Ant Colony Optimization and Contraint Programming Working ...

Ant Colony Optimization and Contraint Programming Working Together for Routing in Smart Cities Negar ZakeriNejad1 , Daniel Riera1 , and Daniel Guimarans2 1

Computing, Multimedia and Telecommunication Department, Universitat Oberta de Catalunya 2 Optimisation Research Group, NICTA April 13, 2015 Abstract The concept of Smart City is entangled with urban transportation and mobility. Increasing density of urban living, and consequently growing commercial establishments, requires the development of sustainable efficient transportation systems able to deal with the movement of large quantities of goods and services for commercial and domestic use. The role of economic development and structural urban variables, the geographical location, and the density of the population together with the associated congestion problems could have a deep influence in determining the transportation strategy. Therefore, promoting more efficient and intelligent systems, which are applicable in various urban domains with different needs and contextual conditions is a subject of increasing attention. The aim of the research activities in this area is to optimize logistic flows in cities with the aim of improving traffic and transport efficiency as well as assuring a high-quality travel experience. This work tries to design and implement a smarter transportation system in the sense that it is flexible and robust enough to respond effectively and promptly to changing circumstances. Thus, our objective is to improve urban transportation strategies by developing a generalized framework to address real-life Vehicle Routing Problems (RVRPs). That is, we aim for a framework that can consider many diverse constraints. Due to the importance of the VRP, there is already a number of studies which present methodologies to face it. Unfortunately, most of these mainly consider one or a limited number of constraints. Furthermore, when modifying the initial problem by adding a new constraint, it is often necessary to reconstruct the whole system in order to re-draw the proposed solution. In other words, the current techniques are not easily extensible for including new constraints. Thus, a challenge for researchers is to develop a framework to address a large class of RVRPs. To satisfy this a methodology is required that allows to join easy fast modeling of different problems and a quick, good search engine.

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In this work, we present an implementation of a flexible model which facilitates adding and removing constraints without the need for changing the search strategy. The proposed framework is made up of two paradigms: Constraint Programming (CP) and the Ant Colony Optimization (ACO) meta-heuristic. The CP part has two main tasks. First, it provides the modeling power to encode any constraint relevant to the RVRP. Combining these constraints, different RVRPs can be quickly modeled as a Constraint Satisfaction Problem (CSP) transparently, without changing the search algorithm. Second, it encompasses a CP solver that provides answers on feasibility of partial or complete solutions to ants movements. The meta-heuristic part of the framework, implemented using ACO, is where the main search through the space of parameters takes place. This part is composed of two nested loops. The inner loop is used to construct a solution variable by variable, while the outer loop searches for a different solution in each iteration. Every certain number of decisions, ants check the feasibility of the current partial solution and react advancing or backtracking to avoid following that path. In this work we show the results of applying our methodology to different VRPs built by combining a set of constraints (i.e. capacity, maximum distances, asymmetric distances, and heterogeneous fleets). The results show the flexibility of the framework to satisfy new constraints, providing solutions of good quality within reasonable running time, and needing no changes to adapt to new situations.

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