Journées de l'optimisation 2018

HEC Montréal, Québec, Canada, 7 — 9 mai 2018

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MA5 Scheduling I

7 mai 2018 10h30 – 12h10

Salle: Manuvie (54)

Présidée par Urbbi Paik

3 présentations

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    10h30 - 10h55

    Flexible versus robust lot-scheduling subject to random production yield and deterministic dynamic demand

    • Stefan Helber, prés., Leibniz Universität Hannover
    • Karl Inderfurth, Otto-von-Guericke-Universität Magdeburg
    • Florian Sahling, Technische Universität Chemnitz
    • Katja Schimmelpfeng, Universität Hohenheim

    We consider the problem of scheduling production lots for multiple products competing for a common production resource that processes the product units serially. The demand for each product and period is assumed to be known with certainty, but the yield per production lot is random as the production process can reach an out-of-control state while processing each single product unit of a lot. A service-level constraint is used to limit the backlog in the presence of this yield uncertainty. We address the question of how to determine static production lots and how to schedule these lots over the discrete periods of a finite planning horizon. The scheduling problem is characterized by a trade-off between the cost of holding inventory and the cost of overtime, whereas the production output is uncertain. For this purpose, we develop a rigid and robust planning approach and two flexible heuristic scheduling approaches. In an extensive numerical study, we compare the different approaches to assess the cost of operating according to a robust plan as opposed to a flexible policy.

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    10h55 - 11h20

    Hospital maintenance scheduling and resource allocation applying multi-agent systems

    • Zahra Yousefli, prés., student
    • Fuzhan Nasiri, Concordia University
    • Osama Moselhi, Professor

    Healthcare buildings and hospitals are included among vulnerable assets. Moreover, these assets have the budget constraints which makes them more vulnerable. According to Reviewing literature illuminates this fact that hospitals across Canada are engaged with deferred maintenance problems. Therefore, budgets constraints have to be managed by hospitals manager with very less impact on the primary expenditures. Solving the issue of deferred maintenance requires a high level of dedication and commitment of the maintenance human resource in hospital buildings. Resource allocation processes include several activities such as scheduling, allocation of personnel and coordination of facility manager’s team. Facility managers are under increasing pressure to prioritize maintenance work orders due to declining human resources and limited budget. Furthermore, it is necessary to optimize the cost of human resource allocation in order to achieve the desired performance. On the other hand, the resource allocation is an interactive, dynamic and complex environment of diverse and independent of facility manager’s team. In such a complex environment, each party’s behavior may be simple, but the aggregate patterns of their interactions can be complex. Despite significant mathematical models contributions to optimize the cost of resource allocation, these approaches have some limitations such as ignorance of unforeseen events and financial losses while a facility waits for maintenance. Solving these kinds of problems is difficult for mathematical formulation. Multi-Agent System (MAS) has viable potential in modeling that complex environment. The purpose of this research is to propose a Multi-Agent Based System (MAS) to simulate and optimize the cost of human resource allocation. This research proposes a MAS to simulate the maintenance human resource allocation of hospitals. Furthermore, it analyzes some scenarios to achieve near optimum resource allocation cost and modify the probabilities related to work defects and poor performance. This model studies the coordination of facility manager’s team as agents to minimize out of service time and maximize utilization of maintenance human resources to increase patients ‘satisfaction in healthcare and hospitals. The proposed MAS model provides an opportunity for facility managers to generate a near optimal schedule and test it under a variety of conditions. In this research, a MAS model is generated using AnyLogic software to re-engineer facility maintenance work order processes in a department of healthcare.

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    11h20 - 11h45

    Optimal scheduling of demand-driven cogeneration biogas plants using mixed integer linear programming

    • Urbbi Paik, prés., McGill University
    • François Bouffard, McGill University

    This work presents a mixed-integer linear programming formulation of demand-driven biogas combined heat and electricity generation units. It analyzes the economic benefits of having a flexible substrate management for biogas generation along with a variable heat-to-electricity conversion ratio for profit maximization of spot market electricity sales.