Inventory-production theory : a linear policy approach

1. The general model.- 2. The linear-quadratic model.- 2.1 Finite horizon case.- 2.2 Least square forecasts.- 2.2.1 Least square property of rk(i).- 2.2.2 Recursive calculation of rk(i).- 2.3 An ideal situation.- 2.4 Infinite horizon case.- 2.5 Appendix to Chapter 2.- 2.5.1 State space representation and Separation Theorem.- 2.5.2 Optimal policies for ARMA-processes.- 3. The linear non-quadratic model 3.- 3.1 The general linear non-quadratic model.- 3.2 The general solution.- 3.3 Special cost functions.- 3.3.1 Piecewise linear costs 4.- 3.3.2 Probability constraints.- 3.3.3 A production smoothing problem.- 3.4 Special stochastic demand sequences.- 3.4.1 Non-correlated demand.- 3.4.2 Exponentially correlated demand.- 3.5 A direct approach solving a LNQ-problem.- 3.5.1 Piecewise linear costs.- 3.5.2 Production smoothing problem.- 3.6 Appendix to Chapter 3: The normality condition.- 4. Comparison with optimal Dynamic Programming solutions.- 4.1 Piecewise linear costs (no set-up costs P=Q=0).- 4.1.1 Dynamic Programming solution.- 4.1.2 Numerical results.- 4.2 Piecewise linear costs (including set-up costs: P and/or Q?O).- 4.2.1 Optimal solution.- 4.2.2 Numerical results.- 4.3 Piecewise linear costs - Gauss-Markov case.- 4.3.1 Dynamic Programming solution.- 4.3.2 Numerical results.- 5. Comparison with deterministic approximations.- 5.1 White noise case.- 5.1.1 Numerical results.- 5.2 Gauss-Markov case.- 5.2.1 Numerical results.- 5.3 Appendix to Chapter 5: Derivation of the deterministic policy.- 6. Comparison with AHM-Inventory Models.- 6.1 No-set-up cost case (P=0).- 6.1.1 The LNQ-approach.- 6.1.2 Comparison of demand distributions.- 6.2 Set-up cost case (P+0).- 6.2.1 Derivation of an optimal S (T)-policy.- 6.2.2 Numerical results.- 7. Summary and concluding remarks.- Literature.