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Hybrid MIP-CP problem solving: sequential solving Description The idea of this example is to use a Constraint Programming
(CP) model to preprocess
data for an LP problem. The constraint propagation performed by the CP solver tightens the bounds on certain decision variables.
- solving a sequence of CP subproblems
- data exchange between several models via shared memory
Further explanation of this example:
Xpress Whitepaper 'Hybrid MIP/CP solving', Section 'Using CP propagation as preprocessor'. The example problem, namely planning the construction of a stadium, is described in the book 'Applications of optimization with Xpress-MP'.
Source Files By clicking on a file name, a preview is opened at the bottom of this page.
Data Files b1stadium_ka.mos (!**************************************************************** CP example problems =================== file b1stadium_ka.mos ````````````````````` Construction of a stadium (See "Applications of optimization with Xpress-MP", Section 7.1 Construction of a stadium) (c) 2008 Fair Isaac Corporation author: S. Heipcke, March 2005, rev. Dec. 2010 *****************************************************************!) model "B-1 Stadium construction (CP)" uses "kalis" declarations N = 19 ! Number of tasks in the project ! (last = fictitious end task) TASKS = 1..N ARC: dynamic array(range,range) of integer ! Matrix of the adjacency graph DUR: array(TASKS) of integer ! Duration of tasks HORIZON : integer ! Time horizon start: array(TASKS) of cpvar ! Start dates of tasks bestend: integer end-declarations initializations from 'Data/b1stadium.dat' DUR ARC end-initializations HORIZON:= sum(j in TASKS) DUR(j) forall(j in TASKS) do 0 <= start(j); start(j) <= HORIZON end-do ! Task i precedes task j forall(i, j in TASKS | exists(ARC(i, j))) do Prec(i,j):= start(i) + DUR(i) <= start(j) if not cp_post(Prec(i,j)) then writeln("Posting precedence ", i, "-", j, " failed") exit(1) end-if end-do ! Since there are no side-constraints, the earliest possible completion ! time is the earliest start of the fictitiuous task N bestend:= getlb(start(N)) start(N) <= bestend writeln("Earliest possible completion time: ", bestend) ! For tasks on the critical path the start/completion times have been fixed ! by setting the bound on the last task. For all other tasks the range of ! possible start/completion times gets displayed. forall(j in TASKS) writeln(j, ": ", start(j)) end-model | |||||||||||||||

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