Organiser: Matthias Ehrgott
11/09/2018, 11:00, Room - County LT Code: OR60A3548 Multi-Objective Mixed Integer Programming: An Exact Objective Space Algorithm
Dr William Pettersson (University of Glasgow) and Dr Melih Ozlen (RMIT University)
This talk will describe one of the first objective space algorithms which can exactly find all supported and non-supported non-dominated solutions to a mixed-integer multi-objective linear program with an arbitrary number of objective functions. This algorithm is operates in three phases. First it builds up a super-set which contains the Pareto front. This super-set is then modified to not contain any intersecting polytopes. Once this is achieved, the algorithm efficiently calculates which portions of the super-set are not part of the Pareto front and removes them, leaving exactly the Pareto front.
What is the nature of your talk? Theoretical
Does your talk require prior knowledge of the subject area? A little Is your talk accessible and relevant to practitioners? Somewhat
11/09/2018, 11:30, Room - County LT Code: OR60A3559 Solving Multi-Criteria Network Interdiction for Information Security
Dr Zhengliang Liu, Dr Arman Khouzani and Prof Pasquale Malacaria (Queen Mary, University of London)
In this study, we propose a methodology that efficiently solves a multi-criteria network interdiction problem for cyber-security. Cyber network has been topologically modelled by attack graph. Given an attack graph, and a set of available security counter-measures (controls) our method constructs a deployment strategy which minimises the security risk and the implementation costs of the strategy. The problem of interest in this study integrates two conflicting agents, the defender and the attacker, who are intertwined in their own decision- making and utility maximisation. On one hand to mitigate disastrous attack the defender deliberately selects and implements security controls from numerous available controls, each of which affects a certain subset of vulnerabilities in different ways. Furthermore, the cyber- security risk extenuation should be leveraged with the monetary expenditure as well as the negative operational side-effects of the counter-measures. On the other hand, the attacker’s penetration procedure is predominantly influenced by the defence strategy. This hierarchical interaction implies bi-level nature of the problem. Hence, we propose a network interdiction model. This model not only captures the probabilistic and multifold effects of counter- measures, but also integrates the adversary agents’ problems. Utilizing techniques such as dualisation and linearisation we convert the problem into a single level MILP optimisation,
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which can be solved exactly. The efficiency of the method is revealed through the computational experiments.
What is the nature of your talk? A mix
Does your talk require prior knowledge of the subject area? A little Is your talk accessible and relevant to practitioners? Relevant
11/09/2018, 12:00, Room - County LT Code: OR60A3415 Semismooth Newton-Type Method for Bilevel Optimization: Theory and Extensive Experiments Dr Alain Zemkoho (University of Southampton)
We consider the optimistic bilevel optimization problem involving twice continuously differentiable functions. Using the lower-level optimal value function, we reformulate the problem into a single-level optimization problem. To construct a tractable Newton method to solve the latter problem, we start by introducing a new stationarity concept that allows us to design a simple, yet powerful Newton scheme to solve the bilevel optimization problem. From numerical experiments conducted on 124 nonlinear bilevel optimization examples from the literature, our method is able to compute, just within a few seconds, the true/best known or better solutions for all the problems with an existing record on their results, i.e. for 114 out of the 124 problems. To the best of our knowledge, this is the first time in the literature where experiments on a method for nonlinear bilevel optimization are conducted at such a scale, and possibly with such a level of success. To achieve this success, appropriate choices had to be made for the exact penalization parameter, usually needed to mitigate the negative effects of the value function constraint. By so doing, we also provide a first benchmark study on the selection of this parameter. Furthermore, it is important to mention that second order sufficient conditions ensuring that the stationary points computed are at least locally optimal are also developed in this paper, that will be presented at the conference. (Ongoing joint work with Andreas Fischer and Shenglong Zhou)
What is the nature of your talk? Theoretical
Does your talk require prior knowledge of the subject area? A little Is your talk accessible and relevant to practitioners? Highly
11/09/2018, 13:30, Room - County LT Code: OR60A3474 Towards Explainable and Transparent MCDA Methods
Dr K.Nadia Papamichail (The University of Manchester) and Prof Theodor Stewart (University of Cape Town)
Making machine decisions explainable and transparent is an ongoing challenge. This work seeks to develop a framework for generating automated explanations for decision analytic tools. Natural language generation techniques have been applied to design a text planner and a sentence generator for orchestrating the dialogue between a decision aiding tool and a decision maker. We have developed explanations for augmenting the ability of an MCDA tool to explain its output and justify its recommendations. The content of the explanations depends on the stage of the decision analysis process. We have identified four stages of interactions between MCDA tools and their users. The natural language tool has been applied to a MAVT (Multi-attribute value theory) setting but can be extended to other MCDA approaches.
What is the nature of your talk? A mix
Does your talk require prior knowledge of the subject area? A little Is your talk accessible and relevant to practitioners? Very
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11/09/2018, 14:00, Room - County LT Code: OR60A3326 Enhancing Knowledge Construction Processes within Multicriteria Decision Analysis: The Delphi-DC-MACBETH Collaborative Approach
Dr Monica Oliveira, Prof Carlos Bana e Costa and Dr Ana Vieira (IST - Universidade de Lisboa) Multicriteria Decision Analysis (MCDA) is commonly used to help decision-makers and other stakeholders in complex evaluation contexts. Further to technical soundness and meaningfulness, for developing evaluation models for practical use it is critical to design adequate social processes to capture participants’ values and perspectives, while promoting a shared understanding around key evaluation issues. Face-to-face workshops and decision conferencing (DC) processes, involving a relatively small number of participants, have been typically adopted for interactive model building and to promote model requisiteness. One such socio-technical approach is the MACBETH decision conferencing (DC-MACBETH) which uses the Measuring Attractiveness by a Categorical Based Evaluation TecHnique (MACBETH) to facilitate the elicitation of value judgements from a small group in a decision conferencing process. The DC-MACBETH approach has proven to be effective, but replicating this environment in participatory contexts that require to capture the views of a large, diverse and dispersed number of stakeholders, requires a different social setting. This paper tackles this challenge by enhancing the social component of the DC-MACBETH approach with the Delphi method. We depart from the classical collaborative knowledge acquisition process in expert systems literature and develop a three-step approach to involve an extended group of participants (“selected crowd”) in different steps of multicriteria value modelling – the Delphi- DC-MACBETH collaborative approach. After an initial planning step, the Delphi-DC-MACBETH approach draws upon the features of the Delphi method to design a participatory knowledge construction process where we extract knowledge from a “selected crowd” within a non-face- to-face web environment. This knowledge is then used to inform a restrict number of participants that collaboratively analyse the information collected, verify and/or complete it in a decision conferencing setting. We describe potential uses of the Delphi-DC-MACBETH approach and exemplify its use in different real cases in which it fostered higher participation and collaboration in multicriteria modelling.
What is the nature of your talk? A mix
Does your talk require prior knowledge of the subject area? A little Is your talk accessible and relevant to practitioners? Very
11/09/2018, 14:30, Room - County LT Code: OR60A3678 Integrating Individual and Aggregate Diversity in Top-N Recommendation
Dr Ibrahim Muter (University of Bath), Dr Tevfik Aytekinand Dr Ethem Canakoglu (Bahcesehir University)
Recommender systems have become one of the main components of web technologies that help people to cope with the information overload. Two of the most important metrics used to analyse the performance of these systems are accuracy and diversity of the recommendation lists. While all the efforts exerted in the prediction of the user interests aim at maximizing the former, the latter emerges in various forms, such as diversity in the lists across all user recommendation lists, referred to as aggregate diversity, and diversity in the lists of individuals, known as individual diversity. To the best of our knowledge, no study has been done to consider both individual diversity and aggregate diversity, along with accuracy. In this paper, we tackle the combination of these three objectives, and justify this approach by showing through experiments that handling these objectives in pairs does not yield satisfactory results in the
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third one. To that end, we develop a mathematical model that is formulated using multi- objective optimization approaches. To cope with the intractability of this non-linear integer programming model, its special structure is exploited by a decomposition technique. For the solution of the resulting formulation, we propose an iterative framework that is composed of a clique generating genetic algorithm and constructive/improvement heuristics. We conduct experiments on three data sets and show that the proposed modelling approach successfully handles all objectives according to the needs of the system.
What is the nature of your talk? A mix
Does your talk require prior knowledge of the subject area? Some Is your talk accessible and relevant to practitioners? Very
13/09/2018, 09:00, Room - County LT Code: OR60A3459 Role of Multi-Criteria Decision Analysis Techniques in Achieving Circular Economy
Dr Maryam Masood and Dr Stuart R Coles (University of Warwick)
The concept of the circular economy, where the value of materials and resources is preserved, is gaining momentum across the majority of industries and research fields. However, similar to sustainability concepts, the circular economy approach is also multifaceted and requires a shift in more than one value domain. If an approach focusses only on one or a few of the important domains i.e. environmental, economic, social and technical, then it often delivers misleading results in terms of the benefits or impacts. The key in shifting towards a circular economy is to have a lifecycle perspective as well as a holistic approach where all relevant parameters and all stakeholders involved are considered. A key contributor to this shift can be the use of Multi-Criteria Decision Analysis (MCDA) techniques. This work explores how effectively MCDA techniques have so far been employed in achieving circular economy principles in multiple disciplines. The selection of MCDA techniques has been critically reviewed based on the nature of the problem and if there is any particular bias towards a certain approach. Findings suggest that the use of MCDA approaches in the literature in the context of the circular economy has so far been limited, however, the nature of defined problems and solutions required are generally very well aligned with the scope of application of MCDA techniques. Examples from existing literature on the circular economy will be used to build a case on the usefulness of MCDA application for this paradigm shift. Future developments must focus on building of a framework that may enable the selection of indicators for a specific problem to accurately and clearly describe benefits and impacts across all domains resulting in a transparent analysis of the shift towards a circular economy.
What is the nature of your talk? Theoretical
Does your talk require prior knowledge of the subject area? A little Is your talk accessible and relevant to practitioners? Very
13/09/2018, 09:30, Room - County LT Code: OR60A3348 Evaluating the Quality of Radiotherapy Treatment Plans for Prostate Cancer
Miss Emma Stubington and Prof Matthias Ehrgott (Lancaster University), Prof Omid Nohadani (Northwestern University) and Prof Glyn Shentall (Royal Preston Hospital)
External beam radiation therapy is a common treatment method for cancer. Radiotherapy is planned with the aim to achieve conflicting goals: while a sufficiently high dose of radiation is necessary for tumour control, a low dose of radiation is desirable to avoid complications in normal, healthy, tissue. These goals are encoded in clinical protocols and a plan that does not
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meet the criteria set out in the protocol may have to be re-optimised using a trial and error process. To support the planning process, we seek plans that would benefit from re- optimisation by proposing a method to evaluate the quality of the treatment plans. First, the clinical protocol is translated into a set of measurable variables and Principal Component Analysis (PCA) is used for dimension reduction to select the most relevant variables. Data Envelopment Analysis (DEA) is then applied to assess the quality of individual treatment plans. Each plan is compared against the entire set of plans to identify the ones that could realistically be improved. We further enhance this procedure with simulation techniques to account for uncertainties in the data for treatment plans. This allows us to make recommendations to the clinicians as to which plans we believe could potentially be improved. In this talk, we present a case study based on prostate cancer treatment plans from the Royal Preston Hospital, UK. Clinicians at Preston then re-plan the identified plans and our findings are presented here. What is the nature of your talk? A mix
Does your talk require prior knowledge of the subject area? A little Is your talk accessible and relevant to practitioners? Somewhat
13/09/2018, 10:00, Room - County LT Code: OR60A3496 Uncertain Data Envelopment Analysis
Prof Matthias Ehrgott (Lancaster University), Prof Allen Holder (Rose-Hulman Institute of Technology) and Prof Omid Nohadani (Northwestern University)
Data Envelopment Analysis (DEA) is a nonparametric, data driven method to conduct relative performance measurements among a set of decision making units (DMUs). Efficiency scores are computed based on assessing input and output data for each DMU by means of linear programming. Traditionally, these data are assumed to be known precisely. We instead consider the situation in which data is uncertain, and in this case, we demonstrate that efficiency scores increase monotonically with uncertainty. This enables inefficient DMUs to leverage uncertainty to counter their assessment of being inefficient. Using the framework of robust optimization, we propose an uncertain DEA (uDEA) model for which an optimal solution determines (1) the maximum possible efficiency score of a DMU over all permissible uncertainties, and (2) the minimal amount of uncertainty that is required to achieve this efficiency score. We show that the uDEA model is a proper generalization of traditional DEA and provide a first-order algorithm to solve the uDEA model with ellipsoidal uncertainty sets. Finally, we present a case study applying uDEA to the problem of deciding efficiency of radiotherapy treatments.
What is the nature of your talk? A mix
Does your talk require prior knowledge of the subject area? A little Is your talk accessible and relevant to practitioners? Relevant
13/09/2018, 11:00, Room - County LT Code: OR60A3652 KEYNOTE: Learning from and for MCDA Practice
Prof Valerie Belton (University of Strathclyde Business School)
Ten years ago, at OR50, myself and Gilberto Montibeller concluded a presentation with thoughts on the key challenges facing MCDA. The integration of theory and practice was one of these, including the question of how to encourage a healthy community of MCDA practice. A decade later, although there is a growing literature describing a wide range of applications of MCDA methods, there is still relatively little that focuses on the actual practice of MCDA – ie
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the practicalities of how MCDA methods are used and what works well in different contexts. This presentation will review existing theory relating to MCDA practice and the extent to which it informs practice before reporting on findings from recent work, including a series of interviews with MCDA practitioners, exploring approaches used and perceptions of key challenges faced, before concluding with discussion of the potential and ways to enhance future practice.
What is the nature of your talk? A mix
Does your talk require prior knowledge of the subject area? A little Is your talk accessible and relevant to practitioners? Relevant
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