Person:
Toriello, Alejandro

Permanent Link
https://hdl.handle.net/1853/71784
Associated Organization(s)
ORCID
ArchiveSpace Name Record

Filters

2
1
2010 - 2018 2
1 1
1 1
2
2
Reset filters

Settings

Publication Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Operational and Tactical Analysis of Same-Day Delivery Distribution Systems
    ( 2018-10-24) Toriello, Alejandro
    E-retail is a highly competitive segment that constantly demands innovation and process improvement. One such innovation rapidly gaining traction is same-day delivery (SDD); large e-retailers like Amazon are quite active in SDD, and over half of all US retailers claim to offer some form of the service. This talk surveys some of our recent and ongoing work studying the distribution component of SDD systems, and includes both (1) operational questions faced by dispatchers, e.g. when should vehicles be dispatched and what orders should they deliver, as well as (2) tactical questions faced by managers, e.g. how late in the day should SDD be offered and how big should the delivery fleet be. The talk is based on join work with Alan Erera, Mathias Klapp (now at PUC Chile) and Alex Stroh.
  • Thumbnail Image
    Item
    Time decomposition of multi-period supply chain models
    (Georgia Institute of Technology, 2010-08-04) Toriello, Alejandro
    Many supply chain problems involve discrete decisions in a dynamic environment. The inventory routing problem is an example that combines the dynamic control of inventory at various facilities in a supply chain with the discrete routing decisions of a fleet of vehicles that moves product between the facilities. We study these problems modeled as mixed-integer programs and propose a time decomposition based on approximate inventory valuation. We generate the approximate value function with an algorithm that combines data fitting, discrete optimization and dynamic programming methodology. Our framework allows the user to specify a class of piecewise linear, concave functions from which the algorithm chooses the value function. The use of piecewise linear concave functions is motivated by intuition, theory and practice. Intuitively, concavity reflects the notion that inventory is marginally more valuable the closer one is to a stock-out. Theoretically, piecewise linear concave functions have certain structural properties that also hold for finite mixed-integer program value functions. (Whether the same properties hold in the infinite case is an open question, to our knowledge.) Practically, piecewise linear concave functions are easily embedded in the objective function of a maximization mixed-integer or linear program, with only a few additional auxiliary continuous variables. We evaluate the solutions generated by our value functions in a case study using maritime inventory routing instances inspired by the petrochemical industry. The thesis also includes two other contributions. First, we review various data fitting optimization models related to piecewise linear concave functions, and introduce new mixed-integer programming formulations for some cases. The formulations may be of independent interest, with applications in engineering, mixed-integer non-linear programming, and other areas. Second, we study a discounted, infinite-horizon version of the canonical single-item lot-sizing problem and characterize its value function, proving that it inherits all properties of interest from its finite counterpart. We then compare its optimal policies to our algorithm's solutions as a proof of concept.