4 présentations

• 
  16h45 - 17h07

  Two-Stage Distributionally Robust Optimization for Service Region Design in Crowdsourced Delivery

  • Aliaa Alnaggar, prés., Toronto Metropolitan University
  • Merve Bodur, The University of Edinburgh

  We consider a service region planning problem faced by a crowdsourced delivery platform where drivers are commuters who are willing to deviate from their original routes to a make a delivery in exchange for a compensation. The availability of drivers and service demand are uncertain, and it is difficult to estimate their probability distributions due to the absence of service history. To mitigate the effects of data ambiguity, we propose a two-stage distributionally robust optimization (DRO) model. The first stage selects which nodes to offer service in, while the second stage matches drivers and orders after uncertainty is realized. We derive an exact reformulation of the DRO model and develop a monolithic approximation based on a convex relaxation of the subproblem. We further strengthen the proposed approximation by a set of valid inequalities inspired by the linearization reformulation technique. The benefit of the proposed approach for service region design in crowdsourced delivery is demonstrated through numerical experiments.

• 
  17h07 - 17h29

  Optimizing Healthcare Access in Rural Areas: An Application of Self-serve Kiosks

  • Gohram Baloch, prés., Simon Fraser University
  • Fatma Gzara, University of Waterloo

  In recent years, self-serve Kiosks have been widely adopted in the healthcare industry to improve healthcare access, streamline processes, and reduce wait times. In this presentation, we explore the application of self-serve pharmacy kiosks in enhancing healthcare access in rural regions. We model the problem as a location-inventory problem to make optimal location and inventory decisions that minimize customer travel distances in the presence of both brick-and-mortar pharmacy and self-serve kiosks. Customer choice is modeled through a multinomial logit (MNL) framework, where the utility they derive from a specific kiosk location depends on expected travel distance, which in turn is influenced by the kiosk’s fill rate. Operational scenarios where these kiosks improve healthcare accessibility are presented

• 
  17h29 - 17h51

  Capacity Planning and Allocation Under Uncertainty: The Case of a Staffing Resource Team

  • Alireza Ghahtarani, prés., University of Montreal
  • Vahid Sarhangian, University of Toronto
  • Andre A. Cire, University of Toronto Scarborough

  We study a capacity planning and allocation problem under demand uncertainty with application to design of a staffing (or nursing) resource team. The decision maker must decide on the number of staff as well as their allocation to multiple periods at the beginning of the horizon. We consider a distributionally robust formulation where the goal is to minimize the maximum expected staffing and shortage costs among all joint demand distributions with the specified mean and covariance matrix. In the extreme case where the adversary can choose the demand for each period independently (the no-correlation model) we characterize the structure of the optimal solution and propose a polynomial-time algorithm to compute it. For the general case, we propose a Benders decomposition algorithm warm-started by the solution of the no-correlation model. We further investigate the performance of the solution of the no-correlation model for the general problem both analytically and numerically.

• 
  17h51 - 18h13

  A Novel Robust Stochastic Possibilistic Programming Model to Design a Global Omni-Channel Sustainable Closed-loop Supply Chain Network

  • Samira Rouhani, prés., Department of Mechanical, Industrial and Mechatronics Engineering, Toronto Metropolitan University, Ontario, Canada
  • Saman Hassanzadeh Amin, Department of Mechanical, Industrial and Mechatronics Engineering, Toronto Metropolitan University, Ontario, Canada
  • Leslie Wadley, Department of Management & Marketing, Cape Breton University, Nova Scotia, Canada

  With the rise of e-commerce, combining online and in-store channels expands product reach and increases sales. However, this integration significantly increases supply chain complexity in both forward and reverse flows. Therefore, managing operational challenges, returns and their environmental impacts is crucial. In this talk, a global omni-channel sustainable Closed-Loop Supply Chain (CLSC) network is described. A new mixed-integer linear programming model is proposed to design this network. In this model, the demand is sensitive to both price and manufacturer sustainability level (i.e., Environmental, Social, and Governance (ESG) score). In this research, a new price-ESG-dependent demand function is developed for the first time in the field of CLSC network design. In addition, to cope with the uncertainty, a novel robust stochastic possibilistic programming model is formulated. The model is applied to a mattress supply chain in Canada, offering managerial insights. The results show that the proposed model is effective.