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I tried the simple following example and it works well. m = ConcreteModel() # decision variables m.x1 = Var(domain=NonNegativeIntegers, name= "x1", bounds=(0, 10)) m.x2 = Var(domain=NonNegativeIntege …

Besides the mentioned answer, there are many cases in which either a simple rule or an algorithm, or a heuristic can solve a practical problem in an efficient manner and still without defining any con …

If you are willing to know about DEA, there are lots of useful materials which can be found by googling. Some of the best ones are: DEA Tranining and Courses DATA ENVELOPMENT ANALYSIS WITH GAMS Implm …

Also, he is the author of the book Sequential Decision Analytics which introduces some policies to tackle some variants of stochastic optimization models. I hope it helps. …

Suppose, the variables ($x$, $z$) $\in \mathbb{R}^+$. To enforce the first conditional expression, you have to define an auxiliary binary variable $w_1$ and the following constraints: $$ x \leq Mw_1 - …

First of all, I assume that the mentioned model is just to reflect what you want to do otherwise, it needs more attention as the problem is infeasible. To capture a variable value and store that into …

As the problem only contains four variables let's define $f$ as the feasible solution space of the problem. Then $f$ contains $12$ feasible solutions: $\{(1,0,0,0), (0,1,0,0), (0,0,1,0), (0,0,0,1), (1 …

To determine the number of AGVs in your production system, their working procedure should be exploded first based on the following states: $$ \{ \text{Idle time, empty travel time, loading time, loade …

If the integer linear programming is a minimization problem then whose linear relaxation is a lower bound for the original MILP model, and for the maximization problem it is an upper bound. In the con …

Suppose we have an array of binary variables $\{x_{i,t,n}, \ \forall i \in I, t \in T, n \in N \}$. If we want to define a condition as, if any of $x_{i,t,n} = 1$ in an arbitrary index $i$, then the r …

AI algorithms, specifically in industries, can be used to work and manipulate the data to estimate or learn this data to achive a solution or prepare trained data to feed that into an optimization model … almost the same data, e.g. number of failures in a specific station in the production line, to either estimate a function to robust some parameters as a final solution or feed these parameters into an optimization …

What you are looking for sounds like a variant of the HUB-location allocation problem, specifically, Uncapacitated Multiple Allocation Hub Location Problem. This problem contains a graph $G = (V, E)$ …

It actually tried to show the concept of an optimization model like the objective, constraints, etc. by a drag-and-drop scheme. …

As a free Excel plugin to solve a 3D packing problem and already with the visualization, the Vehicle Routing and Logistics Optimization within EURO, VeRoLog, has a material that may help to solve such …

If you would like to take into account the inventory model directly in an optimization model, your problem is more likely classified in the following areas: Multi-Depot Location Routing Problem taking …