This paper studies the order allocation between a logistics service integrator (LSI) and multiple functional logistics service providers (FLSPs) with MCLS. To maximize the satisfaction of FLSPs, minimize the total cost of LSI, and maximize the customized degree, this paper establishes a multiobjective order allocation model of LSSC that is constrained by meeting customer demand, customer order decoupling point, and order difference tolerance coefficient. Numerical analysis is performed with Lingo 12 software. This paper also discusses the influences of scale effect coefficient, order difference tolerance coefficient, and relationship cost coefficient on the comprehensive order allocation performance of the LSSC. Results show that LSI prefers FLSPs with better scale effect coefficients and does not need to set an extremely high order difference tolerance coefficient. Similarly, setting a high relationship cost coefficient does not necessarily correspond to better results. For FLSPs, the continuous improvement of largescale operational capacity is required. When the comprehensive order allocation performance of the LSSC is high, the LSI should offer cost compensation to improve the satisfaction of the LSSC.
Outsourcing service, such as information technology services, financial services, and logistics services, has become increasingly popular in recent years due to the fact it appears to be more profitable. Many service integrators and functional service providers develop a longterm service purchasing/supplier relationships to satisfy customers demand and obtain more profit with integrated services and then form a service supply chains (SSC). Specially, in a logistics service supply chain (LSSC), a logistics service integrator (LSI) provides customized logistics services by integrating the service capacities of multiple functional logistics service providers (FLSPs) [
In recent years, customer requirements for specialized and customized logistics services have increased. Many logistics enterprises have also begun considering a change in logistics service mode. These logistics enterprises have attempted to provide mass customization logistics service (MCLS) instead of only mass logistics service [
Under the MCLS environment, the LSI integrates different customer orders and allocates such orders to multiple FLSPs. In contrast to existing order allocation methods, the LSI needs to consider the influence of customer order decoupling points (CODPs). Each customer order has a unique CODP; therefore, when the LSI integrates multiple customer orders, the cost scale effect caused by largescale services and the different CODPs of these orders should be considered. We also need to consider the minimum total cost of LSI and FLSP satisfaction in order allocation processes. Hence, a multiobjective decisionmaking problem exists. This problem involves addressing the diversity of customer orders, minimizing the total cost of the LSI, and considering the optimal satisfaction of FLSPs. This paper studies the order allocation problem (OAP) between LSI and multiFLSPs, considers the characteristics of the MCLS by modeling and analysis, and explores the influence of related factors on the comprehensive performance of order allocation. Moreover, several management insights are provided.
This paper is organized as follows. Section
This paper involves two main research areas: mass customization and order allocation. The literature review focuses on these two areas and elaborates their research progress and inadequacies.
Studies on mass customization mainly include mass customization production and mass customization service. Mass customization production is primarily for manufacturing industry and mass customization service is mainly for service industry.
Most mass customization production studies have focused on the supply chain of the manufacturing industry [
Studies on the decisionmaking problem of CODP have increased in recent years. In enterprise manufacturing activities, CODP is the transition point from maketostock to maketoorder [
The increasing adoption of the service supply chain in recent years has attracted scholars in studying the CODP problem of the service industry under mass customization environments. These previous studies mainly examined how a single service enterprise positions the CODP in service processes while considering mass customization costs and ignoring inventory costs. Moreover, there is a special issue on service optimization and control in mathematical problems in engineering which includes a hot topic of mass customization service. There are also some scholars dedicating research perspective to specific industries, such as logistics services industry. References [
To date, most OAPs are solved by using multiobjective linear programming or nonlinear programming methods, which spread around the produce supply chain. References [
The growth of LSSC has directed considerable research attention on its order allocation. Several studies have examined the OAP in twoechelon LSSC [
The literature review indicates that the MCLS environment has been left unexamined even though scholars have focused on the OAP of LSSC. The MCLS needs to consider the cost advantages of mass service and customized special requirements; therefore, the order allocation in this case is clearly distinct from general cases. Furthermore, cost control is not the most important decisionmaking objective when we consider the customization requirements. Thus, this paper focuses on the OAP around the MCLS environment by modeling its characteristics and exploring the influence of related factors on the comprehensive performance of order allocation. Furthermore, several management insights are also provided.
Section
We considered a twoechelon LSSC, which consists of one LSI and many FLSPs. After receiving orders from multiple customers, the LSI analyzes the customized and universal service requirements of these customer orders and allocates them to more than one FLSP. Every logistic service for customer orders comprises multiple service processes. Each process needs one kind of service that can be provided by many cooperative FLSPs. The service capability of each FLSP is possibly different. We assumed that
Table
Each FLSP
Given the importance of different customizations for each customer, the weight of the
Customer orders are assigned to FLSPs through the LSI. FLSPs will accomplish the order assigned by the LSI. No game exists between LSI and FLSPs.
Model notations.
Notations  Description 


The service ability interval of the 

The value of 

The value of 

The 

Scale effect coefficient 

The demand of the 

The total service procedures of the 

The weight of the 

The satisfaction of the 

The initial satisfaction of the 

The value of 

The value of 

The unit operating cost of the 

The unit operating cost of the 

The weight of the single satisfaction of the 

The weight of the overall satisfaction of the 

The preference of the 

The 

The procedure to start the customization service of the 

The minimum of all 

The CODP for all customers 

Order difference 

Order difference tolerance coefficient of LSI 

Relationship cost coefficient of LSI 

The order quantity of the 

The total satisfaction of FLSPs in LSSC 

The total cost of LSI in LSSC 

The total customized degree of LSSC 

The optimal solution of objective function 

The optimal solution of objective function 

The value of 

The optimal solution of objective function 

The value of 

Weight coefficients of the objective function 

Weight coefficients of the objective function 

The objective function synthesized by 

The optimal solution of 
Previous order allocation models are mostly based on the costs and orders service level [
Maximizing the satisfaction of FLSPs is considered the first process by the LSI in order allocation. Based on the satisfaction function of reference [
Given that each FLSP has different preferences for each customer, satisfaction comprises single satisfaction and overall satisfaction. The weight of the single satisfaction of the
Single satisfaction: the satisfaction of the FLSP for the order allocation result of one customer. Assuming that the preference of the
Overall satisfaction: the satisfaction of the FLSP for the order allocation result of all customers. If the FLSP can service two customers, the FLSP expects that the satisfaction for both customers is high. Usually, we use the average satisfaction of multiple customers to represent the overall satisfaction. Thus, the overall satisfaction of the
The total satisfaction of
Therefore, the objective function of maximizing the satisfaction of FLSPs is expressed as follows:
In the order allocation process, the LSI also considers minimizing the cost. The total cost of the LSI is related not only to the number of orders assigned but also to the position of the CODP. When CODP approaches the customer, the quantity of procedures and order of mass services increase; the order of mass service is also obvious. Therefore, the scale effect is more noticeable.
The total cost of the LSI consists of two parts, namely, mass service cost and customization service cost. The unit operating cost of the
where
When the procedure of mass service is
The cost of all FLSPs is summarized. Thereafter, the objective function of minimizing the total cost of the LSI is obtained:
Under the MCLS environment, each customer will intend to maximize their own customized degree. Therefore, the customized degree for customer demand is one of the most important objectives. However, given that the customized degree for customer demand will affect the total cost of the LSI, the former cannot be too high. Mass customization involves reducing total cost as much as possible and maximizing the customized demand (i.e., increase the cost of customization services). Thus, we multiply the ratio of the customization service cost and total cost by the weight to represent the customized degree of each customer. We then summarize the customized degree of each customer to obtain the customized degree of all customers:
In the order allocation process, the LSI also needs to meet several constraints. First, the total order quantity of all FLSPs should be equal to the customer demand in the
As shown in (
Equation (
Combining the three objective functions given in Sections
In the objective functions, (
For the constraints, (
The model is a multiobjective programming problem with three objectives and three constraints. Multiobjective programming problems have numerous mature solutions, such as the evaluation function method including linear weighting method, reference target method, maximin method [
Based on the previous solving methods, in order allocation, considering these actual situations, we introduce a parameter called the relationship cost coefficient
The consideration of the objective functions
This approach denotes that the LSI can minimize and maintain the total cost within a certain range.
The model has
After determining
In solving the objective function
This section illustrates the validity of the model by numerical analysis, explores the influence of related factors on time scheduling results, and provides several management insights. First, we use the Lingo 12 software to implement the numerical analysis. The results are shown in Section
Figures
Parameter data (



















 



 


15 


18 


16 


20 


22 
 



 

0.2  0.3 

0.35  0.35 

0.15  0.3 

0.3  0.2 

0.25  0.3 
 



 

0.6  0.4 

0.5  0.5 

0.7  0.3 

0.4  0.6 

0.35  0.65 
Parameter data (






0.3  0.3  0.4 

0.2  0.4  0.4 

0.35  0.35  0.3 

0.25  0.35  0.4 

0.3  0.4  0.3 

0.3  0.4  0.3 

60  100  80 

6  5  5 

8  7  7 
Structure of the LSSC.
Logistics service procedure of customer A, B, and C. Note: The red circle denotes the procedure that starts the customization service of each customer.
By using the solution method in Section
Order allocation results.
Procedure  1–5  6–7  8  

Customer 
A  B  C  A  B  C  A 
a  0  4.31  0  31.59  18.20  37.79  31.59 
b  0  6.33  0  6.14  37.86  0.53  6.14 
c  60  4.99  80  4.63  40  37.82  4.63 
d  0  4.93  0  6.41  0.35  0  6.41 
e  0  79.44  0  11.23  3.60  3.86  11.23 
Order allocation performance.
Total cost of LSSC  17472.020 
CODP  5 
Customized degree  0.522 

0.391 
To analyze the influence of
Influence of



CODP 


0  20280  0.596  4  0.403 
0.025  18960.000  0.524  5  0.388 
0.05  17472.020  0.522  5  0.391 
0.075  16320.000  0.580  5  0.417 
0.1  15000.000  0.638  5  0.469 
0.125  13680.000  0.646  5  0.472 
0.15  12360.000  0.783  5  0.582 
Influence of
Figure
Figure
Influence of
If
Influence of
Influence of
Our results differ from those of previous studies that did not include the CODP. The LSI will delay the CODP to acquire the scale effect, which leads to a significant increase in mass service cost and a decrease in customized degree (Figure
If
Further analysis shows that when
To analyze the influence of
Influence of



CODP 


0.4  17472.020  0.522  5  0.391 
0.45  17472.020  0.522  5  0.391 
0.5  17499.530  0.613  5  0.471 
0.55  17499.530  0.613  5  0.471 
0.6  17499.530  0.613  5  0.471 
0.65  17640.000  0.681  4  0.561 
0.7  17640.000  0.681  4  0.561 
0.8  17640.000  0.681  4  0.561 
0.9  17640.000  0.681  4  0.561 
1  17640.000  0.681  4  0.561 
Influence of
The order difference tolerance coefficient
When
When
Influence of
Figure
Influence of
Influence of
Figure
To analyze the influence of
Influence of



CODP 


0.15  15400.000  0.575  5  0.421 
0.2  17472.020  0.522  5  0.391 
0.25  17990.84  0.570  5  0.413 
0.3  18887.720  0.652  4  0.497 
0.35  19845.000  0.668  4  0.465 
0.4  20580.000  0.663  4  0.477 
0.45  21002.100  0.685  4  0.494 
Influence of
Figure
Figure
Influence of
When
Influence of
Influence of
Further analysis shows that if the customized degree is greater than 0.575,
Figure
This observation is attributed to the two parts of
Figure
In this section, the relationship of order allocation results (total cost
In this section, we elaborate the main conclusions and implications for researchers. We also explore the management insights for LSI and FLSP and provide some suggestions.
On the basis of the analysis on Section
The total cost of LSI
The CODP moves back to the customers with increasing scale effect coefficient
The customized degree
The comprehensive performance of order allocation
In line with academic research, this paper elaborates on the management of order allocation under the MCLS environment and the influences caused by allocation results. This paper provides a theoretical basis for the study of control methods about the performance of order allocation in MCLS. For instance, the type of order allocation method needs to be determined in choosing the FLSP. How will the comprehensive performance of the LSSC improve with minimum cost? How will the contradiction between cost and customization be solved? What factors have more influence on the comprehensive performance of the LSSC? This study provides the necessary theoretical basis for further research in the application and scheduling of the theoretical and empirical studies of LSSC under the MCLS environment.
LSI and FLSP managers should understand the factors that need focus and establish longterm strategic partnership for better cooperation.
The LSI should be careful in order allocation and choose the FLSP with a greater scale effect. The order difference tolerance factor should not be set extremely high. Excessive order difference tolerance results in a higher comprehensive performance of order allocation; however, excessive order difference tolerance can also lead to excessive total LSI cost. The order difference tolerance has a reasonable upper limit. Beyond this upper limit, the comprehensive performance of order allocation and the degree of customized experience initially decrease before increasing with increasing relationship cost coefficient. Therefore, a bigger relationship cost coefficient of the LSI does not indicate a better setup; instead, we should consider both the customized degree of the target and comprehensive performance.
For FLSP, the scale effect coefficient is important to enterprises for order allocation. Thus, enterprises need to improve largescale operational capabilities continuously. When the comprehensive performance is high, FLSPs should request for cost compensation from the LSI by increasing the relationship cost coefficient, which improves FLSP satisfaction.
Under increasing customer demand for specialized and customized logistics services, the competitiveness of the LSSC depends on its ability to meet the need for customized requirements with the operating expense of mass service. The satisfaction of FLSPs will be maximized by minimizing the total cost of the LSI and maximizing the customized degree for customer demand. This paper establishes an order allocation model for the LSSC that is constrained by meeting the demand, CODP, and order difference tolerance coefficient. Numerical analysis is conducted with Lingo 12 software. The influences of the scale effect coefficient, order difference tolerance coefficient, and relationship cost coefficient on the order allocation and comprehensive performance of the LSSC are discussed. Management insights are also proposed.
However, the order allocation model also has some disadvantages. For instance, we assumed that each service process in order allocation requires only one service capability, such as transport service capability. In many cases, logistics service is the combination of different services including transportation, storage, loading, and unloading; each process may also be supported by a variety of capacities. The equivalent problem of each capacity needs to be considered in detail in the future. We also assumed that customer demand and service capacity are stable; however, customer demand may be random in actuality. Order allocation with random customer demand is an important direction for future studies. Furthermore, we assumed that the members of the LSSC have longterm cooperation, transparency, and mutual trust. In reality, we need to consider the game behavior among members because of its influence on order allocation. These problems need to be explored in detail in future research.
This research is supported by the National Natural Science Foundation of China (Grant no. 71372156), supported by Humanity and Social Science Youth Foundation of the Ministry of Education of China (Grant no. 2013YJC630098), and sponsored by China State Scholarship Fund and Independent Innovation Foundation of Tianjin University. The suggestions of the reviewers are also gratefully acknowledged.