However, the benefits are much more tangible. This makes it more difficult to explain…at least in my mind.

The job scheduling problem requires less pre-work, but the set-up is more complicated and less intuitive. These problems require a good bit of pre-work related to data gathering and analysis, but the worksheet setup is fairly straightforward, perhaps even intuitive. This was an interesting class of problems that produced valuable information and insights regarding the cost and service components of a distribution network. In the previous post, performing a desktop transportation/ network optimization was discussed. This is the second post in a three-part series that describes how to perform desktop network optimization, job scheduling and vehicle routing with nothing but Microsoft Excel.

Useful ideas for maximizing the power of Microsoft Excel for Supply Chain Analysis, Part II

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Enterprise Portfolio and Program Management Consulting.Business Process Improvement (Lean/Six Sigma).Customer Experience And Design Consulting Services.Other methods for nonlinear optimization include Sequential Quadratic Programming (SQP) and Interior Point or Barrier methods. The GRG method can be viewed as a nonlinear extension of the Simplex method, which selects a basis, determines a search direction, and performs a line search on each major iteration – solving systems of nonlinear equations at each step to maintain feasibility. The GRG Nonlinear Solving method uses the Generalized Reduced Gradient method as implemented in Lasdon and Waren’s GRG2 code. Since a non-convex NLP may have multiple feasible regions and multiple locally optimal points within such regions, there is no simple or fast way to determine with certainty that the problem is infeasible, that the objective function is unbounded, or that an optimal solution is the “global optimum” across all feasible regions. They may be convex or non-convex, and an NLP Solver must compute or approximate derivatives of the problem functions many times during the course of the optimization. Such problems are intrinsically more difficult to solve than linear programming (LP) problems. A model in which the objective function and all of the constraints (other than integer constraints) are smooth nonlinear functions of the decision variables is called a nonlinear programming (NLP) or nonlinear optimization problem.

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