Safe Multi-objective Planning with a Posteriori Preferences

Autonomous planning in safety critical systems is a difficult task where decisions must carefully balance optimisation for performance goals of the system while also keeping the system away from safety hazards. These tasks often conflict, and hence present a challenging multi-objective planning problem where at least one of the objectives relates to safety risk. Recasting safety risk into an objective introduces additional requirements on planning algorithms: safety risk cannot be "averaged out" nor can it be combined with other objectives without loss of information and losing its intended purpose as a tool in risk reduction. Thus, existing algorithms for multi-objective planning cannot be used directly as they do not provide any facility to accurately track and update safety risk. A common work around is to restrict available decisions to those guaranteed safe a priori, but this can be overly conservative and hamper performance significantly. In this paper, we propose a planning algorithm based on multiobjective Monte-Carlo Tree Search to resolve these problems by recognising safety risk as a first class objective. Our algorithm explicitly models the safety of the system separately from the performance of the system, uses safety risk to both optimise and provide constraints for safety in the planning process, and uses an ALARP-based preference selection method to choose an appropriate safe plan from its output. The preference selection method chooses from the set of multiple safe plans to weigh risk against performance. We demonstrate the behaviour of the algorithm using an example representative of safety critical decision-making

Multi-objective optimisation of aircraft flight trajectories in the ATM and avionics context

The continuous increase of air transport demand worldwide and the push for a more economically viable and environmentally sustainable aviation are driving significant evolutions of aircraft, airspace and airport systems design and operations. Although extensive research has been performed on the optimisation of aircraft trajectories and very efficient algorithms were widely adopted for the optimisation of vertical flight profiles, it is only in the last few years that higher levels of automation were proposed for integrated flight planning and re-routing functionalities of innovative Communication Navigation and Surveillance/Air Traffic Management (CNS/ATM) and Avionics (CNS+A) systems. In this context, the implementation of additional environmental targets and of multiple operational constraints introduces the need to efficiently deal with multiple objectives as part of the trajectory optimisation algorithm. This article provides a comprehensive review of Multi-Objective Trajectory Optimisation (MOTO) techniques for transport aircraft flight operations, with a special focus on the recent advances introduced in the CNS+A research context. In the first section, a brief introduction is given, together with an overview of the main international research initiatives where this topic has been studied, and the problem statement is provided. The second section introduces the mathematical formulation and the third section reviews the numerical solution techniques, including discretisation and optimisation methods for the specific problem formulated. The fourth section summarises the strategies to articulate the preferences and to select optimal trajectories when multiple conflicting objectives are introduced. The fifth section introduces a number of models defining the optimality criteria and constraints typically adopted in MOTO studies, including fuel consumption, air pollutant and noise emissions, operational costs, condensation trails, airspace and airport operations

Network Design Model with Evacuation Constraints Under Uncertainty

Abstract: Nepal earthquake, have shown the need for quick response evacuation and assistance routes. Evacuation routes are, mostly, based on the capacities of the roads network. However, in extreme cases, such as earthquakes, roads network infrastructure may adversely affected, and may not supply their required capacities. If for various situations, the potential damage for critical roads can be identify in advance, it is possible to develop an evacuation model, that can be used in various situations to plan the network structure in order to provide fast and safe evacuation. This paper focuses on the development of a model for the design of an optimal evacuation network which simultaneously minimizes construction costs and evacuation time. The model takes into consideration infrastructures vulnerability (as a stochastic function which is dependent on the event location and magnitude), road network, transportation demand and evacuation areas. The paper presents a mathematic model for the presented problem. However, since an optimal solution cannot be found within a reasonable timeframe, a heuristic model is presented as well. The heuristic model is based on evolutionary algorithms, which also provides a mechanism for solving the problem as a stochastic and multi-objective problem

Preference-based evolutionary algorithm for airport surface operations

In addition to time efficiency, minimisation of fuel consumption and related emissions has started to be considered by research on optimisation of airport surface operations as more airports face severe congestion and tightening environmental regulations. Objectives are related to economic cost which can be used as preferences to search for a region of cost efficient and Pareto optimal solutions. A multi-objective evolutionary optimisation framework with preferences is proposed in this paper to solve a complex optimisation problem integrating runway scheduling and airport ground movement problem. The evolutionary search algorithm uses modified crowding distance in the replacement procedure to take into account cost of delay and fuel price. Furthermore, uncertainty inherent in prices is reflected by expressing preferences as an interval. Preference information is used to control the extent of region of interest, which has a beneficial effect on algorithm performance. As a result, the search algorithm can achieve faster convergence and potentially better solutions. A filtering procedure is further proposed to select an evenly distributed subset of Pareto optimal solutions in order to reduce its size and help the decision maker. The computational results with data from major international hub airports show the efficiency of the proposed approach

Real-time trajectory optimisation models for next generation air traffic management systems

This paper presents models and algorithms for real-time 4-Dimensional Flight Trajectory (4DT) operations in next generation Communications, Navigation, Surveillance/Air Traffic Management (CNS/ATM) systems. In particular, the models are employed for multi-objective optimisation of 4DT intents in ground-based 4DT Planning, Negotiation and Validation (4-PNV) systems and in airborne Next Generation Flight Management Systems (NG-FMS). The assumed timeframe convention for offline and online air traffic operations is introduced and discussed. The adopted formulation of the multi-objective 4DT optimisation problem includes a number of environmental objectives and operational constraints. In particular, the paper describes a real-time multi-objective optimisation algorithm and the generalised expression of the cost function adopted for penalties associated with specific airspace volumes, accounting for weather models, condensation trails models and noise models

A Practical Guide to Multi-Objective Reinforcement Learning and Planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems

A multi-objective model for inventory and planned production reassignment to committed orders with homogeneity requirements

[EN] Certain industries are characterized by obtaining non-homogeneous units of the same product. However, customers require homogeneity in some attributes between units of the same and different products requesting in their orders. To commit such orders, an estimation of the homogeneous product to be obtained can be used. Unfortunately, estimations of homogenous product quantities can differ considerably from real distributions. This fact could entail the impossibility of accomplishing the delivery of customer orders in the terms previously committed. To solve this, we propose a multi-objective mathematical programming model to reallocate already available homogeneous products in stock and planned production to committed orders. The main contributions of this model are the consideration of the homogeneity requirement between units of different lines of the same order, the allowance of partial deliveries of order lines, and the specification of some relevant attributes of products to accomplish with the customer homogeneity requirement. Different hypotheses are proved through experiments and statistical analyses applied to a ceramic tile company. The epsilon-constraint method is used to obtain an implementable solution for the company. The weighted sum method is used when proving other hypotheses that offer some managerial insights to companies.This work was supported by the Program of Formation of University Professors (FPU) of the Spanish Ministry of Education, Culture and Sport (FPU15/03595), and by the Spanish Ministry of Economy and Competitiveness Project DPI2011-23597.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á.; Peidro Payá, D. (2018). A multi-objective model for inventory and planned production reassignment to committed orders with homogeneity requirements. Computers & Industrial Engineering. 124:180-194. https://doi.org/10.1016/j.cie.2018.07.025S18019412

Preference-based evolutionary algorithm for airport runway scheduling and ground movement optimisation

As airports all over the world are becoming more congested together with stricter environmental regulations put in place, research on optimisation of airport surface operations started to consider both time and fuel related objectives. However, as both time and fuel can have a monetary cost associated with them, this information can be utilised as preference during the optimisation to guide the search process to a region with the most cost efficient solutions. In this paper, we solve the integrated optimisation problem combining runway scheduling and ground movement problem by using a multi-objective evolutionary framework. The proposed evolutionary algorithm is based on modified crowding distance and outranking relation which considers cost of delay and price of fuel. Moreover, the preferences are expressed in a such way, that they define a certain range in prices reflecting uncertainty. The preliminary results of computational experiments with data from a major airport show the efficiency of the proposed approach