A novel mathematical formulation for solving the dynamic and discrete berth allocation problem by using the Bee Colony Optimisation algorithm

AbstractBerth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model. The problem analysed in this work deals with the Discrete and Dynamic Berth Allocation Problem (DDBAP). We propose a novel mathematical formulation expressed as a Mixed Integer Linear Programming (MILP) for solving the DDBAP. Furthermore, we adapted a metaheuristic solution approach based on the Bee Colony Optimisation (BCO) for solving large-sized combinatorial BAPs. In order to assess the solution performance and efficiency of the proposed model, we introduce a new set of instances based on real data of the Livorno port (Italy), and a comparison between the BCO algorithm and CPLEX in solving the DDBAP is performed. Additionally, the application of the proposed model to a real berth scheduling (Livorno port data) and a comparison with the Ant Colony Optimisation (ACO) metaheuristic are carried out. Results highlight the feasibility of the proposed model and the effectiveness of BCO when compared to both CPLEX and ACO, achieving computation times that ensure a real-time application of the method

Population-based techniques for the multiple objective optimisation of sandwich materials and structures

Sandwich materials, consisting of two thin, stiff facings separated by a low density core, can be used to produce structures that are both light and flexurally rigid. Such assemblies are attractive for applications in transport and construction. However, their optimisation is rarely straightforward. Not only is this due to the complex equations that govern their mechanics, but also because multiple design variables and objectives are often present. The work in this thesis identifies population-based optimisation techniques as a novel solution to this challenge. Three of these techniques have been developed in MATLAB specifically for this purpose and are based on particle swarm optimisation (sandwichPSO), ant colony optimisation (sandwichACO), and simulated annealing (sandwichSA). To assess their suitability, a benchmark problem considered the application of these techniques to a multiple objective sandwich beam optimisation. Optimised for stiffness mass and cost, a selection of 16 materials for both facing and core were available. Several constraints were also present. The sandwichACO technique demonstrated superior ability as it was able to obtain all optimal solutions in most cases. However, the sandwichPSO and sandwichSA techniques struggled to identify local optimum solutions for the multi-ply, fibre-reinforced polymer sandwich facing laminates. A further case study then applied sandwichACO to the optimisation of a sandwich plate for a rail vehicle floor panel. In addition to the benchmark, the problem was extended to include 40 materials. Also, the material and thickness of the top face was allowed to be different to the bottom. Furthermore, orthotropic fibre-reinforced facing constructions were included, as well as a localised load constraint. A broad range of optimal solutions were identified for the applied minimum mass and cost objectives. Sandwich constructions provided a significant (approximately 40%) saving in both mass and cost compared to the existing plywood design. More significant mass saving designs were also identified (of over 40%), but with a cost premium. Overall, population-based techniques have demonstrated successful application to the design of sandwich materials and structures.EThOS - Electronic Theses Online ServiceNewRail : School of Mechanical and Systems Engineering, Newcastle University : Royal Academy of EngineeringGBUnited Kingdo

Development of Practical Design Approaches for Water Distribution Systems

The optimal design of water distribution systems (WDSs) should be economical, consider practical field applicability, and satisfy hydraulic constraints such as nodal pressure and flow velocity. However, the general optimal design of a WDSs approach using a metaheuristic algorithm was difficult to apply for achieving pipe size continuity at the confluence point. Although some studies developed the design approaches considering the pipe continuity, these approaches took many simulation times. For these reasons, this study improves the existing pipe continuity search method by reducing the computation time and enhancing the ability to handle pipe size continuity at complex joints that have more than three nodes. In addition to more practical WDSs designs, the approach considers various system design factors simultaneously in a multi-objective framework. To verify the proposed approach, the three well-known WDSs to apply WDS design problems are applied, and the results are compared with the previous design method, which used a pipe continuity research algorithm. This study can reduce the computation time by 87% and shows an ability to handle complex joints. Finally, the application of this practical design technique, which considers pipe continuity and multiple design factors, can reduce the gap between the theoretical design and the real world because it considers construction conditions and abnormal situations.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

An Evolutionary Approach to Multistage Portfolio Optimization

Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl

An updated annotated bibliography on arc routing problems

The number of arc routing publications has increased significantly in the last decade. Such an increase justifies a second annotated bibliography, a sequel to Corberán and Prins (Networks 56 (2010), 50–69), discussing arc routing studies from 2010 onwards. These studies are grouped into three main sections: single vehicle problems, multiple vehicle problems and applications. Each main section catalogs problems according to their specifics. Section 2 is therefore composed of four subsections, namely: the Chinese Postman Problem, the Rural Postman Problem, the General Routing Problem (GRP) and Arc Routing Problems (ARPs) with profits. Section 3, devoted to the multiple vehicle case, begins with three subsections on the Capacitated Arc Routing Problem (CARP) and then delves into several variants of multiple ARPs, ending with GRPs and problems with profits. Section 4 is devoted to applications, including distribution and collection routes, outdoor activities, post-disaster operations, road cleaning and marking. As new applications emerge and existing applications continue to be used and adapted, the future of arc routing research looks promising.info:eu-repo/semantics/publishedVersio

Utilization of Integer Programming for Scheduling Maintenance at Nuclear Power Plants

This thesis develops a thought that naturally explores three specific motifs for solving the complexities of scheduling maintenance at Nuclear Power Plants (NPP). The first chapter of this paper will develop the initial thought around creating a schedule for a given work week, including all the various constraints inherent to this problem. Such constraints include but are not limited to personnel availability, allowable component out-of-service time, and the Plant Risk Assessment. The objective function being to minimize the total cost of worker’s compensation for that given week. The second chapter addresses the question of whether this simple schedule can be implemented with a long time horizon as the goal. This section delves into the concept of utilizing maintenance task frequencies and extended preventive maintenance frequencies to once again minimize the objective function of cost due to compensation. The third chapter focuses on the ability of the program to respond to adaptive circumstances. One major obstacle in running any large commercial facility is unplanned downtime of required systems or components. Simulating failures of certain components that shorten the overall allowable out-of-service time, the program will be required to still minimize the objective function while navigating these changing timelines