Shakibayifar, M, Hassannayebi, E, Mirzahossein, H, Taghikhah, F & Jafarpur, A 2019, 'An intelligent simulation platform for train traffic control under disturbance', International Journal of Modelling and Simulation, vol. 39, no. 3, pp. 135-156.View/Download from: Publisher's site
© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. Railway disturbance management is inherently a multi-objective optimization problem that concerns both the operators’ cost and passenger’s service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions. Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first.
Taghikhah, F, Raffe, WL, Mitri, G, Toit, SD, Voinov, A & Garcia, JA 2019, 'Last Island: Exploring Transitions to Sustainable Futures through Play', ACM International Conference Proceeding Series, Australasian Computer Science Week Multiconference, ACM, Sydney, Australia, pp. 1-7.View/Download from: Publisher's site
© 2019 Association for Computing Machinery. A serious game was designed and developed with the goal of exploring potential sustainable futures and the transitions towards them. This computer-assisted board game, Last Island, which incorporates a system dynamics model into a board game's core mechanics, attempts to impart knowledge and understanding on sustainability and how an isolated society may transition to various futures to a non-expert community of players. To this end, this collaborativecompetitive game utilizes the Miniworld model which simulates three variables important for the sustainability of a society: Human population, economic production and the state of the environment. The resulting player interaction offers possibilities to collectively discover and validate potential scenarios for transitioning to a sustainable future, encouraging players to work together to balance the model output while also competing on individual objectives to be the individual winner of the game.
Taghikhah, F, Daniel, J & Mooney, G 2017, 'Profit, planet and people in supply chain: Grand challenges and future opportunities', The 25th European Conference on Information Systems (ECIS), European Conference on Information Systems, AIS Electronic Library (AISeL), Guimarães, Portugal.
Recent pressure from governments and customers on supply chain organizations to consider environ-mental and social issues has increased dramatically. The challenge ahead for supply chain managers is how to grow business profit while protecting the planet and respecting people’s rights. The significance of this issue motivates researchers in the fields of “sustainability” and “supply chain” to further integrate these concepts. To identify affected areas, and how sustainability influences them, this research has employed a literature survey of related papers published between 2012 and 2016 within 16 A* indexed journals that are relevant to Information and Computing Science, Transportation/Freight Services and Manufacturing Engineering. Findings show that sustainable supply chain network structure, impact factors, relationship integration and performance evaluation are the main research topics in these streams. The role of decision-making tools within each discipline, the key methodologies and techniques are discussed. Generally speaking, primary challenges in the sustainable supply chain domain devolve from use of inadequate decision-making tools and inappropriate in-formation systems. The holistic picture presented in this paper is important for helping scholars, system developers, and supply chain analysts to become more aware of current grand challenges and future research opportunities within this field.
Taghikhah, F, Daniel, J & Mooney, G 2017, 'Sustainable Supply Chain Analytics: Grand Challenges and Future Opportunities', https://aisel.aisnet.org/pacis2017/44, Pacific-Asia Conference on Information Systems, Langkawi, Malaysia.
Over the last few years, the pressure for decreasing environmental and social footprints has motivated supply chain organizations to significantly progress sustainability initiatives. Since supply chains have implemented sustainability strategies, the volume of economic, environmental and social data has rapidly increased. Dealing with this data, business analytics has already shown its capability for improving supply chain monetary performance. However, there is limited knowledge about how business analytics can be best leveraged to grow social, environmental and financial performance simultaneously. Therefore, in reviewing the literature around sustainable supply chain, this research seeks to
further illuminate the role business analytics plays in addressing this issue. A literature survey methodology is outlined, scrutinizing key papers published between 2012 and 2016 in the research fields of Information/Computing Science, Business and Supply Chain Management. From examination of 311 journal papers, 39 were selected as meeting defined criteria for further categorization into three distinct research groups including: (a) sustainable supply chain configuration; (b)
sustainable supply chain implementation; (c) sustainable supply chain evaluation.
The issues involved within each grouping are identified and the business analytics processes (i.e. prescriptive, predictive, prescriptive analytics) to specifically address them are discussed. This wide-ranging review of sustainable supply chain analytics can assist both scholars and practitioners to better appreciate the current grand challenges and future research opportunities posed by this area.