Download Advances in Computational and Stochastic Optimization, Logic by J. N. Hooker (auth.), David L. Woodruff (eds.) PDF

By J. N. Hooker (auth.), David L. Woodruff (eds.)

Computer technological know-how and Operations learn proceed to have a synergistic courting and this publication - as part of theOperations study and machine technological know-how Interface Series - sits squarely within the heart of the confluence of those technical examine groups. The learn awarded within the quantity is facts of the increasing frontiers of those intersecting disciplines and gives researchers and practitioners with new paintings within the parts of good judgment programming, stochastic optimization, heuristic seek and post-solution research for integer courses.
The bankruptcy themes span the spectrum of software point. many of the chapters are hugely utilized and others symbolize paintings during which the applying strength is barely starting. additionally, every one bankruptcy comprises expository fabric and reports of the literature designed to reinforce the participation of the reader during this increasing interface.

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Extra info for Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search: Interfaces in Computer Science and Operations Research

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1994a). Tutorial in logic-based methods for optimization, CSTS Newsletter, Fall issue. [18] Hooker, J. N. (1995). html. [19] Hooker, J. N. and C. Fedjki (1990). Branch-and-cut solution of inference problems in propositional logic, Annals of Mathematics and AI 1, 123-140. [20] Hooker, J. , and M. A. Osorio (1996). html. , and J. L. Lassez (1987). Constraint logic programming, Proceedings of the 14th Annual ACM Symposium on Principles of Programming Languages (POPL87),111-119. , S. Michaylov, P.

The main decision variables are declared by var Assign {(i1,i2) in TYPE, j in ROOM} integer >= 0; Assign [i1, i2, j] is the number of people of type (it, i2) assigned to room j. The constraints to rule out "isolation" employ some auxiliary zero-one variables: var Any {(i1,i2) in TYPE, j in ROOM} binary; The intent is that Any [i1, i2, j] will take the value one if there are any people of type (i 1 , i2) assigned to room j , and zero otherwise. Upper bounds on the values of the variables Assign [il, i2, j] are also calculated, in terms of previously defined parameters: param upperbnd {(i1,i2) in TYPE, j in ROOM} := min ( ceil ((number[i1,i2]/card PEOPLE) * hiDine[j]) + give[i1,i2], hiTargetTitle[i1,j] + giveTitle[i1] , hiTargetLoc[i2,j] + giveLoc[i2], number [i 1, i2] ); Then three collections of constraints jointly specify the non-isolation requirement: 38 INTERFACES IN COMPUTER SCIENCE AND OPERATIONS RESEARCH subj to IsolationO {(il,i2) in TYPE, j in ROOM}: Assign[il,i2,j] <= upperbnd[ii,i2,j] * Any[ii,i2,j]; subj to Isolationia {(ii,i2) in TYPE, j in ROOM}: Assign[ii,i2,j] >= Any[ii,i2,j]; subj to Isolationib {(ii,i2) in TYPE, j in ROOM}: Assign[il,i2,j] + sum {iii in ADJ[ii]: (iii,i2) in TYPE} Assign[iii,i2,j] >= 2 * Any[il,i2,j]; When Any [il, i2, j] is zero, constraint IsolationO [il, i2, j] says no person of type (il,i2) may be assigned to room j, while Isolation1a[i1,i2,j] and Isolation1b[i1,i2,j] are trivially satisfied.

Thus a natural formulation is provided by using a set-valued variable to represent the subset of cities visited. This example differs from the previous one, however, in that the order of the cities is also significant. We would thus want to further extend the var declaration to encompass ordered set-valued variables: var var-name {indexing} ordered within set-expr ; var var-name {indexing} circular within set-expr ; The keywords ordered and circular would have the same meanings as they currently do in AMPL's set declarations.

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