Lexicographic maximin optimisation for fair bandwidth allocation in computer networks,”

Abstract This paper addresses the problem of bandwidth allocation in multi-application computer network environments. Allocations are determined from the solution of a multiple objective optimisation problem under network constraints, where the lexicographic maximin criterion is applied to solve the problem and guarantees fairness and efficiency properties to the solution. An algorithm based on a series of maximum concurrent multicommodity flow sub-problems is proposed. Numerical results show the advantage of the approach compared to other traditional bandwidth allocation solutions

Disaster Management Cycle-Based Integrated Humanitarian Supply Network Management

While logistics research recently has placed increased focus on disruptionmanagement, few studies have examined the response and recovery phases in post-disaster operations. We present a multiple-objective, integrated network optimizationmodel for making strategic decisions in the supply distribution and network restorationphases of humanitarian logistics operations. Our model provides an equity- or fairness-based solution for constrained capacity, budget, and resource problems in post-disasterlogistics management. We then generate efficient Pareto frontiers to understand the trade-off between the objectives of interest.Next, we present a goal programming-based multiple-objective integratedresponse and recovery model. The model prescribes fairness-based compromise solutionsfor user-desired goals, given limited capacity, budget, and available resources. Anexperimental study demonstrates how different decision making strategies can beformulated to understand important dimensions of decision making.Considering multiple, conflicting objectives of the model, generating Pareto-optimal front with ample, diverse solutions quickly is important for a decision maker tomake a final decision. Thus, we adapt the well-known Non-dominated Sorting GeneticAlgorithm II (NSGA-II) by integrating an evolutionary heuristic with optimization-basedtechniques called the Hybrid NSGA-II for this NP-hard problem. A Hypervolume-basedtechnique is used to assess the algorithm’s effectiveness. The Hazards U.S. Multi-Hazard(Hazus)-generated regional case studies based on earthquake scenarios are used todemonstrate the applicability of our proposed models in post-disaster operations

Optimal Design and Planning of Energy Microgrids

Microgrids are local energy providers which reduce energy expense and gas emissions by utilising distributed energy resources (DERs) and are considered to be promising alternatives to existing centralised systems. However, currently, problems exist concerning their design and utilisation. This thesis investigates the optimal design and planning of microgrids using mathematical programming methods. First, a fair economic settlement scheme is considered for the participants of a microgrid. A mathematical programming formulation is proposed involving the fair electricity transfer price and unit capacity selection based on the Game-theory Nash bargaining approach. The problem is first formulated as a mixed integer non-linear programming (MINLP) model, and is then reformulated as a mixed integer linear programming (MILP) model. Second, an MILP model is formulated for the optimal scheduling of energy consumption of smart homes. DER operation and electricity consumption tasks are scheduled based on real-time electricity pricing, electricity task time windows and forecasted renewable energy output. A peak charge scheme is also adopted to reduce the peak demand from the grid. Next, an MILP model is proposed to optimise the respective costs among multiple customers in a smart building. It is based on the minimisation/maximisation optimisation approach for the lexicographic minimax/maximin method, which guarantees a Pareto-optimal solution. Consequently each customer will pay a fair energy cost based on their respective energy consumption. Finally, optimum electric vehicle (EV) battery operation scheduling and its related degradation are addressed within smart homes. EV batteries can be used as electricity storage for domestic appliances and provide vehicle to grid (V2G) services. However, they increase the battery degradation and decrease the battery performance. Therefore the objective is to minimise the total electricity cost and degradation cost while maintaining the demand under the agreed threshold by scheduling the operation of EV batteries