Dispatching Design for Customized Bus of Hybrid Vehicles Based on Reservation Data

Proper vehicle operation and route planning are critical for achieving the best match between bus operation and passenger services. In order to enhance the attractiveness of public transportation, a new type of the public transportation dispatching method based on passenger reservation data is proposed. This mode can meet the requirements of multiple lines in urban centers during peak hours, which can realize direct service between two stations. Then, taking the lowest operating cost of the enterprise and the lowest passenger waiting cost as the optimization goal, a customized dynamic dispatching model of multiline and hybrid vehicles was established. Finally, a calculation example is designed and the genetic algorithm is used to solve the model. The results show that the hybrid vehicle solution is more reasonable than the traditional single-vehicle solution and reveal that the model is feasible to optimize scheduling plan. The conclusions obtained in this research lay a theoretical foundation for APP setup and operation plan formulation.

Research on Airport Scheduling of FGAP Multi-Objective Programming Model Based on Uncertainty Theory

Based on the current background of airport management and flight-gate scheduling in China, this paper takes Shanghai Pudong International Airport’s flight number of the rising and landing aircraft in a certain day as the research object, and it establishes an uncertain FGAP (Flight-Gate Assignment Problem) multi-objective programming model under the framework of uncertainty theory. Using genetic algorithm to solve the model, the specific flight-gate assignment scheduling plan is given. The research results show that the model in this paper can effectively alleviate the problems, such as unbalanced flight-gate allocation and excessive operating pressure of a single gate, in the conventional model, and make the allocation and scheduling more reasonable and efficient. Finally, we give the future application of uncertainty theory in finance and management, as well as the prospect of combining it with symmetry in physics.

Investigation of Vehicle Logistics Cost Control Model Based on Internet of Things Mode Based on E-commerce Platform

Objectives: The rise of the Internet of Things and e-commerce platform has enabled logistics companies to embark on a fast-growing road. How to effectively control the cost of vehicle transportation under the condition of continuous increase in the total volume of logistics business is the key to influencing the sustainable development of logistics enterprises. Methods: In order to reduce the cost of vehicle transportation, the use of artificial intelligence ant colony algorithm to build intelligent deployment model is explored. Results: After analyzing the principle and implementation flow of the traditional ant colony algorithm, the ant colony algorithm is updated and optimized from the perspective of many dynamic factors and large changes in the logistics vehicle. Conclusion: A combination of optimal algorithm parameters is constructed to help logistics companies find the most cost-effective vehicle scheduling plan. Simulation tests show that the optimized ant colony algorithm can quickly find the optimal cost-effective route and effectively control the vehicle logistics costs.

Parallel Hybrid Particle Swarm Algorithm for Workshop Scheduling Based on Spark

In hybrid mixed-flow workshop scheduling, there are problems such as mass production, mass manufacturing, mass assembly and mass synthesis of products. In order to solve these problems, combined with the Spark platform, a hybrid particle swarm algorithm that will be parallelized is proposed. Compared with the existing intelligent algorithms, the parallel hybrid particle swarm algorithm is more conducive to the realization of the global optimal solution. In the loader manufacturing workshop, the optimization goal is to minimize the maximum completion time and a parallelized hybrid particle swarm algorithm is used. The results show that in the case of relatively large batches, the parallel hybrid particle swarm algorithm can effectively obtain the scheduling plan and avoid falling into the local optimal solution. Compared with algorithm serialization, algorithm parallelization improves algorithm efficiency by 2–4 times. The larger the batches, the more obvious the algorithm parallelization improves computational efficiency.

Model and algorithm for the first-level maintenance operation optimization of EMU trains

As a key operation for the daily maintenance of electric multiple units (EMU), the first-level maintenance operation directly affects the utilization efficiency of the EMU. The fixed operation sequence of EMU trains, the limitation of the track capacity and inconsistent arrival time of EMU trains give rise to such problems as extended waiting time, idle tracks and waste of maintenance capacity. To solve these problems and optimize the assignment of EMU-to-track, we propose a flexible job-shop sequence scheduling (Flexible-JSS) mode for the first-level maintenance of EMU trains, and a flexible sequence and tracks sharing (FSTS) model for the first-level maintenance at electric multiple units depot (EMUD) has also been proposed in this paper. The FSTS model is designed to shorten the latest completion time after taking into account the constraints such as the train length, track capacity, the operation sequence of all EMU trains, the operation process of a single EMU train, and the train-set scheduling plan. A modified genetic algorithm is used to solve the model. The feasibility and effectiveness of the model and algorithm are verified by a real case, and the comparison with the other two fixed job-shop sequence scheduling (Fixed-JSS) modes proves that the Flexible-JSS mode can improve the efficiency and ability of the first-level maintenance at EMUD impressively.

Water Pipe Replacement Scheduling Based on Life Cycle Cost Assessment and Optimization Algorithm

Water distribution networks (WDNs) comprise a complex network of pipes and are crucial for providing potable water to urban communities. Therefore, WDNs must be carefully managed to avoid problems such as water contamination and service failures; however, this requires a large budget. Because WDN components have different statuses depending on their installation year, location, transmission pressure, and flow rate, it is difficult to plan the rehabilitation schedule within budgetary constraints. This study, therefore, proposes a new pipe replacement scheduling approach to smooth the investment time series based on a life cycle cost (LCC) assessment for a large-scale WDN. The proposed scheduling plan simultaneously considers both the annual budget limitation and the optimum expenditure on the useful life of pipes. A multi-objective optimization problem consisting of three decision-making objectives—minimum imposed LCC on the network, minimum standard deviation of annual investment, and minimum average age of the network—is thus solved using a nondominated sorting genetic algorithm to obtain an optimal plan. Three scenarios with different pipe replacement time spans and different annual budget constraints are considered accordingly. The results indicate that the proposed scheduling framework provides an efficient water pipe replacement scheduling plan with a smooth management budget.

A Multi-objective Optimal Dispatch Method for Integrated Energy System Considering Multiple Loads Variations

This paper proposes a multi-objective optimization dispatching method for the Integrated Energy System (IES), in which cooling/heating/electricity multi-load demand are included, and IES’s requirements in terms of energy efficiency, economy, and emissions are considered. We established an IES model for multi-objective optimal dispatch, and simulated cooling/heating/electric multiple load variations in it; then we established multiple objective functions based on IES’s scheduling requirements in terms of energy efficiency, economy, environmental issues. The model is solved by finding the pareto front based on genetic algorithm (GA), so that the decision makers can filter out the scheduling plan based on their preference, in which single-objective optimization is used first to find out the initial population for GA. Finally, the proposed method’s effectiveness is verified by case studies.

Mission reliability-oriented integrated scheduling optimization model by combining production planning and maintenance activities

Machine utilization and production efficiency of manufacturing systems can be effectively improved through reasonable production scheduling. Traditionally, production scheduling and maintenance planning are considered as two independent issues, but it may lead to a suboptimal solution that is unable to maximize the productivity of the manufacturing system. Therefore, a mission reliability-oriented integrated scheduling model that considers production planning and maintenance activities is proposed. Firstly, the mission reliability that takes into account product type and equipment performance is defined to characterize production rhythm. Secondly, the maintenance strategy based on machine degradation cumulative failure and stochastic failure is proposed to guarantee the mission reliability of the machine effectively. Thirdly, an integrated scheduling model is established with the goal of minimizing total operational time, and Genetic algorithm is tailored to find the best production scheduling plan. Finally, a case study and comparative study of the cylinder head manufacturing system are presented to demonstrate the effectiveness of the proposed method. Results show that the proposed method is more suitable for production practice than the previous production scheduling strategy.

Multi-Objective Optimization of Workshop Scheduling with Multiprocess Route Considering Logistics Intensity

In order to solve the problems of flexible process route and workshop scheduling scheme changes frequently in the multi-variety small batch production mode, a multiprocess route scheduling optimization model with carbon emissions and cost as the multi-objective was established. At the same time, it is considered to optimize under the existing machine tool conditions in the workshop, then the theory of logistics intensity between equipment is introduced into the model. By designing efficient constraints to ensure reasonable processing logic, and then applying multilayer coding genetic algorithm to solve the case. The optimization results under single-target and multi-target conditions are contrasted and analyzed, so as to guide enterprises to choose a reasonable scheduling plan, improve the carbon efficiency of the production line, and save costs.