Maximize Customer Satisfaction Research Articles

Do all complain the same? Examining the role of luxury hotels reviewer attributes using text mining

Purpose Although extant studies found the determinants of customer complaints in luxury hotels, comparison of complaint severity using reviewer characteristics (nationality, reviewer type and expertise) is limited. This study aims to identify the variation in customers’ complaint attributes among different customer categories. Design/methodology/approach This study tried to address this gap by using structural topic modelling and text mining techniques to find the sentiment score, word count, complaints attributes and severity score. The research sample covers luxury hotels in India, and online review text and their associated data has been collected and analysed. Findings The authors found that leisure customers wrote lengthier reviews and complained about the facility-related aspects (valet parking, entertainment and amenities) and food-related aspects (food variety, service and menu) while the business customers’ complaint severity score is more on salient aspects (room size, bathroom and staff related services). The international customers wrote lengthier reviews and complained more about the facility-related aspects (amenities, pick-up and entertainment) and core service offerings (stay experience, bathroom, dirtiness and complaint resolution). Whereas domestic customers complained more about stay-related aspects (food variety, quality, services, staff behaviour and stay experience). Also, this study used correlation analysis to find the dependencies between topics and uncovered underlying patterns, trends and associations, aiding in a deeper understanding and interpretation of the complaints. This study can help service providers tailor their services based on customer attributes to ensure maximum customer satisfaction. Originality/value The findings of this study can enable hotel service providers to customise their services based on customer attributes to reduce dissatisfaction among customers and promote customer loyalty. The findings of this study can also be used to devise differential promotional strategies for different types of customers.

Stage-Specific Multi-Objective Five-Element Cycle Optimization Algorithm in Green Vehicle-Routing Problem with Symmetric Distance Matrix: Balancing Carbon Emissions and Customer Satisfaction

This study presents a bi-objective optimization model for the Green Vehicle-Routing Problem in cold chain logistics, with a focus on symmetric distance matrices, aiming to minimize total costs, including carbon emissions, while maximizing customer satisfaction. To address this complex challenge, we developed a Stage-Specific Multi-Objective Five-Element Cycle Optimization algorithm (MOFECO-SS), which dynamically adjusts optimization strategies across different stages of the process, thereby enhancing overall efficiency. Extensive comparative analyses with existing algorithms demonstrate that MOFECO-SS consistently outperforms in solving the multi-objective optimization model, particularly in reducing total costs and carbon emissions while maintaining high levels of customer satisfaction. The symmetric nature of the distance matrix further aids in achieving balanced and optimized route planning. The results highlight that MOFECO-SS offers decision-makers flexible route planning options that balance cost efficiency with environmental sustainability, ultimately improving the effectiveness of cold chain logistics operations.

Delivery Route Scheduling of Heterogeneous Robotic System with Customers Satisfaction by Using Multi-Objective Artificial Bee Colony Algorithm

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an unmanned aerial vehicle (UAV), which is capable of traversing terrain but has limitations in terms of energy and payload. The problem is formulated as an optimal route scheduling problem in a road network, where the goal is to find the route with minimum delivery cost and maximum customer satisfaction (CS) enabling the UAV to deliver packages to customers. We propose a new method of route scheduling based on an improved artificial bee colony algorithm (ABC) and the non-dominated sorting genetic algorithm II (NSGA-II) that provides the optimal delivery route. The effectiveness and superiority of the method we proposed are demonstrated by comparison in simulations. Moreover, the physical experiments further validate the practicality of the model and method.

A GRASP algorithm for the concrete delivery problem

This paper addresses a novel variant of the Concrete Delivery Problem (CDP), which involves the efficient scheduling of ready-mixed concrete deliveries to construction sites while balancing the conflicting goals of minimizing transportation costs and maximizing customer satisfaction. In this study, we propose an exact formulation and a heuristic approach based on the Greedy Randomized Adaptive Search Procedure (GRASP) to tackle this challenging CDP variant. This variant introduces realistic side constraints, including driver working shifts, a minimum driver working time, and overtime penalties. Additionally, it considers the case where customers may request multiple types of concrete delivered within the same time window. We assess the performance of our heuristic using new instances generated for this problem and provide a comparative analysis with another CDP variant from the literature to demonstrate its effectiveness.

Twenty-Four Hour Live Chat Feature and Customer Satisfaction

Purpose –This paper wants to describe the e-service quality of customer support in handling crypto asset consumer problems using the 24-hour live chat feature, and an overview of customer satisfaction ratings at PT Akunku Dotcom Indonesia Methodology/approach –The survey was conducted on 100 Reku friend respondents who had interacted with customer support via the 24-hour live chat feature. The instrument used is a questionnaire with 4 Likert scales and customer satisfaction rating data. The assessment of e-service quality is presented in the form of an average answer score and the level of agreement between Reku friends. Findings –E-Service Quality through the 24 Hour Live Chat Feature on Reku received a positive response from Reku users with an agreement rate of almost 90%. Reku Indonesia's Customer Satisfaction Rating is dominated by the Good Rate category. Both the Good Rate and Bad Rate sections are mostly influenced by Customer Support services. Novelty/value –Customer support needs to provide a sense of security to customers of digital products such as crypto assets, through maximum e-service quality, so that this can have an impact on maximum customer satisfaction ratings too

Bi-Objective Mixed Integer Nonlinear Programming Model for Low Carbon Location-Inventory-Routing Problem with Time Windows and Customer Satisfaction

In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time window constraints that satisfies the normal distribution of stochastic customer demand. The proposed model aims to find Pareto optimal solutions for total cost minimization and customer satisfaction maximization. An improved non-dominated sorting genetic algorithm II (IMNSGA-II) with an elite strategy is developed to solve the model. The model considers cost factors, ensuring that out-of-stock inventory is not allowed. Factors such as a carbon trading mechanism and random variables to address customer needs are also included. An entropy weight method is used to derive the total cost, which is comprised of fixed costs, transportation costs, inventory costs, punishment costs, and the weight of carbon emissions costs. The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. Additionally, a sensitivity analysis is performed to evaluate the influence of carbon pricing on carbon emissions and customer satisfaction.

Streamlining Project Management in the Production Sector: A Case Study of Industrial Engineering

The objective of the research is to pinpoint issues in project management, as this is crucial for the development of a successful project. There is a need to expand the existing knowledge in project management, as it contributes to enhancing a company’s project management system. Additionally, the study presents effective remedies for project management challenges. Initially, we introduce the specific project, followed by an examination of the current state of production processes. In this context, we endeavour to propose solutions. The concluding section focuses on evaluating these solutions, summarizing the study, and achieving the study’s objective. These outcomes contribute to cost savings and ensure maximum customer satisfaction.

Customised product design optimisation considering module synergy effects and expert preferences

ABSTRACT Fierce marketing competition and varied customer requirements on products make a lot of companies transfer to the customisation strategy. However, customers may be overwhelmed by the vast assortment of products and options. This paper proposes a methodology aimed at optimising the range of customised product varieties considering synergy effects of modules and preferences of experts. The methodology outlines key steps required for a successful customised product configuration. Firstly, a questionnaire based on the Kano model is designed to identify customer preferences for product module customisation. The potential customers can be segmented based on the distance among their multidimensional preferences. Secondly, within each segmented market, the customer satisfaction function that considers module synergies is established using the quantified Kano model and coefficient matrix. Thirdly, the cost function that considers experts' preferences is established based on the prospect theory. Finally, a bi-objective optimisation model is formulated to maximise customer satisfaction and minimise cost under technical constraints. Pareto solutions are obtained by solving the model with the non-dominated sorting genetic algorithm II (NSGA-II). Modules and attributes available to customers can be determined by the solutions. An illustrative example of modular laptop computers confirms the methodology's effectiveness in optimising product configuration.

E-Commerce Demand Forecasting Using SARIMA Model and K-means Clustering Analysis

E-commerce has become a significant driver in the retail industry within the digital era, with escalating online transaction volumes. Accurate demand forecasting and effective inventory management are crucial for retailers to minimize costs related to overstock and stockouts while maximizing customer satisfaction and revenue. This study explores the application of advanced time series models for demand forecasting by e-commerce retailers, with a particular emphasis on the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The SARIMA model, an extension of the ARIMA model, incorporates autoregression, moving averages, and accounts for seasonal variations, making it suitable for analyzing data with regular periodic patterns, such as seasonal fluctuations in consumer purchasing behavior. The study methodology includes data preprocessing, establishment of the ARIMA model, construction of a Linear Regression (LR) model, development of the SARIMA model, demand forecasting, and clustering analysis. The SARIMA model demonstrated higher fit and predictive accuracy compared to traditional LR and ARIMA models, as substantiated by the 1-mWAPE and RMSE metrics. The application of the K-means clustering algorithm enhanced the homogeneity of demand forecasting and improved the model's adaptability to new sequence data. Cluster analysis categorized products into four classes, with more similar demand characteristics within each class, aiding the model in more accurately capturing potential demand changes. In conclusion, this research confirms the effectiveness and practicality of combining the SARIMA model with the K-means clustering algorithm for e-commerce demand forecasting.

A predictive analytics approach for forecasting bike rental demand

The demand for rental bikes in urban areas fluctuates, leading to localized surpluses and shortages. To address this challenge, effective bike relocation strategies are essential for ensuring equitable distribution and maximizing customer satisfaction. This study aims to employ advanced machine learning techniques to forecast bike rental demand in urban areas, thereby enhancing the efficiency and accessibility of bike rental services and contributing to sustainable urban mobility. The study comprehensively analyzes various influencing factors using machine learning models, including Ordinary Least Squares regression, MLP Regression, Gradient Boosting Regression, Random Forest Regression, Polynomial Regression, and Decision Tree Regression. The primary objective is to identify the most accurate predictor by comparing key metrics such as R-squared (R2), Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson Correlation Coefficient. Insights gained from this analysis aid in identifying influential variables and ensure the development of resource-efficient and adaptable models, leading to more informed decision-making for rental bike businesses. Additionally, future research directions involve the implementation of artificial intelligence technology to predict overall bike demand based on urban cities’ criteria, including the number of national and international tourists. By addressing these objectives, this study seeks to provide valuable insights and tools for rental bike businesses to optimize operations, make strategic decisions, and enhance customer experience in competitive urban markets.

A fully polynomial time approximation scheme for a stop schedule of a transportation line without branches

For a transportation line without branches, passenger service scheduling is mainly concerned with the determination of stop schedules for planned service trips. A mixed-integer programming method is proposed to maximize customer satisfaction by minimizing the total number of in-vehicle passengers during trips. A heuristic algorithm is proposed for a given number of service trips, in which the error bound of the heuristic is restricted by the number of stations. When the given number of service trips is sufficient to provide express services for all origin–destination pairs, the heuristic provides an optimal solution. However, the error bound of the heuristic approaches infinity as the number of stations increases. A fully polynomial time-approximation algorithm is proposed, where the number of service trips is a decision variable. Finally, the proposed algorithm was applied to the stop-schedule problem of a river bus operating along the Han River in Seoul, Korea.

Waiting Strategy for the Dynamic Meal Delivery Routing Problem with Time-Sensitive Customers Using a Hybrid Adaptive Genetic Algorithm and Adaptive Large Neighborhood Search Algorithm

In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy is proposed to divide the dynamic problem into a series of static subproblems. This waiting strategy utilizes the decision threshold to determine rerouting points based on the number of dynamic meal orders. Meanwhile, time-sensitive priority is introduced to accelerate assignment and routing decisions for orders from customers with high time sensitivity. For each static subproblem, a hybrid AGA–ALNS algorithm that incorporates the adaptive genetic algorithm and adaptive large neighborhood search is developed to improve both the global and local search capabilities of the genetic algorithm. We validate the performance of the proposed waiting strategy and the AGA–ALNS algorithm through numerical instances. In addition, managerial insights are obtained from sensitivity analysis experiments.

Optimization of fresh produce delivery path based on C-W saving algorithm

Because fresh agricultural products have the characteristics of difficulty in preserving freshness, short shelf life, and are perishable and fragile, so they have extremely high requirements for warehousing, distribution, and other multiple links, and their distribution costs have caused some economic pressure on Z. To reduce the distribution costs of Z's distribution centers and to maximize the satisfaction of customers' needs, it is very important to carry out reasonable planning of the distribution paths. How to scientifically and reasonably formulate the vehicle distribution plan and arrange the distribution path program, shorten the time of fresh agricultural products in transit and reduce the company's logistics and distribution costs, maximize customer satisfaction, and accurately and timely provide customers with high-quality fresh agricultural products and high-quality distribution services are of great significance. In this paper, by summarizing the existing literature on the optimization of the distribution path of fresh agricultural products cold chain logistics, the C.W saving algorithm is used to optimize the path of the distribution center of Z company.

Resource Scheduling Optimisation Study Considering Both Supply and Demand Sides of Services under Cloud Manufacturing

In cloud manufacturing environments, the scheduling of multi-user manufacturing tasks often fails to consider the impact of service supply on resource allocation. This study addresses this gap by proposing a bi-objective multi-user multi-task scheduling model aimed at simultaneously minimising workload and maximising customer satisfaction. To accurately capture customer satisfaction, a novel comprehensive rating index is introduced, integrating the actual completion cost, time, and processing quality against customer expectations. Furthermore, vehicle constraints are incorporated into the model to accommodate potential delays in transport vehicle availability, thereby enhancing its alignment with real-world manufacturing settings. The proposed mathematical model is solved using an improved three-stage genetic algorithm, which integrates the k-means algorithm and a real-time sequence scheduling strategy to optimise solution quality. Validation against alternative algorithms across various case scales demonstrates the efficacy of the approach in providing practical scheduling solutions for real-case scenarios.

Order Distribution and Routing Optimization for Takeout Delivery under Drone–Rider Joint Delivery Mode

Order distribution and routing optimization of takeout delivery is a challenging research topic in the field of e-commerce. In this paper, we propose a drone–rider joint delivery mode with multi-distribution center collaboration for the problems of limited-service range, unreasonable distribution, high delivery cost, and tight time windows in the takeout delivery process. The model is constructed with the minimum delivery cost and the overall maximum customer satisfaction as the objective function, and a two-stage heuristic algorithm is designed to solve the model. In the first stage, Euclidean distance is used to classify customers into the regions belonging to different distribution centers, and the affinity propagation (AP) clustering algorithm is applied to allocate orders from different distribution centers. The second stage uses an improved tabu search algorithm for route optimization based on specifying the number of rider and drone calls. This paper takes China’s Ele.me and Meituan takeout as the reference object and uses the Solomon data set for research. The experimental results show that compared with the traditional rider delivery mode, the drone–rider joint delivery mode with multiple distribution center collaboration can effectively reduce the number of riders used, lower the delivery cost, and improve the overall customer satisfaction.

An application of integrated AHP and TOPSIS methodology on the choice of transshipment container port in the Eastern Mediterranean basin

In container transportation by sea, it is crucial to define the transshipment design. Liner operators desire not only to reduce operational costs but also to maximize customer satisfaction. Therefore, a transshipment hub port selection problem becomes at the front to gaining productive transshipment ability. The study aims to apply an alternative approach to determine the best transshipment hub port for a northbound container ship entering the Eastern Mediterranean basin through the Suez Channel. The proposed model consists of criteria that are weighted with the AHP method and numerical data regarding each criterion is used to rank the alternatives. Thus, as a decision-maker, the line operator can determine the transshipment port quickly using an easily applicable method upon determining the port calls and the schedules. The findings imply that connectivity is the most important criterion for the choice of transshipment hub port for containerized goods. The location was also found important for this choice, but to improve transshipment function, the proper way may be to draw beneficial strategies on maritime connectivity. On the other hand, capital investments such as superstructure improvement were found to be less important for an attractive transshipment hub.

The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing

Exploration of the green flexible job-shop scheduling problem is essential for enterprises aiming for sustainable practices, including energy conservation, emissions reduction, and enhanced economic and social benefits. While existing research has predominantly focused on carbon emissions or energy consumption as green scheduling objectives, this paper addresses the broader scope by incorporating the impact of variable energy prices on energy cost. Through the introduction of an energy cost model based on time-of-use electricity pricing, the study formulates a multi-objective optimization model for green flexible job-shop scheduling. The objectives include minimizing cost, reducing carbon emissions, and maximizing customer satisfaction. To prevent premature convergence and maintain population diversity, an enhanced genetic algorithm is employed for solving. The validation of the algorithm’s effectiveness is demonstrated through specific examples, providing decision results for optimal scheduling under various weight combinations. The research outcomes hold substantial practical value as they can significantly reduce energy expenses, lower carbon emissions, and elevate customer satisfaction while safeguarding production efficiency. This contributes to enhancing the market competitiveness and green brand image of businesses.

A customer satisfaction-based optimization model for the charging and discharging path and battery swapping stations’ site selection of electric vehicles

This paper studies the problem of considering customer satisfaction in the no-battery-swap mode and in the power-swap mode. First, with the goal of maximizing customer satisfaction, the total cost of charging and discharging and the minimum construction cost of swapping stations, the customer time window, and the load constraints of electric vehicles are considered. A model of electric vehicle charging and discharging route optimization and replacement station location without battery swapping behavior, considering customer satisfaction, is established, and then, a two-stage improved ant colony–genetic algorithm is designed to solve the model, and finally, the comparative analysis considers customer satisfaction. Based on the path optimization results and location decisions considering the cost of charging and discharging, the following conclusions are obtained: 1) electric vehicle route optimization and swap station location planning considering customer satisfaction can not only effectively reduce logistics distribution costs and replacement costs but also improve customer satisfaction levels. 2) Reducing the number of route crossings in the process of logistics distribution routes can save electricity costs for electric vehicles and logistics distribution costs, and help reduce the total cost of the entire logistics distribution network. 3) The gradient setting of the electricity price for electricity exchange will reduce the cost of electricity exchange, improve the utilization efficiency of the battery, reduce the cost of logistics and distribution, and improve the electricity exchange revenue of the electricity exchange station.

Phasing real estate projects considering profitability and customer satisfaction

PurposeThe aggressive competition in the real estate market forces real estate developers to tackle the challenge of selecting the best project construction phasing alternative. The real estate industry is characterized by high costs, high profit and high risks. The schedules of real estate projects are also characterized by having large number of repetitive activities that are executed over a long duration. The repetitiveness, long duration of execution, the high amounts of money involved and the high risk made it desirable to leverage the impact of changes in phasing plans on net present value of amounts incurred and received over the long execution and selling duration. This also changes the project progress, and delivery time as well as their respective impact on customer degree of satisfaction. This research addresses the problem of selecting the best phasing alternative for real estate development projects while maximizing customer satisfaction and project profit.Design/methodology/approachThe research proposes a model that generates all construction phasing alternatives and performs decision-making to rank all possible phasing alternatives. The proposed model consists of five modules: (1) Phasing Sequencing module, (2) Customer Satisfaction module, (3) Cash-In calculation module, (4) Cost Estimation module and (5) Decision-making module. A case study was presented to demonstrate the practicality of the model.FindingsThe proposed model satisfies the real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model generates all construction phasing alternatives and performs multi-criteria decision making to rank all possible phasing alternatives. It quantifies the score of the two previously mentioned criteria and ranks all solutions according to their overall score.Research limitations/implicationsThe research proposes a model that assist real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model can be used to conclude general guidelines and common successful practices to be used by real estate developers when deciding the construction phasing plan. In this study the model is based on business models where all the project units are sold, rental cases are not considered. Also, the budget limitations that might exist when phasing is not considered in the model computations.Originality/valueThe model can be used as a complete platform that can hold all real estate project data, process revenues and cost information for estimating profit, plotting cash flow profiles, quantifying the degree of customer satisfaction attributable to each phasing alternative and providing recommendation showing the best one. The model can be used to conclude general guidelines and common successful practices to be used by real estate developers when tackling the challenge of selecting construction phasing plans.

Continuous Process Improvement and Customer Satisfaction of Selected Food and Beverage Companies in South-West, Nigeria

The food and beverage manufacturing sector is a highly competitive and dynamic industry. Organizations in this sector must be proactive in identifying corporate strategies to enhance business sustainability. One of the key strategies for optimum performance is the application of continuous process improvement through customer satisfaction. This study examined the influence of Continuous Process Improvement on Customer Satisfaction in some selected Food and Beverage Companies in South-West Nigeria. A descriptive research design was adopted for the study, with a questionnaire as the research instrument. The sample size was restricted to 400, using a stratified random sampling technique for sample selection. The basic criteria for inclusion in the sample were job rank and organizational tenure of five years or more. A Four-rating Likert Scale questionnaire with reliability coefficients above 0.76 was used to obtain data from the respondents. Data collected were analysed by using both descriptive and inferential statistics. The main hypothesis for the study was tested by using regression analysis at 0.05 level of significance. The findings of the study revealed that Continuous Process Improvement does not have a statistically significant influence on Customer Satisfaction. The study concluded that Continuous Process Improvement is very important in maximizing customer satisfaction in the Food and Beverage sector. However, it was recommended that as a result of the insignificant relationship between continuous process improvement and customer satisfaction, companies in this industry should focus on other factors such as product quality, customer service, and pricing to improve customer satisfaction. In addition, these organizations also need to align their improvement process needs with the demands of customers in order to boost customers’ delight.