A Comparative Analysis of Application of Genetic Algorithm and Particle Swarm Optimization in Solving Traveling Tournament Problem (TTP)

Traveling Tournament Problem (TTP) has been a major area of research due to its huge application in developing smooth and healthy match schedules in a tournament. The primary objective of a similar problem is to minimize the travel distance for the participating teams. This would incur better quality of the tournament as the players would experience least travel; hence restore better energy level. Besides, there would be a great benefit to the tournament organizers from the economic point of view as well. A well constructed schedule, comprising of diverse combinations of the home and away matches in a round robin tournament would keep the fans more attracted, resulting in turnouts in a large number in the stadiums and a considerable amount of revenue generated from the match tickets. Hence, an optimal solution to the problem is necessary from all respects; although it becomes progressively harder to identify the optimal solution with increasing number of teams. In this work, we have described how to solve the problem using Genetic algorithm and particle swarm optimization

A Cooperative Local Search Method for Solving the Traveling Tournament Problem

Constrained optimization is the process of optimizing a certain objective function subject to a set of constraints. The goal is not necessarily to find the global optimum. We try to explore the search space more efficiently in order to find a good approximate solution. The obtained solution should verify the hard constraints that are required to be satisfied. In this paper, we propose a cooperative search method that handles optimality and feasibility separately. We take the traveling tournament problem (TTP) as a case study to show the applicability of the proposed idea. TTP is the problem of scheduling a double round-robin tournament that satisfies a set of related constraints and minimizes the total distance traveled by the teams. The proposed method for TTP consists of two main steps. In the first step, we ignore the optimization criterion. We reduce the search only to feasible solutions satisfying the problem's constraints. For this purpose, we use constraints programming model to ensure the feasibility of solutions. In the second step, we propose a stochastic local search method to handle the optimization criterion and find a good approximate solution that verifies the hard constraints. The overall method is evaluated on benchmarks and compared with other well-known techniques for TTP. The computational results are promising and show the effectiveness of the proposed idea for TTP

Solving Challenging Real-World Scheduling Problems

This work contains a series of studies on the optimization of three real-world scheduling problems, school timetabling, sports scheduling and staff scheduling. These challenging problems are solved to customer satisfaction using the proposed PEAST algorithm. The customer satisfaction refers to the fact that implementations of the algorithm are in industry use. The PEAST algorithm is a product of long-term research and development. The first version of it was introduced in 1998. This thesis is a result of a five-year development of the algorithm. One of the most valuable characteristics of the algorithm has proven to be the ability to solve a wide range of scheduling problems. It is likely that it can be tuned to tackle also a range of other combinatorial problems. The algorithm uses features from numerous different metaheuristics which is the main reason for its success. In addition, the implementation of the algorithm is fast enough for real-world use.Siirretty Doriast

An Ant Colony Optimisation Algorithm for Timetabling Problem

The University Course Timetabling Problem (UCTP) is a combinatorial optimization problem which involves the placement of events into timeslots and assignment of venues to these events. Different institutions have their peculiar problems; therefore there is a need to get an adequate knowledge of the problem especially in the area of constraints before applying an efficient method that will get a feasible solution in a reasonable amount of time. Several methods have been applied to solve this problem; they include evolutionary algorithms, tabu search, local search and swarm optimization methods like the Ant Colony Optimisation (ACO) algorithm. A variant of ACO called the MAX-MIN Ant System (MMAS) is implemented with two local search procedures (one main and one auxiliary) to tackle the UCTP using Covenant University problem instance. The local search design proposed was tailored to suit the problem tackled and was compared with other designs to emphasise the effect of neighbourhood combination pattern on the algorithm performance. From the experimental procedures, it was observed that the local search design proposed significantly bettered the existing one used for the comparison. The results obtained by the implemented algorithm proved that metaheuristics are highly effective when tackling real-world cases of the UCTP and not just generated instances of the problem and can even be better if some tangible modifications are made to it to perfectly suit a problem domain

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Developing sustainable supply chains in regional Australia considering demand uncertainty, government subsidies and carbon tax regulation

There is a tremendous opportunity to implement sustainable supply chain management practices in terms of logistics, operations, and transport network in regional Australia. Unfortunately, this opportunity has not been investigated and there is a lack of academic studies in this body of knowledge. This thesis is made up by three related, but independent models designed to efficiently distribute products from a regional hub to other part of the country. This research aims to develop efficient and sustainable supply chain practices to deliver regional Australian products across the country and overseas. As the airports of most Australian capital cities are over-crowded while many regional airports are under-utilised, the first model examines the ways to promote the use of regional airports. Australia is a significant food producer and the agricultural products are primarily produced in regional areas. In the other two models, we focus on the distribution of perishable products from regional Australia. The first model presented in Chapter 2 outlines how different government subsidy schemes can be used to influence airfreight distributions that favour the use of regional airports and promote regional economic development. The model simultaneously considers time-window and release-time constraints as well as the heterogeneous fleet for ground distribution where fuel consumption is subject to load, travel distance, speed and vehicle characteristics. A real-world case study in the state of Queensland, Australia is used to demonstrate the application of the model. The results suggest that the regional airport's advantages can be promoted with suitable subsidy programs and the logistics costs can be reduced by using the regional airport from the industry’s perspective. The second model presented in Chapter 3 examines the impacts of carbon emissions arising from the storage and transportation of perishable products on logistical decisions in the cold supply chain considering carbon tax regulation and uncertain demand. The problem is formulated as a two-stage stochastic programming model where Monte Carlo approach is used to generate scenarios. The aim of the model is to determine optimal replenishment policies and transportation schedules to minimise both operational and emissions costs. A matheuristic algorithm based on the Iterated Local Search (ILS) algorithm and a mixed integer programming is developed to solve the problem in realistic sizes. The proposed model was implemented in a real-world case study in the state of Queensland, Australia to demonstrate the application of the model. The results highlight that a higher emissions price does not always contribute to the efficiency of the cold supply chain system. The third model presented in Chapter 4 investigates the impacts of two different transport modes - road and rail - on the efficiency and sustainability of transport network to deliver meat and livestock from regional Queensland to large cities and seaports. The model is formulated as a mixed-integer linear programming model that considers road traffic congestions, animal welfare, quality of meat products and environmental impacts from fuel consumption of different transport modes. The aim of the model is to determine an optimal network configuration where each leg of journey is conducted by the most reliable, sustainable and efficient transport mode. The results indicate that it would be possible to significantly decrease total cost if a road-rail intermodal network is used. Considering animal welfare, product quality and traffic congestion can have a significant effect on the decisions related to transport mode selection

On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p