Australia

Automated driving systems (ADS) will redefine human transportation. Due to recent breakthroughs in artificial intelligence and computational processing, mass public transportation via autonomous vehicles is no longer in our distant future. However, the transition from current roadways to fully automated roadways will not be instantaneous. This opens up many interesting research opportunities in OR and DS some of which I shall discuss in the talk: ethical decisions in ADS, request to intervene decision support and multiagent decisions in ADS.

ICORES 2022

Operations Research Opportunities in Automated Driving Systems

The purpose of the International Conference on Operations Research and Enterprise Systems (ICORES) is to bring together researchers, engineers, faculty, and practitioners interested in both theoretical advances and practical applications in the field of operations research. Two tracks cover on one side domain independent methodologies and technologies and on the other side practical work developed in specific application areas.<br><br>
CONFERENCE AREAS<br>
1 . Methodologies and Technologies<br>
2 . Applications

The purpose of the International Conference on Operations Research and Enterprise Systems (ICORES) is to bring together researchers, engineers, faculty, and practitioners interested in both theoretical advances and practical applications in the field of operations research. 

keynote

This work addresses an operational problem of a logistics service provider that consists of finding an optimal route for a vehicle carrying customer parcels from a central depot to selected facilities, from where autonomous devices like robots are launched to perform last-mile deliveries. The objective is to minimize a tardiness indicator based on the customer delivery deadlines. We provide a better understanding of how three major tardiness indicators can be used to improve the quality of service by minimizing the maximum tardiness, the total tardiness, or the number of late deliveries. We study the problem complexity, devise a unifying Mixed Integer Programming formulation and propose an efficient branch-and-Benders-cut scheme to deal with instances of realistic size. Numerical results show that this novel Benders approach with a tailored combinatorial algorithm for generating Benders cuts largely outperforms all other alternatives. In our managerial study, we vary the number of available facilities, the coverage radius of autonomous robots and their speed, to assess their impact on the quality of service and environmental costs. Joint work with: L. Alfandari and M.M. de Silva

A Tailored Benders Decomposition Approach for Last-mile Delivery with Autonomous Robots

Multiple Criteria Decision Analysis/Making (MCDA/MCDM) is constantly increasing its presence in various scientific fields but also in new fields of its application. This expansion became possible mainly due to the integration and implementation of its methodologies in the Decision Support Systems (DSS). This enabled their successful applications in many new fields resulting in their further expansion as Multi-Criteria Decision Support Systems (MCDSS). Their combination with Artificial Intelligence (intelligent agents, recommender systems, expert systems) in general and especially in recent years with the field of Machine Learning and Data Mining has led to the development of Intelligent Multi-Criteria Decision Support Systems (IMCDSS) with enormous application in analytics. At the same time, MCDA was used to solve various problems and to develop new methodologies in the field of Machine Learning. The aim of this speech is to present on the one hand the theoretical issues and methodologies of the cooperation of the Multi-Criteria DSS with the Artificial Intelligence and on the other hand to present a series of such IMCDSS and their applications in various fields such as marketing, health, defense, agricultural, etc.

Intelligent Multi-Criteria Decision Support Systems

Strategic planners need two distinct intellectual capabilities. First, extensive topical or disciplinary expertise, supported by a broad understanding of the world, is needed for dealing with complex subtleties of human affairs. Second, methodological expertise in decisions under uncertainty is needed for dealing with unique situations involving innovation, discovery, and surprise by friend or foe. We employ info-gap decision theory, and the concept of robust-satisficing, in support of strategic planning. Examples from national security, economic forecasting, and project management are presented. Three arguments support our claim. First, this dichotomy of intellectual capabilities is based on the uniqueness of historical circumstance, which often induces unprecedented behavior. Each strategic planning situation has many unique attributes of culture, geography, technology, ideology, etc. E.g. Britain’s counterinsurgency (COIN) strategy in Malaya was, in many respects, quite different from its COIN in Northern Ireland, and both were different from British COIN in Kenya, Brunei, Malaysia, Radfan (Yemen) and Dhofar (in Oman). While there are generic aspects of all conflicts, historical distinctiveness and innovation are also characteristic. This makes the identification of useful concrete rules of strategy difficult. Thus, strategists need both profound understanding of human affairs and societies, and expertise in managing surprise and uncertainty. The second argument for expertise in managing uncertainty is based on Shackle-Popper indeterminism (SPI), which will be discussed. SPI provides a generic epistemic framework for understanding historical idiosyncracy and the prevalence of non-probabilistic Knightian uncertainty. The third argument is that consensus of analysts' assessments is demanded by decision makers, but pluralism of understanding is prevalent in complex uncertain environments. We propose nurturing plurality of assessment, and embedding those assessments in the analysis of robustness to uncertainty. Specifically, for any proposed policy, the analyst evaluates the robustness (of that policy) to uncertainty (plurality) of assessment. A more robust policy is preferred over a less robust policy. In order to do this, the analyst must have both topical expertise in the relevant disciplines, as well as decision-theoretic expertise in managing uncertainty.

Operations Research Opportunities in Automated Driving Systems

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A Tailored Benders Decomposition Approach for Last-mile Delivery with Autonomous Robots

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