Service network design and delivery have a wide range of applications, spanning industries such as manufacturing-oriented product service networks, emergency medical services, agricultural maintenance networks, supply chain logistics, and maintenance service networks. In many cases, it becomes crucial to select a group of service providers within the entire network. This problem can be categorized as a multi-objective optimization challenge, for which various AI-based decision tools, particularly artificial neural networks (ANNs), have been employed for classifying schemes. ANN-based approaches have been developed to assess and select potential service providers, determine industrial site locations, and map mineral resources. Furthermore, ANNs can be integrated with fuzzy logic, and a two-phase decision model can be used to select suppliers in third-party reverse logistics networks. Additionally, an adaptive neural network-based decision support system, combined with fuzzy expert-designed rules, can be utilized to select the optimal industrial site.
A flexible fuzzy-neuro approach can be developed to select service providers for building a maintenance service network and for making the final decision. To assist managers in making informed choices, it's crucial to categorize the set of potential schemes and narrow them down to a manageable number of carefully selected alternatives through decision-making methods.
PROCESS:
The process begins with identifying the evaluation criteria. Relevant data must be gathered to meet the basic requirements based on these criteria.
The second step, which is the most critical, involves classifying the set of service providers and presenting decision-makers with an optimized set of alternatives.
The final step is the human decision-making phase, where the optimal group is chosen.
For data collection, in certain contexts such as agricultural maintenance service network, geographic information systems (GIS) and enterprise information systems can be utilized to gather the necessary data for evaluation.
During the classification phase, an innovative adaptive fuzzy-neuro approach, informed by expert knowledge, can be employed to aid decision-making. This (combined) methodology can address the challenges of service provider selection and service network construction.
The fuzzy-neuro network integrates the reasoning capabilities of fuzzy logic with the interconnected structure of neural networks, offering an effective approach for a range of applications, including medical services, production scheduling, robotics control, product evaluation, and fault diagnosis. A fuzzy-neuro network is typically structured as a five-layer neural network, as described below:
-
Layer 1 (Input Layer): This layer is responsible for receiving and importing the input samples.
-
Layer 2 (Fuzzification Layer): Here, the inputs are fuzzified using a set of predefined membership functions, transforming crisp values into fuzzy values.
-
Layer 3 (Rule Layer): This layer generates initial rules from the input samples, with each rule corresponding to a neuron in this layer. The connections between the fuzzification layer and the rule layer are determined by these rules.
-
Layer 4 (Defuzzification Layer): In this layer, the fuzzy input is defuzzified, converting fuzzy values back into crisp values.
-
Layer 5 (Output Layer): The final processed results are presented in this layer, providing the output of the fuzzy-neuro network.
Recently, the adaptive fuzzy-neuro network, which combines adaptive techniques with fuzzy logic, has gained widespread application. The Takagi–Sugeno model is often used to represent fuzzy rules in an IF-THEN format. Unlike the standard fuzzy-neuro network, the adaptive fuzzy-neuro network is structured with six layers.
-
Layer 1 (Input Layer): This layer handles the import of sample data into the fuzzy neural network.
-
Layer 2 (Fuzzification Layer): In this layer, input characteristic variables are transformed into fuzzy variables using a set of membership functions.
-
Layer 3 (Rule Layer): This layer contains a set of fuzzy rules, which are expressed according to the Takagi–Sugeno model.
-
Layer 4 (Normalized Firing Strengths Layer): Each rule is evaluated, and normalization is applied to the firing strengths of the rules.
-
Layer 5 (Defuzzification Layer): The fuzzy values generated by the fuzzy inference system are processed. Various methods, such as the mean of maximum method and weighted average defuzzification method, are used to eliminate fuzziness.
-
Layer 6 (Output Layer): This layer has a single node that generates the output data.
A fuzzy reference system is developed to capture and represent the expertise and knowledge of specialists. Meanwhile, an adaptive neural network is used to fine-tune the parameters. The combination of these components enhances both learning accuracy and generalization capabilities.
USE CASE:
The service provider plays a vital role in the maintenance service network, working alongside the manufacturer and the customer to form a comprehensive maintenance service system. Currently, such networks are in place across various industries, including agricultural machinery, the automobile sector, military equipment, and other complex machinery.
Agricultural machinery operates in a dynamic and continuous manner, which makes it prone to failures, particularly during peak farming seasons. Designing the layout for agricultural equipment maintenance services is therefore more complex. To address this, multiple service providers are selected to create a dynamic service network that can deliver cross-regional maintenance for agricultural machinery. This approach aims to enhance equipment reliability and extend the operational lifespan of the machinery.
The challenge of service network design and the joint selection of service providers can be framed as follows:
-
The service area is divided into multiple service districts.
-
Each service district has a set of potential service providers, and only one provider can be chosen per district.
-
The selected service providers together form the maintenance service network.
Given the large number of possible permutations and combinations in selecting service providers for different districts, the potential number of configurations for the maintenance service network can be enormous. As a result, it becomes essential to identify and select the best possible solutions from a vast array of options.
The goal of intelligent decision-making system is to determine the optimal group of service providers to deliver the best maintenance service for customers at the right time in maintenance service network. A set of evaluation criteria have to be defined prior to evaluate prospective service providers in the maintenance service network. These evaluation criteria could include reputation service provider, size and quality of fixed assets, geographic advantage, operational performance, experience in similar products, cost of services, quality of service, long-term relationship, relationship with neighboring service providers, total delivery cost of spare parts, service network construction cost, and geographical spread and range of service provided. Some features, such as cost of services, total delivery cost of spare parts and service network construction cost, are quantitative, while others are qualitative.
These evaluation criteria can be categorized into various clusters. The first cluster focuses on service provider's performance: reputation, size and quality of fixed assets, and geographical advantage. The second measures the business capability of service providers in four aspects: operational performance, experience in similar products, cost of services, and quality of service. The third is related to the manufacturer and whole service network: long-term relationship, relationship with neighboring service providers, total delivery cost of spare parts in the whole network, service network construction cost, geographical spread and range of service provided. Some features are developed to evaluate the service providers in whole service network by evaluating the service provider in each area, and others are defined to assess the performance of all service providers in the service network.
The decision-making methodology can be divided into four stages: generating alternatives and data collection, expert analysis, classification and final decision.
The first step is to generate a set of potential alternatives and assess the value of evaluation criteria for each service provider group. The data collected includes information on the service providers, such as their location, the frequency of failures in the maintenance service network, geographic details, and service-related costs—such as labor, logistics, spare parts, and service station construction costs. Given the complexity and time-consuming nature of this task, data is gathered through various methods, including maps, GPS, computer records, and reports generated by the service providers. Additionally, the list of available service providers in each service area can be sourced from their enterprise information system. Similarly, the evaluation criteria values must be established for expert analysis. For quantitative features, values can be calculated using specific formulas based on the collected data. However, for qualitative features, direct calculation is not possible. In this case, a group of leading experts and relevant stakeholders is consulted to determine the value of these criteria and evaluate each service provider group. The experts are selected from the company’s expert database, and their roles vary from professors and maintenance service center managers to field service managers. The stakeholders involved come from various departments, such as finance, marketing, planning, and human resources.
As a second step, leading experts and stakeholders collaborate to review the information on each service provider and assess the performance of each group. For every service provider group, the decision group analyzes and discusses the available data, ultimately reaching a consensus among all members.
An adaptive fuzzy-neuro-based approach can be used in the third stage to assess the suitability of each service provider group by determining their ranking scores. This classification method combines the adaptive fuzzy-neuro approach with a two-class classification method, where varying ranking scores reflect the differences in the quality of service provider groups within the maintenance service network. Several parameters need to be adjusted in this approach, and the adaptive fuzzy-neuro method is found to be more appropriate than other machine learning techniques, such as the fuzzy inference system (FIS), particularly when sufficient training samples are available. Unlike the fuzzy rules used in FIS, which are typically defined by experts, the adaptive fuzzy-neuro method allows for both offline training and online learning, enabling the system to self-adjust its fuzzy inference rules.
Therefore, a fuzzy-neuro network-based selection system is better suited for evaluating service provider groups with a fuzzy knowledge base and large datasets. Additionally, the two-class classification technique is employed during the classification process, with the advantage of presenting the results in an ordered sequence.
n two-class classifiers can be used, and the method generates return values in a cascading mode, starting with i = 1. The computational process ranks the alternatives into n classes.
The adaptive fuzzy-neuro classifiers can be utilized to compute ranking scores representing the class of each service provider group. These classifiers can be refined using a set of training samples. In this way, the adaptive classification approach integrates FIS with neural networks. The adaptive fuzzy-neuro network consists of six layers, and unlike previous fuzzy-neuro implementations, detailed information for each layer is provided as follows:
-
Layer 1 (Input Layer): The adaptive fuzzy-neuro approach receives a set of evaluation criteria for the service provider group.
-
Layer 2 (Fuzzification Layer): Gaussian membership functions are used to fuzzify the inputs.
-
Layer 3 (Rule Layer): Rules arre defined by the decision group, and fuzzy rules from the knowledge base are applied for approximate reasoning.
-
Layer 4 (Normalized Firing Strengths Layer): The firing strengths are normalized.
-
Layer 5 (Defuzzification Layer): The weighted average defuzzification method is applied in this layer.
- Layer 6 (Output layer): The floating-point data is the output and the range of output is between the values 1 to n. The outputs of the method are rounded to the nearest integers to represent the ranking score of these group.
Adaptive fuzzy-neuro method can be trained in a supervised manner. Least squares estimation method and back propagation approach can be integrated and applied to train the proposed n two-class adaptive fuzzy-neuro classifier.