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Minimise the amount of cash that should be allocated among all available bonds which will maintain the required cash inflow over a number of periods.
A Markov decision problem for moving funds between cash and an interest bearing account in order to minimise the average cost.
Locate plants in such a way that demand is satisfied in each target city, potential plant capacity is not exceeded, and overall operating costs are minimised.
Minimise the weighted tardiness when sequencing jobs on a machine from a given set of jobs. Each job has a processing time, due date and weight.
Minimise weekly payroll cost while meeting manpower requirements for a five-day shift
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What'sBest! is an add-in to Excel that allows you to build large scale optimization models in a free form layout within a spreadsheet. What'sBest! combines the proven power of Linear, Nonlinear (convex and nonconvex/Global), Quadratic, Quadratically Constrained, Second Order Cone, Stochastic, and Integer optimization with Microsoft Excel -- the most popular and flexible business modeling environment in use today. What'sBest! 17.0 includes a wide range of performance enhancements and new features.
Enhancements to the Simplex solvers boost performance on linear models. Large models solve an average of 20% faster using primal simplex and 15% faster for dual simplex.
New symmetry detection capabilities dramatically reduce the time required to prove optimality on certain classes of models with integer variables. Performance has been improved on Markowitz portfolio problems with minimum buy quantities, and/or limit on number of instruments at nonzero level. Other enhancements provide faster solutions on certain task assignment-like models.
Stability and robustness of the Global solver has been improved through several enhancements to quadratic recognition and range reduction. Improved exploitation of convexity of certain ratio constraints, e.g., as found in heat exchanger network design problems.
Several new functions and constraint types are recognized, e.g., the =WBALLDIFF() All Different constraint, for general integer variables. The =WBALLDIFF() function allows one to specify a set of integer variables, such that each variable in the set must have a unique value, different from all other variables in the set.
What'sBest! will efficiently solve your biggest, toughest models. The linear, integer, nonlinear and global solvers in What'sBest! have been designed for large scale commercial use and field tested on real world models by companies around the world. For optimization modeling in Excel, What'sBest! offers unrivaled speed and capacity.
Excel users will find What'sBest! to be an easy and powerful tool for solving optimization problems. Most users are able to begin modeling within minutes of installation.
What'sBest! is an ideal tool for creating optimization applications for use by others. What'sBest! allows you to provide the application in a form that is best suited to the user. For managers, you can build a simple, easy-to-understand spreadsheet. For clerical workers, you can create turn-key applications with custom interfaces.
What'sBest! provides all of the tools you will need to get up and running quickly. You get the What'sBest! User Manual (in printed form and available via the online Help) that fully describes the commands and features of the program. Also included in the manual is discussion of the major classes of linear, integer and nonlinear optimization problems along with over two dozen real world based examples that you can modify and expand.
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The Barrier Option provides the ability to deal with quadratic problems. This option includes a Barrier/Interior Point solver that provides quadratic programming capabilities. This option also provides an alternative method of solving linear models that may be faster than the linear solvers included in the base version.
The Global Option provides the global optimisation capability. Local search solvers are generally designed to search only until they have identified a local optimum. If the model is non-convex, other local optima may exist that yield significantly better solutions. Rather than stopping after the first local optimum is found, the global solver will search until the global optimum is confirmed. The global solver converts the original non-convex, nonlinear problem into several convex, linear subproblems. Then, it uses the branch-and-bound technique to exhaustively search over these subproblems for the global solution. The nonlinear and global license options are required to utilize the global optmization capabilities.
The Nonlinear Option provides the ability for dealing with general nonlinear problems. This option includes a Generalised Reduced Gradient (GRG) based solver that is capable of finding locally optimal solutions to general nonlinear models.