Logistics planning technique and applications

Résumé du document

Exercise 1: We have to solve a linear programming system, concerning a cost transport optimisation. To minimise the total transport cost, we have to use the linear programming mathematical model. Exercise 2: We have to find an appropriate place for a new distribution centre by using the Gravity model to place it at an optimum position. Exercise 3: We have to find an appropriate routing for the new distribution centre by using one of the Sweep or Saving algorithms.

Extraits

[...] London: Geraldine Lyons. 530.) In order to define this transportation model we have to find out three kinds of information: - the variables: are the number of unit that is going to be delivered to each retailer, - the constraints: depends on the number available and the demand, - an objective function: which here, is the minimum total transport cost. Here the variables are the total number of unit delivered from the distribution centre DCi to the retailer Rj, so we can establish the name of these variables on the same scheme than the "Table given in the Coursework paper. [...]

[...] Part A In this part, we have to solve a linear programming system, concerning a cost transport optimisation. A.1: The mathematical model minimising the total transportation cost To minimise the total transport cost, we have to use the linear programming mathematical model. “Linear programming describes graphical or mathematical procedures that seek the optimum allocation of scarce or limited resource to competing products or activities.” (Curwin, J and Slater, R. (2008). Linear Programming. In: Thomson Quantitative methods for business decisions. 6th ed. [...]

[...] If we didn't change our constraint we will have the following inequations : If we would represent the solution on an axis it would be : The group of solution would be the intersection between these two ensembles: meaning that the group of solution is equal to ø because there is no intersection between the intervals. "If the constraints are mutually exclusive, no feasible area can be defined and no optimum solution can exist." (Curwin, J and Slater, R. (2008). Linear Programming. In: Thomson Quantitative methods for business decisions. 6th ed. London: Geraldine Lyons. 540.) That is why we have to adapt our constraints. [...]