Download e-book for iPad: Demand Flexibility in Supply Chain Planning by Joseph Geunes

By Joseph Geunes

ISBN-10: 1441993479

ISBN-13: 9781441993472

This paintings encapsulates the basic advancements during this box right into a unmarried source, in addition to to set an time table for extra improvement within the box. This short specializes in the call for flexibility in provide chains with fragmented effects allotted through the literature. those effects have powerful implications for handling real-world complicated operations making plans problems.

This booklet exploits dimensions of call for flexibility in offer chains and characterizes the easiest healthy among call for houses and operations functions and constraints. The origins and seminal works are traced in built-in call for and operations making plans and an in-depth documentation is supplied for the present state-of-the-art. platforms with inherent expenses and constraints that needs to reply to a few set of calls for at a minimal rate are tested. the most important unanswered questions are explored and the high-value examine instructions are highlighted for either perform and for the advance of recent and engaging optimization versions and algorithms.

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2) t = 1, . . 3) t = 1, . . 4) t = 1, . . 5) j = 1, . . , nt yt ∈ {0, 1}, t = 1, . . , T . 6) The DSP formulation is based on the standard formulation of the ELSP. , it is possible to reformulate the problem such that the linear programming (LP) relaxation value provides a better upper bound on the optimal solution to the DSP. This reformulation of the LP relaxation is closely related to the facility location reformulation of ELSP discussed in Sect. 6. We begin by defining the modified revenue parameter ρj t = dj t (rj t + Tτ=t Ht ) as well as the modified cost parameter Cˆ t = Ct + Tτ=t Ht .

9) τ = t, . . , T , τ = 1, . . 10) j = 1, . . , nτ yt , Qj tτ ≥ 0, t = 1, . . , T , τ = t, . . , T , j = 1, . . , nt . 9), Mtτ , is a large number that ensures the problem remains uncapacitated, and can be replaced with τ dj τ without loss of optimality. To demonstrate the tightness of the Mtτ = nj =1 LP relaxation FDSP, the next section provides an algorithm for solving the dual of FDSP. We then show that the resulting dual has an optimal solution with a complementary primal solution that is feasible for DSP, and therefore optimal.

16) j = 1, . . , nt . 15) that an optimal solution will exist such that ωtτ is equal to the maximum between zero and μj τ − Cˆ t for all t, τ , and j . 14), at optimality, we will have −πj τ equal to the minimum between zero and dj τ μj τ − ρj τ for every j and τ . 17) τ =1 j =1 T nt Subject to dj τ max{0, μj τ − Cˆ t } ≤ St , t = 1, . . , T . 18) 44 5 Dynamic Lot Sizing with Demand Selection and the Pricing Analog Inspection of CDP leads to some interesting and useful observations. Note first that an optimal solution exists such that μj τ is no greater than ρj τ /dj τ for every j, τ pair.

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Demand Flexibility in Supply Chain Planning by Joseph Geunes

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