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In this chapter, an air cargo shipment planning problem is considered by including individual risk factors of any sub-contracted agents. Due to competitive market conditions, air cargo forwarders are advised to remain flexible in operations. A mixed integer linear programming formulation including the potential for divisible activities is developed to model the shipment planning problem. To solve this complicated problem, we employ an ant colony optimization (ACO) methodology. Numerical examples are generated using data from both the extant literature and from a global air cargo company, allowing investigation of the viability of the novel methodology. We find that the algorithm/methodology provides effective solutions for small problem sizes.

This chapter studies the technical efficiencies of Chinese airports by using a meta-frontier production function model which accounts for airports in different regions accessing different technologies. Our empirical results show that the technical efficiency scores of airports and provincial output in the coastal region are higher than their counterparts in the inland region. However, the technical efficiency scores of airports and provincial output in inland region are steadily increasing while the counterparts of airports and provincial output in coastal region are slowly declining. In addition, our analysis of provincial efficiency changes shows that airport productivity has a positive and statistically significant effect on the technical changes of provincial output. Our results partially confirm the success of the government policy of promoting airport construction and development in the western inland region.

Considers an input‐output production model with random‐input variables. Presents statistical control charts to monitor the output quality, the random‐input variables, and the stability of the production process. These monitoring procedures serve the purposes of acceptance inspection of product output, acceptance inspection of input materials, and production process control. Gives an example in cotton spinning to illustrate the control of a random‐input production process.

Delay is an important issue for service providers. Indeed, previous studies have widely shown the negative effect of waiting time on consumer service satisfaction. However, being satisfied with the service seems to be insufficient for customers to remain loyal. Creating customer loyalty is even more crucial than just satisfying them. The paper aims to investigate how customers weigh up their service satisfaction and waiting time satisfaction in order to determine whether they will remain loyal or not.

A survey was conducted in the Belgian health care industry. The final sample includes 946 respondents. Regression analyses were performed and the Baron and Kenny method used to test moderator and mediator impacts of variables.

The results confirm that waiting time satisfaction is not only a service satisfaction determinant, but it also moderates the satisfaction‐loyalty relationship. Moreover, determinants of customer waiting time satisfaction include the perceived waiting time, the satisfaction with information provided in case of delays, and the satisfaction with the waiting environment. In addition, it is shown that waiting time satisfaction is a complete mediating variable in the perceived waiting time and service satisfaction link.

The paper suggests several implications about the waiting time impacts on service satisfaction and customer loyalty. They show the importance of this variable in the service process and explain how to improve it.

Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking.

Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency.

The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers.

The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.

Although customer satisfaction and loyalty have attracted a lot of attention in service management research, relatively few studies have examined the impact of waiting time and service quality on customer satisfaction and repurchase frequency. In this study, we model the relationships between customer satisfaction, repurchase frequency, waiting time and other service quality factors in fast food outlets. The results indicate that waiting time and other service factors such as staff attitude, environment, seat availability and food quality significantly influence the customers’ return frequency. Results also show that waiting time, staff attitude, food quality and food variety all significantly affect customer satisfaction. It is also found that the significance of the relationship depends on the timing of the visits. These models will help managers to understand the critical factors that influence customer loyalty and satisfaction in the fast food industry and help them make improvements accordingly.

The reliability of a product is generally concerned with failures during its operating life. Accelerated (stress) burn‐in before shipment will reject poor quality products and avoid a high initial mortality rate in residual life. It is justified when the expected proportion of a product being defective is high in the infant stage and where early removal of defects will improve reliability in service. Studies economic designs of stress screening plans where every finished product is subject to burn‐in. Examines the optimal burn‐in time in minimizing total testing, manufacturing, quality and reliability costs. Discusses the effect of burn‐in stress level.

Burn‐in is an effective means of screening out the infant mortality components of a system. Most related studies investigated burn‐in by curve fitting for a failure model and cost for an optimal burn‐in time model and environmental stress model. However, those investigations did not provide an effective method to determine the optimal burn‐in condition for a practical operation. As a result, this study presents an effective procedure that uses part stress analysis and robust design techniques to determine the optimal burn‐in operational environment. A case study of the production of a switch mode rectifier is performed and compared with the traditional approach, to examine the proposed procedure’s effectiveness. Those results show that the proposed procedure generalizes well, and can screen out the early failure of material and manufacturing process.

The research presented in this study replicates and extends the QSport-10 scale proposed by Rial, Varela, Rial and Real in their 2010 study, by incorporating the Programme dimension into the original measurement of service quality in Physical Activity and Sports Centres (PSCs). The objective of this research is to examine the dimensionality of the QSport-10 scale and extend it to capture the additional Programme service quality dimension. Study results confirmed the dimensionality of the service quality measurement offered by the QSport-10 scale and presented strong empirical support for the existence of Programme, Installations and Staff dimensions. Programme dimension was considered the most important factor for the largest consumer segment in the study.