Fleet Mix Problem Research Articles

Studying flexibility in modularisable vehicle systems

Some systems continue working under partial failure; errors can be made in assessing the performance of such systems if static measures are used. Systems that appear to be equal on the basis of idealised data can perform differently under component loss. For some system features, such as component modularity, the evidence of benefits, based on static measures, is equivocal. We propose that a modular design will perform better than a nonmodular design under component loss. We consider two systems, each designed to a particular budget and completely effective over all variations contained in the design context. One of the systems has modular components. We use the mission criticality model to assess the benefits of introducing this component modularity and compare the results with a related static assessment. When demands are variable, the modular system is superior under component loss, due to its greater component redundancy. References A. Bender, F. Bowden, A. Pincombe, and P. Williams. Role of mission criticality and component reliability in defining and evaluating system effectiveness. In Andrew Stacey, Bill Blyth, John Shepherd, and A. J. Roberts, editors, Proceedings of the 7th Biennial Engineering Mathematics and Applications Conference, EMAC-2005, volume 47 of ANZIAM J., pages C760--C775, June 2007. \protect http://anziamj.austms.org.au/V47EMAC2005/Bender [June 26, 2007]. James Whitacre, Axel Bender, Stephen Baker, Qi Fan, Hussein A. Abbass, and Ruhul Sarker. Network topology and time criticality effects in the modularised fleet mix problem. Accepted for publication simtect2008. Matti A. Hiltunen, Richard D. Schlichting, and Carlos A. Ugarte. Building survivable services using redundancy and adaptation. IEEE Transactions on Computers, 52(2):181--194, February 2003. doi:10.1109/TC.2003.1176985 J. F. Meyer. On evaluating the performability of degradable computer systems. IEEE Transactions on Computers, c-29:720--731, 1980.

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.

The best rail fleet mix problem

Every rail operator wishes to minimise the size of rail freight car fleet in order to reduce costs. The best rail fleet mix problem for railway is to determine an optimal rail freight car fleet in order to respond to the actual transport demand. A rail company must have capabilities to respond to the actual transport demand if it wants to reach a raised level of competitiveness on the transport market. In other words, it is necessary that the structure of freight rolling stock correspond to the structure of the transport demand. Various types of railcars are used for carrying goods by rail, caused by different performances of goods, for example, by manifestation form, aggregate condition and resistance to atmosphere influences. This paper treats a problem of determining an optimal rail fleet mix. A successful application of a multicriteria decision method, analytic hierarchical process (AHP) for solving of this problem is proposed. According to the selected number and types of criteria, as well as given level of importance, optimal type of rail freight car for all homogenous types of goods is derived. Obtained result shows a potential of practical application of AHP method for the problem of determining the best rail fleet mix.

Fleet Size and Mix Optimization for Paratransit Services

Most paratransit agencies use a mix of different types of vehicles ranging from small sedans to large converted vans as a cost-effective way to meet the diverse travel needs and seating requirements of their clients. Currently, decisions on what types of vehicles and how many vehicles to use are mostly made by service managers on an ad hoc basis without much systematic analysis and optimization. The objective of this research is to address the underlying fleet size and mix problem and to develop a practical procedure that can be used to determine the optimal fleet mix for a given application. A real-life example illustrates the relationship between the performance of a paratransit service system and the size of its service vehicles. A heuristic procedure identifies the optimal fleet mix that maximizes the operating efficiency of a service system. A set of recommendations is offered for future research; the most important is the need to incorporate a life-cycle cost framework into the paratransit service planning process.

A TABU SEARCH APPROACH FOR THE DETERMINISTIC AND STOCHASTIC VEHICLE FLEET MIX AND ROUTING PROBLEM

ABSTRACT This study relaxes the typical assumption made in traditional research of the vehicle routing problems, i.e., single vehicle type and unlimited number of vehicles, and proposes a tabu search method for solving the vehicle fleet mix and routing problem, in which vehicles of different types and limited number of vehicles for each type are considered. In reality, the amount of demand and the probability of occurring a demand are not known in advance, they are stochastic in nature. We use the concept of chance constraint programming and modify the above tabu search algorithm for solving the stochastic vehicle routing problem. Parameter combinations that can quickly result in good solutions are analyzed and reported.

A multi-level composite heuristic for the multi-depot vehicle fleet mix problem

The problem of simultaneously allocating customers to depots, finding the delivery routes and determining the vehicle fleet composition is addressed. A multi-level composite heuristic is proposed and two reduction tests are designed to enhance its efficiency. The proposed heuristic is tested on benchmark problems involving up to 360 customers, 2 to 9 depots and 5 different vehicle capacities. When tested on the special case, the multi-depot vehicle routing, our heuristic yields solutions almost as good as those found by the best known heuristics but using only 5 to 10% of their computing time. Encouraging results were also obtained for the case where the vehicles have different capacities.

The fleet size and mix problem for capacitated arc routing

There have been several attempts to solve the capacitated arc routing problem with m vehicles starting their tours from a central node. The objective has been to minimize the total distance travelled. In the problem treated here we also have the fixed costs of the vehicles included in the objective function. A set of vehicle capacities with their respective costs are used. Thus the objective function becomes a combination of fixed and variable costs. The solution procedure consists of four phases. In the first phase, a Chinese or rural postman problem is solved depending on whether all or some of the arcs in the network demand service with the objective of minimizing the total distance travelled. It results in a tour called the giant tour. In the second phase, the giant tour is partitioned into single vehicle subtours feasible with respect to the constraints. A new network is constructed with the node set corresponding to the arcs of the giant tour and with the arc set consisting of the subtours of the giant tour. The arc costs include both the fixed and variable costs of the subtours. The third phase consists of solving the shortest path problem on this new network to result in the least cost set of subtours represented on the new network. In the last phase a postprocessor is applied to the solution to improve it. The procedure is repeated for different giant tours to improve the final solution. The problem is extended to the case where there can be upper bounds on the number of vehicles with given capacities using a branch and bound method. Extension to directed networks is given. Some computational results are reported.