Risk management in some types of location problems under uncertainty


 Abraham Bell
 1 years ago
 Views:
Transcription
1 Risk management in some types of location problems under uncertainty Laureano F. Escudero Joint work with Antonio AlonsoAyuso and Celeste Pizarro Universidad Rey Juan Carlos, Móstoles (Madrid), Spain Exploratory Workshop on Location Analysis: Trends on Theory and Applications IMUS, Universidad de Sevilla, November 2830, 2011
2 Table of Contents 1 Location problems under uncertainty that can be treated 2 Basic notions Risk Neutral strategy Seminal papers in 3 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures
3 Table of Contents 1 Location problems under uncertainty that can be treated 2 Basic notions Risk Neutral strategy Seminal papers in 3 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures
4 Table of Contents 1 Location problems under uncertainty that can be treated 2 Basic notions Risk Neutral strategy Seminal papers in 3 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures
5 Table of Contents 1 Location problems under uncertainty that can be treated 2 Basic notions Risk Neutral strategy Seminal papers in 3 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures
6 Table of Contents 1 Location problems under uncertainty that can be treated 2 Basic notions Risk Neutral strategy Seminal papers in 3 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures
7 Table of Contents 1 Location problems under uncertainty that can be treated 2 Basic notions Risk Neutral strategy Seminal papers in 3 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures
8 Table of Contents 1 Location problems under uncertainty that can be treated 2 Basic notions Risk Neutral strategy Seminal papers in 3 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures
9 Location problems: Special mixed 01 programs máx s.t. ax + cy Ax + By = b x {0, 1} n, y 0, (1)
10 Our experience for large scale location problems Production planning dimensioning and location Plant location & sizing in Strategic Supply Chain Mgmnt Designing connected rapid transit networks Road construction selection in Forestry harvesting planning Tactical portfolio planning in the Natural Gas Supply Chain Prison Facility Site selection under Uncertainty Air traffic Flow Management planning. Flight routes selection Cluster location in multiperiod copper extraction planning in mining Multiperiod locationassignment Energy generation capacity expansion planning and sources location Combinatorial nonlocation most difficult problem: Stochastic SPP
11 Production planning dimensioning and location Aim: Determining product selection and plant location and capacity (i.e., sizing). Main Uncertainties: product price and demand and production cost. VaR and meanrisk averse strategies for twostage stochastic mixed 01 optimization problem for maximizing expected net profit. Method: A twostage BFC algorithmic approach. Ref. AlonsoAyuso, Escudero, Garín, Ortuño, Pérez. OMEGA 05.
12 Plant location & sizing in Strategic Supply Chain Mgmnt Aim: Strategic planning for supply chains and, so, deciding on production topology, plant location and capacity (i.e, sizing), product selection and allocation, and vendor selection for raw materials. Main Uncertainties: product net profit and demand, raw material cost and production cost along time horizon. A risk neutral twostage stochastic mixed 01 optimization problem for maximizing expected net profit. Ref. AlonsoAyuso, Escudero, Garín, Ortuño, Pérez. JOGO 03, and AlonsoAyuso, Escudero, Ortuño. SORT 07.
13 Designing connected rapid transit networks Aim: Location of stations to be constructed and the links between them for in 1st step maximizing the expected number of trips and minimizing the route length, and in 2nd step attempting to minimize an estimation of the total number of transfers that should be made by the users to arrive at their destinations. Uncertainty: Trip demand between origin and destination pairs of demand. Deterministic method. 1st step: Plain CPLEX for the pure combinatorial model network designing, 2nd step: ad.hoc heuristic. Ref. Escudero, Muñoz. 1st paper TOP 09; 2nd paper submitted 2nd evaluation 2011.
14 Road construction selection in Forestry harvesting planning Aim: Managing land designated for timber production and divided into harvest cells. High volatility; timber price scenarios over time. For each time period: Decision on decide which cells to cut and location of access roads to build. A risk neutral multistage stochastic mixed 01 optimization problem for maximizing expected net profit. Method: A specialization of the BFC algorithmic approach (AEO EJOR 03). Ref. AlonsoAyuso, Escudero, Guignard, Quinteros, Weintraub. ANOR 11.
15 Tactical portfolio planning in the Natural Gas Supply Chain Aim: Natural Gas SCM for selecting production fields, transportation capacity booking, bilateral contracts and spot markets. Uncertainty: Buyer s nomination and prices, as well a spot prices. A risk neutral multistage stochastic mixed 01 optimization problem for maximizing expected profit. Method: A nonsymmetric BFCMS algorithmic approach. Ref. Fodstad, Midhum, Romo, Tomasgard, in Bertocchi, Consigli, Dempster (eds.) book, Springer, Ref. Escudero, Garín, Merino, Pérez. COR 12.
16 Prison Facility Site selection under Uncertainty Aim: Solving the strategic problem of timing location and capacity (i.e., sizing) of prison facilities in the Chilean prison system for minimizing expected total cost. Other applications: locating public (hospitals) or private (malls) centers. Main uncertainty: demand for capacity along time horizon. A risk neutral multistage stochastic mixed 01 optimization problem for minimizing expected total cost. Method: A methaheuristic BCC algorithmic approach. based on BFC. Ref. Hernández, AlonsoAyuso, Bravo, Escudero, Guignard, Marianov, Weintraub. COR, accepted for publication 2011.
17 Air traffic Flow Management planning. Flight routes selection Aim: Air traffic flow management for deciding the flight scheduling and location of the routes for each flight in an air network. Uncertainties: airport arrival and departure capacity, the air sector capacity and the flight demand. A risk neutral multistage stochastic mixed 01 optimization problem for minimizing expected total cost. Method: Plain use of MIP solver CPLEX. Ref. Agustín, AlonsoAyuso, Escudero, Pizarro. Submitted, 2nd evaluation, 2011.
18 Cluster location in multiperiod copper extraction planning in mining Aim: Supporting decisions on sequencing and locating clusters for material extraction copper mines. Main uncertainty: Highly volatility of copper prices along a time horizon. A variety of risk averse strategies for multistage stochastic mixed 01 optimization problem for maximizing total profit. Plain use of MIP solver CPLEX. Ref. AlonsoAyuso, Carvallo, Escudero, Guignard, Pi, Puranmalka, Weintraub. To be submitted 2011.
19 Multiperiod locationassignment Aim: Solving the strategic problem of timing the location of facilities and assigning of customers to facilities in a multiperiod environment. Main uncertainties: facilities setup and maintenance costs, customers assignment cost to the facilities, timing at which the customers need to be assigned to the facilities. A risk neutral multistage stochastic pure combinatorial problem for minimizing expected total cost. Method: A methaheuristic FRC algorithmic approach. Ref. AlbaredaSambola, AlonsoAyuso, Escudero, Fernández, Pizarro. To be submitted.
20 Energy generation capacity expansion planning and sources location Aim: Deciding on the optimal mix of different electric power technologies and plant location and sizing along a time horizon for a price taker. Main uncertainty: Fuel prices. A CVaR and Stochastic Dominance Constraints risk averse strategies for multistage stochastic mixed 01 optimization problem for maximizing total profit. Plain use of MIP solver CPLEX. Ref. L.F. Escudero, M. Inorta, M.T. Vespucci. In preparation.
21 Combinatorial nonlocation most difficult problem: Stochastic SPP Aim: Maximizing the meanrisk averse strategy: Maximizing the objective function expected value of the Stochastic SSP minus the weighted risk of obtaining a scenario whose objective function value is worse than a given threshold. Uncertainty: Objective function coeffs. Method: A onestage DEM splitting variable decomposition approach by using a deterministic SPP and a 01 knapsack problem for each scenario at each Lagrangean iter. Ref. Escudero, Landete, RodriguezChia, EJOR 11.
22 Risk Mngment in above types of Location problems Deterministic approach on expected values of uncertain parameters. It can be a big fiasco. Stochasticity treatment via Scenario Analysis. Risk Management: Risk neutral and Risk averse strategies. Feasibility for each scenario. Simple, partial, full recourse. multistage (dynamic), twostage (dynamic), one stage (static). Risk measures: minmax; risk neutral: mean; risk averse: VaR, CVaR, meanrisk, stochastic dominance cons.
23 Basic notions Risk Neutral strategy Seminal papers in Scenario trees in A stage of a given time horizon is a set of consecutive time periods where the realization of the uncertain parameters takes place. A scenario is a realization of the uncertain parameters along the stages of a given time horizon. A scenario group for a given stage is the set of scenarios with the same realization of the uncertain parameters up to the stage.
24 t = 1 t = 2 t = 3 t = 4 Basic notions Risk Neutral strategy Seminal papers in Ω = Ω 1 = {10, 11,...,17}; Ω 2 = {10, 11, 12} G = {1,...,17}; G 2 = {2, 3, 4} 14 N 9 = {1, 4, 9}; σ(9) = Multistage nonsymmetric scenario tree
25 Basic notions Risk Neutral strategy Seminal papers in Let the following notation related to the scenario tree: T, set of the T stages along the time horizon. Ω, set of scenarios. G, set of scenario groups, so that we have a directed graph where G is the set of nodes. G t, set of scenario groups in stage t, for t T (G t G). Ω g, set of scenarios in group g, for g G (Ω g Ω). t(g), stage to which scenario group g belongs to, for g G. σ(g), immediate ancestor node of node g, for g G. w ω, likelihood assigned by the modeler to scenario ω Ω. N g, set of scenario groups {k} such that Ω g Ω k, for g G (N g G). That is, set of ancestor scenario groups to scenario group g, including itself.
26 Basic notions Risk Neutral strategy Seminal papers in Risk Neutral strategy. Max objective function expected value over scenarios máx Q E = g G w g (a g x g + c g y g ) s.t. A gx σ(g) + A g x g + B gy σ(g) + B g y g = b g g G x g {0, 1} n t, y g 0 g G. (2) DEM, Nonanticipativity constraints (NAC) (Wets, SIAM Review 74 for twostage problems and many others after)
27 Basic notions Risk Neutral strategy Seminal papers in 6 earlier seminal papers in Stochastic Programming. Chronological order E.M.L. Beale. On minimizing a convex function subject to linear inequalities, Journal of the Royal Statistical Society, Ser. B, 17: , G.B. Dantzig. Linear programming under uncertainty, Management Science 1: , A. Charnes and W.W. Cooper. Chanceconstrained programming, Management Science 5:7379, R. JB. Wets. Programming under uncertainty: the equivalent convex program, SIAM Journal on Applied Mathematics 14:89105, R. Van Slyke and R.JB. Wets. Lshaped linear programs with application to optimal control and stochastic programming, SIAM Journal on Applied Mathematics 17: , R.JB Wets. Stochastic programs with fixed recourse: The equivalent Deterministic program, SIAM Review 16: , BUT WHAT ABOUT TWOSTAGE AND MULTISTAGE STOCHASTIC MIXED INTEGER OPTIMIZATION THEORY, MODELS AND ALGORITHMS?
28 Basic Risk Averse measures Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures max mean subject to an upper bound on semideviation max mean  weighted semideviation min semideviation subject to a lower bound on mean min expected deviation from scenarios optimal value max ValueatRisk (VaR) max Conditional VaR max mean  weighted probability of nondesired scenarios stochastic dominance cons strategies (SDC)
29 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #1: max mean Q E subject to an upper bound on semideviation σ 2 máx Q E = g G w g (a g x g + c g y g ) s.t. A gx σ(g) + A g x g + B gy σ(g) + B g y g = b g g G σ 2 σ 2 x g {0, 1} n t, y g 0 g G. (3) Inspired in Markowitz, JoFinance 52 and 1959 book, Wiley, Ogryczak & Ruszczynski, EJOR 99 and Ahmed, MP 06 for twostages and continuous vars.
30 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Semideviation σ 2 σ 2 = ω Ω w ω (Q ω Q E ) 2 where Q E : max obj. fun. expected value is given in model (2) and Q ω = g N d (a gˆx g + c g ŷ g ) of the solution ˆx ω, ŷ ω of the variables x ω, y ω in that model, where ω Ω d, d G T.
31 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #2: min expected deviation from optimal obj. fun. value over scenarios Let the Scenario Immunization model (Dembo, ANOR 91 for twostages and continuous vars) D = mín ω Ω wω (Q ω Q ω ) l s.t. A g x σ(g) + A g x g + B g y σ(g) + B g y g = b g g G x g {0, 1} n t, y g 0 g G. (4) See in (Escudero, 1995, in Zenios (ed.), Quantitative Methods. AI and Supercomputers in Finance) a twostep strategy (, 1) for solving model (4).
32 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #3: max mean Q E & VaR for β success probability, a 01 γ parameter, a ρ weighting parameter and a big enough M ω parameter. máx γ g G w g (a g x g + c g y g )+ρα s.t. A gx σ(g) + A g x g + B gy σ(g) + B g y g = b g G (a g x g + c g y g )+M ω ν ω α ω Ω g N d w ω ν ω 1 β ω Ω x g {0, 1} n t, y g 0 g G ν ω {0, 1} ω Ω. See Gaivoronski & Pflug, 1999, JoR 05; Charpentier & Oulidi MMOR 08, among others for two stages and continuous vars. (5)
33 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #4: max mean & weighted VaR & Conditional VaR (i.e., obj. fun expected shortfall on reaching VaR) máx γ g G w g (a g x g + c g y g )+ ( ( ρ α 1 β d G T w d α ) ) g N d (a g x g + c g y g ) + s.t. A gx σ(g) + A g x g + B gy σ(g) + B g y g = b g G x g {0, 1} n t, y g 0 g G α R. Inspired in Rockafellar & Uryasev, JoR 00 and Ahmed, MP06 for twostages and continuous vars; Schultz & Tiedemann, MP 06 for twostages. (6)
34 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #5: max VaR for given success probability & weighted Conditional over VaR (i.e., obj fun expected excess on reaching VaR) máx α+ρ ( ) w d w g (a g x g + c g y g ) α + d G T g G d s.t. A g x σ(g) + A g x g + B g y σ(g) + B g y g = b g G (a g x g + c g y g )+M ω ν ω α ω Ω g N d w ω ν ω 1 β ω Ω x g {0, 1} n t, y g 0 g G ν ω {0, 1} ω Ω. (7)
35 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #6: max mean  weighted obj fun expected shortfall over given threshold φ ( ) máx w d w g (a g x g + c g y g ) ρ w ω v ω d G T g G d ω Ω s.t. A gx σ(g) + A g x g + B gy σ(g) + B g y g = b g G w g (a g x g + c g y g )+v ω φ ω Ω g G d x g {0, 1} n t, y g 0 g G v ω 0 ω Ω. (8) Inspired in Eppen, Martin & Schrage, OR 89 for twostages. Related to ICC of Klein Haneveld book (1986).
36 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #7: max mean  weighted probability of nondesired scenario máx w g (a g x g + c g y g ) ρ w ω ν ω g G ω Ω s.t. A gx σ(g) + A g x g + B gy σ(g) + B g y g = b g G (a g x g + c g y g )+M ω ν ω φ ω Ω g N d x g {0, 1} n t, y g 0 g G ν ω {0, 1} ω Ω. (9) See Schultz & Tiedemann, SIAM JoO 03 for twostages.
37 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #8: stochastic dominance cons (firstorder SDC) for threshold profile φ p with β p success probability p P máx w g (a g x g + c g y g ) g G s.t. A g x σ(g) + A g x g + B g y σ(g) + B g y g = b g G (a g x g + c g y g )+M ω ν ωp φ p ω Ω, p P g N d w ω ν ωp 1 β p p P ω Ω x g {0, 1} n t, y g 0 g G ν ωp {0, 1} ω Ω, p P. (10)
38 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #8: stochastic dominance cons (firstorder SDC) for threshold profile φ p with β p success probability p P (cont.) Note: The objective fun. value of scenario ω is not below the threshold φ p with β p probability, for p P, where P is the set of profiles under consideration. Inspired in Gollmer, Neise & Schultz, SIAM JoO 08 for twostages.
39 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #9: stochastic dominance cons (secondorder SDC) for obj fun expected shortfall e p bound on reaching threshold φ p, p P máx w g (a g x g + c g y g ) g G s.t. A gx σ(g) + A g x g + B gy σ(g) + B g y g = b g G φ p (a g x g + c g y g ) v ωp ω Ω, p P g N d w ω v ωp e p p P ω Ω x g {0, 1} n t, y g 0 g G v ωp 0 ω Ω, p P. (11)
40 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse alternative #9: stochastic dominance cons (secondorder SDC) for obj fun expected shortfall e p bound on reaching threshold φ p, p P (cont.) Inspired in Gollmer, Gotzes & Schultz, MP 11 for twostages. The concept of the obj fun expected shortfall on reaching a given threshold may have its roots in the ICC concept due to Klein Haneveld book (1986). See also Eppen, Martin & Schrage, OR 89 and Klein Haneveld & van der Vlerk, CMS 06 for twostages.
41 Solution considerations: Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures The strategies #3: Mean & VaR, #5: CVaRover, #8: SDC1 and #9: SDC2 have a computational disadvantage over the strategies #2: Scen Imm, #4: CVaR, #6: VAR & SHORTFALL and #7: MEAN & RISK, since they have constraints linking 01 variables from different scenarios. In any case, a decomposition approach must be used for problem solving of huge instances, e.g., exact BFCMS (Escudero, Garín, Merino & Pérez, COR 12) and inexact FRC (AlonsoAyuso, Escudero, Olaso & Pizarro, in preparation) for very large scale stochastic mixed 01 problems plus a device for treating those linking constraints at given steps in exact BFCMS and inexact FRC
42 Solution considerations: Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures The strategies #3: Mean & VaR, #5: CVaRover, #8: SDC1 and #9: SDC2 have a computational disadvantage over the strategies #2: Scen Imm, #4: CVaR, #6: VAR & SHORTFALL and #7: MEAN & RISK, since they have constraints linking 01 variables from different scenarios. In any case, a decomposition approach must be used for problem solving of huge instances, e.g., exact BFCMS (Escudero, Garín, Merino & Pérez, COR 12) and inexact FRC (AlonsoAyuso, Escudero, Olaso & Pizarro, in preparation) for very large scale stochastic mixed 01 problems plus a device for treating those linking constraints at given steps in exact BFCMS and inexact FRC
43 Solution considerations: Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures The strategies #3: Mean & VaR, #5: CVaRover, #8: SDC1 and #9: SDC2 have a computational disadvantage over the strategies #2: Scen Imm, #4: CVaR, #6: VAR & SHORTFALL and #7: MEAN & RISK, since they have constraints linking 01 variables from different scenarios. In any case, a decomposition approach must be used for problem solving of huge instances, e.g., exact BFCMS (Escudero, Garín, Merino & Pérez, COR 12) and inexact FRC (AlonsoAyuso, Escudero, Olaso & Pizarro, in preparation) for very large scale stochastic mixed 01 problems plus a device for treating those linking constraints at given steps in exact BFCMS and inexact FRC
44 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse bibliography. Chronological order H.M. Markowitz. Portfolio selection, Journal of Finance 7:7791, H.M. Markowitz. Portfolio selection, Wiley, P.C. Fishburn. Meanrisk analysis with risk associated with below target returns, The American Economic Review 67: , G.D. Eppen, R.K. Martin, L. Schrage. Scenario approach to capacity planning, Operations Research 34: , R. Dembo. Scenario immunization, Annals of Operations Research 30:6390, L.F. Escudero. Robust Portfolios for MortgageBacked Securities. In S.A. Zenios, editor, Quantitative Methods. AI and Supercomputers in Finance. Unicom, London, , P. Artzner, F. Delbaen, L. Eber, D. Health. Coherent measures of risk, Mathematical Finance 9: , A.A. Gaivoronski, G. Pflug. Finding optimal portfolios with constraints on valueatrisk. In B. Green, editor, Proceedings of the Third International Stockholm Seminar on Risk Behaviour and Risk Management. Stockholm University, W. Ogryczak, A. Ruszczynski. From stochastic dominance to meanrisk models: semideviations as risk measures, European Journal of Operations Research 116: 3350, R. T. Rockafellar, S. Uryasev. Optimization on conditional valueatrisk, Journal of Risk 2:2141, D. Dentcheva, A. Ruszczynski. Optimization with stochastic dominance constraints, SIAM Journal on Optimization 14: , W. Ogryczak, A. Ruszczynski. Dual stochastic dominance and related risk models, SIAM Journal on Optimization 13:6078, R. Schultz, S. Tiedemann. Risk Averse via excess probabilities in stochastic programs with mixedinteger recourse, SIAM Journal on Optimization 14: , 2003.
45 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse bibliography. Chronological order (cont.) D. Dentcheva, A. Ruszczynski. Optimality and duality theory for stochastic optimization with stochastic nonlinear dominance constraints, Mathematical Programming, Ser. B 99: , A.A. Gaivoronski, G. Plug. Valueatrisk in portfolio optimization: properties and computational approach, Journal of Risk 7:1131, W.K. Klein, M.H. van der Vlerk. Integrated chance constraints: reduced forms an algorithm, Computational Management Science 3: , R. Schultz, S. Tiedemann. Conditional valueatrisk in stochastic programs with mixed integer recourse, Mathematical Programming, Ser. B 105: , S. Ahmed. Convexity and decomposition of meanrisk stochastic programs, Mathematical Programming, Ser. B 106: , D. Dentcheva, R. Henrion, A. Ruszczynski. Stability and sensitivity of optimization problems with first order stochastic dominance constraints, SIAM Journal on Optimization 18: , A. Charpentier, A. Oulidi. Estimating allocations for ValueatRisk portfolio optimization, Mathematical Methods of Operations Research, doi /s , R. Gollmer, F. Neise, R. Schultz. Stochastic programs with firstorder stochastic dominance constraints induced by mixedinteger linear recourse, SIAM Journal on Optimization 19: , L.F. Escudero, On a mixture of the FixandRelax Coordination and Lagrangean Substitution schemes for multistage stochastic mixed integer programming, TOP 17:529, A. AlonsoAyuso, L.F. Escudero, C. Pizarro. On SIP algorithms for minimizing the meanrisk function in the MultiPeriod Single Source Problem under uncertainty, Annals of Operations Research 166: , 2009.
46 Risk Averse alternatives to Risk Neutral strategy Recommended bibliography for Risk Averse measures Risk Averse bibliography. Chronological order (cont.) the book A. Shapiro, D. Dentcheva, A. Ruszczynski. Lectures on Stochastic Programming, MPSSIAM Series on Optimization, , L.F. Escudero, M. Landete, A. RodriguezChia. Stochastic Set Packing Problem, European Journal of Operational Research 211: , R. Gollmer, U. Gotzes, R. Schultz. A note on secondorder stochastic dominance constraints induced by mixedinteger linear recourse, Mathematical Programming, Ser. A 126: , N. Miller, A. Ruszczynski. Riskaverse twostage stochastic linear programming: Modeling and decomposition, Operations Research 59: , L. Aranburu, L.F. Escudero, M.A. Garín, G. Pérez. Stochastic models for optimizing immunization strategies in fixedincome security portfolios under some sources of uncertainty, Submitted for publication P. Beraldi, M. Costabile, I. Massabo, E. Russo, F. de Simeone, A. Violi. A multistage stochastic programming approach for capital budgeting problems under uncertainty, Submitted for publication 2011.
47 Illustrative examples Note: COINOR, CPLEX v9 and v.12 failed to find a feas sol in several hours. Mixed integer model 1. Pilot case: Tactical portfolio planning in the Natural Gas Supply Chain. Risk neutral. Ω =1000 scenarios, m=98456 constraints, n01= vars, nc=22221 continuous vars, elapsed time = 182 secs, SUN WS, 2.6Ghz, 16Gb RAM, linux GAP=0 % (optimal soln). Escudero, Garín, Merino, Pérez, COR 12.
48 Illustrative examples (cont.) CPLEX v.12.0 did not fail to obtain optimal soln, but bigger cases require decomposition approaches. Mixed integer model 2. Pilot case: Air Traffic Flow Management under uncertainty. Randomly generated. Risk neutral. Ω =48 scenarios, m= constraints, n= (mostly, 01 vars), elapsed time = 415 secs, PC under Ubuntu, Inter Core Duo, 2Ghz, 4Gb RAM. GAP=0 % (optimal soln). Agustín, AlonsoAyuso Escudero, Pizarro, Submitted 2nd evaluation, 2011.
49 Illustrative examples (cont.) CPLEX v.12.0 has problems for obtaining optimal soln. Decomposition approaches are a must. Mixed integer model 3. Pilot case: Copper extraction planning under uncertainty in future cooper prices. Realistic case. Many risk averse measures. Ω =45 scenarios, m=480490, n01=167951, nc=823, elapsed time: from 398 to secs. Big HW/SW platform: 2 quadcore Xeon E Ghz 64bit processors with 6Mb of cache each, GAMS, CPLEX v12.2. GAP=0 % (optimal soln). AlonsoAyuso, Carvallo, Escudero, Guiganrd, Pi, Puranmalka, Weintraub. To be submitted 2011.
50 Illustrative examples (cont.) Note: COINOR, CPLEX v9 and v.12 failed to find a feas sol in 6 hours of elapsed time. Pure combinatorial model 1. Pilot case: Stochastic Set Packing Problem. Randomly generated. Risk adverse: meanrisk. Ω =100 scenarios, m=220 constraints, n01= vars, elapsed time = secs, SUN WS, 2.2Ghz, 4Gb RAM, GAP=6.82 %. Escudero, Landete, RodriguezChia, EJOR 11.
51 Illustrative examples (cont.) CPLEX v.12.3 has problems for obtaining optimal soln. Decomposition approaches are a must. Pure combinatorial model 2. Pilot case: On solving the multiperiod locationassignment problem under uncertainty. Randomly generated. Risk neutral. Ω =158 scenarios, m=121979, n01=99700, elapsed time: 313 secs, Intel Core 2 Duo, 2.60Ghz, 3Gb RAM, inexact FRC algorithm implemented in C++ code with CPLEX v12.3 as a MIP solver. GAP=0 % (optimal soln), Time GAP=44.6 %. AlbaredaSambola, AlonsoAyuso, Escudero, Fernández, Pizarro. To be submitted.
52 Illustrative examples (cont.) CPLEX v.12.3 has problems for obtaining optimal soln. Decomposition approaches are a must. Pure combinatorial model 2. Pilot case: On solving the multiperiod locationassignment problem under uncertainty. Randomly generated. Risk neutral. Ω =241 scenarios, m=188713, n01=154220, elapsed time: 241 secs, Intel Core 2 Duo, 2.60Ghz, 3Gb RAM, inexact FRC algorithm implemented in C++ code with CPLEX v12.3 as a MIP solver. GAP=0 % (optimal soln), Time GAP=60.8 %. AlbaredaSambola, AlonsoAyuso, Escudero, Fernández, Pizarro. To be submitted.
53 Illustrative examples (cont.) CPLEX v.12.3 doesn t obtained a soln in 8 hours of elaped time. Pure combinatorial model 2. Pilot case: On solving the multiperiod locationassignment problem under uncertainty. Randomly generated. Risk neutral. Ω =147 scenarios, m=18231, n01=116860, elapsed time: 2958 secs, Intel Core 2 Duo, 2.60Ghz, 3Gb RAM, inexact FRC algorithm implemented in C++ code with CPLEX v12.3 as a MIP solver. AlbaredaSambola, AlonsoAyuso, Escudero, Fernández, Pizarro. To be submitted.
54 Stochastic papers published in scientific journals A. AlonsoAyuso, L.F. Escudero, M.T. Ortuño. BFC, a BranchandFix Coordination algorithmic framework for solving some types of stochastic pure and mixed 01 programs, European Journal of Operational Research 151: , L.F. Escudero, J. Salmerón. On a FixandRelax framework for largescale resourceconstrainedproject scheduling, Annals of Operations Research 140: , L.F. Escudero, A. Garín, M. Merino, G. Pérez, A twostage stochastic integer programming approach as a mixture of BranchandFix Coordination and Benders Decomposition schemes, Annals of Operations Research 152: , L.F. Escudero, M.A. Garín, M. Merino, G. Pérez. The value of the stochastic solution in multistage problems, TOP 15:4864, A. AlonsoAyuso, L.F. Escudero, C. Pizarro. On SIP algorithms for minimizing the meanrisk function in the MultiPeriod Single Source Problem under uncertainty, Annals of Operations Research 166: , M. P. Cristóbal, L.F. Escudero, J.F. Monge. On Stochastic Dynamic Programming for solving largescale tactical production planning problems, Computers & Operations Research 36: , A. AlonsoAyuso, N. Domenica, L.F. Escudero, C. Pizarro. Structuring bilateral energy contract portfolios in competitive markets, in G. Consigli, M.I. Bertocchi, D.A.H. Dempster (eds.), Stochastic optimization methods in Finance and Energy, Springer, 2011, pp L.F. Escudero, M.A. Garín, M. Merino, G. Pérez, An algorithmic framework for solving large scale multistage stochastic mixed 01 problems with nonsymmetric scenario trees, Computers & Operations Research 39: , 2012.
An approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 01 Programming
An approach for Strategic Supply Chain Planning under Uncertainty based on Stochastic 01 Programming A. AlonsoAyuso 1, L.F. Escudero 2, A. Garín 3, M.T. Ortuño 4, G. Pérez 5 1 Dpto. de CC. Experimentales
More informationAbstract. 1. Introduction. Caparica, Portugal b CEG, ISTUTL, Av. Rovisco Pais, 1049001 Lisboa, Portugal
Ian David Lockhart Bogle and Michael Fairweather (Editors), Proceedings of the 22nd European Symposium on Computer Aided Process Engineering, 1720 June 2012, London. 2012 Elsevier B.V. All rights reserved.
More informationSupply Chain Design and Inventory Management Optimization in the Motors Industry
A publication of 1171 CHEMICAL ENGINEERING TRANSACTIONS VOL. 32, 2013 Chief Editors: Sauro Pierucci, Jiří J. Klemeš Copyright 2013, AIDIC Servizi S.r.l., ISBN 9788895608235; ISSN 19749791 The Italian
More informationCASH FLOW MATCHING PROBLEM WITH CVaR CONSTRAINTS: A CASE STUDY WITH PORTFOLIO SAFEGUARD. Danjue Shang and Stan Uryasev
CASH FLOW MATCHING PROBLEM WITH CVaR CONSTRAINTS: A CASE STUDY WITH PORTFOLIO SAFEGUARD Danjue Shang and Stan Uryasev PROJECT REPORT #20111 Risk Management and Financial Engineering Lab Department of
More informationA WeightedSum Mixed Integer Program for BiObjective Dynamic Portfolio Optimization
AUTOMATYKA 2009 Tom 3 Zeszyt 2 Bartosz Sawik* A WeightedSum Mixed Integer Program for BiObjective Dynamic Portfolio Optimization. Introduction The optimal security selection is a classical portfolio
More informationLAGRANGIAN RELAXATION TECHNIQUES FOR LARGE SCALE OPTIMIZATION
LAGRANGIAN RELAXATION TECHNIQUES FOR LARGE SCALE OPTIMIZATION Kartik Sivaramakrishnan Department of Mathematics NC State University kksivara@ncsu.edu http://www4.ncsu.edu/ kksivara SIAM/MGSA Brown Bag
More informationNan Kong, Andrew J. Schaefer. Department of Industrial Engineering, Univeristy of Pittsburgh, PA 15261, USA
A Factor 1 2 Approximation Algorithm for TwoStage Stochastic Matching Problems Nan Kong, Andrew J. Schaefer Department of Industrial Engineering, Univeristy of Pittsburgh, PA 15261, USA Abstract We introduce
More informationOptimization under uncertainty: modeling and solution methods
Optimization under uncertainty: modeling and solution methods Paolo Brandimarte Dipartimento di Scienze Matematiche Politecnico di Torino email: paolo.brandimarte@polito.it URL: http://staff.polito.it/paolo.brandimarte
More informationChapter 9 ShortTerm Trading for Electricity Producers
Chapter 9 ShortTerm Trading for Electricity Producers Chefi Triki, Antonio J. Conejo, and Lina P. Garcés Abstract This chapter considers a pricetaker power producer that trades in an electricity pool
More informationHighperformance local search for planning maintenance of EDF nuclear park
Highperformance local search for planning maintenance of EDF nuclear park Frédéric Gardi Karim Nouioua Bouygues elab, Paris fgardi@bouygues.com Laboratoire d'informatique Fondamentale  CNRS UMR 6166,
More informationBranchandPrice Approach to the Vehicle Routing Problem with Time Windows
TECHNISCHE UNIVERSITEIT EINDHOVEN BranchandPrice Approach to the Vehicle Routing Problem with Time Windows Lloyd A. Fasting May 2014 Supervisors: dr. M. Firat dr.ir. M.A.A. Boon J. van Twist MSc. Contents
More informationDynamic Capacity Acquisition and Assignment under Uncertainty
Dynamic Capacity Acquisition and Assignment under Uncertainty Shabbir Ahmed and Renan Garcia School of Industrial & Systems Engineering Georgia Institute of Technology Atlanta, GA 30332. March 29, 2002
More informationA MULTIPERIOD INVESTMENT SELECTION MODEL FOR STRATEGIC RAILWAY CAPACITY PLANNING
A MULTIPERIOD INVESTMENT SELECTION MODEL FOR STRATEGIC RAILWAY YungCheng (Rex) Lai, Assistant Professor, Department of Civil Engineering, National Taiwan University, Rm 313, Civil Engineering Building,
More informationOptimal Scheduling for Dependent Details Processing Using MS Excel Solver
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 8, No 2 Sofia 2008 Optimal Scheduling for Dependent Details Processing Using MS Excel Solver Daniela Borissova Institute of
More informationRobust Optimization for Unit Commitment and Dispatch in Energy Markets
1/41 Robust Optimization for Unit Commitment and Dispatch in Energy Markets Marco Zugno, Juan Miguel Morales, Henrik Madsen (DTU Compute) and Antonio Conejo (Ohio State University) email: mazu@dtu.dk Modeling
More informationElectric Company Portfolio Optimization Under Interval Stochastic Dominance Constraints
4th International Symposium on Imprecise Probabilities and Their Applications, Pittsburgh, Pennsylvania, 2005 Electric Company Portfolio Optimization Under Interval Stochastic Dominance Constraints D.
More informationManaging Financial Risk in Planning under Uncertainty
PROCESS SYSTEMS ENGINEERING Managing Financial Risk in Planning under Uncertainty Andres Barbaro and Miguel J. Bagajewicz School of Chemical Engineering and Materials Science, University of Oklahoma, Norman,
More informationA stochastic programming approach for supply chain network design under uncertainty
A stochastic programming approach for supply chain network design under uncertainty Tjendera Santoso, Shabbir Ahmed, Marc Goetschalckx, Alexander Shapiro School of Industrial & Systems Engineering, Georgia
More informationARTICLE IN PRESS. European Journal of Operational Research xxx (2004) xxx xxx. Discrete Optimization. Nan Kong, Andrew J.
A factor 1 European Journal of Operational Research xxx (00) xxx xxx Discrete Optimization approximation algorithm for twostage stochastic matching problems Nan Kong, Andrew J. Schaefer * Department of
More informationJoint LocationTwoEchelonInventory Supply chain Model with Stochastic Demand
Joint LocationTwoEchelonInventory Supply chain Model with Stochastic Demand Malek Abu Alhaj, Ali Diabat Department of Engineering Systems and Management, Masdar Institute, Abu Dhabi, UAE P.O. Box: 54224.
More informationFrancisco J. Nogales. Education. Professional Experience. Avda. de la Universidad, 30
Francisco J. Nogales Department of Statistics Universidad Carlos III de Madrid  Spain Avda. de la Universidad, 30 28911Legane s (Madrid) Tel: 916248773 Fax: 916248749 FcoJavier.Nogales@uc3m.es http://www.est.uc3m.es/nogales
More informationAppendix: Simple Methods for Shift Scheduling in MultiSkill Call Centers
MSOM.1070.0172 Appendix: Simple Methods for Shift Scheduling in MultiSkill Call Centers In Bhulai et al. (2006) we presented a method for computing optimal schedules, separately, after the optimal staffing
More informationIntroduction to Stochastic Optimization in Supply Chain and Logistic Optimization
Introduction to Stochastic Optimization in Supply Chain and Logistic Optimization John R. Birge Northwestern University IMA Tutorial, Stochastic Optimization, September 00 1 Outline Overview Part I  Models
More informationSTOCHASTIC SERVICE NETWORK DESIGN: THE IMPORTANCE OF TAKING UNCERTAINTY INTO ACCOUNT
Advanced OR and AI Methods in Transportation STOCHASTIC SERVICE NETWORK DESIGN: THE IMPORTANCE OF TAKING UNCERTAINTY INTO ACCOUNT ArntGunnar LIUM, Stein W. WALLACE, Teodor Gabriel CRAINIC Abstract. The
More informationA joint control framework for supply chain planning
17 th European Symposium on Computer Aided Process Engineering ESCAPE17 V. Plesu and P.S. Agachi (Editors) 2007 Elsevier B.V. All rights reserved. 1 A joint control framework for supply chain planning
More informationCentro de Investigación Operativa
Centro de Investigación Operativa I20083 WISCHE: A decision support system for water irrigation scheduling M. Almiñana, L.F. Escudero, M. Landete, J.F. Monge and J. SánchezSoriano May 2008 ISSN 15767264
More informationHYBRID GENETIC ALGORITHMS FOR SCHEDULING ADVERTISEMENTS ON A WEB PAGE
HYBRID GENETIC ALGORITHMS FOR SCHEDULING ADVERTISEMENTS ON A WEB PAGE Subodha Kumar University of Washington subodha@u.washington.edu Varghese S. Jacob University of Texas at Dallas vjacob@utdallas.edu
More informationPractical Financial Optimization. A Library of GAMS Models. The Wiley Finance Series
Brochure More information from http://www.researchandmarkets.com/reports/2218577/ Practical Financial Optimization. A Library of GAMS Models. The Wiley Finance Series Description: In Practical Financial
More informationA MILP Scheduling Model for Multistage Batch Plants
A MILP Scheduling Model for Multistage Batch Plants Georgios M. Kopanos, Luis Puigjaner Universitat Politècnica de Catalunya  ETSEIB, Diagonal, 647, E08028, Barcelona, Spain, Email: luis.puigjaner@upc.edu
More informationLocating and sizing bankbranches by opening, closing or maintaining facilities
Locating and sizing bankbranches by opening, closing or maintaining facilities Marta S. Rodrigues Monteiro 1,2 and Dalila B. M. M. Fontes 2 1 DMCT  Universidade do Minho Campus de Azurém, 4800 Guimarães,
More informationA Scalable Decomposition Algorithm for Solving Stochastic Transmission and Generation Investment Planning Problems
Photos placed in horizontal position with even amount of white space between photos and header Photos placed in horizontal position with even amount of white space between photos and header A Scalable
More informationA Constraint Programming based Column Generation Approach to Nurse Rostering Problems
Abstract A Constraint Programming based Column Generation Approach to Nurse Rostering Problems Fang He and Rong Qu The Automated Scheduling, Optimisation and Planning (ASAP) Group School of Computer Science,
More informationA Reference Point Method to TripleObjective Assignment of Supporting Services in a Healthcare Institution. Bartosz Sawik
Decision Making in Manufacturing and Services Vol. 4 2010 No. 1 2 pp. 37 46 A Reference Point Method to TripleObjective Assignment of Supporting Services in a Healthcare Institution Bartosz Sawik Abstract.
More informationDistributed Control in Transportation and Supply Networks
Distributed Control in Transportation and Supply Networks Marco Laumanns, Institute for Operations Research, ETH Zurich Joint work with Harold Tiemessen, Stefan Wörner (IBM Research Zurich) Apostolos Fertis,
More informationA Robust Formulation of the Uncertain Set Covering Problem
A Robust Formulation of the Uncertain Set Covering Problem Dirk Degel Pascal Lutter Chair of Management, especially Operations Research RuhrUniversity Bochum Universitaetsstrasse 150, 44801 Bochum, Germany
More informationOptimization Modeling for Mining Engineers
Optimization Modeling for Mining Engineers Alexandra M. Newman Division of Economics and Business Slide 1 Colorado School of Mines Seminar Outline Linear Programming Integer Linear Programming Slide 2
More informationRegulatory Impacts on Credit Portfolio Management
Regulatory Impacts on Credit Portfolio Management Ursula Theiler*, Vladimir Bugera **, Alla Revenko **, Stanislav Uryasev ** *Risk Training, CarlZeissStr. 11, D83052 Bruckmuehl, Germany, mailto: theiler@risktraining.org,
More informationTwo objective functions for a real life Split Delivery Vehicle Routing Problem
International Conference on Industrial Engineering and Systems Management IESM 2011 May 25  May 27 METZ  FRANCE Two objective functions for a real life Split Delivery Vehicle Routing Problem Marc Uldry
More informationMoral Hazard. Itay Goldstein. Wharton School, University of Pennsylvania
Moral Hazard Itay Goldstein Wharton School, University of Pennsylvania 1 PrincipalAgent Problem Basic problem in corporate finance: separation of ownership and control: o The owners of the firm are typically
More informationApproximation Algorithms
Approximation Algorithms or: How I Learned to Stop Worrying and Deal with NPCompleteness Ong Jit Sheng, Jonathan (A0073924B) March, 2012 Overview Key Results (I) General techniques: Greedy algorithms
More informationA Column Generation Model for Truck Routing in the Chilean Forest Industry
A Column Generation Model for Truck Routing in the Chilean Forest Industry Pablo A. Rey Escuela de Ingeniería Industrial, Facultad de Ingeniería, Universidad Diego Portales, Santiago, Chile, email: pablo.rey@udp.cl
More informationTwoStage Stochastic Linear Programs
TwoStage Stochastic Linear Programs Operations Research Anthony Papavasiliou 1 / 27 TwoStage Stochastic Linear Programs 1 Short Reviews Probability Spaces and Random Variables Convex Analysis 2 Deterministic
More informationWITH the growing economy, the increasing amount of disposed
IEEE TRANSACTIONS ON ELECTRONICS PACKAGING MANUFACTURING, VOL. 30, NO. 2, APRIL 2007 147 Fast Heuristics for Designing Integrated EWaste Reverse Logistics Networks ILin Wang and WenCheng Yang Abstract
More informationScheduling Home Health Care with Separating Benders Cuts in Decision Diagrams
Scheduling Home Health Care with Separating Benders Cuts in Decision Diagrams André Ciré University of Toronto John Hooker Carnegie Mellon University INFORMS 2014 Home Health Care Home health care delivery
More informationBIG DATA PROBLEMS AND LARGESCALE OPTIMIZATION: A DISTRIBUTED ALGORITHM FOR MATRIX FACTORIZATION
BIG DATA PROBLEMS AND LARGESCALE OPTIMIZATION: A DISTRIBUTED ALGORITHM FOR MATRIX FACTORIZATION Ş. İlker Birbil Sabancı University Ali Taylan Cemgil 1, Hazal Koptagel 1, Figen Öztoprak 2, Umut Şimşekli
More informationStrategic planning in LTL logistics increasing the capacity utilization of trucks
Strategic planning in LTL logistics increasing the capacity utilization of trucks J. Fabian Meier 1,2 Institute of Transport Logistics TU Dortmund, Germany Uwe Clausen 3 Fraunhofer Institute for Material
More informationA scenario aggregation based approach for determining a robust airline fleet composition
Econometric Institute Reports EI 200217 A scenario aggregation based approach for determining a robust airline fleet composition Ovidiu Listes, Rommert Dekker Erasmus University Rotterdam, P.O. Box 1738,
More informationA BiObjective Portfolio Optimization with Conditional ValueatRisk. Bartosz Sawik
Decision Making in Manufacturing and Services Vol. 4 2010 No. 1 2 pp. 47 69 A BiObjective Portfolio Optimization with Conditional ValueatRisk Bartosz Sawik Abstract. This paper presents a biobjective
More informationLecture 10 Scheduling 1
Lecture 10 Scheduling 1 Transportation Models 1 large variety of models due to the many modes of transportation roads railroad shipping airlines as a consequence different type of equipment and resources
More informationa.dariano@dia.uniroma3.it
Dynamic Control of Railway Traffic A stateoftheart realtime train scheduler based on optimization models and algorithms 20/06/2013 a.dariano@dia.uniroma3.it 1 The Aut.O.R.I. Lab (Rome Tre University)
More informationA New Solution for Rail Service Network Design Problem
A New Solution for Rail Service Network Design Problem E.Zhu 1 T.G.Crainic 2 M.Gendreau 3 1 Département d informatique et de recherche opérationnelle Université de Montréal 2 École des sciences de la gestion
More informationMathematical finance and linear programming (optimization)
Mathematical finance and linear programming (optimization) Geir Dahl September 15, 2009 1 Introduction The purpose of this short note is to explain how linear programming (LP) (=linear optimization) may
More informationA Robust Optimization Approach to Supply Chain Management
A Robust Optimization Approach to Supply Chain Management Dimitris Bertsimas and Aurélie Thiele Massachusetts Institute of Technology, Cambridge MA 0139, dbertsim@mit.edu, aurelie@mit.edu Abstract. We
More informationINTEGER PROGRAMMING. Integer Programming. Prototype example. BIP model. BIP models
Integer Programming INTEGER PROGRAMMING In many problems the decision variables must have integer values. Example: assign people, machines, and vehicles to activities in integer quantities. If this is
More informationLoad Balancing of Telecommunication Networks based on Multiple Spanning Trees
Load Balancing of Telecommunication Networks based on Multiple Spanning Trees Dorabella Santos Amaro de Sousa Filipe Alvelos Instituto de Telecomunicações 3810193 Aveiro, Portugal dorabella@av.it.pt Instituto
More informationA hierarchical multicriteria routing model with traffic splitting for MPLS networks
A hierarchical multicriteria routing model with traffic splitting for MPLS networks João Clímaco, José Craveirinha, Marta Pascoal jclimaco@inesccpt, jcrav@deecucpt, marta@matucpt University of Coimbra
More informationA Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem
A Genetic Algorithm Approach for Solving a Flexible Job Shop Scheduling Problem Sayedmohammadreza Vaghefinezhad 1, Kuan Yew Wong 2 1 Department of Manufacturing & Industrial Engineering, Faculty of Mechanical
More informationA Maximal Covering Model for Helicopter Emergency Medical Systems
The Ninth International Symposium on Operations Research and Its Applications (ISORA 10) ChengduJiuzhaigou, China, August 19 23, 2010 Copyright 2010 ORSC & APORC, pp. 324 331 A Maximal Covering Model
More informationModels in Transportation. Tim Nieberg
Models in Transportation Tim Nieberg Transportation Models large variety of models due to the many modes of transportation roads railroad shipping airlines as a consequence different type of equipment
More informationAirline Schedule Development
Airline Schedule Development 16.75J/1.234J Airline Management Dr. Peter Belobaba February 22, 2006 Airline Schedule Development 1. Schedule Development Process Airline supply terminology Sequential approach
More informationAdvanced Lecture on Mathematical Science and Information Science I. Optimization in Finance
Advanced Lecture on Mathematical Science and Information Science I Optimization in Finance Reha H. Tütüncü Visiting Associate Professor Dept. of Mathematical and Computing Sciences Tokyo Institute of Technology
More informationAgenda. Real System, Transactional IT, Analytic IT. What s the Supply Chain. Levels of Decision Making. Supply Chain Optimization
Agenda Supply Chain Optimization KUBO Mikio Definition of the Supply Chain (SC) and Logistics Decision Levels of the SC Classification of Basic Models in the SC Logistics Network Design Production Planning
More informationRobust Investment Management with Uncertainty in Fund Managers Asset Allocation
Robust Investment Management with Uncertainty in Fund Managers Asset Allocation Yang Dong Aurélie Thiele April 2014, revised December 2014 Abstract We consider a problem where an investment manager must
More informationSoftware tools for Stochastic Programming: A Stochastic Programming Integrated Environment. (SPInE)
Software tools for Stochastic Programming: A Stochastic Programming Integrated Environment (SPInE) Patrick Valente, Gautam Mitra, Chandra A. Poojari Centre for the Analysis of Risk and Optimisation Modelling
More informationCOORDINATION PRODUCTION AND TRANSPORTATION SCHEDULING IN THE SUPPLY CHAIN ABSTRACT
Technical Report #98T010, Department of Industrial & Mfg. Systems Egnieering, Lehigh Univerisity (1998) COORDINATION PRODUCTION AND TRANSPORTATION SCHEDULING IN THE SUPPLY CHAIN Kadir Ertogral, S. David
More informationLecture 3. Linear Programming. 3B1B Optimization Michaelmas 2015 A. Zisserman. Extreme solutions. Simplex method. Interior point method
Lecture 3 3B1B Optimization Michaelmas 2015 A. Zisserman Linear Programming Extreme solutions Simplex method Interior point method Integer programming and relaxation The Optimization Tree Linear Programming
More informationOptimization Models for Differentiating Quality of Service Levels in Probabilistic Network Capacity Design Problems
Optimization Models for Differentiating Quality of Service Levels in Probabilistic Network Capacity Design Problems Siqian Shen Department of Industrial and Operations Engineering University of Michigan,
More informationNetwork Optimization using AIMMS in the Analytics & Visualization Era
Network Optimization using AIMMS in the Analytics & Visualization Era Dr. Ovidiu Listes Senior Consultant AIMMS Analytics and Optimization Outline Analytics, Optimization, Networks AIMMS: The Modeling
More informationRandomization Approaches for Network Revenue Management with Customer Choice Behavior
Randomization Approaches for Network Revenue Management with Customer Choice Behavior Sumit Kunnumkal Indian School of Business, Gachibowli, Hyderabad, 500032, India sumit kunnumkal@isb.edu March 9, 2011
More informationModeling and Solving the Capacitated Vehicle Routing Problem on Trees
in The Vehicle Routing Problem: Latest Advances and New Challenges Modeling and Solving the Capacitated Vehicle Routing Problem on Trees Bala Chandran 1 and S. Raghavan 2 1 Department of Industrial Engineering
More informationAn optimisation framework for determination of capacity in railway networks
CASPT 2015 An optimisation framework for determination of capacity in railway networks Lars Wittrup Jensen Abstract Within the railway industry, high quality estimates on railway capacity is crucial information,
More informationOptimization applications in finance, securities, banking and insurance
IBM Software IBM ILOG Optimization and Analytical Decision Support Solutions White Paper Optimization applications in finance, securities, banking and insurance 2 Optimization applications in finance,
More informationOptimization Methods in Finance
Optimization Methods in Finance Gerard Cornuejols Reha Tütüncü Carnegie Mellon University, Pittsburgh, PA 15213 USA January 2006 2 Foreword Optimization models play an increasingly important role in financial
More informationMaking Hard Decision. ENCE 627 Decision Analysis for Engineering
CHAPTER Duxbury Thomson Learning Making Hard Decision Third Edition SENSITIVITY ANALYSIS A. J. Clark School of Engineering Department of Civil and Environmental Engineering 5 FALL 2003 By Dr. Ibrahim.
More informationR u t c o r Research R e p o r t. A Method to Schedule Both Transportation and Production at the Same Time in a Special FMS.
R u t c o r Research R e p o r t A Method to Schedule Both Transportation and Production at the Same Time in a Special FMS Navid Hashemian a Béla Vizvári b RRR 32011, February 21, 2011 RUTCOR Rutgers
More informationReSolving Stochastic Programming Models for Airline Revenue Management
ReSolving Stochastic Programming Models for Airline Revenue Management Lijian Chen Department of Industrial, Welding and Systems Engineering The Ohio State University Columbus, OH 43210 chen.855@osu.edu
More informationA SetPartitioningBased Model for the Stochastic Vehicle Routing Problem
A SetPartitioningBased Model for the Stochastic Vehicle Routing Problem Clara Novoa Department of Engineering and Technology Texas State University 601 University Drive San Marcos, TX 78666 cn17@txstate.edu
More informationLocation Problems in Supply Chain Management
Location Problems in SCM Location Problems in Supply Chain Management Stefan Nickel Suppliers Plants Distribution Centers Institute for Operations Research Karlsruhe Institute of Technology (KIT) Customers
More informationErhan Deniz ALL RIGHTS RESERVED
2009 Erhan Deniz ALL RIGHTS RESERVED MULTIPERIOD SCENARIO GENERATION TO SUPPORT PORTFOLIO OPTIMIZATION by ERHAN DENIZ A dissertation submitted to the Graduate School  New Brunswick Rutgers, The State
More informationAn Overview Of Software For Convex Optimization. Brian Borchers Department of Mathematics New Mexico Tech Socorro, NM 87801 borchers@nmt.
An Overview Of Software For Convex Optimization Brian Borchers Department of Mathematics New Mexico Tech Socorro, NM 87801 borchers@nmt.edu In fact, the great watershed in optimization isn t between linearity
More informationHigh Performance Computing for Operation Research
High Performance Computing for Operation Research IEF  Paris Sud University claude.tadonki@upsud.fr INRIAAlchemy seminar, Thursday March 17 Research topics Fundamental Aspects of Algorithms and Complexity
More informationMultiperiod and stochastic formulations for a closed loop supply chain with incentives
Multiperiod and stochastic formulations for a closed loop supply chain with incentives L. G. HernándezLanda, 1, I. Litvinchev, 1 Y. A. RiosSolis, 1 and D. Özdemir2, 1 Graduate Program in Systems Engineering,
More informationMemory Allocation Technique for Segregated Free List Based on Genetic Algorithm
Journal of AlNahrain University Vol.15 (2), June, 2012, pp.161168 Science Memory Allocation Technique for Segregated Free List Based on Genetic Algorithm Manal F. Younis Computer Department, College
More informationRole of Stochastic Optimization in Revenue Management. Huseyin Topaloglu School of Operations Research and Information Engineering Cornell University
Role of Stochastic Optimization in Revenue Management Huseyin Topaloglu School of Operations Research and Information Engineering Cornell University Revenue Management Revenue management involves making
More informationFUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM
International Journal of Innovative Computing, Information and Control ICIC International c 0 ISSN 3448 Volume 8, Number 8, August 0 pp. 4 FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT
More informationDynamic Asset Allocation Using Stochastic Programming and Stochastic Dynamic Programming Techniques
Dynamic Asset Allocation Using Stochastic Programming and Stochastic Dynamic Programming Techniques Gerd Infanger Stanford University Winter 2011/2012 MS&E348/Infanger 1 Outline Motivation Background and
More informationScheduling Shop Scheduling. Tim Nieberg
Scheduling Shop Scheduling Tim Nieberg Shop models: General Introduction Remark: Consider non preemptive problems with regular objectives Notation Shop Problems: m machines, n jobs 1,..., n operations
More informationCopula Simulation in Portfolio Allocation Decisions
Copula Simulation in Portfolio Allocation Decisions Gyöngyi Bugár Gyöngyi Bugár and Máté Uzsoki University of Pécs Faculty of Business and Economics This presentation has been prepared for the Actuaries
More informationSolving convex MINLP problems with AIMMS
Solving convex MINLP problems with AIMMS By Marcel Hunting Paragon Decision Technology BV An AIMMS White Paper August, 2012 Abstract This document describes the Quesada and Grossman algorithm that is implemented
More informationWorstCase Conditional ValueatRisk with Application to Robust Portfolio Management
WorstCase Conditional ValueatRisk with Application to Robust Portfolio Management ShuShang Zhu Department of Management Science, School of Management, Fudan University, Shanghai 200433, China, sszhu@fudan.edu.cn
More informationWater networks security: A twostage mixedinteger stochastic program for sensor placement under uncertainty
Computers and Chemical Engineering 31 (2007) 565 573 Water networks security: A twostage mixedinteger stochastic program for sensor placement under uncertainty Vicente RicoRamirez a,, Sergio FraustoHernandez
More informationAn interval linear programming contractor
An interval linear programming contractor Introduction Milan Hladík Abstract. We consider linear programming with interval data. One of the most challenging problems in this topic is to determine or tight
More informationBandwidth Trading: A Comparison of the Combinatorial and Multicommodity Approach
Paper Bandwidth Trading: A Comparison of the Combinatorial and Multicommodity Approach Kamil Kołtyś, Piotr Pałka, Eugeniusz Toczyłowski, and Izabela Żółtowska Abstract Since the telecommunication market
More informationDimensioning an inbound call center using constraint programming
Dimensioning an inbound call center using constraint programming Cyril Canon 1,2, JeanCharles Billaut 2, and JeanLouis Bouquard 2 1 Vitalicom, 643 avenue du grain d or, 41350 Vineuil, France ccanon@fr.snt.com
More informationAsset Liability Management / Liability Driven Investment Optimization (LDIOpt)
Asset Liability Management / Liability Driven Investment Optimization (LDIOpt) Introduction ALM CASH FLOWS OptiRisk Liability Driven Investment Optimization LDIOpt is an asset and liability management
More informationFleet Management. Warren B. Powell and Huseyin Topaloglu. June, 2002
Fleet Management Warren B. Powell and Huseyin Topaloglu June, 2002 Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544 1 1. Introduction The fleet management
More informationGAMS, Condor and the Grid: Solving Hard Optimization Models in Parallel. Michael C. Ferris University of Wisconsin
GAMS, Condor and the Grid: Solving Hard Optimization Models in Parallel Michael C. Ferris University of Wisconsin Parallel Optimization Aid search for global solutions (typically in nonconvex or discrete)
More informationChapter 13: Binary and MixedInteger Programming
Chapter 3: Binary and MixedInteger Programming The general branch and bound approach described in the previous chapter can be customized for special situations. This chapter addresses two special situations:
More informationAirport management: taxi planning
Ann Oper Res (2006) 143: 191 202 DOI 10.1007/s1047900673812 Airport management: taxi planning Ángel G. Marín C Science + Business Media, Inc. 2006 Abstract The Taxi Planning studies the aircraft routing
More information