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Concerns the logistics system of the Taiwan power company, Taipower, and the problem it faces of coal allocation and fleet deployment of dedicated coal carriers. The coal allocation problem can be viewed as a multi‐index transportation problem requiring the formulation of a coal allocation model. Owing to the high uncertainty of shipping operations, formulates a dynamic fleet assignment model to dispatch vessels and maximize ship usage. An integrated coal allocation and fleet assignment decision support system based on these models was developed to assist the decision maker. It not only guides Taipower marine operations management in annual schedule planning, but also assists operating personnel to allocate vessels to the schedules and to make rapid adjustments. Demonstrates the potential for substantial cost improvement in Taipower coal trade.

The purpose of this paper is to improve the tactical planning of the stakeholders of the midstream liquefied natural gas (LNG) supply chain, using an optimisation approach. The results can contribute to enhance the proactivity on significant investment decisions.

A decision support tool (DST) is proposed to minimise the operational cost of a fleet of vessels. Mixed integer linear programming (MILP) used to perform contract assignment combined with a genetic algorithm solution are the foundations of the DST. The aforementioned methods present a formulation of the maritime transportation problem from the scope of tramp shipping companies.

The validation of the DST through a realistic case study illustrates its potential in generating quantitative data about the cost of the midstream LNG supply chain and the annual operations schedule for a fleet of LNG vessels.

The LNG transportation scenarios included assumptions, which were required for resource reasons, such as omission of stochasticity. Notwithstanding the assumptions made, it is to the authors’ belief that the paper meets its objectives as described above.

Potential practitioners may exploit the results to make informed decisions on the operation of LNG vessels, charter rate quotes and/or redeployment of existing fleet.

The research has a novel approach as it combines the creation of practical management tool, with a comprehensive mathematical modelling, for the midstream LNG supply chain. Quantifying future fleet costs is an alternative approach, which may improve the planning procedure of a tramp shipping company.

The purpose of this paper is twofold: first to carry out a comprehensive literature review for state of the art regarding airline schedule planning and second to identify some new research directions that might help academic researchers and practitioners.

The authors mainly focus on the research work appeared in the last three decades. The search process was conducted in database searches using four keywords: “Flight scheduling,” “Fleet assignment,” “Aircraft maintenance routing” (AMR), and “Crew scheduling”. Moreover, the combination of the keywords was used to find the integrated models. Any duplications due to database variety and the articles that were written in non-English language were discarded.

The authors studied 106 research papers and categorized them into five categories. In addition, according to the model features, subcategories were further identified. Moreover, after discussing up-to-date research work, the authors suggested some future directions in order to contribute to the existing literature.

The presented categories and subcategories were based on the model characteristics rather than the model formulation and solution methodology that are commonly used in the literature. One advantage of this classification is that it might help scholars to deeply understand the main variation between the models. On the other hand, identifying future research opportunities should help academic researchers and practitioners to develop new models and improve the performance of the existing models.

This study proposed some considerations in order to enhance the efficiency of the schedule planning process practically, for example, using the dynamic Stackelberg game strategy for market competition in flight scheduling, considering re-fleeting mechanism under heterogeneous fleet for fleet assignment, and considering the stochastic departure and arrival times for AMR.

In the literature, all the review papers focused only on one category of the five categories. Then, this category was classified according to the model formulation and solution methodology. However, in this work, the authors attempted to propose a comprehensive review for all categories for the first time and develop new classifications for each category. The proposed classifications are hence novel and significant.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.

The purpose of this paper is to provide some heuristic and meta heuristic tools to aid airline companies in flight planning while taking into account maintenance planning. The objective is to maximize aircraft utilization before the maintenance interventions and to smooth the long‐term flight load of the aircraft so that maintenance checks are regular for all the fleet.

The proposed methods, based on operational research solution technics, build a flight planning for an airline company. The simulation of the proposed methods is observed over 40 weeks and evaluated with different problem instances and maintenance policies.

The longest path based method with increasing priority and the simulated annealing are shown to have the best aircraft utilization results.

Further research could propose some methods which build simultaneous maintenance and flight planning.

The economic value and legal considerations of maintenance activities in the airline industry show the importance of maximizing aircraft utilization. The proposed methods are compared to decide the best maintenance policy. These methods are simple and efficient.

This paper provides a connection between an industrial problem of aircraft maximization under maintenance constraints and operational research. Simple but efficient methods are evaluated in terms of two criteria: aircraft maximization and flight load smoothing.

The life span of an aircraft is usually around 30 years in the commercial aviation industry. During this time span, aircraft needs maintenance to stay in service. The cost of maintenance, repair and overhaul (MRO) activities in its pure nature is a significant portion of operations, accounting around 10 percent of all cost drivers. The purpose of this paper is to design/develop and critically assess a comprehensive model of operations at Turkish Technic – the MRO department of Turkish Airlines.

A comprehensive systems dynamics model is designed and developed to holistically represent and critically assess the different facets of MRO operations to help in analyzing various decision scenarios at Turkish Airlines.

The developed system dynamics (SD) model presented unique opportunities to test various MRO operations’ work load and aircraft fleet expansion policy alternatives. The model can also be used as a “learning laboratory” by altering various system parameters and testing different policies. The case study results suggested that MRO operations have a direct impact on the available number of airworthy aircrafts and hence, the usable fleet seat capacity; to sustain a profitable airline fleet, the airline companies should take into account the unique characteristics/needs of MRO operations for both existing and new/prospective aircrafts.

There are several SD studies in the literature focusing on the airline industry, but the MRO operations are virtually neglected in them. Hence, the proposed SD model contributed to the extant literature. The value of the developed model stems from its potential use in the critical analysis of decision scenarios as well as being leveraged as a training/learning laboratory.

The purpose of this paper is to investigate the feasibility of solving an integrated flight scheduling, fleet assignment and crew pairing problem for an on-demand service using a small, up to 19-seater, aircraft.

Evolutionary algorithm is developed to solve the problem. Algorithm design assumes indirect solution representation that allows to evaluate partially feasible solutions only and speed up calculations. Tested algorithm implementation takes advantage of the graphic processing unit.

Performed tests confirm that the algorithm can successfully solve the defined integrated scheduling problem.

The presented algorithm allows to optimise on-demand transport service operation within minutes.

Optimisation of operation cost contributes to better accessibility of transport.

The presented integrated formulation allows to avoid sub optimal solutions that are results of solving sequential sub problems. Indirect representation and evaluation strategy can be applied to speed up calculations in other problems as well.

The objective of this work is to provide a novel aircraft allocation model for fractional business aviation. This model may provide decision-makers with alternative routing solutions that take into consideration preventive maintenance and failure prognostics information. The expected results are more efficient routing solutions when compared to conventional planning models, to help decision-makers improve operations and maintenance planning.

The model is a mixed integer linear problem formulation addressing and considering preventive maintenance and failure prognostics for optimal operations. Numerical experiments were performed using both field and synthetic data to validate the proposed method. All instances are solved using branch, price and cut algorithms from open-source software.

The results obtained in this study show that the use of failure prognostics information in aircraft routing can provide improvements in overall planning. By choosing slightly longer flight legs, the flight cost will increase, but putting an aircraft with a higher risk of failure on a leg inbound to a maintenance base can reduce maintenance and overall operating cost.

The model and method provide decision-makers with routing solutions that consider new aspects of planning, not used in previous works, such as failure. Most of the literature focuses on solving routing problems for large commercial airlines. Considering that, few solutions are found in literature for fractional business operators, which have their own operational particularities, such as a company managing a fleet of aircraft belonging to multiple shareowners. In such operation, clients may not always fly in the aircraft that they are shareowners, but an aircraft from the fractional fleet of the same category. Here, the company managing the aircraft guarantees that an aircraft will be ready to attend client demands in minimum time. One of the major differences from other models of operation is the dynamic nature of its flight demands, thus requiring flexible and agile planning limiting the available time to find a routing solution.

The purpose of this paper is to develop an original model and a solution procedure for solving jointly three main strategic fleet management problems (fleet composition, replacement and make-or-buy), taking into account interdependencies between them.

The three main strategic fleet management problems were analyzed in detail to identify interdependencies between them, mathematically modeled in terms of integer nonlinear programing (INLP) and solved using evolutionary based method of a solver compatible with a spreadsheet.

There are no optimization methods combining the analyzed problems, but it is possible to mathematically model them jointly and solve together using a solver compatible with a spreadsheet obtaining a solution/fleet management strategy answering the questions: Keep currently exploited vehicles in a fleet or remove them? If keep, how often to replace them? If remove then when? How many perspective/new vehicles, of what types, brand new or used ones and when should be put into a fleet? The relatively large scale instance of problem (50 vehicles) was solved based on a real-life data. The obtained results occurred to be better/cheaper by 10% than the two reference solutions – random and do-nothing ones.

The methodology of developing optimal fleet management strategy by solving jointly three main strategic fleet management problems is proposed allowing for the reduction of the fleet exploitation costs by adjusting fleet size, types of exploited vehicles and their exploitation periods.