Uncapacitated Single Allocation Research Articles

Solving the Uncapacitated Single Allocation p-Hub Location Problem with Fixed Cost Using Anti-Predatory NIA

Background: Hub Location Problem (HLP) deals with long-term strategic decision planning in various application domains with the aim of reducing overall transportation cost. It deals with identifying hubs and allocating spokes to hubs in order to route the flow of goods between origin-destination locations. Due to the complex nature of the problem, meta-heuristic algorithms are best suited to solve HLPs. The existing algorithms face the accuracy and consistency related issues for solving the HLPs. Objective: This paper attempts to solve a variant of HLP, which is known as Uncapacitated Single Allocation p-Hub Location Problem with Fixed Cost (USApHLP-FC), using Anti-Predatory Nature- Inspired Algorithm (APNIA) to improve accuracy and consistency in results. Methods: APNIA is a recently proposed meta-heuristic nature-inspired algorithm, which is based on the anti-predatory behavior of frogs. For solving the HLP, APNIA is used for both identifying the hubs and allocating the spokes to hubs in order to reduce the total cost of goods transportation. Results: A numerical problem with 10 locations is used for empirical study. The experimental result shows that APNIA outperforms other leading proposals in terms of total cost and gap value. The obtained results of APNIA are compared with the genetic algorithm, particle swarm optimization, artificial bee colony, firefly algorithm, teacher learning based optimization and Jaya algorithm. The comparative study indicates at least 0.86% improvement in accuracy and at least a 10% gain in consistency by APNIA for the different number of generations. Conclusion: The experimental evaluation and performance comparison signify that APNIA based approach has improved accuracy and consistency in solutions than other compared algorithms. It establishes the robustness of anti-predatory NIA for solving the hub location problems.

A simheuristic algorithm for video streaming flows optimisation with QoS threshold modelled as a stochastic single-allocation p-hub median problem

ABSTRACT Modern telecommunication networks are comprised of a countless number of nodes exchanging data among them. In particular, multimedia traffic – composed of audio, video, and images – represents a challenging scenario requiring link optimisation techniques. The hub-and-spoke topology is frequently used to design more effective telecommunication networks. This work considers a hub-and-spoke network in which a large number of nodes are exchanging real-time multimedia data, and where the quantity of data sent from one node to another is a random variable. Given a fixed number of hubs, , the goal is to select the best location for these hubs in order to minimise the total expected cost of transmission. This scenario is modelled as an uncapacitated single-allocation -hub median problem under uncertainty conditions. Additionally, with the purpose of considering the effect of transmission delays on the video signal, service quality thresholds are assigned to every potential hub in the network. Since real-life networks tend to be large in size, we propose a simheuristic algorithm to cope with this stochastic and large-scale optimisation problem. A series of computational experiments illustrate these concepts and allow for testing the performance of our simheuristic approach. Finally, a statistical analysis of the obtained results is provided.

Oracle-based algorithms for binary two-stage robust optimization

In this work we study binary two-stage robust optimization problems with objective uncertainty. We present an algorithm to calculate efficiently lower bounds for the binary two-stage robust problem by solving alternately the underlying deterministic problem and an adversarial problem. For the deterministic problem any oracle can be used which returns an optimal solution for every possible scenario. We show that the latter lower bound can be implemented in a branch and bound procedure, where the branching is performed only over the first-stage decision variables. All results even hold for non-linear objective functions which are concave in the uncertain parameters. As an alternative solution method we apply a column-and-constraint generation algorithm to the binary two-stage robust problem with objective uncertainty. We test both algorithms on benchmark instances of the uncapacitated single-allocation hub-location problem and of the capital budgeting problem. Our results show that the branch and bound procedure outperforms the column-and-constraint generation algorithm.

Designing of Public Transportation Network Using Uncapacitated Single Allocation p Hub Median Model: Case Study Nonthaburi Province

Designing of Public Transportation Network Using Uncapacitated Single Allocation p Hub Median Model: Case Study Nonthaburi Province