponding Author, E-mail: kurilovkyu@mail.ru, d.research_r@yahoo.com
March 12, 2021 April 12, 2021 April 19, 2021

ABSTRACT


Location and allocation in queuing systems is one of the most important decisions to improve the quality of service. Optimizing these two important decisions can have many positive effects on customer service. In this research, the problem of location-hierarchical allocation of facilities has been investigated by considering the priority of customers in the framework of queuing systems. To this end, a multi-objective nonlinear mathematical model has been developed. The objective functions of this issue include reducing the total waiting time of customers and maximizing the unemployment of facilities. To solve this mathematical model, a multi-objective genetic algorithm has been used. Also, the parameters of this algorithm are adjusted using Taguchi method. The results show that the establishment of customer priority system reduces the average waiting time of all customers. Therefore, it can be concluded that in the design of hierarchical facilities, if the goal is to reduce the waiting time of a particular segment of customers, they should be prioritized.



초록


    1. INTRODUCTION

    In many businesses and organizations, facilities are categorized into multiple levels, each offering a different service level. These multi-level hierarchical structures are common in both public and private sectors, for example in systems that deliver health and education services, bank branches, ATMs, etc. (Marianov and Serra, 2001; Amouzad Mahdiraji and Yousefi Talouki, 2021; Nasiri and Ahmadi Daryakenari, 2021; Sharma and Sharma, 2021). Facility location is a very important issue for these systems. However, in many real-world cases of hierarchical location, the service time of facilities does not match the volume of demand, resulting in considerable queuing or congestion. To address this prevalent problem, the generated queue systems need to be analyzed in terms of criteria such as queue length or average waiting time of customers. Because of the uncertainty involved in the time that it might take to serve arriving customers, the problem of location of congested facilities could be difficult to model and solve. The present study aims to bridge the gap in the literature on the multi-level congested facility location and the impact of prioritizing services compared to using non-prioritized (turn-based) queues. The rest of this paper is structured as follows. In the next part of this section, we review the literature on the subject of hierarchical facility location-allocation and congested facility location-allocation. In section 2, we present the problem statement and the proposed formulation and describe the model indices, parameters, and assumptions. In section 3, we provide a detailed description of how the model is solved and report the results for a numerical example. Finally, in Section (4), we present the conclusions and offer some suggestions for future research.

    In a study by Mitropoulos et al. (2006) they formulated a hierarchical facility location model with the objective of maximizing coverage and equity in a healthcare system with a two-level structure comprised of local health centers and hospitals. They assumed that efficiency and equity of distribution of facilities among citizens are the two most important factors for the optimal design of healthcare systems. The efficiency was optimized in the form of an objective function seeking to minimize the total distance between patients and facilities. The equity of service distribution was optimized by an objective function that minimizes the maximum distance between facilities and patients. Hodgson (1986) modeled the location of primary healthcare facilities in a three-level structure with the objective function of minimizing the total distance between demand points and the closest facility with the appropriate service level. The model of this study is a logical yet practically unrealistic hierarchical model in which demand at every level is met at the nearest appropriate facility. This study also presented an interactive model in which the objective is to minimize the distance of individuals from the higher-level facility they anticipate to be referred to from the first place they visit, and help choose the first place based on the sum of distances in question over the entire hierarchical system.

    Since the early 1940s, many researchers have proposed mathematical formulations for more realistic modeling of customer travel behaviors. These include researchers such as Mitropoulos, Hodgson, and Jacobsen, who have point out the unrealistic nature of the assumption that customers will visit the nearest facility. According to Mitropoulos et al. (2006) the attractiveness of facilities depends not only on proximity but also on the quality of services provided. Hodgson and Jacobsen proposed an interactive model called the expected referral distance, which models several types of customer travel behaviors based on their expected referrals. Teixeira and Antunes (2008) also stated that in the location of public facilities, it is very important to pay attention to the limitations of customer-facility allocation and analyze the spatial patterns of this allocation.

    Fard and Hajaghaei-Keshteli (2018) modeled the problem of multi-level hierarchical location-allocation with reverse and forward flows between different levels. In this study, researchers considered a series of customer zones, distribution centers, and recovery centers in the model structure and formulated it with the help of Stackelberg game theory. The model was solved with three well-known algorithms called Tabu search, particle swarm optimization, and variable neighborhood search, and also with two new algorithms called the Keshtel algorithm and water wave optimization. Since the performance of meta-heuristic algorithms highly depends on their parameters, the optimal parameter settings for these algorithms were determined with the Taguchi method.

    In a study by Mousazadeh et al. (2018) they proposed a model for the problem of designing a health service network with a three-level hierarchical structure. The proposed objective functions were focused on minimizing the cost of building the facilities and minimizing the distance between the demand points and the facility at each level. The proposed model was formulated in the form of a nonlinear programming problem and solved with four robust optimization approaches. Finally, the performance of these four approaches was analyzed in a case study.

    Zarrinpoor et al. (2018) developed a hierarchical location- allocation model for designing a health service network. This model was constructed with a two-level multi-flow hierarchical structure that takes into account the uncertainty of demand and services and geographical access. In this model, patients are prioritized based on urgency in receiving services and service quality is measured in terms of the expected waiting times considering the priority queuing system and risk of unexpected disruptive events.

    One of the earliest studies explicitly focused on congested facility location is the research carried out by Wang et al. (2002). In this study, they proposed a facility location model with immobile servers where a maximum of one server can be deployed in each facility. The queue model developed in this study is M/M/1.

    Berman and Drezner (2006) expanded the model of Wang et al. from single-server to multi-server mode and investigated the performance of M/M/c systems in congested facility location with immobile servers.

    The model presented by Pasandideh and Niaki (2012) is one of the few models where both customer and service aspects are simultaneously considered in the objective function. In this study, a bi-objective model was developed to simultaneously minimize the total time spent by customers (travel times plus waiting times) and the idle time of service providers. In this model, it is assumed that only one server can be deployed in each facility, which makes the proposed queue model of the M/M/1 type.

    Chambari et al. (2011) also presented a bi-objective model in which customers and service providers are both simultaneously considered. The objective functions of this model simultaneously minimize customer travel and waiting times and the idle rate of service providers. The main assumption that distinguishes this model from the previous model is the limited capacity of waiting space in each facility, which makes the queuing system of the M/M/1/k type.

    In a study by Hamaguchi and Nakade (2010), they modeled the problem of location of fixed facilities with the M/G/1 queuing system, in which times between consecutive customer arrivals follow an exponential distribution, but service times have a general distribution.

    Harewood (2002) modeled the problem of location of multiple emergency service/ambulance facilities with multiple objective functions including the maximization of the covered population and minimization of the demand coverage costs. Also, the Queuing Probabilistic Location Set Covering Problem approach was used to estimate the probability that all service providers in the area are busy. For this purpose, the facilities were assumed to behave according to the M/M/m/K model, and having the minimum required confidence level and the probability that all employees of each facility are busy, the minimum number of employees required to cover the demand points with a specific probability was calculated.

    Aboolian et al. (2009) formulated a new model for server facility location with the objective function of minimizing the maximum time spent by customers, including travel time and waiting time. In this study, the facilities were considered as M/M/m queuing systems and service allocation was also incorporated into the model formulation.

    In another study, Aboolian et al. (2008) modeled the location-allocation problem in a congested network with the objective of minimizing system-wide costs (including fixed facility construction costs and variable costs associated with travel and waiting). In this study, The queue model used for the facilities was of the M/M/c type.

    Rahmati et al. (2013) developed a location model with an M/M/c queuing system and server allocation, in which the maximum number of servers per facility was limited. They also incorporated the cost of allocating staff to the facility and the service level into the model formulation.

    In a study by Hajipour et al. (2016), they presented a multi-objective model for multi-level congested facility location within the M/M/1 queue model. This model has three objective functions: minimizing the total travel time and waiting time of customers, minimizing the maximum probability of facility idleness, and minimizing the number of facilities (or in other words, minimizing the cost of constructing facilities). In this model, the system is structured in such a way that the facilities of different levels provide different services and each customer must receive service from facilities of all service levels. They solved the proposed model with two meta-heuristic methods, namely the multi-objective vibration damping optimization and the multi-objective harmony search algorithm, as well as the non-dominated sorting genetic algorithm and multi-objective simulated annealing and compared the obtained results.

    Tavakkoli-Moghaddam et al. (2017) presented the new facility location model with the M/M/c/K queue model combined with pricing and facility capacity determination. In this work, in addition to location and allocation variables, the number of employees and queue capacity, and the price of service in each facility were included in the model and the number of employees and capacity in each facility was assumed to be limited. This model defines the satisfaction of customers as a function of the price of service/product and their distance from the facility, with different customers having different levels of sensitivity to this price and distance. Therefore, the queue length in this model is controlled by the pricing policy and the number of service providers in each facility.

    Pasandideh et al. (2013) formulated a multiobjective facility location model with batch arrivals and an Mx/M/1 queuing system. In this model, each batch of customers is assigned to a facility and the size of these batches is a random variable. To balance the goals of customers and service providers, they considered three objective functions that minimize the total travel time and waiting time of customers, minimize the maximum idleness of each facility, and minimize the total fixed cost of constructing facilities. They considered different weights for the terms in the first objective function. These researchers stated that minimizing the average probability of idleness does not necessarily lead to minimizing the probability of idleness of all facilities, but minimizing the maximum idleness reduces the likelihood of idleness of all facilities. To improve the quality of service, they placed a coefficient alongside the service rate of servers in the capacity constraint.

    In another study by Hajipour et al. (2014) they modeled a facility location problem with the allocation of service providers to facilities. In this study, they introduced the notion of reliability to the M/M/c queuing system and developed a model with the objectives of maximizing system reliability, minimizing system costs, and minimizing waiting time. The model was solved with multiobjective vibration damping optimization algorithms, multi-objective genetic algorithm, and simulated annealing algorithm. The results showed that the vibration damping optimization algorithm is more efficient than the other two meta-heuristic algorithms.

    Araz et al. (2014) proposed a p-median location model for a certain number of facilities to provide urban public health emergency services in Arizona. The model was developed for the location of dispenser centers deployed in response to an anthrax attack, the allocation of demand points and human resources to these centers. Each dispenser center in this network was considered as an M/G/c queuing system with the objective of minimizing the total average waiting time and the total travel time of customers, which were assumed the main criteria for measuring system performance.

    Khezerlou et al. (2021) developed a model for optimizing a biomass-to-biofuel supply chain for reliability under different risk items.

    2. METHODOLOGY

    The considered location-allocation problem is a hierarchical problem where each level provides its own services and the facilities at the same level provide the same type of services. In other words, the facilities at each level are similar to each other. To be served completely, customers must visit one facility from each level. Customers are assumed to be positioned in specific predetermined locations, which are divided into several demand points. Customer demand is probabilistic and follows a Poisson distribution with a known mean. There are a certain number of potential locations for establishing facilities of each level. In this problem, the goal is to select suitable places for establishing a suitable number of facilities from among potential locations and then allocating customers to the facilities of each level using two queue modes: priority-based queue and turn-based queue. In this model, the possibility of congestion in facilities is also taken into account. Since customer demand follows the Poisson distribution, the demand allocated to each facility, which is the sum of multiple Poisson distributions, also has a Poisson distribution. In addition, each facility is assumed to have a server with an exponential service time and therefore an M/M/1 queuing system. The facilities do not have capacity constraints and the long-term efficiency coefficient of the system is assumed less than one. The conceptual model of the considered problem is illustrated in Figure 1.

    In the following, we first present a multi-objective mixed integer nonlinear programming model, where the first objective function minimizes the total waiting time of customers in the facility queues and the second objective function minimizes the maximum facility idleness. Obviously, these objectives are conflicting, because as the idleness probability decreases, the waiting time increases and vice versa. These objective functions create a balance between the goals of customers and service providers because the first function works in the customers’ favor while the second function works in the favor of service providers.

    2.1 Problem Assumptions

    • - Each customer receives each level of service only once and cannot revisit a facility or opt out of receiving a service.

    • - Each facility operates as an M/M/1 queuing system.

    • - The time between customer arrivals and service times are independent of each other.

    • - The efficiency coefficient of each facility is less than one.

    • - Service providers (facilities) are immobile and customers must visit them to receive the offered services.

    • - All demands must be fully satisfied (demand loss is not allowed).

    • - In the priority-based queue, customers with higher priority are placed in front of the queue, but in the turn-based queue, services are provided based on the first-in-first-out (FIFO) policy.

    2.2 Model Sets and Indices

    • i : Set of customer points (demand); i=1,…,M

    • j,j’ : Set of potential locations for the facilities of each level; j,j’=1,…, N

    • l : Index of facility level; I=1, …, K

    2.3 Model Parameters

    • Di : Average demand of customer i

    • μl : Service rate of each facility at level l

    2.4 Model Decision Variables

    • yjl=1 if facility j is constructed at level l, and yjl=0 otherwise.

    • xijl=1 if customer i is allocated to facility j of level l, and xijl=0 otherwise

    • z1 : Minimizes the total waiting time of customers in facility queues

    • z2 : Minimizes the maximum facility idleness

    • λjl : Average arrival rate of customers at facility j of level l

    • wqjl : Waiting time of customers in facility j of level l

    • Wqjl(i) : Waiting time of customer with priority i in facility j of level l

    2.5 Mathematical Formulation of the Problem

    M i n z 1 = i = 1 M j = 1 N l = 1 K w q j l
    (1)

    M i n z 2 = m a x j = 1 , 2 , , N l = 1 , 2 , , k { π 0 j l y j l }
    (2)

    λ j l = i = 1 M d i x i j l j = 1 ,   , N l = 1 , , K
    (3)

    λ j l μ l l = 1 , ,   K
    (4)

    π 0 j l = ( 1 λ j l μ l ) j = 1 , , N l = 1 , , K  
    (5)

    j = 1 N y j l 1 l = 1 , , K
    (6)

    y j l i = 1 M x i j l M y j l j = 1 , , N l = 1 , , K  
    (7)

    j = 1 N x i j l = 1 j = 1 , , N l = 1 , , K
    (8)

    j ' = 1 N j = 1 N x i j ' ( l 1 ) x i j l = 1 l = 1 , , K i = 1 , .... , M
    (9)

    y j l . x i j l { 0.1 }
    (10)

    Equation (1) minimizes the total waiting time of customers in the facility queue. Equation (2) minimizes the maximum facility idleness. Obviously, there is a conflict between these two objective functions, because as the service rate of a facility increases, although the total waiting time in the queue decreases, the probability of facility idleness also increases (and vice versa). Therefore, facilities need to be established in such a way as to reach a balance between these objectives. Equation (3) calculates the average customer arrival rate at each facility. Equation (4) guarantees the long-term stability of the system. Equation (5) computes the probability of a facility remaining idle over a long period. Equation (6) guarantees that at least one facility should be established at each level. Equation (7) ensures that customers should only be allocated to the facilities that are established, and that each established facility must have at least one customer and at most, the total number of customers allocated to it. Equation (8) ensures that each customer visits only one facility from each service level. Equation (9) ensures that customers receive all levels of service. Equation (10) defines the range and type of decision variables.

    2.6 Analysis of Waiting Times in Different Queuing Systems

    Given the mathematical complexity of the prioritybased queuing system, there are limited results for models of this system. In the model considered in this study, customers arrive at facilities of different levels based on the Poisson process with parameter (λ). Regardless of their priority, all customers receive the service at the same rate, which is equal to (μ). In this model, customers are prioritized in levels 1, 2, 3,..., M. Customers with priority 1 receive service ahead of customers with priority 2 and by the same token, customers with priority M-1 receive service ahead of customers with priority M. The number of servers in each facility is 1. Therefore, based on the above assumptions, the waiting time of a customer with priority k is given by the following equation (Tirkolaee et al. 2020).

    w q j l ( k ) = 1 A j l B j l ( k 1 ) B j l ( k ) ; j = 1 , , N l = 1 , , K k = 1 , 2 , , M
    (11)

    A j l = ( μ l ) 2 λ j l ; j = 1 , , N l = 1 , , K
    (12)

    { B j l ( 0 ) = 1 B j l ( k ) = 1 s = 1 k d s x s j l μ l ; j = 1 , , N l = 1 , , K s = 1 , 2 , , M
    (13)

    w q j l = i = 1 M d i x i j l λ j l w q j l ( i ) ; j = 1 , , N l = 1 , , K
    (14)

    In the turn-based system, customers arrive at facilities of different levels based on the Poisson process with parameter (λ). The service rate is the same for all customers and is equal to (μ). In this model, customers do not have any priority over each other and receive the service based on the FIFO policy. The number of employees in each facility is 1. Based on these assumptions, the waiting time of customers can be obtained from the following equation (Mohtashami et al., 2020).

    w q j l = 1 μ l ( μ l λ j l ) ; j = 1 , , N l = 1 , , K
    (15)

    3. RESULTS AND DISCUSSION

    To evaluate the model, in this section, several numerical examples of different dimensions are solved and the results are analyzed. Since Sherali and Nordai have proven that the multi-facility location-allocation problem (where facilities can be located anywhere in Euclidean space) with definite parameters is an NP-hard problem, the best approach to solving these problems is to use heuristic and meta-heuristic methods. Therefore, a multiobjective non-dominated sorting genetic algorithm is used to solve the proposed model.

    Since the authors found no similar model in the literature, the data for testing the model were generated at random. In addition, since in most articles on locationallocation, the required data have been produced with a uniform distribution, this article also used a uniform distribution for this purpose. The stochastic parameters of the problem are given in Table 1.

    The non-dominated sorting genetic algorithm (NSGA) has been developed to solve multi-objective problems. The second version of this algorithm, which was developed by Deb et al. (2000) uses the concept of crowing distance instead of sharing function and employs an elitist approach to ensure diversity in Pareto solutions. To solve the proposed model in its different dimensions, this study used the second version of this algorithm, which is called NSGA-II.

    In this paper, a special method is used to increase the feasibility of solutions and satisfying more constraints. The chromosomes representing the solutions are structured in the form of a matrix where:

    • 1. There are as many rows as there are customers.

    • 2. There are as many columns as there are service levels.

    • 3. Each element is a random number between 1 and the total number of facilities in the corresponding level.

    The structure of this chromosome is shown in Equation (16).

    [ p 11 = R a n d i ( 1. J ) p 1 K = R a n d i ( 1. J ) p M 1 = R a n d i ( 1. J ) p M k = R a n d i ( 1. J ) ]
    (16)

    In this chromosome, the number of rows is equal to the number of customers (M) and the number of columns is equal to the number of service levels (k). Each element of this matrix is a random number between one and the number of facilities in that level (J). To decode the chromosome, the variables related to the customers allocated to the facility are set to one. With this allocation, the facility will be constructed and its corresponding variable will be set to one.

    Since this way of defining chromosomes satisfies most of the model constraints, the solutions generated in each iteration of the algorithm may not necessarily be feasible. This way of defining chromosomes ensures that constraints (6) to (9) are always satisfied. One of the common methods for dealing with such issues in constrained optimization problems is to use a penalty function to turns these problems into unconstrained problems. Since the model presented in this study is multi-objective, the penalty function should be added to both objective functions. The penalty functions used in this study are:

    p ( x ) = U * M a x { 0. a ( x ) b 1 }
    (17)

    F ( x ) = { F ( x )                             ;       i f     x   f e a s i b l e   r e g i o n F ( x ) + p ( x ) ;   i f   x   i n f e a s i b l e   r e g i o n
    (18)

    Where U is a large positive value, g(x) is the target constraint, and p(x) is the penalty considered for the infeasible chromosome. It should be noted that the above equation has been designed for constraints where (x) ≤ b.

    Using a sorting algorithm, the solutions obtained in each population must be converted to a local Pareto front. In this research, the non-dominated sorting algorithm was used for this purpose (Coello et al., 2007).

    In the genetic algorithm, the members of the parent population were produced at random and the population of children was generated using the single-point crossover and mutation operators.

    The solutions obtained from meta-heuristic methods are sensitive to the values selected for the algorithm parameters (Rahmati et al., 2013). In this study, the optimal values of the parameters of the multi-objective NSGA-II for problem instances were obtained using the Taguchi method at three levels. After reviewing the previous studies and some trial and error, the parameter levels were defined as shown in Table 2.

    According to the standard Taguchi table, for four factors with three levels, one can use either L9 or L27 arrays (Montgomery, 2017). In this study, the L9 array was used as it involves fewer calculations. Finally, using the Minitab software, the optimal values of algorithm parameters were determined as shown in Table 3. The objective function values in the solutions of multi-objective NSGA-II with these parameter settings for problems of different sizes with priority-based and turn-based queue systems are reported in Tables 4 and 5, respectively.

    As shown in Figure 2, using the priority-based queues reduces not only the total waiting time of each customer in the queue but also the total waiting time of all customers.

    4. CONCLUSIONS

    The hierarchical congested facility locationallocation problem is a widely used type of location problem that has attracted the attention of many researchers over the past two decades. This study investigated the effect of considering priority-based queues and turn-based queues in the service-provider facilities on this type of location-allocation problem. Since this problem is NPhard, the multi-objective NSGA-II was used to solve the model. The optimal parameter settings for this algorithm were determined using the Taguchi method. Given the obtained results, it can be stated that if the goal of hierarchical facility location is to reduce the waiting time of a particular group of customers, it is best to establish a priority-based queuing system or in other words, give that group of customers higher priority, as it will reduce the average waiting time of the entire system. Considering priority-based queues in the service delivery system will mean that once arrived at a facility, customers that have a higher priority will be automatically placed in front of the queue, replacing the customer with lower priority and leaving his service half-finished. Therefore, this could be an attractive subject for future studies.

    Figure

    IEMS-20-2-339_F1.gif

    Schematic diagram of the considered problem.

    IEMS-20-2-339_F2.gif

    Graphical comparison of the average value of the first objective function in the priority-based and turn-based queue systems

    Table

    Parameter values considered for the proposed hierarchical location-allocation problem

    Levels of parameters of NSGA-II

    Optimal parameter settings for multi-objective NSGA-II based on Taguchi method

    Objective function values for problems of different dimensions with the turn-based queue

    Objective function values for problems of different dimensions with the priority-based queue

    REFERENCES

    1. Aboolian, R. , Berman, O. , and Drezner, Z. (2008), Location and allocation of service units on a congested network, IIE Transactions, 40(4), 422-433.
    2. Aboolian, R. , Berman, O. , and Drezner, Z. (2009), The multiple server center location problem, Annals of Operations Research, 167(1), 337-352.
    3. Amouzad Mahdiraji, E. and Yousefi Talouki, A. (2021), Voltage stability of wind turbines equipped with DFIG based on PID-based control method, Journal of Chemical Reviews, 3(1), 40-49.
    4. Araz, O. M. , Fowler, J. W. , and Nafarrate, A. R. (2014), Optimizing service times for a public health emergency using a genetic algorithm: Locating dispensing sites and allocating medical staff, IIE Transactions on Healthcare Systems Engineering, 4(4), 178-190.
    5. Berman, O. and Drezner, Z. (2006), Location of congested capacitated facilities with distance-sensitive demand, IIE Transactions, 38(3), 213-221.
    6. Chambari, A. H. , Rahmaty, S. H. , Hajipour, V. , and Karimi, A. (2011), A bi-objective model for location-allocation problem within queuing framework, World Academy of Science. Engineering and Technology, 78, 138-145.
    7. Coello, C. A. C. , Lamont, G. B. , and Van Veldhuizen, D. A. (2007), Evolutionary Algorithms for Solving Multi-Objective Problems (2nd Ed.), New York: Springer.
    8. Deb, K. , Agrawal, S. , Pratap, A. , and Meyarivan, T. (2000), A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, In International Conference on Parallel Problem Solving From Nature, Springer, Berlin, Heidelberg, 849-858.
    9. Fard, A. M. F. and Hajaghaei-Keshteli, M. (2018), A tri-level location-allocation model for forward/reverse supply chain, Applied Soft Computing, 62, 328-346.
    10. Hajipour, V. , Fattahi, P. , Tavana, M. , and Di Caprio, D. (2016), Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based meta-heuristics, Applied Mathematical Modelling, 40(7-8), 4948-4969.
    11. Hajipour, V. , Khodakarami, V. , and Tavana, M. (2014), The redundancy queuing-location-allocation problem: A novel approach, IEEE transactions on Engineering Management, 61(3), 534-544.
    12. Hamaguchi, T. and Nakade, K. (2010), Optimal location of facilities on a network in which each facility is operating as an M/G/1 queue, Journal of Service Science and Management, 3, 287-297.
    13. Harewood, S. I. (2002), Emergency ambulance deployment in Barbados: A multi-objective approach, Journal of the Operational Research Society, 53(2), 185-192.
    14. Hodgson, M. J. (1986), A hierarchical location-allocation model with allocations based on facility size, Annals of Operations Research, 6(8), 273-289
    15. Khezerlou, H. S. , Vahdani, B. , and Yazdani, M. (2021), Designing a resilient and reliable biomass-to-biofuel supply chain under risk pooling and congestion effects and fleet management, Journal of Cleaner Production, 281, 125101.
    16. Marianov, V. and Serra, D. (2001), Hierarchical location–allocation models for congested systems, European Journal of Operational Research, 135(1), 195-208.
    17. Mitropoulos, P. , Mitropoulos, I. , Giannikos, I. , and Sissouras, A. (2006), A biobjective model for the locational planning of hospitals and health centers, Health Care Management Science, 9(2), 171-179.
    18. Mohtashami, Z. , Aghsami, A. , and Jolai, F. (2020), A green closed loop supply chain design using queuing system for reducing environmental impact and energy consumption, Journal of Cleaner Production, 242, 118452.
    19. Montgomery, D. C. (2017), Design and Analysis of Experiments, John Wiley & Sons.
    20. Mousazadeh, M. , Torabi, S. A. , Pishvaee, M. S. , and Abolhassani, F. (2018), Health service network design: A robust possibilistic approach, International Transactions in Operational Research, 25(1), 337-373.
    21. Nasiri, H. R. and Ahmadi Daryakenari, F. (2021), Simulation and optimization of the urban energy system based on the combination of technologies and energy production and distribution network, Journal of Engineering in Industrial Research, 2(3), 129-148.
    22. Pasandideh, S. H. R. and Niaki, S. T. A. (2012), Genetic application in a facility location problem with random demand within queuing framework, Journal of Intelligent Manufacturing, 23(3), 651-659.
    23. Pasandideh, S. H. R. , Niaki, S. T. A. , and Hajipour, V. (2013), A multi-objective facility location model with batch arrivals: Two parameter-tuned meta-heuristic algorithms, Journal of Intelligent Manufacturing, 24(2), 331-348.
    24. Rahmati, S. H. A. , Hajipour, V. , and Niaki, S. T. A. (2013), A soft-computing Pareto-based meta-heuristic algorithm for a multi-objective multi-server facility location problem, Applied Soft Computing, 13(4), 1728-1740.
    25. Sharma, G. and Sharma, S. B. (2021), Synthetic impatienol analogues as potential cyclooxygenase-2 inhibitors: A preliminary study, Journal Applied Organometallic Chemistry, 1(2), 66-75.
    26. Tavakkoli-Moghaddam, R. , Vazifeh-Noshafagh, S. , Taleizadeh, A. A. , Hajipour, V. , and Mahmoudi, A. (2017), Pricing and location decisions in multi-objective facility location problem with M/M/m/k queuing systems, Engineering Optimization, 49(1), 136-160.
    27. Teixeira, J. C. and Antunes, A. P. (2008), A hierarchical location model for public facility planning, European Journal of Operational Research, 185(1), 92-104.
    28. Tirkolaee, E. B. , Goli, A. , Faridnia, A. , Soltani, M. , and Weber, G. W. (2020), Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms, Journal of Cleaner Production, 276, 122927.
    29. Wang, Q. , Batta, R. , and Rump, C. M. (2002), Algorithms for a facility location problem with stochastic customer demand and immobile servers, Annals of operations Research, 111(1-4), 17-34.
    30. Zarrinpoor, N. , Fallahnezhad, M. S. , and Pishvaee, M. S. (2018), The design of a reliable and robust hierarchical health service network using an accelerated Benders decomposition algorithm, European Journal of Operational Research, 265(3), 1013-1032.
    Do not open for a day Close