Random Neural Networks and Optimisation

In this thesis we introduce new models and learning algorithms for the Random Neural Network (RNN), and we develop RNN-based and other approaches for the solution of emergency management optimisation problems. With respect to RNN developments, two novel supervised learning algorithms are proposed. The first, is a gradient descent algorithm for an RNN extension model that we have introduced, the RNN with synchronised interactions (RNNSI), which was inspired from the synchronised firing activity observed in brain neural circuits. The second algorithm is based on modelling the signal-flow equations in RNN as a nonnegative least squares (NNLS) problem. NNLS is solved using a limited-memory quasi-Newton algorithm specifically designed for the RNN case. Regarding the investigation of emergency management optimisation problems, we examine combinatorial assignment problems that require fast, distributed and close to optimal solution, under information uncertainty. We consider three different problems with the above characteristics associated with the assignment of emergency units to incidents with injured civilians (AEUI), the assignment of assets to tasks under execution uncertainty (ATAU), and the deployment of a robotic network to establish communication with trapped civilians (DRNCTC). AEUI is solved by training an RNN tool with instances of the optimisation problem and then using the trained RNN for decision making; training is achieved using the developed learning algorithms. For the solution of ATAU problem, we introduce two different approaches. The first is based on mapping parameters of the optimisation problem to RNN parameters, and the second on solving a sequence of minimum cost flow problems on appropriately constructed networks with estimated arc costs. For the exact solution of DRNCTC problem, we develop a mixed-integer linear programming formulation, which is based on network flows. Finally, we design and implement distributed heuristic algorithms for the deployment of robots when the civilian locations are known or uncertain

A combined Mixed Integer Programming model of seaside operations arising in container ports

This paper puts forward an integrated optimisation model that combines three distinct problems, namely the Berth Allocation Problem, the Quay Crane Assignment Problem, and the Quay Crane Scheduling problem, which have to be solved to carry out these seaside operations in container ports. Each one of these problems is complex to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence the need to solve them in a combined form. The problem is formulated as a mixed-integer programming model with the objective being to minimise the tardiness of vessels. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX

A Classification of Hyper-heuristic Approaches

The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Design and Analysis of Efficient Freight Transportation Networks in a Collaborative Logistics Environment

The increase in total freight volumes, reducing volume per freight unit, and delivery deadlines have increased the burden on freight transportation systems of today. With the evolution of freight demand trends, there also needs to be an evolution in the freight distribution processes. Today\u27s freight transportation processes have a lot of inefficiencies that could be streamlined, thus preventing concerns like increased operational costs, road congestion, and environmental degradation. Collaborative logistics is one of the approaches where supply chain partners collaborate horizontally or/and vertically to create a centralized network that is more efficient and serves towards a common goal or objective. In this dissertation, we study intermodal transportation, and cross-docking, two major pillars of efficient, cheap, and faster freight transportation in a collaborative environment. We design an intermodal network from a centralized network perspective where all the participants intermodal operators, shippers, carriers, and customers strive towards a synchronized and cost-efficient freight network. Also, a cross-dock scheduling problem is presented for competitive shippers using a centralized cross-dock facility. The problem develops a fast heuristic and meta-heuristic approach to solve large-scale real-world problems and draws key insights from a cross-dock operator and inbound carrier\u27s perspectives

Computation Offloading and Scheduling in Edge-Fog Cloud Computing

Resource allocation and task scheduling in the Cloud environment faces many challenges, such as time delay, energy consumption, and security. Also, executing computation tasks of mobile applications on mobile devices (MDs) requires a lot of resources, so they can offload to the Cloud. But Cloud is far from MDs and has challenges as high delay and power consumption. Edge computing with processing near the Internet of Things (IoT) devices have been able to reduce the delay to some extent, but the problem is distancing itself from the Cloud. The fog computing (FC), with the placement of sensors and Cloud, increase the speed and reduce the energy consumption. Thus, FC is suitable for IoT applications. In this article, we review the resource allocation and task scheduling methods in Cloud, Edge and Fog environments, such as traditional, heuristic, and meta-heuristics. We also categorize the researches related to task offloading in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Mobile Fog Computing (MFC). Our categorization criteria include the issue, proposed strategy, objectives, framework, and test environment.

On the investigation of the large-scale grouping constrained storage location assignment problem

The primary focus of this study is a novel optimisation problem, namely Storage Location Assignment Problem with Grouping Constraint (SLAP-GC). The problem stems from real-world applications and is significant in theoretical values and applicability in resource allocation tasks where groupings must be considered. The aim of this problem is to minimise the total operational cost in a warehouse through stock rearrangement. The problem consists of two interdependent subproblems, grouping same product items and assigning items to minimize picking distance. The interactions between these two subproblems make this problem significantly different from previous Storage Location Assignment Problems (SLAP), a well-studied field in logistics. Existing approaches for SLAP are not directly applicable for SLAP-GC. This dissertation lays a foundation for research on grouping constraints and other optimisation problems with similar interactions between subproblems. Firstly this study presents a formal definition of SLAP-GC. Then it others a formal proof of NP-completeness of SLAP-GC by reducing from a well-known 3-Partition problem to SLAP-GC. This suggests that the real-world instances of SLAP-GC should not be tackled with exact approaches, but with approximation and heuristic approaches. Then, we explored decomposition and modelling techniques for SLAP-GC and developed three types of promising heuristic approaches: a hyperheuristic approach, a metaheuristic approach and a matheuristic approach. Comprehensive experimental studies are conducted on both synthetic benchmark instances and real-world instances to examine their efficiency, efficacy, and scalability. Through the analysis of the experimental results, the suitability of proposed methods is verified on various SLAP-GC scenarios. In addition, we demonstrate in this study that with the proposed decomposition, large-scale SLAP-GC can be handled efficiently by the three proposed heuristic-based approaches