- There are several ways of solving a supply chain optimization problem with CPLEX.
- These settings are made in both supply planning applications as well as off the shelf optimizers.\
- There is both a simplex method and a duplex method.
The solution procedure is the optimization method that is applied. I often describe and differentiate optimizers based upon their objective function. Therefore, optimizers with an objective function of minimizing costs, I call cost optimizers. Those that attempt to minimize inventory at a set service level, or maximize service level at a set inventory level are called inventory optimizers.
To read about this type of optimization see this article.
However, something I have discussed significantly less is the optimization solution selected, which is a subset of the optimization method.
There are some methods, but a small number of them are the most popular. For applications like supply planning, the following would apply.
Where this is set in many optimizers is very clear. This is a screenshot of the Solution Methods tab of the SAP SNP Optimizer. The decomposition methods describe how the problem is segmented to improve run times.
More on this topic can be read about in this article.
However, notice the options at the bottom of the screenshot under LP Solution Procedure.
There are three LP Solution Procedures available to choose from. This is Primal Simplex, Dual Simplex Method and Interior Point Method, which can be used along with either of the first two options. As the CPLEX solver is actually what is being used, these are the same options provided by CPLEX.
These are described by Wikipedia below:
The IBM ILOG CPLEX Optimizer solves integer programming problems, very large linear programming problems using either primal or dual variants of the simplex method or the barrier interior point method, convex and non-convex quadratic programming problems, and convex quadratically constrained problems (solved via Second-order cone programming, or SOCP).– Wikipedia
The methods move from the most simple, being the Primal Simplex to the most complex, the Interior Point Method. The Simplex is the most commonly used. The simplex method must work with equalities, not inequalities, and thus requires the introduction of slack variables, which measures the amount of unused capacity in the resource.
Dual Simplex Method
The Dual Simplex method is used for a particular type of problem where the equality constraints are set up in a specific way. This quote is from Elmer G. Wiens site on operations research:
Like the primal simplex method (or just the simplex), the standard form of the dual simplex method assumes all constraints are <= or =, but places no restrictions on the signs of the RHS (right hand side variables — to read more about right hand side variables see this article. The dual simplex method algorithm consists of three phases.
Phase 0 is identical to Phase 0 of the primal simplex method, as the artificial variables are replaced by the primal variables in the basis. However, the dual simplex method algorithm in Phase 1 searches for a feasible dual program, while in Phase 2, it searches for the optimal dual program, simultaneously generating the optimal primal program. – Elmer G. Weins
The interior point method solves problems differently from the primal or the dual method simplex in that the interior point begins from the interior of the problem, rather than looking across the surface. Where the optimizer starts it search is of great importance as to the final solution it develops.
For instance, MatLab in their documentation (a separate optimizer not associated with SAP), describes how to “change the initial point” of the optimizer in at least one of its online documentation pages.
This is not the only way to change the starting point. The Heuristic First Solution selection will also change the optimizers’ first point by estimating the best solution with a heuristic before the optimizer even begins.
The Frontline Solver offers different options which are listed in the screenshot below:
Something which is interesting is that Frontline recommends only using the Simplex LP method for non-linear problems. However, CPLEX (which is inside of SNP) uses Simplex for non-linear problems (realistic supply planning problems are non-linear).
This discrepancy is something that I will update this post with when I figure out the reason for this.
The solution method is always of great emphasis for those using a general solver, which requires that the users get very much into the detail of the optimization. However, on enterprise optimization projects, often the particulars of the optimizer parameter setup can be overlooked due to other issues and distractions.
However, it is both interesting and relevant to know what solution methods are being employed and to have a good reason for their selection in a documented format.
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Supply Planning Book
Showing the Pathway for Improvement
Supply planning software, and by extension supply planning itself, could be used much more efficiently than it currently is. Why aren’t things better?
Providing an Overall Understanding of Supply Planning in Software
Unlike most books about software, this book showcases more than one vendor. Focusing an entire book on a single software application is beneficial for those that want to use the application in question solely. However, this book is designed for people that want to understand supply planning in systems.
- What methods fall into APS?
- How do the different methods work and how do they differ in how they generate output?
- What is the sequence of supply planning runs?
These types of questions are answered for readers in this book.
This book explains the primary methods that are used for supply planning, the supply planning parameters that control the planning output as well as how they relate to one another.
Who is This Book For?
- Chapter 1: Introduction
- Chapter 2: Where Supply Planning Fits Within the Supply Chain Planning Footprint
- Chapter 3: MRP Explained
- Chapter 4: DRP Explained
- Chapter 5: APS Supply Planning Methods
- Chapter 6: APS for Deployment
- Chapter 7: Constraint-based Planning
- Chapter 8: Reorder Point Planning
- Chapter 9: Planning Parameters
- Chapter 10: How MRP, DRP, and APS Relate to One Another
- Chapter 11: Supply Planning Visibility and Master Data Management
- Chapter 12: Understanding the Difference Between Production Versus Simulation