Optimal Vehicle Allocation Research Articles

Resilience-based optimization model for emergency bus bridging and dispatching in response to metro operational disruptions.

Bus-bridging evacuation services can significantly enhance metro resilience during operational disruptions. A resilience-based optimization model was proposed to generate a bus bridging and dispatching plan. The objective of the model is to maximize the resilience index of evacuated passengers while meeting pre-established restrictions on operational indicators and resources. The proposed approach consists of three steps: representing an integrated network based on a hyper-network, generating candidate bus-bridging routes using the K-shortest paths algorithm, and solving the optimization model using a genetic algorithm to determine the optimal vehicle allocation among the candidate routes. The Nanjing metro network was used to demonstrate the proposed model. The results show that the average waiting time is the main reason for travel delays, especially in short-distance travel. Furthermore, the cycling strategy is beneficial for reducing the average travel delay and improving evacuation efficiency with limited vehicles. In particular, when resources are very limited, the vehicle cycling strategy may have significant advantages over fixed vehicles for servicing fixed lines. The proposed model could be widely used in emergency response to quickly and efficiently evacuate passengers.

The Research on Meeting Arrangement

With the progress of modern society, the meeting plays a very important role in communicating information, exploring the latest development in a certain field and discussing the solution of the problem, and it is a popular hope to arrange meetings by computer instead of person. This paper introduces the research on the establishment of decision algorithm for automatic meeting arrangement system. The meeting organizer should not only consider meeting time and venue, but also consider representatives’ satisfaction, which includes two aspects: the first is the room cost, and the second is whether or not to meet the reservation requirements. Considering these factors, a linear programming model is given in this paper. With this model, the reasonable allocation of rooms and the optimal allocation of vehicles can be realized. On the other hand, the appropriate meeting time and venue can also be determined.

A mathematical model for collecting and distributing perishable products by considering costs minimisation and CO<SUB align="right">2 emissions

This paper considers the problem of allocating vehicles to collect and distribute fruit to producer associations in Colombia. In particular, the problem seeks to determine the optimal allocation of vehicles for fruit collection minimising both total transportation costs and CO2 emissions. This problem has multiple objectives, and the well-known e-constraint method has been used as solution technique for the proposed mathematical models. The efficiency of the former methodology has been tested by using a case study involving the distribution of blackberry (Rubus glaucus) by an association of producers in Cundinamarca Department, Colombia. In particular, we considered 12 different scenarios related to supply levels, route outsourcing, and collection frequency. The results show the efficiency of the proposed methodology in solving vehicle allocation problems related to collection and distribution. The case study reveals that, in general, collecting fruit three days/week yields lower costs and fewer emissions than performing collections four days/week. Furthermore, increased supply leads to greater differences between costs and emissions.

Optimal allocation of vehicles to bus routes using automatically collected data and simulation modelling

Monitoring the service quality of high-frequency bus transit is important both to agencies running their own operations and those contracting out, where performance measures can be used to assess contract penalties or bonuses. The availability of automatically collected vehicle movement and demand data enables detecting changes in running times and demand, which may present opportunities to improve service quality and fleet utilization. This research develops a framework to maximize service performance in a set of high-frequency bus routes, given their planned headways and a total fleet size constraint. Using automatically collected data and simulation modelling to evaluate the performance of each route with varying fleet sizes, a greedy algorithm adjusts allocation toward optimality. A simplified case study involving morning peak service on nine bus routes in Boston demonstrates the feasibility and potential benefits of the approach. A potential application is automated detection of routes operating with insufficient or excessive resources.

Dynamic vehicle allocation control for automated material handling system in semiconductor manufacturing

The current study examines the dynamic vehicle allocation problems of the automated material handling system (AMHS) in semiconductor manufacturing. With the uncertainty involved in wafer lot movement, dynamically allocating vehicles to each intrabay is very difficult. The cycle time and overall tool productivity of the wafer lots are affected when a vehicle takes too long to arrive. In the current study, a Markov decision model is developed to study the vehicle allocation control problem in the AMHS. The objective is to minimize the sum of the expected long-run average transport job waiting cost. An interesting exhaustive structure in the optimal vehicle allocation control is found in accordance with the Markov decision model. Based on this exhaustive structure, an efficient algorithm is then developed to solve the vehicle allocation control problem numerically. The performance of the proposed method is verified by a simulation study. Compared with other methods, the proposed method can significantly reduce the waiting cost of wafer lots for AMHS vehicle transportation.

Management of Dynamic Vehicle Allocation for Carsharing Systems

Carsharing offers innovative mobility solutions and has been gaining popularity around the world as an environmentally sustainable, socially responsible, and economically feasible form of mobility. Carsharing allows members to benefit from private vehicle use without the costs and responsibilities of ownership and provides individuals with access to a fleet of shared-use vehicles in a network of locations on a short-term, as-needed basis. This paper seeks to develop a stochastic optimization framework to address the dynamic vehicle allocation problem for carsharing systems, in which the service operator needs to manage and determine the optimal vehicle allocation in both time and space to maximize profits. A multistage stochastic linear programming model with recourse, which can account for system uncertainties such as car-sharing demand variation, is formulated and solved. Numerical results are discussed and computational insights are presented on the basis of a seven-stage experimental network pilot study.

Optimal vehicle allocation for an Automated Materials Handling System using simulation optimisation

The Automated Materials Handling System (AMHS) in the semiconductor industry plays a vital role in reducing wafer cycle times and enhancing fabrication facility (fab) productivity. Due to the complexity of the manufacturing process and the stochasticity introduced by the inherent variability of processing times, the vehicle allocation for the AMHS is a challenging task, especially in 300 mm wafer fabs where the AMHS comprises both the interbay and intrabay systems to perform the timely deliveries. This paper studied the vehicle allocation problem in a typical 300 mm wafer fab. We formulated it as a simulation optimisation problem and proposed a conceptual framework to handle the problem. A discrete event simulation model was developed to characterise the AMHS, and the technique of simulation optimisation was applied to obtain the optimal vehicle allocation for both the interbay and intrabay systems. To demonstrate the feasibility and advantages of the simulation optimisation approach, a photobay example was used to compare the solution derived from the analytical model and simulation optimisation model. Finally, an empirical problem based on real data was conducted to show the viability of the proposed framework in practice.

An efficient multi-vehicle multi-criteria mission planning and control system for autonomous underwater vehicles

In their study, Tavana and Bourgeois (2010) proposed a multi-criteria decision analysis model that considered dynamic and episodic phenomenon on the surface of the ocean and provided specific navigation plans. They showed that the transect for an autonomous underwater vehicle (AUV) includes not only the desired horizontal path, but also the depth range that the vehicle will operate in. They argued that the current models should be extended to include considerations that change vertically, that is, with different ocean depths. They also suggested expansion of the current models to cooperative teams of AUVs and showed that working together will allow underwater vehicles to complete tasks that could not be completed by a single vehicle. This study extends their model by: reducing the number of judgements required to generate the navigation scores within the decision region; considering ocean depth and finding the navigation scores for the ocean surface and interior; and developing a model which derives an optimal allocation of multiple vehicles to various locations within the decision region. The proposed framework is an efficient multi-vehicle multi-criteria mission planning and control system that considers ocean phenomenon on the surface and in the interior and provides an optimal allocation of vehicles with respect to the stated objective and subjective mission goals.

A heuristic for vehicle fleet mix problem using tabu search and set partitioning

The vehicle fleet mix problem is a special case of the vehicle routing problem where customers are served by a heterogeneous fleet of vehicles with various capacities. An efficient heuristic for determining the composition of a vehicle fleet and travelling routes was developed using tabu search and by solving set partitioning problems. Two kinds of problems have appeared in the literature, concerning fixed cost and variable cost, and these were tested for evaluation. Initial solutions were found using the modified sweeping method. Whenever a new solution in an iteration of the tabu search was obtained, optimal vehicle allocation was performed for the set of routes, which are constructed from the current solution by making a giant tour. Experiments were performed for the benchmark problems that appeared in the literature and new best-known solutions were found.

A Solution to Optimal Vehicle Allocation Problems in Product Shipping Systems

Due to continually rising fuel costs, it has become very important to solve optimal vehicle allocation problems in shipping products from a supplier to customers by using different types of vehicles. The problems consist of choosing the optimal combination of products, packing them into available load spaces, and selecting the optimal vehicles for any order so as to minimize the total cost of delivery. This is a kind of combinatorial problem involving the difficulties of large-scale variables. In order to overcome these difficulties, we have developed a two-stage planning method which consists of packing pattern generation and optimal pattern selection. The objective of this paper is to discuss a formulation of this method and generation rules for the packing patterns to obtain a near-optimal solution.