mixed-integer-programming

问题 I'm using DOCPLEX to build up a Mixed Integer Linear Programming (MILP) problem which is then solved via CPLEX on Python. However, upon trying to solve the MILP problem using IF-THEN constraints, I receive the following error: DOcplexException: Model.if_then(), nbBus40 >= 3.0 is not discrete This is happening because I have declared nbbus40 variable to be continuous , as shown in the code below: from docplex.mp.model import Model mdl = Model(name='buses') nbbus40 = mdl.continuous_var(name=

问题 I'm using DOCPLEX to build up a Mixed Integer Linear Programming (MILP) problem which is then solved via CPLEX on Python. However, upon trying to solve the MILP problem using IF-THEN constraints, I receive the following error: DOcplexException: Model.if_then(), nbBus40 >= 3.0 is not discrete This is happening because I have declared nbbus40 variable to be continuous , as shown in the code below: from docplex.mp.model import Model mdl = Model(name='buses') nbbus40 = mdl.continuous_var(name=

问题 I'm new to Google OR-Tools. Using Python, I implemented a MIP model with SCIP as a solver. The objective function is for minimization ( solver.Minimize(C) ) and I am accessing the final solution through solver.Objective().Value() . However, I also need to access the intermediate solutions that the solver finds, before reaching the final one, and their timestamp . (The final goal is to plot a graph with the solutions evolution through time). I tried to use a while loop: solutions_evolution = {

问题 I have a Mixed Integer Programming problem. I can use JuMP to find the optimal solution. But how can I find the second best solution? Or the third-best etc. This potentially might be another equally optimal solution, or it might be a worse solution, or it might be :Infeasible -- there might be no most solutions. I know for a TSP-like problem, I can find additional solutions by progressively removing links that are on the optimal path (I.e setting the distances between some of the cities to be

问题 I am new to mystic and working on an optimization problem.My mystic code looks like this: def find_loss(e,lmd,q,k): edge_pmf=find_final_dist(e,lmd,q) l_e=sum(edge_pmf[k+1:]) return l_e def objective(x): s=0 for i in range(len(x)): s+=find_loss(edge_enum[i],lamd,q,x[i]) return s added=lambda x: [i for i in x] cons=lambda x: my.constraints.impose_sum(total_cap,added(x)) @integers() def round(x): return x bounds=[(0,None)]*a if __name__=='_main_': result=diffev2(objective,x0=bounds,bounds=bounds

问题 I need to find a solution to an optimisation problem. In my simplified example, I have a prediction of prices for the next year. I have inventory that can contain max 25 products. I can either sell or buy each month. I cannot buy more than 4 products or sell more than 8 products per month. I am looking for profit by buying for lower price than selling. Is there a package/function that can indicate when to buy and when to sell? The objective is to maximise the profit at the end of the period

问题 I need to find a solution to an optimisation problem. In my simplified example, I have a prediction of prices for the next year. I have inventory that can contain max 25 products. I can either sell or buy each month. I cannot buy more than 4 products or sell more than 8 products per month. I am looking for profit by buying for lower price than selling. Is there a package/function that can indicate when to buy and when to sell? The objective is to maximise the profit at the end of the period

问题 I would like to find a solution to an optimisation problem. The aim is to maximise profit by buying for low price and selling for a higher one. There are constraints such as maximum stock level, and max buy/sell number of units. Moreover, sell and buy limits depend on the inventory levels. I have asked a similar question albeit without the last condition here R optimisation buy sell. Here is an example: price = c(12, 11, 12, 13, 16, 17, 18, 17, 18, 16, 17, 13) capacity = 25 max_units_buy_30 =

问题 I would like to find a solution to an optimisation problem. The aim is to maximise profit by buying for low price and selling for a higher one. There are constraints such as maximum stock level, and max buy/sell number of units. Moreover, sell and buy limits depend on the inventory levels. I have asked a similar question albeit without the last condition here R optimisation buy sell. Here is an example: price = c(12, 11, 12, 13, 16, 17, 18, 17, 18, 16, 17, 13) capacity = 25 max_units_buy_30 =

问题 I want to solve an integer programming model with cplex python. I have this model: a and h are matrixes with 0s and 1s. p is a set of numbers. here is a part of my cplex code for this model: p=[i for i in range (len(h))] x=mdl.binary_var_dict(p,name='x') #objective mdl.minimize(0) #constraints #1 mdl.add_constraints(mdl.sum(h[i][k]*x[i] for i in p)==4 for k in T) #2 mdl.add_constraints(mdl.sum(a[i][k]*x[i] for i in p)==4 for k in T) mdl.print_information() Solution = mdl.solve(log_output