I Introduction
Order scheduling is a key decisionmaking problem in supply chain management of manufacturing industry, and plays an important role in rational resource allocation and utilization, which makes the companies more competitive in the global market [1, 2]. As a typical representative of laborintensive industries, the fashion industry is characterized by short product life cycles, volatile customer demands, rising labor costs, tremendous product varieties, and long supply processes [3]. Therefore, more attention needs to be paid to fashion order scheduling problems.
For the past few decades, order scheduling problems in the fashion industry have been widely investigated [4, 5, 6]. In these researches, it is assumed that all the orders are ready for production when the production process begins. However, in realworld apparel production, a whole series of activities need to be carried out before an order can be put into production. And these activities are known as preproduction events. In the fashion industry, preproduction events include order fabric, order trims, sample approval, issue markers, and so on [7]. Multiple parties, e.g., suppliers, manufacturers, and customers, need to collaborate with each other to complete a preproduction event. And negotiations among them are timeconsuming and uncertain, which results in the late completion of some preproduction events and the delay of producing the related order [8]. In addition, industrial data concerning the preproduction process are difficult to collect. Therefore, preproduction events have been largely overlooked in the order scheduling research of the fashion industry, which makes the first incentive of this paper.
In recent years, as a powerful optimization tool [9, 10], evolutionary algorithms (EAs) have been introduced to solve the order scheduling problems in the fashion industry [6, 11]. In the studies above, when the schedules were made before the real production, it was assumed that the daily production quantity of each order was fixed. However, in most realworld manufacturing environments of the fashion industry, order scheduling is an ongoing reactive process in which the occurrence of various unexpected disruptions are usually inevitable [12]. These disruptions consist of machine breakdown, operator absenteeism, and so on. Therefore, the daily production quantity of each order is not always as expected in the production process. As a result, the preestablished order schedules are shifted very often after the production starts. However, a frequent modification of order schedules will increase labor and time cost, which may reduce production efficiency and fail to complete the orders before their delivery dates. Therefore, the second incentive of this paper originates from considering robust order schedules, which are not sensitive to the variation of the daily production quantity during the process of the real production.
For the past decade and more, robust optimization has gained increasing attention, and has been incorporated into the framework of singleobjective evolutionary optimization [13, 14]. In the context of multiobjective optimization, Deb and Gupta [15] did some pioneering works by suggesting two different ways of introducing robustness in multiobjective optimization. Up to now, the integration of robust multiobjective optimization and MOEA has been gradually applied to deal with a variety of applications, such as the welded beam design problem [15]
and the controllability of complex neuronal networks
[16]. Therefore, in searching for a candidate for robust order schedules in the fashion industry, robust MOEA can be selected as a promising one.Based on the above discussion, in this paper, robust order scheduling is presented via introducing robust multiobjective optimization into order scheduling problems in the fashion industry. In addition, the preproduction events in apparel manufacturing are also taken into account and the order scheduling problem is modelled as a multiobjective optimization problem. A MOEA called nondominated sorting adaptive differential evolution (NSJADE) is utilized to search the order schedules in the fashion industry that achieve the following three objectives: 1) the schedules can minimize the total preproduction event clashes of all orders; 2) the schedules can minimize the total tardiness of all orders; 3) the schedules are not sensitive to variation of the daily production quantity during the process of real production. The contributions of this paper are mainly threefold: 1) to the best of our knowledge, it is the first attempt in which the preproduction events are considered for the order scheduling research in the fashion industry; 2) robust order schedules are obtained with the aid of robust multiobjective optimization combined with NSJADE; 3) compared with the results obtained by adaptive differential evolution (JADE) and NSJADE without uncertainty, it is revealed that the preproduction events and the existence of the uncertainties in the daily production quantity heavily affect the order scheduling.
Ii Problem Description and Formulation
In this section, the problem of robust order scheduling is described in detail. The settings of the notations and the variables considered in the problem are on the basis of a business software called Fast React [17], which is specifically for the fashion industry.
[Ca]the day when making the order schedule[Cb]the day when the production begins[Ac]mthe number of production lines[Ad]the ith production line ()[Be]nthe number of production orders[Bf]the jth production order ()[Cg]pthe number of product types[Ch]the lth product type ()[Ai]efficiency of production line for producing order of type [Aj]capacity minutes per day of production line [Bk]product type of order [Bl]quantity of order [Bm]standard minutes per piece for order [Bn]scheduled starting date of order [Bo]scheduled finishing date of order [Bp]due date of order [Bq]present conservative starting date of order [Br]the number of the preproduction events of order [Bs]offset days of the kth preproduction event of order ()[Bt]indicates if the kth preproduction event of order is finished on day , ; otherwise, ().[Cu]a function indicating the learning curve of product type [Bu1]efficiency of producing order [Cu2]consecutive days of producing order of type [Bu3]production time of order [Bu4]time needed to reach the next efficiency level for order [Bu5]time spent on order on the current efficiency level[Bu6]quantity of order completed on the dth day of processing ()[Bu7]total quantity of order completed from the first day till the dth day of processing ()[Bv]qthe number of suborders of order [Bw]the rth suborder of order ()[Bx]split percentage of order [Cy]uncertainty factor of daily production quantity
Iia Problem Description
The robust order scheduling problem in the fashion industry considers m production lines and n production orders, and n orders are assigned to appropriate lines for production. An illustration is displayed in Fig. 1. In Fig. 1, the order bar represents the duration of producing the related order.
IiA1 Description of Production Line
The production lines consist of a number of different machines so that a garment can start at the top of the line in its cut state and come off the line once completed. On a production line, the machines are responsible for cutting, embroidery, printing, sewing, pressing and packing, respectively; and sewing is the most timeconsuming process. Since we focus on the impact of preproduction events and the uncertainties in the daily production quantity on the fashion order scheduling, to simplify the problem, we only consider the sewing process during the production in this paper, instead of all the processes. In this research, production lines are productspecific lines, which means that the line’s efficiency is lower than its peak when there is a mismatch of product to production line.





100  80  6720  
80  100  6240 


Quantity 

Due Date 


Skirts  870  6  10  14.20  
Blouses  800  0  11  18.20 
An example is provided in Table I to better illustrate the production lines. In Table I, the second and third columns show that is blousespecific line and is jacketspecific line; if a mismatch of product to production line occurs, the efficiency and will reduce to 80%. The last column gives the value of “capacity minutes per day” of each line: and .
IiA2 Description of Production Order
Each order has five attributes, and an example of 2 production orders is given in Table II. For convenience, the day when the production begins is set as day 0 in this paper. “Present conservative starting date” denotes the earliest safe starting date of this order’s production, which is determined by its preproduction events which have not been finished on the day . More details of this attribute will be explained in Section IIB. “Due date” shows when the order needs to be completed and delivered to customers. The attribute “standard minutes per piece” is used to represent the workload of the sewing process of each order. In this research, orders can be split into q suborders, with the purpose of realizing flexible production; the split percentage is denoted by .
IiA3 Description of Other Notations
When a new type of product is introduced into production, it takes a period of time for the operators on the production line to get familiar with the production of this type of product. As time goes on, the efficiency of the production line improves day by day as the operators become more familiar with the product and any new manufacturing techniques or skills required. The increase of the efficiency during this period of time is illustrated by “learning curve” (), which is specific according to different types of the product (see Fig. 2). The uncertainties in the daily production quantity are taken into account in this paper, and represented by .
In the following, the problem formulation is separated into two parts, in which the critical variables concerning preproduction events and the uncertainties in the daily production quantity are explained in detail, respectively.
IiB Variables Regarding PreProduction Events
In the robust order scheduling problems, four variables are related to the launch and the termination of order ’s production. They are present conservative starting date of order (), due date of order (), scheduled starting date of order (), and scheduled finishing date of order (). Among them, is one of the attributes of order , and and of can be obtained when the schedule is made. For , the value is determined by ’s preproduction events which have not been finished on the day . In the following, the process of calculating is introduced in detail.
Event Name  Offset Days  Event Name  Offset Days  
PO Receive  60  PO Receive  60  
Order Fabric  55  Order Fabric  55  
Order Trims  40  Order Trims  40  
Lab Dip Submit  35  Lab Dip Submit  35  
Lab Dip Approval  20  Lab Dip Approval  20  
Sample Approval  15  Sample Approval  15  
Fabric Receipt  10  Fabric Receipt  10  
Issue Markers  7  Issue Markers  7  
Trims Receipt  7  Trims Receipt  7  
Factory PP Meeting  7  Factory PP Meeting  7 
For each order, a whole series of activities need to be accomplished before the order can be put into production, and these events are called preproduction events. According to preproduction events, we can set up a timetable, which contains the name of events and the number of working days before the start by which each event needs to be finished. An illustration is displayed in the left half of Table III. Some of the events are closely linked, like “Lab Dip Submit” and “Lab Dip Approval”, and the only requirement to begin “Lab Dip Approval” is that “Lab Dip Submit” must be finished.
As it gets closer to the start of the production, the progress of the preproduction events will be updated. The right half of Table III shows the updated progress of the preproduction events of order when the schedule is made 14 days before the production, i.e., . The unfinished preproduction events of are highlighted in grey background. And the earliest safe starting date of producing is determined by the unfinished preproduction event with the largest offset days, e.g., “Sample Approval” in Table III. In this case, the earliest safe starting date of producing should be , since . The method of calculating is concluded below.
For order , assume that th preproduction event is not completed and has the largest value of , that is, , where . Therefore, order ’s present conservative starting date is calculated as follows:
(1) 
To sum up, the introduction of preproduction events influences the order’s present conservative starting date, which mainly depends on the order’s preproduction events which have not been finished on the day when making the order schedule.
Remark 1.
In this research, preproduction events are integrated in the scheduling as one of the objective functions. We didn’t consider preproduction events as individual tasks, which was the research paradigm mentioned in other preparation actionrelated works [18, 19]. The reasons can be summarized as follows: 1) In the manufacturing environment of the fashion industry, production is closely related to preproduction events, and an order cannot be put into production until its preproduction events are all completed; 2) In a schedule, preproduction events influence the starting date of an order, while production determines the finishing date of this order. In addition, the negotiations among multiple parties to fulfill an order’s preproduction events are timeconsuming and uncertain, which means some events cannot be completed as originally planned. Therefore, a dynamic adjustment of the schedules can be made in terms of the realtime updating information of preproduction events, which is a prominent advantage of integrating preproduction events in the order scheduling. Furthermore, in Section IVB, we will discuss the difference between integrating and not integrating preproduction events in the scheduling. The experimental results will show that preproduction events are closely linked with the production, and have an important impact on the scheduling problems in the fashion industry.
IiC Variables Regarding the Uncertainties in the Daily Production Quantity
In a schedule, when order is assigned to production line , ’s scheduled finishing date can be computed in terms of its scheduled starting date and its production time . In this research, the uncertainties in the daily production quantity are taken into consideration, hence the calculation of is related to , which is the uncertainty factor of daily production quantity.
indicates the date when the production of order is completed, which is planned in the order schedule made on the day . If order is not split into suborders during the production, is the date when the production of ends:
(2) 
If order is split into q suborders, represents the ending date of producing suborder () in the schedule, where is the last suborder to be finished among all the q suborders:
(3) 
In the following, we will explain how to calculate the production time of an order. The calculation process is the same no matter whether the order is split. Therefore, we take order as an example, which is not split during the production. Assume that order is of type and processed on production line , and here we have the procedure to calculate .
First, determine the efficiency of producing order : and the time needed to reach the next efficiency level for order : . There are two circumstances:
1) If is not the first order processed on production line , we assume that the order processed right before is order (). If is of the same type as , the consecutive days of producing order of type : can keep accumulating instead of reinitialization. Then can be computed according to . can be obtained by the following equation:
(4) 
where is the time spent on order on its current efficiency level.
2) If is not the first order processed on production line and is of the different type from , or is the first order processed on production line , is reinitialized as 1, and can be obtained according to . is initialized as .
Second, after having the values of and , we can calculate the value of according to Algorithm 1. In Algorithm 1, Line 11 shows how to calculate the quantity of completed on the dth day of processing , which reflects the impact of considering the uncertainties in the daily production quantity. The while loop terminates when the total quantity of order completed from the first day till the dth day of processing reaches the quantity of . Finally, and can be obtained.
In a word, the uncertainties in the daily production quantity affect the total processing time of the order, and hence has an impact on the order’s scheduled finishing date.
IiD Objective Functions
The investigated order scheduling problem aims at making robust order schedules in the fashion industry, in which the preproduction events and the uncertainties in the daily production quantity are both considered. In a schedule, if an order is assigned to be processed on a day before the present conservative starting date, we say that a preproduction event clash occurs; if the order is scheduled to be completed after its due date, we say that there is tardiness for delivering this order. Therefore, we have two objectives for the robust order scheduling problems: 1) minimizing the total tardiness of all orders; 2) minimizing the total preproduction event clashes of all orders. In the following, these two objectives are introduced in detail.
The first objective is to minimize the total tardiness of all orders, which is described as follows:
(5) 
where and are due date and scheduled finishing date of order , respectively. is defined as follows:
(6) 
The second objective is to minimize the total preproduction event clashes of all orders, which is expressed as follows:
(7) 
where and are scheduled starting date and present conservative starting date of order , respectively. These two objectives determine the assignment of all the orders on the production lines, and they conflict with each other, which means the solution leading to a smaller (less total tardiness) can cause a larger (more total preproduction event clashes). Therefore, the robust order scheduling problem in the fashion industry can be modelled as a multiobjective optimization problem.
Remark 2.
In apparel manufacturing, the production starts as planned even if some preproduction events of individual orders have not been completed, which is realized by moving the orders with unfinished preproduction events to the later stage of the production, and arranging the orders of which the preproduction events are all finished to be produced at first. Fashion order scheduling problems depend much on the progress of the orders’ preproduction events. It is quite often that some events of individual orders may fail to be finished as planned since the negotiation process is full of uncertainty. On the basis of our experience in the fashion industry, the schedules are made a period of time preceding the production, and we keep modifying these schedules according to the dynamic updating information of the preproduction events as production approaching. It is worth mentioning that the intention of this paper is to provide the planners with early warnings of the orders with unfinished preproduction events. And by means of evolutionary algorithms, the schedules can be promptly and intelligently updated.
Iii Robust MultiObjective Optimization for Order Scheduling Problems in the Fashion Industry
In this section, the concept of robust multiobjective optimization is first provided. Then a nondominated sorting adaptive differential evolution (NSJADE)based optimization process is proposed to obtain the robust order schedules in the fashion industry.
Iiia Robust MultiObjective Optimization
As stated in [15], robustness is introduced in multiobjective optimization by means of optimizing the mean effective objective functions instead of optimizing the original objective functions. Hence the robust multiobjective optimization problem can be formulated as follows:
Problem: A solution is called a multiobjective robust solution, if it is the global feasible Paretooptimal solution to the following multiobjective minimization problem (defined with respect to a neighborhood of a solution x):
(8) 
where is defined as follows:
(9) 
is a neighborhood of a solution x, is the hypervolume of the neighborhood; is the feasible decision space,
is a decision vector, and
D is the dimension size, representing the number of the decision variables involved in the problem; are mean effective objective functions for optimization. An illustration of robust solutions is given in Fig. S.1 in the supplementary file.For the robust order scheduling problem in the fashion industry, we set . And is provided in Eqs. (5) and (7), hence the objectives of the problem in this paper are transformed into: and . For the calculation of , a practical way is to generate a finite set of solutions in a randomly or structured manner, which are selected around a neighborhood of a solution x in the decision space; then the value of the mean effective objective function can be calculated by averaging the function values of the neighboring solutions.
IiiB NSJADEBased Optimization Process
NSJADE is developed based on two EAs: nondominated sorting genetic algorithmII (NSGAII)
[20] and adaptive differential evolution (JADE) [21], which aims to combine the advantages of these two EAs. In this research, NSJADE serves as the optimization tool in the optimization process, and the flowchart of NSJADE is provided in Fig. 3. In the optimization process, there are three important issues we need to elaborate: encoding scheme, population initialization, and evaluation of the population. Encoding scheme and population initialization are related to the first box (marked by ) of the flowchart in Fig. 3, evaluation of the population is related to the second and eighth boxes (marked by ) of the flowchart in Fig. 3.IiiB1 Encoding Scheme
The first step of the optimization process is to encode potential order scheduling solutions into individuals. In this research, a feasible solution needs to be able to determine the assignment of each production order to a proper production line. In addition, an individual should reflect the split information of each order, and the sequence of the orders on the same production line. Hence, each individual consists of three parts: the assignment of each production order to the production line, the split information of each order, and the sequence of the orders on the same production line. Since this research focuses on the impact of preproduction events and the uncertainties in the daily production quantity on the fashion order scheduling, to simplify the problem, we assume that during the production process, each order can be divided into at most two suborders. Therefore, the length of an individual is four times the number of the orders: . Fig. 4 illustrates the encoding of the individual. In Part A, every two bits represent the production lines to which the suborders of an order are assigned; and the length of Part A is . In Part B, each single bit denotes the split percentage of every order; the length of Part B is . Part C assigns the label to each order, which determines the sequence of the orders on the same production line; the length of Part C is also .
Remark 3.
We assume that each order can be divided into at most 2 suborders, hence the dimension size of the individual equals . If we assume that an order can be split into suborders (), then . As the value of increases, which leads to the increase of , more individuals and generations are needed for NSJADE to search for the optimal schedules. When increases to vastly more than 100 [22], in order to have efficient performance, evolutionary algorithms specially designed for highdimensional optimization problems are required. On the other hand, it is worth pointing out that learning curve is one of the factors that affect the efficiency of apparel manufacturing. If an order is split into fewer suborders, the quantity of each suborder will increase, which means the operator will repeat the production process of this product for more times, and hence improves the production efficiency. In addition, the increase in the number of suborders will raise the likelihood that the neighbouring orders on the same production lines are of different product types, which then lowers the production efficiency and increases the production time of the orders. Therefore, it is not often to split an order into many suborders in apparel manufacturing.
IiiB2 Population Initialization
For the initialization of Part A, uniform random integers in the range are assigned to each dimension of Part A, where is the number of the production line. It is worth noting that if certain lines cannot absolutely accept one type of product (the efficiency is 0 for this type of product), then related modifications should be made when initializing Part A. For example, if order 1 cannot be processed on line (), then the first two bits of the encoding will be initialized in the range . Part B shows the split percentage of each order. To simplify the optimization process, each bit of Part B is selected from in a uniformly random way. For initializing Part C, each bit is assigned with a uniform random integer in the range , where is the number of the production order.
IiiB3 Evaluation of the Population
After the population initialization, the fitness value of each individual needs to be evaluated. To calculate , neighbouring points will be selected around the individual within a predefined range. As illustrated in Section II, is set as the uncertainty factor of daily production quantity . The detailed evaluation process of each individual is explained as follows, which involves six steps:

Split the orders according to the split percentage in Part B of the individual’s encoding if the orders are scheduled to be processed on different production lines in terms of Part A.

According to Part A of the individual’s encoding, assign the orders or suborders to the production lines.

Based on Part C of the individual’s encoding, determine the sequence of the orders assigned to the same production line.

Generate neighbouring points of the individual, and for each neighbouring point, the daily production quantity can be calculated according to Line 11 in Algorithm 1.

Average the fitness values of neighbouring points, and the fitness value of the individual can be obtained.
After initializing the population, several operations, e.g., fast nondominated sorting, crowdingdistance assignment, mutation, crossover, and selection, are carried out. Because of the page limit, the details of these operations are provided in Section S.I in the supplementary file.
Iv Experiments and Analysis of Results
Iva Experimental Information
Because of the page length limit, the experimental setup, including the information of the test data, the production orders and the production lines, is given in Section S.II in the supplementary file.
The parameters of NSJADE are set as the same as that in JADE [21]. It should be mentioned that in this paper, we do not use the maximum number of function evaluations (MAX_FES) to control the termination of NSJADE. The reason is that MAX_FES relies on the value of , which is the number of the neighbouring points of an individual. In order to simply control the termination of NSJADE, we utilize the maximum number of generations as the stopping criterion; and , where D is the dimension size, and is a predefined parameter, which controls the evolution generations of the algorithm. The value of is set as 10 according to the analysis in Section S.III in the supplementary file. It is noticed that a differential evolution (DE) algorithm is employed as the search engine of NSJADE; and the population size of DE is recommended as [23]. Therefore, the population size in this paper is , since the dimension size of the problem investigated is 80. In the experiments, the number of the neighbouring points is set as , and the uncertainty factor of daily production quantity is set as . NSJADE is conducted for 30 runs for eliminating discrepancy, and the PFs are sorted out from the solutions obtained after 30 runs.
IvB Significance of Considering PreProduction Events
One of the contributions of this research is to consider preproduction events when making the order schedules in the fashion industry. In this subsection, two groups of experiments will be conducted to show the significance of including preproduction events.
In the first group of experiments, no preproduction events are taken into consideration, which means that all the orders are ready for production after the production begins. The problem is transformed into a singleobjective optimization problem, and the only objective is to minimize the total tardiness of all orders. A singleobjective evolutionary algorithm JADE [21] is utilized to search the optimal solution for order schedules. We provide the evolution of the average objective value of 30 runs for the problem optimized by JADE when preproduction events are not considered in Fig. 5. From Fig. 5, it shows that the average objective value drops to 0 within 800 generations, which means that the schedule with no tardiness can be achieved easily.
In the second group of experiments, preproduction events are taken into consideration when making the schedule. In realworld production, based on the progress of the orders’ preproduction events, planners make or update order schedules at regular intervals before the production begins. Here, we make the schedules 3 days, 7 days, and 14 days before the production begins, i.e., , , and . The problem is optimized by our proposed NSJADE and the PFs are provided in Fig. 5. The results illustrate that the schedule with no preproduction event clashes and no tardiness cannot be obtained when preproduction events are included in the order scheduling. Therefore, it can be concluded that preproduction events have a big impact on the order scheduling problems in the fashion industry, which should not be neglected in the research of order scheduling.
Remark 4.
In multiobjective optimization, the ideal solution denotes an array of the lower bound of all objective functions (for minimization problems) [24]. For the order scheduling problem in this paper, the ideal solution is , which means the total tardiness of all orders is 0, and the total preproduction event clashes of all orders are also 0. However, in realworld production, multiple parties, e.g., suppliers, manufacturers and customers, need to collaborate with each other to complete a preproduction event. And negotiations among them are timeconsuming and uncertain. In addition, some events are closely linked, which means the only requirement to begin an event is that another event must be finished. Therefore, when making the schedules, the total preproduction event clashes of all orders mostly exist. If we hope that the total clashes become 0, we need to postpone the starting date of certain orders when making the schedule. However, the late starting date of an order might defer its finishing date, which means the tardiness happens. So minimizing the total tardiness of all orders and minimizing the total preproduction event clashes of all orders are two conflicting objectives of the order scheduling problem in this paper. By means of NSJADE, a set of schedules which balance these two objectives can be provided.
Remark 5.
It is worth mentioning that the PFs obtained by NSJADE when , , and are different. This is because the progress of the orders’ preproduction events will keep updating when the production is approaching, which then influences the present conservative starting date of each order. According to Eq. (7), is involved in the second objective; therefore, the PFs obtained by NSJADE may be in different shapes when is distinct. From Fig. 6, it can be noticed that the robust PF for offers the best result when compared with those for and ; and the higher flexibility (i.e., ) in the time schedule does not increase the chance for less clashes, which is also caused by the present conservative starting date of each order. By taking a look at , , , , , , and in Table S.1 in the supplementary file, we find that for (i.e., 6 or 11) is larger than that for (i.e., 0) and (i.e., 0). This means these orders can only be arranged on the 6th or 11th day after the production begins when , and we can say more restrictions are put on scheduling these orders, which also explains why the higher flexibility in the time schedule does not decrease the clashes.
IvC Comparison of NonRobust and Robust Order Schedules by NSJADE
In this subsection, we will compare the nonrobust and robust order schedules obtained in the experiments, which aims at illustrating the significance of introducing robust multiobjective optimization into order scheduling problems in the fashion industry. The results are provided in Fig. 6.
From Fig. 6, it can be observed that the schedules obtained achieve a balance between the two objectives, i.e., minimizing the total tardiness of all orders and minimizing the total preproduction event clashes of all orders. The results unveil that when the daily production quantity is not fixed, which means the uncertainty is considered, the PFs shown in Fig. 6 are different from that of the cases without uncertainty. The differences appear due to the uncertain daily production quantity during the production. In robust schedules, the daily production quantity of each order is allowed to vary within a certain range, and we can say that the robust schedules (represented by blue triangles in Fig. 6) are less sensitive to the variation of each order’s daily production quantity. As a result, robust order schedules can be shifted less often after the production starts than nonrobust ones, which saves labor cost and enhances the production efficiency. In addition, based on the robust order schedules obtained by NSJADE, planners can pay close attention to the unfinished preproduction events as early as possible.
In the following, we randomly take two of the Pareto solutions obtained in a nonrobust scenario as an example, and investigate how the solutions will be affected when uncertainty is considered. In detail, a solution A ( in the objective space) and a solution B ( in the objective space) are selected from the PF by NSJADE without uncertainty in Fig. 6(c). The schedules represented by solution A and solution B are marked as schedule SA and schedule SB. To save space, we only list the details of schedule SA in Table IV; the details of schedule SB are provided in Table S.3 in the supplementary file. The figures in the parentheses denote the specific order size after order split.

Order Assignments  
1 


2 


3 


4  , , ,  
5  , , , ,  
6  , , , 
In schedule SA, three orders have the preproduction event clashes or delay in delivery: starts 1 day earlier before all the preproduction events are ready, and finishes 1 day later than its due date; and start 3 days and 1 day earlier before all the preproduction events are ready, respectively. When uncertainty is introduced into schedule SA, we calculate the updated objective values , and get . Three more orders (i.e., , and ) encounter the delay in delivery besides , which leads to the increase of the first objective. Similarly, when uncertainty is introduced into schedule SB, we get . One more order (i.e., ) encounters the delay in delivery besides and , which leads to the increase of the first objective. It can be observed that uncertainty has a big impact on the schedules, and it is meaningful to consider uncertainty in the order scheduling problem.
IvD Effect of on Robust Order Scheduling
In this subsection, we will study the impact of the uncertainty factor on robust order scheduling. is set as , and the results are displayed in Fig. 7. From Fig. 7, it can be observed that as increases, the shift in the PF moves away from the original PF, i.e., uncertainty is not considered. This phenomenon is natural, since the increase of brings more uncertainties in the daily production quantity of each order, which then causes more differences from the original PF.
IvE Effect of on Robust Order Scheduling
As described in Section IIIA, neighbouring solutions are selected to compute the mean effective objective function . Intuitively, if more neighbouring solutions are chosen, the objective values will be closer to the true average values. In the previous experiments of this section, the value of is set as 5. Here, we depict the effect of using different values of on robust order scheduling problems in Fig. 8. From Fig. 8, it can be observed that despite having a smaller computational time, the mean effective front using a small overestimates the true robust front. However, compared with , the mean effective front with can also achieve a satisfactory approximation. In applications, users can choose the values of according to practical situations.
IvF Comparison with Existing MOEAs
In order to show the superiority of NSJADE, we compare it with two existing MOEAs: NSGAII [20] and NSCDE [25]. The results are given in Fig. 9. In this paper, the computational resource is limited (i.e., 800 generations) for each algorithm. Therefore, we need to select the algorithm that is powerful enough to obtain the solutions with better convergence performance with limited computational resource. It can be observed from Fig. 9 that in a fixed period of searching, the solutions obtained by NSJADE have the best convergence performance among the three MOEAs; and we give the credit to the powerful search engine of NSJADE: JADE. Furthermore, since boundary points are one of the most important components in a PF, in Table V, we record the mean values of the boundary points of the PF obtained in each run of 30 runs for these three algorithms. The nondominated boundary points are highlighted in gray background. According to the results in Table V, it can be figured out that, 1) all the boundary points in the PF obtained by NSJADE are nondomindated; 2) only half of the boundary points in the PF obtained by NSCDE are nondominated; 3) and none of the boundary points in the PF obtained by NSGAII are nondominated, which also means that NSJADE offers the best results when compared with NSGAII and NSCDE.
boundary point 1  boundary point 2  
NSGAII  8.34.4  12.44.2  23.25.3  0.20.5 
NSCDE  0.70.7  14.54.2  17.33.2  0.00.0 
NSJADE  0.91.3  10.82.6  12.52.3  0.00.0 
boundary point 1  boundary point 2  
NSGAII  7.53.8  6.93.1  19.06.0  0.00.0 
NSCDE  0.10.4  7.53.0  9.22.6  0.00.0 
NSJADE  0.40.8  5.01.7  6.21.9  0.00.0 
boundary point 1  boundary point 2  
NSGAII  9.85.0  14.15.8  31.216.1  1.20.9 
NSCDE  0.20.5  12.93.4  21.69.7  0.00.2 
NSJADE  1.51.7  9.13.2  15.88.3  0.00.2 
The experiments were carried out on a PC with i7 Processor 3.60GHz CPU and 8GB RAM. The processing time of each generation is 2 seconds, and it takes around 26.67 minutes each run for generating robust order schedules. It is worth mentioning that order scheduling is performed before the production, which can be regarded as an offline scheduling. In addition, if highperformance computers and parallel computing are introduced to make the schedules in the factory, the scheduling time will further reduce. Meanwhile, intelligent order scheduling requires less manpower and fewer resources, which also saves the cost and increases the efficiency.
V Conclusion
This paper has tackled the robust order scheduling problems in the fashion industry, which is a significant component of flexible and intelligent supply chain management. Preproduction events in apparel manufacturing are taken into account for the first time, hence the order scheduling problem is modelled as a multiobjective optimization problem. In addition, in this paper, the daily production quantity of each order is assumed to be uncertain according to most realworld manufacturing environment of the fashion industry, which makes the problem into a robust multiobjective optimization problem. And NSJADE is utilized to search the robust order schedules.
A set of experiments have been carried out. The observations from the experiments show that preproduction events greatly influence the arrangements of the orders in the fashion industry. Moreover, it can be observed that the uncertainty in the daily production quantity of each order has a paramount impact on the order scheduling. The corresponding robust PFs are also provided under various settings of parameters including the uncertainty factor and the number of the neighbouring solutions . It is found that robust order schedules can be shifted less often after the production starts than nonrobust ones, which saves labor cost and enhances the production efficiency. Meanwhile, with the help of robust order schedules, planners can pay close attention to the unfinished preproduction events as early as possible, negotiate earlier with the customers who place the orders about the delay in delivery, or arrange operators to work extra hours for these orders.
References
 [1] H. K. Chan and F. T. Chan, “Early order completion contract approach to minimize the impact of demand uncertainty on supply chains,” IEEE Transactions on Industrial Informatics, vol. 2, no. 1, pp. 48–58, 2006.

[2]
J. Branke, S. Nguyen, C. Pickardt, and M. Zhang, “Automated design of production scheduling heuristics: A review,”
IEEE Transactions on Evolutionary Computation
, vol. 20, no. 1, pp. 110–124, 2016.  [3] A. Şen, “The US fashion industry: a supply chain review,” International Journal of Production Economics, vol. 114, no. 2, pp. 571–593, 2008.
 [4] Z.L. Chen and G. Pundoor, “Order assignment and scheduling in a supply chain,” Operations Research, vol. 54, no. 3, pp. 555–572, 2006.
 [5] Z. Guo, W. K. Wong, Z. Li, and P. Ren, “Modeling and pareto optimization of multiobjective order scheduling problems in production planning,” Computers & Industrial Engineering, vol. 64, no. 4, pp. 972–986, 2013.
 [6] W.K. Wong, Z. Guo, and S. Leung, “Intelligent multiobjective decisionmaking model with RFID technology for production planning,” International Journal of Production Economics, vol. 147, pp. 647–658, 2014.
 [7] T. Hines and M. Bruce, Fashion marketing. Routledge, 2007.
 [8] Are you in control of your preproduction process? http://icm.fastreact.com/assets/case_studies/pdfs/Management_Brief_Ref_Fast_React_Evolve_Management_Brief_Critical_Path_2015.pdf, 2015.
 [9] A. E. Eiben and J. Smith, “From evolutionary computation to the evolution of things,” Nature, vol. 521, no. 7553, pp. 476–482, 2015.
 [10] W. Du, S. Y. S. Leung, Y. Tang, and A. V. Vasilakos, “Differential evolution with eventtriggered impulsive control,” IEEE Transactions on Cybernetics, vol. 47, no. 1, pp. 244–257, 2017.
 [11] Z. Guo, W. K. Wong, and S. Leung, “A hybrid intelligent model for order allocation planning in maketoorder manufacturing,” Applied Soft Computing, vol. 13, no. 3, pp. 1376–1390, 2013.
 [12] D. Ouelhadj and S. Petrovic, “A survey of dynamic scheduling in manufacturing systems,” Journal of Scheduling, vol. 12, no. 4, pp. 417–431, 2009.
 [13] Y. Jin and B. Sendhoff, “Tradeoff between performance and robustness: an evolutionary multiobjective approach,” in Evolutionary MultiCriterion Optimization, pp. 237–251, Springer, 2003.
 [14] Y. Jin and J. Branke, “Evolutionary optimization in uncertain environmentsa survey,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 3, pp. 303–317, 2005.
 [15] K. Deb and H. Gupta, “Introducing robustness in multiobjective optimization,” Evolutionary Computation, vol. 14, no. 4, pp. 463–494, 2006.
 [16] Y. Tang, H. Gao, W. Du, J. Lu, A. Vasilakos, and J. Kurths, “Robust multiobjective controllability of complex neuronal networks,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 4, pp. 778–791, 2016.
 [17] Fast React. http://www.fastreact.com/, 2015.
 [18] P. D. Berger, A. Gerstenfeld, and A. Z. Zeng, “How many suppliers are best? A decisionanalysis approach,” Omega, vol. 32, no. 1, pp. 9–15, 2004.
 [19] T. Sawik, “Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. dual sourcing,” Omega, vol. 43, pp. 83–95, 2014.
 [20] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGAII,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002.
 [21] J. Zhang and A. C. Sanderson, “JADE: adaptive differential evolution with optional external archive,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 945–958, 2009.
 [22] R. Cheng and Y. Jin, “A competitive swarm optimizer for large scale optimization,” IEEE transactions on cybernetics, vol. 45, no. 2, pp. 191–204, 2015.
 [23] S. Das and P. N. Suganthan, “Differential evolution: a survey of the stateoftheart,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, 2011.
 [24] K. Deb, Multiobjective optimization using evolutionary algorithms. Chichester, U.K.: Wiley, 2001.
 [25] Y. Tang, H. Gao, and J. Kurths, “Multiobjective identification of controlling areas in neuronal networks,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 3, pp. 708–720, 2013.
Supplementary file
Section Captions

Section S.I The Other Operations

Section S.II Experimental Setup

Section S.III The Effect of on Search Performance
Table Captions
Figure Captions

Fig. S.1 An illustration of robust solutions from decision space to objective space. Solution A is more robust to perturbations in variable than solution B when two objectives and are optimized.

Fig. S.2 The learning curves of producing different products. (a) Leaning curve of producing skirts and pants; (b) Learning curve of producing blouses; (c) Learning curve of producing jackets.

Fig. S.3 The Pareto fronts sorted out from the solutions obtained after 30 runs by NSJADE when .
S.I The Other Operations
Va Fast Nondominated Sorting and CrowdingDistance Assignment
In our proposed NSJADE, we keep the fast nondominated sorting and crowdingdistance assignment, which are two effective mechanisms presented in NSGAII [20]. After evaluating the population, a fast nondominated sorting approach is employed to sort the population into different nondomination levels with a lower computational complexity compared to the traditional approach. For each individual, the average distance of two individuals on either side of this individual is calculated along each of the objectives, and the distance is called crowding distance. When two individuals are in the same nondomination level, the individual with a larger value of crowding distance is preferred. For more details of these two mechanisms, one can refer to [20].
VB Mutation and Crossover
In the developed NSJADE, adaptive differential evolution (JADE) is elected as the search engine instead of the nonadaptive genetic algorithm for promoting both exploration and exploitation abilities of the population. Therefore, the mutation and crossover strategies of JADE are adopted in NSJADE. One can refer to [21] for the details of the mutation and crossover strategies.
VC Selection
In a single evolution, after each individual in the parent population goes through the mutation and crossover, a new generation needs to be selected from the combined parent and the offspring population. It is assumed that the parent population contains NP individuals; therefore, after a single evolution, there are 2NP individuals (each parent generates one offspring) in the candidate pool. Sort the population into different nondomination levels by means of the fast nondominated sorting approach, and calculate the crowding distance of each individual. The individual with the lower nondomination level and the larger crowding distance is preferred. By following this principle, NP individuals are chosen from the candidate pool as the population of a new generation.
S.II Experimental Setup
In this section, we introduce the experimental setup in detail, including the information of the test data, the production orders, and the production lines.
A. Test Data Information
The test data used for the following experiments are gathered from a business software called Fast React [17]. Fast React is a production planning software for the fashion industry, which considers practical factors in realworld production, like preproduction events, learning effect, etc. And Fast React has been used by global fashion brands, high street retailers and prominent worldwide manufacturers involved in the real business ranging from carpet, cloth and lace weavers to shoe manufacturers, clothing companies and so on. Fast React provides a lot of industrial data collected from their customers, and the data include different types of orders and production lines as well as the information of the orders’ preproduction events. Therefore, these data are utilized as the test data in our research.
Although Fast React simulates the real production in the fashion industry, it does not consider various uncertainties that exist in the realworld manufacturing. In addition, planners are required to manually place the orders on the production lines in Fast React, which is not intelligent. In this research, uncertainties in the daily production quantity are taken into consideration; and robust order schedules can be intelligently obtained by using our proposed NSJADE.
B. Production Order Information
Each production order has five attributes: product type, quantity, present conservative starting date, due date, and standard minutes per piece of this order. The detailed descriptions of each attribute can be found in Section IIA. There are total four categories of orders: skirts, blouses, pants and jackets. The learning curves of producing these four types of products are collected from Fast React [17] and provided in Fig. S.2 in the supplementary file. A total of 20 orders are collected for the following experiments. The details of these orders are given in Table S.1 in the supplementary file.
C. Production Line Information
As in Fast React [17], there are total 6 production lines considered in the experiments. These production lines are productspecific lines, which means that the line’s efficiency is lower than its peak value when there is a mismatch of product to production line. The details of the production lines are listed in Table S.2 in the supplementary file.
S.III The Effect of on Search Performance
The parameter is to balance the search accuracy and the computational complexity of NSJADE. A large is apt to enhance the search accuracy and result in a heavy computational complexity; a small is able to reduce the computational burdens and may bring about unsatisfactory search performance. Therefore, an appropriate tuning of must be carried out. In this subsection, a set of tests are conducted to select a proper for the order scheduling problem studied in this paper. We make the order schedule 3 days before the production begins, i.e., , and uncertainty is not considered. is set as . The Pareto fronts (PFs) obtained by NSJADE with three different values of are provided in Fig. S.3. It can be observed that the PFs overlap completely when and , and the solutions on these two PFs dominate the solutions on the PF when . As a result, in order to achieve a balance between search accuracy and computational complexity, we select for NSJADE in this paper.


Quantity 

Due Date 


1  Skirts  870  0, 0, 6  10  14.20  
2  Skirts  700  0, 0, 6  7  18.20  
3  Blouses  800  0, 0, 6  11  18.20  
4  Skirts  500  0, 3, 0  9  18.20  
5  Skirts  1000  0, 0, 0  11  16.70  
6  Skirts  1000  7, 3, 0  10  16.70  
7  Blouses  800  0, 0, 6  7  32.20  
8  Jackets  850  12, 8, 1  15  54.60  
9  Skirts  800  0, 0, 6  10  16.70  
10  Pants  780  12, 8, 1  15  34.00  
11  Blouses  1000  0, 0, 11  15  15.00  
12  Jackets  1000  0, 0, 6  8  53.78  
13  Skirts  400  22, 18, 11  24  26.50  
14  Blouses  2000  0, 13, 6  15  12.60  
15  Blouses  1000  4, 0, 0  11  12.60  
16  Jackets  500  12, 8, 1  18  44.10  
17  Skirts  800  0, 0, 11  15  20.55  
18  Skirts  800  0, 0, 0  19  20.55  
19  Jackets  700  0, 18, 11  20  44.10  
20  Blouses  3000  17, 13, 6  19  12.60 






1  100  80  80  6720  
2  100  80  80  6720  
3  80  100  80  6240  
4  80  100  80  6240  
5  80  80  100  6720  
6  80  80  100  6720 

Order Assignments  
1 


2 


3 


4 


5  , , , 
Comments
There are no comments yet.