Search results

Under the constraints of given passenger service level and coupling travel demand with train departure time, this study optimizes the train operational plan in an urban rail corridor to minimize the numbers of train trips and rolling stocks considering the time-varying demand of urban rail passenger flow.

The authors optimize the train operational plan in a special network layout, i.e. an urban rail corridor with dead-end terminal yard, by decomposing it into two sub-problems: train timetable optimization and rolling stock circulation optimization. As for train timetable optimization, the authors propose a schedule-based passenger flow assignment method, construct the corresponding timetabling optimization model and design the bi-directional coordinated sequential optimization algorithm. For the optimization of rolling stock circulation, the authors construct the corresponding optimization assignment model and adopt the Hungary algorithm for solving the model.

The case study shows that the train operational plan developed by the study's approach meets requirements on the passenger service quality and reduces the operational cost to the maximum by minimizing the numbers of train trips and rolling stocks.

The example verifies the efficiency of the model and algorithm.

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 global energy industry transports supplies and personnel via helicopter to offshore locations and is increasingly focusing on optimizing upstream logistics. This paper aims to and achieves a mutually beneficial balance between research and practice by providing generalizable methods to a problem routinely encountered in practice. Overall, the development and execution of the heterogeneous capacitated helicopter routing problem with split deliveries and multiple depots is validated by the networks’ results.

Using a unique sample of deepwater and ultra-deepwater permanent offshore locations in the Gulf of Mexico, transportation networks consisting of 57 locations operated by 19 firms are optimized via a randomized greedy algorithm. The study’s randomized greedy algorithm yields depot assignment, vehicle assignment, passenger assignment and routing. All data processing techniques and iterative algorithm processes are defined and explained.

Results show that the model effectively solves the complex transportation networks consisting of subject firms’ offshore nodes and eligible depots. Specifically, average load factors related to seat capacity and effective vehicle capacity of 87.7 and 95.7% are realized, respectively. The study’s model is a unique contribution to the extant literature and provides researchers and practitioners a practical approach to model development and solution deliverance.

The extant literature encompasses works that inadequately observe the complexity associated with the transportation of personnel. Specifically, this research, unlike many works in the extant literature, uses a heterogeneous versus homogeneous fleet, includes multiple depots versus a single depot and allows split deliveries. Also, the current research ensures all relevant aircraft capabilities and limitations are observed. In particular, the paper takes into account vehicles’ seat capacities, effective capacities via maximum gross takeoff weights and reserve fuel requirements. The current model, which is built upon a heterogeneous capacitated helicopter routing problem with split deliveries and multiple depots (HCHRPSDMD), sufficiently provides a practical approach to model development and solution deliverance while promoting future research endeavors. Future research may use these findings for other geographical regions and similar transportation networks and could adopt firm-specific actual cost parameters instead of the estimated average hourly costs of operating different helicopters. Furthermore, future endeavors may employ other techniques for the derivation of solutions. Future works may be enhanced with actual cost data in lieu of estimations. In the current study, cost data were not available; however, estimations do not inherently proscribe sound interpretations of the models’ outputs. Also, future research endeavors including manual method results may enable comparative results to establish cost variance analysis. Although the current study is, to some extent, limited, the practicality for practitioners and contribution to researchers is comprehensible. Due to the idiosyncrasies and complexity prevalent in modern transportation networks, optimization is and will continue to be a rich opportunity for implementation and research.

As described by previous researchers, energy firms may more efficiently use their contracted aircraft via implementation of a decision-making mechanism for passenger assignment, aircraft selection, depot selection and aircraft routing. Most energy firms possess numerous and spatially segregated offshore facilities and, therefore, are unable to efficiently and effectively make such decisions. Ultimately, the efficient use of firms’ contracted helicopters can enhance profitability via reduced costs without compromising operational performance. Reduced costs are likely to be realized by a potential workforce or workload reduction, reduced flight hours and enhanced bargaining power with commercial helicopter operators. Specifically, enhanced bargaining power may be realized as a result of minimized depots from which the aircraft are operated and an overall reduction of aircraft via increased asset utilization. In essence, the efficient use of commercial helicopters may yield systemic efficiencies that can be shared among all stakeholders, contracting energy firms and commercial helicopter operators. The achievement of operational efficiencies, ultimately, may determine the realization of target performance or solvency of a plethora of firms in the future (Krishnan et al., 2019).

For economies, communities and industries depending on crude oil and natural gas production, people’s livelihoods are significantly impacted due to price fluctuations (Rostan and Rostan, 2020; Solaymani, 2019). Based on a unique set of inputs and outputs, the International Energy Agency region (IEA), which includes the current study’s sample set, was found to achieve greater overall production efficiency relative to the Organization of the Petroleum Exporting Countries (OPEC) and the Organization of Arab Petroleum Exporting Countries (OAPEC) (Ohene-Asare et al., 2018). Therefore, enhanced logistics efficiency within the energy industry’s transportation sector across the globe is reasonably likely. For countries relying on these commodities’ exportation, production efficiency is and will continue to be a priority. With limited resources available in industry and society, efficiency is prone to yield advantageous results for all stakeholders. Furthermore, in the context of this study, a reduction of carbon dioxide and noise pollution in air, above water and on land will contribute to society’s drive to protect the environment and preserve our natural resources for future generations.

The current study represents the lone or one of few research endeavors to evaluate the heterogeneous capacitated helicopter routing problem with split deliveries and multiple depots. Furthermore, research pertaining to transportation via helicopter in the Gulf of Mexico’s offshore basin is unprecedented. Lastly, this work yields actionable knowledge for practitioners while enhancing current and promoting future research endeavors.

This study aims to propose a new airport gate assignment method to effectively improve the comprehensive operation capacity and efficiency of hub airport. Gate assignment is one of the most important tasks for airport ground operations, which assigns appropriate airport gates with high efficiency reasonable arrangement.

In this paper, on the basis of analyzing the characteristics of airport gates and flights, an efficient multi-objective optimization model of airport gate assignment based on the objectives of the most balanced idle time, the shortest walking distances of passengers and the least number of flights at apron is constructed. Then an improved ant colony optimization (ICQACO) algorithm based on the ant colony collaborative strategy and pheromone update strategy is designed to solve the constructed model to fast realize the gate assignment and obtain a rational and effective gate assignment result for all flights in the different period.

In the designed ICQACO algorithm, the ant colony collaborative strategy is used to avoid the rapid convergence to the local optimal solution, and the pheromone update strategy is used to quickly increase the pheromone amount, eliminate the interference of the poor path and greatly accelerate the convergence speed.

The actual flight data from Guangzhou Baiyun airport of China is selected to verify the feasibility and effectiveness of the constructed multi-objective optimization model and the designed ICQACO algorithm. The experimental results show that the designed ICQACO algorithm can increase the pheromone amount, accelerate the convergence speed and avoid to fall into the local optimal solution. The constructed multi-objective optimization model can effectively improve the comprehensive operation capacity and efficiency. This study is a very meaningful work for airport gate assignment.

An efficient multi-objective optimization model for hub airport gate assignment problem is proposed in this paper. An improved ant colony optimization algorithm based on ant colony collaborative strategy and the pheromone update strategy is deeply studied to speed up the convergence and avoid to fall into the local optimal solution.

The purpose of this paper is to provide feasible and fast solutions for the multi-objective airport gate assignment problem (AGAP) considering both passenger-oriented and airline-oriented objectives, which is the total walking distance from gate to baggage carousels (TWD) and the total aircraft fuel consumption during taxi operations (TFC). In addition, obtaining feasible and near-optimal solutions in a short time reduces the gate planning time to be spent by air traffic controllers.

The mixed integer linear programming (MILP) approach is implemented to solve the multi-objective AGAP. The weighted sum approach technique was applied in the model to obtain non-dominated solutions. Because of the complexity of the problem, the simulated annealing (SA) algorithm was used for the proposed model. The results were compared with baseline results, which were obtained from the algorithm using the fastest gate assignment and baggage carousel combinations without any conflict taking place at the gate assignments.

The proposed model noticeably decreased both the TWD and TFC. The improvement of the TWD and TFC changed from 22.8% to 46.9% and from 4.7% to 7.1%, respectively, according to the priorities of the objectives. Additionally, the average number of non-dominated solutions was calculated as 6.94, which presents many feasible solutions for air traffic controllers to manage ground traffic while taking the airline and passenger objectives into consideration.

The proposed MILP model includes the objectives of different stakeholders: air traffic controllers, passengers and airlines. In addition, the proposed model can provide feasible gate and baggage carousel assignments together in a short time. Therefore, the model creates a flexibility for air traffic controllers to re-arrange assignments if any unexpected situations take place.

The proposed MILP model combines the TWD and TFC together for the AGAP problem using the SA. Moreover, the proposed model integrates passenger-oriented and airline-oriented objectives together and reveals the relationships between the objectives in only a short time.