Customer Requests Research Articles

Multiple plan approach for a dynamic dial-a-ride problem

Abstract In densely populated areas, ridepooling services are becoming more and more popular and can contribute to promote sustainable mobility concepts. In this paper, we study a dynamic dial-a-ride problem and propose a novel two-phase multiple plan approach. By maintaining multiple plans simultaneously over the course of the optimization procedure, we increase the likelihood and speed of a successful insertion for dynamically arising customer requests. Additionally, we present a new metric to replace the commonly used distance minimization as guidance within the optimization procedure. In order to examine the impact of certain instance characteristics on the quality of the solutions, we evaluate our design decisions on various instance sets. The results show that the proposed method consistently outperforms commonly used single plan approaches.

A comparison of reinforcement learning policies for dynamic vehicle routing problems with stochastic customer requests

This paper presents directions for using reinforcement learning with neural networks for dynamic vehicle routing problems (DVRPs). DVRPs involve sequential decision-making under uncertainty where the expected future consequences are ideally included in current decision-making. A frequently used framework for these problems is approximate dynamic programming (ADP) or reinforcement learning (RL), often in conjunction with a parametric value function approximation (VFA). A straightforward way to use VFA in DVRP is linear regression (LVFA), but more complex, non-linear predictors, e.g., neural network VFAs (NNVFA), are also widely used. Alternatively, we may represent the policy directly, using a linear policy function approximation (LPFA) or neural network PFA (NNPFA). The abundance of policies and design choices complicate the use of neural networks for DVRPs in research and practice. We provide a structured overview of the similarities and differences between the policy classes. Furthermore, we present an empirical comparison of LVFA, LPFA, NNVFA, and NNPFA policies. The comparison is conducted on several problem variants of the DVRP with stochastic customer requests. To validate our findings, we study realistic extensions of the stylized problem on (i) a same-day parcel pickup and delivery case in the city of Amsterdam, the Netherlands, and (ii) the routing of robots in an automated storage and retrieval system (AS/RS). Based on our empirical evaluation, we provide insights into the advantages and disadvantages of neural network policies compared to linear policies, and value-based approaches compared to policy-based approaches.

Recommendations for the development and implementation of a balanced scorecard based on studying the experience of Russian organisations

The article provides a comparative analysis of the current practice of Russian organisations in the development and implementation of a balanced score-card. The reasons for low efficiency of the companies are described separately. The expediency of using a strategic management mechanism in the activities of the companies is substantiated in detail, and also, based on the application of tabular data, the scale of scientific research in the field of key performance indicators is indicated. The recommendations formulated by the authors reflect cause-and-effect relationships between customer requests and the needs of suppliers of goods and services. Particular attention is paid to assessing the potential effect of introducing the balanced scorecard into the activities of the Russian companies. As the article is written, patterns are revealed in the processes of implementation of personnel policies by the objects of research, formation of strategic goals, and realisation of a mechanism for disseminating information about the declared strategy among all official levels of the company. The authors of this work state detailed conclusions for each section indicating the detected motives for actions and predicting potential results. The algorithm for formulating recommendations for the development and implementation of the balanced scorecard identified at the beginning of the article finds its step-by-step development within the framework of the ongoing research and is accompanied by reasoned comments based both on previously published sources of information and on provisions proposed by the authors.

Analisa Kepuasan Pelanggan terhadap Kualitas Pelayanan pada PT Tj Bros dengan Metode SERVQUAL (Service Quality)

This study aims to evaluate the service quality of PT TJ Bros using the SERVQUAL method, which includes five dimensions: tangibility, reliability, responsiveness, assurance, and empathy. Data were collected through a Likert-scale questionnaire from 50 active customers selected using purposive sampling. The analysis measured the gap between customer expectations and their perceptions of the company's services.The findings indicate that all service dimensions exhibit negative gaps, meaning the service quality does not fully meet customer expectations. The responsiveness dimension shows the largest gap (-0.80), reflecting delays in responding to customer complaints or requests. The tangibility dimension also demonstrates a gap (-0.40), indicating a need for improvements in physical facilities, such as cleanliness, waiting area comfort, and employee appearance. In the empathy dimension, customers feel a lack of personalized attention, with a gap of -0.60.To enhance customer satisfaction, the company is recommended to improve its customer response system using technology-based complaint management, upgrade physical facilities, and train employees in communication and personalized service. Adjustments to employee uniforms and training to maintain service consistency are also suggested.In conclusion, comprehensive improvements in operational systems, facilities, and employee skills are necessary to enhance service quality. By meeting or exceeding customer expectations, PT TJ Bros can boost customer loyalty and strengthen its competitive position in the market.

On the Concept of Opportunity Cost in Integrated Demand Management and Vehicle Routing

Integrated demand management and vehicle routing problems are characterized by a stream of customers arriving dynamically over a booking horizon and requesting logistical services, fulfilled by a given fleet of vehicles during a service horizon. Prominent examples are attended home delivery and same-day delivery problems, where customers commonly have heterogeneous preferences regarding service fulfillment and requests differ in profitability. Thus, demand management methods are applied to steer the booking process to maximize total profit considering the cost of the routing decisions for the resulting orders. To measure the requests’ profitability for any demand management method, it is common to estimate their opportunity cost. In the context of integrated demand management and vehicle routing problems, this estimation differs substantially from the estimation in the well-examined demand management problems of traditional revenue management applications as, for example, found in the airline or car rental industry. This is because of the unique interrelation of demand control decisions and vehicle routing decisions as it inhibits a clear quantification and attribution of cost, and of displaced revenue, to certain customer requests. In this paper, we extend the theoretical foundation of opportunity cost in integrated demand management and vehicle routing problems. By defining and analyzing a generic Markov decision process model, we formally derive a definition of opportunity cost and prove opportunity cost properties on a general level. Hence, our findings are valid for a wide range of specific problems. Further, based on these theoretical findings, we propose approximation approaches that have not yet been applied in the existing literature, and evaluate their potential in a computational study. Thereby, we provide evidence that the theoretical results can be practically exploited in the development of solution algorithms. Funding: This work was supported by the University of the Bundeswehr Munich. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0644 .

OVERVIEW OF THE FACTORS OF ENSURING THE QUALITY OF PRE-SCHOOL EDUCATION UNDER THE CONDITIONS OF THE MARTIAL STATE

The review article is devoted to the analysis of the quality of preschool education, a categorical-conceptual approach to understanding the essence of the concepts of «quality of education», «quality of Preschool Education» and «safe educational environment». It is outlined that geopolitical challenges determine the urgent need to create a safe educational environment. In order to understand the problems and prospects of organizing the educational process by pedagogical workers for the youngest citizens of the state, it is necessary to consolidate the efforts of specialists in the field of Preschool Education in order to solve strategic tasks for further development in this area. It is summarized that turbulent realities, in particular the challenges associated with the Russian-Ukrainian war, regulate new requirements for the organization of the educational process in preschool education. The psycho-emotional state of children, the requests of customers of educational services (parents and persons replacing them), families who have suffered as a result of armed aggression, as well as the need to modify educational programs to the conditions of martial law require special knowledge, adaptation strategies and psychological and pedagogical support from teachers. The authors note that ensuring the quality of Preschool Education in wartime has a legislative basis, namely: ensuring the right to education, which is decisive for the individual and society as a whole. Ensuring the quality of Preschool Education is considered not only as an administrative task, but also as a socio-cultural responsibility, which should be based on a comprehensive approach taking into account the challenges of today, the need for multidisciplinary support and strategic planning in conditions of instability. We have verified that within the sample of institutional institutions (preschool educational institutions), approaches to the quality of the educational process vary, and with martial law, «quality» remains a rhetorical remark.

A Proposed Framework for the Role of Inter-Cost Management Techniques in Reducing the Costs of Industrial Cluster Enterprises to Support their Business Environment Competitiveness: A Field Study in the Saudi Arabia

The research aims to demonstrate the role of intra-cost management in reducing the costs of industrial clusters in small and medium enterprises to support their competitiveness. In addition building a comprehensive proposal framework for inter-cost management tools that are compatible with the characteristics of industrial clusters in small and medium enterprises in the modern business environment. And the extent of the contribution of the dimensions of this proposed framework in reducing the costs of the industrial cluster in the Saudi industrial enterprises. The research reached the building of a comprehensive proposed framework for inter-cost management tools that are compatible with the characteristics of industrial clusters, and the development of the mechanism of action of this framework. In the field study, the contribution of the dimensions of the proposed framework was also reached in reducing the costs of industrial clusters to support their competitiveness in the Saudi business environment. This was done by achieving several advantages for the industrial cluster enterprises, including: Reducing the total costs along the supply chain to the lowest possible level. Improving the quality levels of products. Improving the indicators of profitability within all the industrial cluster parties. Generating innovative designs that meet the needs and desires of all customers. And the speed to the change in the tastes and requests of customers within the industrial cluster. And in terms of the relative importance of each of the dimensions of the proposed framework. In the first order, in terms of relative importance, came the contribution of inter-cost management tools associated with suppliers. In the second order, the contribution of inter-cost management tools in the pre-industrial .And in the third order, the contribution of inter-cost management methods in the manufacturing. Finally, the contribution of inter-cost management methods associated with customers t came in the fourth and final order in reducing costs and increasing the competitiveness of the industrial cluster facilities in the Saudi business environment.

Navigating Dietary Complexities: Understanding Gluten Intolerance Awareness among Stakeholders

Background: Eating out is not only a common trend these days but also necessity for many who live away from their families especially students pursuing higher education. With growing trend of food hypersensitivity catering industry needs to get driven for such special requirement. Objective: This study aimed to check the awareness of stakeholders for gluten intolerance with respect to the market and sensory evaluation of the developed alternative. Methodology: A cross-sectional study was conducted on consumers and food business operators of Central Delhi. Consumers were 18 years and above and claiming to be gluten intolerant. Food business operators were local shopkeepers running food stalls and bakery. A self -structured questionnaire was designed for stakeholders respectively. Developed gluten alternative was sensory evaluated by consumers on 9-point hedonic rating scale for its overall acceptability. Data was analyzed for the stakeholders by using descriptive statistics. Results: The results indicate that majority (53.65%) consumers were familiar with the symptoms of gluten intolerance like headache and digestive issues. Furthermore, majority (68.3%) identified rice and corn to be an alternative for gluten. Level of satisfaction from market for availability of alternatives is medium (61%).Sensory score for developed alternative was 7.19 ±1.53. Regarding food business operators’ majority (53.12%) receive customer request for availability of gluten free products, 46.87% are affirmative to provide alternatives subjected to demand of consumers. 53.12% also showcased interest in receiving training or resources for gluten intolerance and lactose free cooking skills and outsourcing of such food items. Conclusion: In general stakeholders showcased awareness about their food intolerance and improved availability of alternatives in market. At the same time willingness of food business operators to offer such products on menu. The study recommends to provide training to food business operators for provision of diversified menu for prospective consumers.

Spore-Forming Clostridia in Raw Cow Milk from Northern Italy: A Trend Analysis over the Past 20 Years.

Clostridium species are known for their impact on animal and human health, but also for the spoilage of foodstuffs. Their spores contaminate milk and result in germination and gas production, the latter being particularly evident in the cheeses that suffer severe depreciation. To address this issue, the Primary Production Department of the IZSLER institute in Brescia, Italy conducts the Most Probable Number (MPN) method on bovine milk samples collected from Northern Italian dairies between 2004 and 2023. This approach leverages two semi-quantitative protocols, S2 and S3, to detect Clostridium species spore forms upon customer request. Here, we would like to present an a-posteriori analysis on the results of the S2 and S3 protocols. The goal of this study is to highlight the differences between these two methods and provide evidence of the actual decrease in Clostridium species in raw cow milk over a 20-year period. Our analysis shows that client demand for S2 has progressively decreased, while S3's has remained constant, and both protocols reveal a significant reduction in positives; furthermore, S3's greater sensitivity made it more responsive to environmental changes. This highlights the necessity of choosing the appropriate testing protocol that accounts for both regulatory standards and environmental factors. Overall, our findings underscore the importance of continued monitoring to manage Clostridium species contamination and ensure milk quality.

Dynamic Contract Design in the Presence of Double Moral Hazard

We consider a stylized incentive management problem over an infinite time horizon, where the principal hires an agent to provide services to customers. Customers request service in one of two ways: either via an online or a traditional offline channel. The principal does not observe the offline customers’ arrivals, nor does she observe whether the agent exerts (costly) effort that can increase the arrival rate of customers. This creates an opportunity for the agent (i) to divert cash (that is, to under-report the number of offline customers and pocket respective revenues) and also (ii) to shirk (that is, not to exert effort), thus leading to a novel and thus far unexplored double moral hazard problem. To address this problem, we formulate a constrained, continuous-time, stochastic optimal control problem and derive an optimal contract with a simple intuitive structure that includes a payment scheme and a potential termination time of the agent. We enrich the model to allow the principal to either (i) dynamically adjust the prices for the services in both channels or (ii) monitor the agent. Both tools help the principal to alleviate the double moral hazard problem. We derive respective optimal strategies for using those tools that guarantee the highest profits. We show that the worse the agent’s past performance is, the lower the prices should be set and the more the principal should monitor the agent. This paper was accepted by Ilia Tsetlin, behavioral economics and decision analysis. Funding: The work of F. Tian is financially supported by the Hong Kong RGC Funding [RGC General Research Fund 2023/24 17502023], HKU Seed Fund for Basic Research for New Staff (2022), and the HKU Business School Shenzhen Research Institute [SZRI2023-BRF-04]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01169 .

A two-phase algorithm for the dynamic time-dependent green vehicle routing problem in decoration waste collection

Transportation activities associated with construction waste generate substantial carbon emissions, an issue that is attracting increasing environmental concern. To raise construction waste transportation efficiency and reduce carbon emission, we address the dynamic time-dependent green vehicle routing problem for decoration waste collection (DTDGVRP-DWC). In the existing literature, many deterministic approaches to the dynamic vehicle routing problem are myopic, as they only react to already arrived requests. In this paper, we propose a stochastic sampling method to tackle with uncertain customer request in the real world. The DTDGVRP-DWC is formulated as an 0-1 programming. In the new model, we consider urban traffic congestion and variable speeds over time, and factors that significantly influence both carbon emissions and fuel costs. To quickly solve the complicated optimization problem, we develop a two-phase algorithm. In the first phase, we embed a competitive simulated annealing algorithm to determine visitation schedules for anticipated and early-request customers. In the second phase, we propose an event-trigger mechanism to decompose the dynamic problem into a series of static sub-problems, and an effective heuristic to solve each sub-problem. Computational results show that as long as the prediction accuracy exceeds a threshold, the forecast routing method consistently performs better than reactive routing. A real-world case demonstrates that an early commitment waiting time strategy benefits timely service requirements, whereas a late or hybrid waiting time strategy takes overall efficiency and customer satisfaction into account.

AI-Driven Resource Provisioning: Enhancing Elasticity and Efficiency with Hybrid RNN-LSTM Models

Automatic resource provisioning techniques that dynamically modify resources in response to service demand are essential to implementing elasticity services. For systems with strict latency or reaction time requirements, such as Enterprise Resource Planning (ERP) systems under high traffic loads, this flexibility is crucial for lowering power consumption and guaranteeing quality of service (QoS). Determining the best point at which to scale resources is still a difficult task. In this research, we provide an AI-integrated system that adjusts resources according to anticipated demand. To predict load requests, the system uses an LSTM model and a Hybrid Recurrent Neural Network (RNN). This strategy seeks to minimize overprovisioning, which will lower the cost of infrastructure and energy usage. The deep learning model aims to estimate the resources required to improve service response time and satisfy customer requests, as well as to anticipate with high accuracy the processing load of distributed servers. Proactive provisioning decisions are made for the servers based on the anticipated load. Our tests on a common server request dataset show that the suggested RNN+LSTM model performs better than traditional deep learning models in terms of efficient resource management and prediction accuracy.

Preprocessing Text Data for the Doc2Vec Model in the Automatic Classification of Tickets in Support Centers of Software Providers

Leading software providers typically implement customer technical support functions, which are crucial for promoting and enhancing the competitiveness of their products and services in global markets. The high volume and heterogeneity of support tickets (functional, temporal, linguistic, etc.) highlight the importance of efficient classification systems. Effective classification optimizes the distribution of these tickets among support center specialists and automates their processing using an established knowledge base. However, classifying these tickets is a loosely formalized task. For companies that have accumulated substantial data on customer requests, automating classification through machine learning methods and natural language processing models, such as Word2Vec, FastText, BERT, and GPT, becomes feasible. It is generally accepted that classification effectiveness primarily depends on the model employed. Nevertheless, the quality of these models is significantly influenced by the nature of the training data. Literature review of the reveals significant research interest in methods for the automatic classification of tickets specifically tailored to the operational conditions of software provider support centers. However, there is a noticeable gap in the literature regarding the impact of data preprocessing on the quality of these models. The article aims to clarify the techniques of data preprocessing and analyze their impact on the effectiveness of text classification, considering the specificity of software provider support centers. This study examines the stages of the automatic classification process for tickets, accounting for the unique characteristics of the data (customer text requests). A relevant set of specified methodological and instrumental tools was developed and tested using open data from a global software provider (DevExpress). The testing involved a database of 165,000 tickets. The study's results indicate that preprocessing can improve classification metrics such as F-measure, Precision, and Recall from 77% to 79%. Additionally, preprocessing significantly reduces the dimensionality of text data (by 48.2%) and increases model training speed (by 26.5%) without loss of accuracy, ensuring cost-efficiency and operational efficiency in the use of computational resources.

Scheduling multi-skill technicians and reassignable tasks in a cloud computing company

We investigate a multi-skill technician and reassignable task scheduling problem in a cloud computing company. In the problem, multi-skill technicians are assigned to process a large number of tasks from customer requests in a certain scheduling horizon. The tasks are allowed to be reassigned to another technician multiple times, and one technician can process multiple tasks in parallel. The company not only focuses on processing efficiency, but also expects to improve customers’ experience and technicians’ satisfaction. We characterize the feasible solutions and introduce a weighted objective with three metrics: processing efficiency, response delay, and workload balance. An effective two-stage hierarchical optimization method embedded in a greedy randomized adaptive search procedure framework is proposed. In the first stage, initial solutions are generated by a greedy randomized construction procedure, and then improved by local search with an ejection chain operator to optimize processing efficiency. In the second stage, two local search procedures with five operators for improving response delay or workload balance are designed. Computational experiments are conducted to evaluate the effectiveness of our algorithm. The results show that the proposed algorithm is competent in fast computing a schedule of high quality. It also reveals that reassignments are helpful in reducing response delay and balancing workloads in the scheduling.

Optimizing a Dynamic Vehicle Routing Problem with Deep Reinforcement Learning: Analyzing State-Space Components

Background: The dynamic vehicle routing problem (DVRP) is a complex optimization problem that is crucial for applications such as last-mile delivery. Our goal is to develop an application that can make real-time decisions to maximize total performance while adapting to the dynamic nature of incoming orders. We formulate the DVRP as a vehicle routing problem where new customer requests arrive dynamically, requiring immediate acceptance or rejection decisions. Methods: This study leverages reinforcement learning (RL), a machine learning paradigm that operates via feedback-driven decisions, to tackle the DVRP. We present a detailed RL formulation and systematically investigate the impacts of various state-space components on algorithm performance. Our approach involves incrementally modifying the state space, including analyzing the impacts of individual components, applying data transformation methods, and incorporating derived features. Results: Our findings demonstrate that a carefully designed state space in the formulation of the DVRP significantly improves RL performance. Notably, incorporating derived features and selectively applying feature transformation enhanced the model’s decision-making capabilities. The combination of all enhancements led to a statistically significant improvement in the results compared with the basic state formulation. Conclusions: This research provides insights into RL modeling for DVRPs, highlighting the importance of state-space design. The proposed approach offers a flexible framework that is applicable to various variants of the DVRP, with potential for validation using real-world data.

Dynamic pickup-and-delivery for collaborative platforms with time-dependent travel and crowdshipping

We study a pickup-and-delivery problem that arises when customers randomly submit requests over the course of a day from a choice of vendors on a collaborative e-commerce portal. Based on the attributes of a customer request, a dispatcher dynamically schedules the delivery service on either a dedicated vehicle or a crowdshipper, both of whom experience time-dependent travel times. While dedicated vehicles are available throughout the day, the availability of crowdshippers is unknown a priori and they appear randomly for only portions of the day. With an objective of minimizing the sum of routing costs, piece-rate crowdshipper payments, and lateness charges, we model the uncertainty in request arrivals and crowdshipper appearances as a Markov decision process. To determine an action at each decision epoch, we employ a heuristic that partially destroys the existing routes and repairs them under the guidance of a parameterized cost function approximation that accounts for the remaining temporal capacity of delivery vehicles. We benchmark our real-time heuristic with an adaptive large neighborhood search and demonstrate the effectiveness of our method with several performance metrics. In addition, we conduct computational experiments to demonstrate the impact of inserting wait time in the route scheduling and the benefit of explicitly modeling time-dependent travel times. Through our computational testing, we also investigate the potential of demand management mechanisms that facilitate many-to-one request bundles or one-to-many request bundles to reduce the cost to service requests.

Survey on the salt usage among Fish Cracker (Keropok lekor) manufacturers in several districts of Kuala Terengganu, Malaysia

Fish crackers (keropok lekor) are known for their tastiness. Although rich in protein and iodine, it contains a high amount of salt or sodium. Therefore, this study aimed to determine the manufacturers’ practice in the preparation of keropok lekor, perception and challenges concerning salt usage, and motivation to reduce salt content. This study also aimed to estimate the sodium content in keropok lekor. Seven manufacturers of keropok lekor in Terengganu were interviewed either face-to-face or online to obtain relevant information. All interviews were recorded, transcribed, and analyzed based on a thematic analysis framework. The recipes of keropok lekor were analyzed using the Nutritionist Pro software to determine the nutritional and sodium content, calculated based on the Malaysian and ASEAN Food Composition Tables. Based on the interviews, most manufacturers (90%) supported the effort to reduce salt in keropok lekor. However, the effort was met with several challenges, such as customer acceptance and negative perception of the taste, decreased sales, and increased material costs. The enabling factors include maintaining the original flavor of keropok lekor, customer requests, marketing opportunities, and the availability of salt substitutes. Support for marketing and financial technology know-how is also necessary. An estimate of 606.43 mg/100g of sodium content in keropok lekor was obtained in this study, which was mainly derived from the usage of sodium chloride and monosodium glutamate (MSG) during the preparation. This study revealed the high salt content in keropok lekor due to the addition of salt and seasonings. There is a potential to reduce salt content in keropok lekor with appropriate support provided to the manufacturers. Nevertheless, further research is needed to produce keropok lekor with reduced salt content, improved sensory, and enhanced microbial and physicochemical characteristics, which can improve customer perception.

Application of artificial intelligence in business management and financial market forecasting

In today's business world, artificial intelligence as a new technology has provided dynamic and flexible capabilities in organizational decision-making processes, considering that organizational decision-making often faces challenges due to the complexity and multiplicity of factors affecting it. The application of artificial intelligence as A powerful tool can be effective in improving the organizational decision-making process and improving the performance of organizational managers. Artificial intelligence is one of the popular and widely used new sciences in almost all aspects of life and can do things faster and more accurately. The numerous advantages of artificial intelligence and its wide applications today are not hidden from anyone. Financial services companies are constantly looking for ways to gain superiority in the capital market and surpass their competitors. To do this, companies are moving away from old systems and models They adopt a more agile business that includes artificial intelligence techniques in capital markets, artificial intelligence in financial markets and machine learning (ML). In capital markets, companies use artificial intelligence and ML projects in coordination with RPA robotic process automation technologies to meet customer requests, speed up financial market analysis, and more accurately identify risk in the business process.

A Profit Maximization Problem of Equipment Rental and Scheduling with Split Services

This study addresses a scheduling problem arising from the “Rent Instead of Buy” business model where rental service platforms rent equipment from suppliers to satisfy customer requests in exchange for a lump-sum payment. Suppliers often implement piecewise linear concave charging policies to encourage longer rental duration. This policy prompts the platform to consolidate service periods into longer rental agreements for lower rental rates, potentially necessitating the splitting of a customer request among multiple facilities. We establish an integer programming model to maximize the platform’s profit, which is equal to the total revenue from serving customers minus the total rental and scheduling costs. To solve the problem, we propose two Dantzig-Wolfe decomposition reformulations and design branch-and-price algorithms accordingly. Furthermore, by exploiting the property of the concave rental cost functions, we derive the optimal structure regarding the start and end times of services, significantly reducing the subproblem’s search space. Numerical experiments based on real-life data validate the efficiency of the designed algorithms compared to commercial solvers and highlight the benefits of split services. Sensitivity analysis reveals that as the rental cost function becomes more segmented, more requests are split-serviced, but the times for splitting a request are seldom more than once.

Optimalisasi Kualitas Pelayanan Jasa Menggunakan Metode Service Quality dan TRIZ

This study aims to measure the quality of service of Health Center X towards customer satisfaction and analyze the improvement of service quality of Health Center X towards customer satisfaction based on the Servqual method and develop improvements that can be made to improve service quality using the TRIZ method. The results of the analysis showed that there were 19 negative gaps with 5 attributes that had the largest gap values, including the politeness of officers in serving patients, the attention of officers to customer needs, the accuracy of officers in checking patient conditions, officers having enough time for patient consultations, and comfortable and beautiful health center rooms. The focus of improvements to be developed by Health Center X is that officers help customers by directly handling customer requests or solving customer problems, patient examinations are carried out carefully and seriously so that mistakes do not occur, doctors provide more time and attention to patients, even the latest information about the development of the patient's condition must be conveyed directly by the doctor to the patient, even changing the layout of the benches which were initially too close together when sitting and prone to contact with others, replacing facilities that are not suitable for use or repaired and adding entertainment facilities such as TV and adding decorations such as placing plants and flowers in the room.