A new hybrid simulation-based assignment approach for evaluating airlines with multiple service quality criteria

Evaluation of airlines based on service quality criteria can help to improve the processes of airlines, and also can give guidance to travel agencies to provide better choices for passengers and tourists. In this study, a hybrid simulation-based assignment approach is proposed to deal with multi-criteria decision-making problems with a group of decision-makers. A probability distribution is used to model decision-makers’ opinions and constructing a stochastic decision matrix. Then some efficient multi-criteria decision-making methods are utilized for evaluating alternatives in a simulation process. The proposed approach is applied to a problem of evaluation of five airlines with respect to opinions of 58 experts on 28 criteria. The results show the efficiency of the proposed to handle decision-making problems with a large number of experts. Moreover, the evaluation results are more reliable than the other decision-making approaches because of simulating decision-makers’ opinions, using multiple methods and evaluating based on aggregative results.     

Balanced scorecard based performance measurement of European airlines using a hybrid multicriteria decision making approach under the fuzzy environment

The purpose of this study is to evaluate the performance of the European airlines, using a balanced scorecard perspective. Within this scope, a hybrid multi-criteria approach was used by combining the Fuzzy DEMATEL, Fuzzy ANP, and MOORA methods. The results demonstrate that customer dimensions and profit per customer are the most significant key factors in the balanced scorecard perspective. Additionally, the airline companies with the largest profit (per employee) and highest number of passengers and flights (per employee) had the best scores in the multidimensional performance results. Furthermore, the airline companies with the highest profitability and efficiency are more successful than other companies. Therefore, we recommend European airlines to focus on these aspects in order to improve their performance. This study makes an important contribution to literature by helping to solve a significant problem in the market with the proposed methodology.     

Ecotourism branding in protected areas of Iran: Using an efficient hybrid multi-criteria decision-making method model

Ecotourism, as a nature-based sustainable tourism, raises awareness about the protection of natural and cultural landscape values. This study aims to determine the ecotourism criteria to develop strategies for a sustainable ecotourism branding in Golestan, located in the northeast of Iran. To reach this aim, Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis, multi-criteria decision-making methods consisting of Analytic Hierarchy Process (AHP), and Best and Worst Method (BWM) were integrated. The most appropriate ecotourism strategies for the branding of the study area were ranked according to their priorities. The results of this study reveal that among the 5 main and 20 sub ecotourism criteria, “having natural protected areas,” which is an opportunity, and “having ecological values,” which is a strength, were determined as the highest priority strategies for ecotourism branding. The methods and results of this quantitative-based research present an exemplary hybrid model that prioritizes the strategies required for branding ecotourism in similar protected areas.     

Progress on outbound tourism expenditure research: A review

ABSTRACT

This study aims to identify how the paradigm of outbound tourism expenditure (OTE) research transforms from economic to social concern. It also explicates the evolution of OTE from an advocacy platform to a sustainability platform. This study adopts a hybrid of narrative and systematic reviews to study OTE as a complex social phenomenon. This hybrid review is complemented by a thematic review and semantic network analysis on gaps and future directions of relevant studies. The results reveal that the paradigm of OTE research is directed from economic toward social thinking. This study proposes an application of socially related antecedent configurations, social theories, pragmatic methods, and various scales of study contexts as promising solutions to address the complexity and heterogeneity of OTE. The study concludes that the conceptual structure of OTE is premised on a sustainability platform, which is influenced by socio-cultural, environmental, economic, and political issues. This study provides a road map that enlightens the current state of OTE, prevailing topics, and pathways for further research.     

An adaptive approach to implementing innovative urban transport solutions

Urban transport is facing an increasing number of problems. Innovative technological solutions have been proposed for many of these problems. The implementation of these solutions, however, is surrounded by many uncertainties—for example, future relevant developments for urban transport demand and supply, the possible consequences of these developments for urban transportation system performance, and the way crucial stakeholders will value these consequences. In order to deal with these uncertainties, a flexible or adaptive policy is proposed that takes some actions right away and creates a framework for future actions that allow for adaptations over time as knowledge about urban transport technologies accumulates and critical events with respect to the implementation of these technologies take place. The adaptive approach is illustrated for three promising technological solutions for urban transport problems: (1) intelligent speed adaptation, (2) personal intelligent travel assistant, and (3) underground freight transport.     

Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches

In this study, two hybrid approaches based on seasonal decomposition and least squares support vector regression (LSSVR) model are proposed for short-term forecasting of air passenger. In the formulation of the proposed hybrid approaches, the air passenger time series is first decomposed into three components: trend-cycle component, seasonal factor and irregular component. Then the LSSVR model is used to predict the components independently and these prediction results of the components are combined as an aggregated output. Empirical analysis shows that the proposed hybrid approaches are better than other time series models, indicating that they are promising tools to predict complex time series with high volatility and irregularity.     

Combining Lagrangian heuristic and Ant Colony System to solve the Single Source Capacitated Facility Location Problem

The facility location problems have been applied extensively in practice. We describe a Multiple Ant Colony System (MACS) to solve the Single Source Capacitated Facility Location Problem (SSCFLP). Lagrangian heuristics have been shown to produce good solutions for the SSCFLP. A hybrid algorithm, which combines Lagrangian heuristic and Ant Colony System (ACS), LH–ACS, is developed for the SSCFLP. The performance of the proposed methods are tested on two sets of benchmark instances and compared with other heuristic algorithms in the literature. The computational results indicate that both MACS and LH–ACS are effective and efficient for the SSCFLP and competitive with other well-known algorithms.     

An emergency logistics distribution approach for quick response to urgent relief demand in disasters

Quick response to the urgent relief needs right after natural disasters through efficient emergency logistics distribution is vital to the alleviation of disaster impact in the affected areas, which remains challenging in the field of logistics and related study areas. This paper presents a hybrid fuzzy clustering-optimization approach to the operation of emergency logistics co-distribution responding to the urgent relief demands in the crucial rescue period. Based on a proposed three-layer emergency logistics co-distribution conceptual framework, the proposed methodology involves two recursive mechanisms: (1) disaster-affected area grouping, and (2) relief co-distribution. Numerical studies with a real large-scale earthquake disaster occurring in Taiwan are conducted, and the corresponding results indicate the applicability of the proposed method and its potential advantages. We hope that this study can not only make the proposed emergency logistics system available with more benefits to the development of emergency logistics systems for the urgent needs of disaster areas around the world but also stimulate more excellent researches concerning emergency logistics management.     

Reducing the wasted transportation capacity of Personal Rapid Transit systems: An integrated model and multi-objective optimization approach

This study addresses the problem of wasted transportation capacity in Personal Rapid Transit (PRT) systems. We propose a two-tier transportation model that integrates PRT and capillary transportation systems. We study a related multi-objective empty vehicle redistribution problem that attempts to minimize empty movement and the number of vehicles used. Furthermore, we design a hybrid multi-objective genetic algorithm that integrates multiple crossover operators and linear programming techniques to solve the proposed problem. Evaluations indicate that our algorithm produces satisfactory results, and simulations confirm the efficiency of our proposed two-tier transportation system.     

A heuristic algorithm for yard truck scheduling and storage allocation problems

The yard truck scheduling and the storage allocation are two important decision problems affecting the efficiency of container terminal operations. This paper proposes a novel approach that integrates these two problems into a whole. The objective is to minimize the weighted sum of total delay of requests and the total travel time of yard trucks. Due to the intractability of the proposed problem, a hybrid insertion algorithm is designed for effective problem solutions. Computational experiments are conducted to examine the key factors of the problem and the performance of the proposed heuristic algorithm.     

A hybrid parallel genetic algorithm for yard crane scheduling

This paper aims at postulating a novel strategy in terms of yard crane scheduling. In this study, a dynamic scheduling model using objective programming for yard cranes is initially developed based on rolling-horizon approach. To resolve the NP-complete problem regarding the yard crane scheduling, a hybrid algorithm, which employs heuristic rules and parallel genetic algorithm (PGA), is then employed. Then a simulation model is developed for evaluating this approach. Finally, numerical experiments on a specific container terminal yard are used for system illustration. Computational results suggest that the proposed method is able to solve the problem efficiently.     

FIPIA with information entropy: A new hybrid method to assess airline service quality

This study proposes “FIPIA with information entropy” as a new, hybrid method to assess airline service quality by identifying the most important priorities for airline passengers and producing recommendations to airline management for optimal resource allocation to improve service quality and customer satisfaction. The proposed method is an improvement over IPA, IPIA and FIPA methods, through the introduction of information entropy and fuzzy logic to the analysis of importance, performance and impact dimensions of airline service quality to improve interpretability and actionability of analysis results. This study also offers airline managers a list of what they should improve in resource allocation in order to increase service quality considering customer satisfaction and create value by managing the relational capital more effectively. The new hybrid method was field-tested by administering a 26-item questionnaire to passengers of a major airline operator, analyzing the responses using the Importance-Performance-Impact Analysis (IPIA) method, fuzzy logic and information entropy. The analysis revealed four main dimensions of airline service quality, namely reliability, assurance, tangibles, empathy and responsiveness with 17 constituent attributes. The case study revealed that (1) resource allocation was adequate only on four attributes; (2) seven service quality attributes were identified as needing further management focus on resource allocation; (3) six service quality attributes received more resources than necessary which should be shifted to other attributes; (4) dimensions of reliability and tangibles needed more focus than others. The proposed hybrid method of FIPIA with information entropy can be employed for any industry where service quality depends on multiple attributes.     

Performance evaluation of Italian airports: A data envelopment analysis

This paper addresses empirically financial and operational performance of Italian airports using data envelopment analysis (DEA) methodology. With panel data for 2001–2003, the study tests variable relationships—the relative roles of dimension, managerial status and workload unit—to measure the proximity of the airports to the frontiers of best practices. The implications of the research for managerial purposes are then drawn to validate prior findings of the same relationships in other geographical contexts.     

Economic appraisal in Norwegian aviation

The Norwegian Civil Aviation Administration (NCAA) is responsible for investments and operations regarding the Norwegian airport system, and the air traffic management systems. This paper presents methods for economic appraisal in the aviation sector, proposed for use within the NCAA's jurisdiction. Methodological problems will be addressed, such as the handling of project risk and uncertainty, projects with mutual dependency, and projects influencing accident risks. The cost–benefit analysis methodology proposed here may give conflicting recommendations compared with financial analyses. This raises decision-making problems for public agencies like the NCAA.     

A hybrid population heuristic for the heterogeneous vehicle routing problems

The heterogeneous vehicle routing problem (HVRP) plays an important role in supply chain logistics. Two variants of HVRP are treated in this paper: one with fixed and variable costs (HVRPFD), and the other with only variable cost (HVRPD). A hybrid population heuristic that is able to solve both variants is proposed, in which a population of solutions are progressively evolved by crossovers and local searches. Computational results on a set of eight benchmark test problems from literature show that the proposed heuristic produces excellent solutions in short computing times.     

Joint implementation of tradable credit and road pricing in public-private partnership networks considering mixed equilibrium behaviors

This paper investigates joint road charging schemes in a public-private partnership (PPP) network by simultaneously taking into account Cournot-Nash (CN) players and user equilibrium (UE) players. Each joint scheme comprises a tradable credit plan for public roads and a regular tolling plan for private roads. We show that, under UE-CN mixed equilibrium, there exist anonymous nonnegative joint schemes that can support a system optimum link flow pattern. By using preemptive approach, we further design three bi-objective optimization models with hybrid implementation of tradable credit and road pricing. Numerical examples demonstrate that the proposed methods are effective in managing PPP networks.     

Path finding with stowage planning consideration in a mixed pickup–delivery and specified-node network

This paper considers the mixed pickup–delivery shortest path problem with specified nodes and stowage planning (mixed-PDSPSNSP), which combines path finding and three-dimensional stowage planning. The mixed-PDSPSNSP appears in many operational problems in various industries. However, to the best of our knowledge, there is no study in the literature for such a problem. The mixed-PDSPSNSP is NP-hard. Our proposed solution algorithm is shown to be effective and efficient when applied to a real-life mixed-PDSPSNSP.     

Revealing travel patterns and city structure with taxi trip data

Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of taxi-trip data from Shanghai, we built spatially embedded networks to model intra-city spatial interactions and to introduce network science methods into the analysis. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long- and short-distance trips, we reveal a two-level hierarchical polycentric city structure in Shanghai. Further explorations of sub-network structures demonstrate that urban sub-regions have broader internal spatial interactions, while suburban centers are more influential on local traffic. By incorporating the land use of centers from a travel-pattern perspective, we investigate sub-region formation and the interaction patterns of center–local places. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in developing and applying urban transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than for other structures, such as administrative divisions.     

Aftermarket vehicle hybridization: Potential market penetration and environmental benefits of a hybrid-solar kit

The possibility of upgrading conventional vehicles to hybrid electric vehicles has recently gained considerable interest. Among the diverse options for hybridization, researchers of the Energy and Propulsion Laboratory at the University of Salerno (Italy) have patented a system based on the electrification of the rear wheels in front-wheel-drive vehicles by adopting in-wheel motors and adding a lithium-ion battery, which is rechargeable via plug-in and solar mode. The technology integrates various components, in-vehicle Intelligent Transportation Systems and advanced on-line management algorithms. With respect to the proposed technological context, this paper uses a real case study to investigate the potential market penetration, the achievable environmental benefits and relevant policy issues. To this aim, the paper first analyses the main behavioral determinants that may affect users' perception of this new technology, then specifies and calibrates an installation choice model, and finally develops bottom-up methodology to estimate the environmental benefits to be obtained from different commercialization scenarios. The proposed methodology was applied to the city of Salerno and the achievable benefits were compared to those obtainable through the implementation of traditional transport policies.     

Integrating berth allocation and quay crane assignments

In this study, a dynamic allocation model using objective programming for berth allocation and quay crane assignments was preliminarily developed based on rolling-horizon approach. Afterwards, a hybrid parallel genetic algorithm (HPGA), which combined parallel genetic algorithm (PGA) and heuristic algorithm, was employed to resolve the proposed model. Furthermore, a simulation was conducted to evaluate the HPGA and to execute relevant gene repair techniques. Eventually, the numerical experiments on a specific container terminal were applied to illustrate the proposed models and algorithms. In so doing, the effectiveness of the proposed approach was verified.