Title page for ETD etd-11172011-203803

Type of Document Dissertation
Author Markowski, Adria Elizabeth
Author's Email Address adria83@vt.edu
URN etd-11172011-203803
Title Planning for Army Force Generation Using Lot Streaming, and Extensions
Degree PhD
Department Industrial and Systems Engineering
Advisory Committee
Advisor Name Title
Sarin, Subhash C. Committee Chair
Bish, Douglas R. Committee Member
Halstead, John B. Committee Member
Koelling, Charles Patrick Committee Member
  • lot streaming
  • flow time
  • wait time
  • optimization
  • column generation
  • Dantzig-Wolfe
  • lot splitting
  • hybrid flow shop
  • agile manufacturing
Date of Defense 2011-11-04
Availability restricted
As the Army transitions to the Army Force Generation (ARFORGEN) deployment cycle, it must adjust its many operations in support of ARFORGEN. Specifically, the Initial Military Training (IMT) must be able to adjust the scheduling of its classes for newly enlisted service members to finish training such that they fulfill Brigade Combat Team (BCT) requirements within their common due windows. We formulate this problem as a lot streaming problem. Lot streaming splits a batch of jobs into sublots,which are then processed over the machines in an overlapping fashion. To schedule classes for the IMT, there are two stages that must be coordinated: Basic Training (BT) and Advanced Individual Training (AIT). For the Army Force Generation problem, the classes are considered as sublots that are streamed from one stage to the next. For this process, the model formulation must address determination of class sizes and scheduling of soldiers and classes at the two stages such that (1) the start times of the soldiers at Stage 2 are greater than their completion times at Stage 1, and (2) the assignment of requisite number of soldiers is made to each BCT, so as to minimize the total flow time.

We propose a decomposition-based approach for the solution of this problem. In an effort to decompose the problem, the original lot streaming problem is reformulated such that the master problem selects an optimal combination of schedules for training classes and assigning soldiers to BCTs. A complete schedule selected in the master problem includes the assignments of soldiers to classes in BT, AIT, and their assignments to the BCTs, so as to minimize the total flow time as well as earliness and tardiness for regular Army units. Earliness and Tardiness are defined as the length of the time a soldier waits before and after the due date, respectively, of the BCT to which he or she is assigned. Our decomposition-based method enables solution of larger problem instances without running out of memory, and it affords CPU time reductions when compared with the CPU times required for these problem instances obtained via direct application of CPLEX 12.1.

Our investigation into the structure of the problem has enabled further improvement of the proposed decomposition-based method. This improvement is achieved because of a result, which we show, that the first and second-stage scheduling problems need not be solved as one combined subproblem, but rather, they can be solved sequentially, the first stage problem followed by the second stage problem. The combination of Stage 1 and Stage 2 problems as one subproblem creates several additional enumerations of possible schedules the model must generate. By reducing this number of enumerations, the computational effort involved in solving the model reduces significantly, thereby allowing reductions in CPU time. In the Sequential approach, the completion times of soldiers determined at Stage 1 are passed to Stage 2 as bounds on their completion times at Stage 2. We prove that solving the combined subproblem sequentially as two subproblems is optimal when the first stage has no limit on the batch size and the ready times of the soldiers at Stage 1 are the same. For the Army Force Generation problem, we use unequal ready times, and therefore, solving the scheduling problems for the first two stages as sequential subproblems can lead to suboptimal solutions. Our experimental investigation shows efficacy of solving larger-sized problem instances with this method. We also recommend various potential additions to improve the Sequential approach for application to the overall Army problem. We have also demonstrated the use of our methodology to a real-life problem instance. Our methodology results in schedules for IMT with an estimated 28% reduction in mean flow time for soldiers over what is currently experienced in practice.

We apply this Sequential approach to various extensions of the problem on hand that pertain to hybrid flow shop and agile manufacturing environments. Results of our computational investigation show the effectiveness of using the Sequential approach over direct solution by CPLEX from the viewpoint of both optimality gap and the CPU time required. In particular, we consider two different model configurations for a hybrid flow shop and three different model configurations for an agile manufacturing facility.

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