By Oktay Günlük, Jeff Linderoth (auth.), Andrea Lodi, Alessandro Panconesi, Giovanni Rinaldi (eds.)
The quantity includes the papers chosen for presentation at IPCO 2008, the thirteenth foreign convention on Integer Programming and Combinatorial - timization that was once held in Bertinoro (Italy), might 26–28, 2008. The IPCO sequence of meetings, subsidized by way of the Mathematical Progr- ming Society, highlights contemporary advancements in idea, computation, and app- cation of integer programming and combinatorial optimization. The ?rst conf- ence happened in 1990; ranging from IPCO 1995, the court cases are released within the Lecture Notes in laptop technology sequence. The 12 prior IPCO meetings have been held in Waterloo (Canada) 1990, Pittsburgh (USA) 1992, Erice (Italy) 1993, Copenhagen (Denmark) 1995 [LNCS 920], Vancouver (Canada) 1996 [LNCS 1084], Houston (USA) 1998 [LNCS 1412], Graz (Austria) 1999 [LNCS 1610], Utrecht (The Netherlands) 2001 [LNCS 2081], Boston (USA) 2002 [LNCS 2337], big apple (USA) 2004 [LNCS 2986], Berlin (Germany) 2005 [LNCS 3509], and Ithaca (USA) 2007 [LNCS 4168]. The c- ference isn't held within the years while the overseas Symposium of the Ma- ematical Programming Society takes place.
Read Online or Download Integer Programming and Combinatorial Optimization: 13th International Conference, IPCO 2008 Bertinoro, Italy, May 26-28, 2008 Proceedings PDF
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Extra resources for Integer Programming and Combinatorial Optimization: 13th International Conference, IPCO 2008 Bertinoro, Italy, May 26-28, 2008 Proceedings
Sample text
UGMIP) Another idea that has played a significant role in the successful application of general-purpose cutting planes in MILP is that of cut diversification [8,4]. Cut diversification refers to the strategy of adding a batch of cuts each of which affects a different part of the incumbent solution thereby triggering a collaborative action and yielding improvements that cannot be obtained by a single cut. For instance, the tremendous practical performance of Mixed Integer Gomory Cuts is often attributed to their well-diversified nature (see [7]).
A. Lodi, A. Panconesi, and G. ): IPCO 2008, LNCS 5035, pp. 34–46, 2008. c Springer-Verlag Berlin Heidelberg 2008 The ATFM Problem: An Integer Optimization Approach 35 However, it has become increasingly evident that very significant delays and system throughput degradations have arisen from en-route airspace problems and limitations. The problem posed by the en-route sector capacity constraints is persistent and may take at least one more decade to resolve [4]. One of the implications of the simultaneous presence of airport and en-route airspace constraints is that devising good strategies is a much more complicated task.
Analogously the subset of sectors that precede i is denoted by Pif ⊂ Sf (see Figure 1). , Sector i and Sector h in Figure 1, while those sectors preceded by more than one sector are called joints, Sector j in the same figure. 1 The Mathematical Formulation The model’s formulation requires definition of the following notation: K ≡ set of airports, S ≡ set of sectors, f S ⊆ S ≡ set of sectors that can be flown by flight f, F ≡ set of flights, T ≡ set of time periods, C ≡ set of pairs of flights that are continued, Pif ≡ set of sector i’s subsequent sectors, Lfi ≡ set of sector i’s previous sectors, Dk (t) ≡ departure capacity of airport k at time t, Ak (t) ≡ arrival capacity of airport k at time t, Sj (t) ≡ capacity of sector j at time t, df ≡ scheduled departure time of flight f, af ≡ scheduled arrival time of flight f, sf ≡ turnaround time of an airplane after flight f, origf ≡ airport of departure of flight f, destf ≡ airport of arrival of flight f, lf j ≡ number of time units that flight f must spend in sector j, f f ¯f Tj = [T j , Tj ] ≡ set of feasible time periods for flight f to arrive in sector j, T fj ≡ first time period in the setTjf , T¯jf ≡ last time period in the setTjf .