431 research outputs found

    Roles of inventory and reserve capacity in mitigating supply chain disruption risk

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    This research focuses on managing disruption risk in supply chains using inventory and reserve capacity under stochastic demand. While inventory can be considered as a speculative risk mitigation lever, reserve capacity can be used in a reactive fashion when a disruption occurs. We determine optimal inventory levels and reserve capacity production rates for a firm that is exposed to supply chain disruption risk. We fully characterize four main risk mitigation strategies: inventory strategy, reserve capacity strategy, mixed strategy and passive acceptance. We illustrate how the optimal risk mitigation strategy depends on product characteristics (functional versus innovative) and supply chain characteristics (agile versus efficient). This work is inspired from a risk management problem of a leading pharmaceutical company

    Two-echelon spare parts inventory system subject to a service constraint

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    Department of Logistics2004-2005 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

    Fully polynomial-time approximation schemes for time–cost tradeoff problems in series–parallel project networks

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    2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

    Approximating the Nonlinear Newsvendor and Single-Item Stochastic Lot-Sizing Problems When Data Is Given by an Oracle

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    The single-item stochastic lot-sizing problem is to find an inventory replenishment policy in the presence of discrete stochastic demands under periodic review and finite time horizon. A closely related problem is the single-period newsvendor model. It is well known that the newsvendor problem admits a closed formula for the optimal order quantity whenever the revenue and salvage values are linear increasing functions and the procurement (ordering) cost is fixed plus linear. The optimal policy for the single-item lot-sizing model is also well known under similar assumptions. In this paper we show that the classical (single-period) newsvendor model with fixed plus linear ordering cost cannot be approximated to any degree of accuracy when either the demand distribution or the cost functions are given by an oracle. We provide a fully polynomial time approximation scheme for the nonlinear single-item stochastic lot-sizing problem, when demand distribution is given by an oracle, procurement costs are provided as nondecreasing oracles, holding/backlogging/disposal costs are linear, and lead time is positive. Similar results exist for the nonlinear newsvendor problem. These approximation schemes are designed by extending the technique of K-approximation sets and functions.National Science Foundation (U.S.) (Contract CMMI-0758069)United States. Office of Naval Research (Grant N000141110056

    How does firm innovativeness enable supply chain resilience?:The moderating role of supply uncertainty and interdependence

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    Despite its potential benefits in a wide range of circumstances, firm innovativeness received scant attention in relation to managing the various risks and uncertainties in the global business environment. Likewise, there is still a limited understanding of firms’ supply chain resilience (SCR) and its related antecedents in the strategic management literature. This research focuses on exploring the relationship between firm innovativeness and SCR in an attempt to facilitate bridging the gap between two important research streams and shed some light on the contingent value of firm innovativeness against disruptions and adversities. The moderating role of supply uncertainty and interdependence in the focal relationship was also hypothesised and tested. Findings suggest that firm innovativeness is positively associated with firm SCR, and supply uncertainty negatively moderates this relationship but interdependence does not. We argue that this could be due to the dual nature of interdependence in supply networks

    Sourcing Flexibility, Spot Trading, and Procurement Contract Structure

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    We analyze the structure and pricing of option contracts for an industrial good in the presence of spot trading. We combine the analysis of spot trading and buyers' disparate private valuations for different suppliers' products, and we jointly endogenize the determination of three major dimensions in contract design: (i) sales contracts versus options contracts, (ii) flat-price versus volume-dependent contracts, and (iii) volume discounts versus volume premia. We build a model in which a supplier of an industrial good transacts with a manufacturer who uses the supplier's product to produce an end good with an uncertain demand. We show that, consistent with industry observations, volume-dependent optimal sales contracts always demonstrate volume discounts (i.e., involve concave pricing). However, options are more complex agreements, and optimal option contracts can involve both volume discounts and volume premia. Three major contract structures commonly emerge in optimality. First, if the seller has a high discount rate relative to the buyer and the seller's production costs or the production capacity is low, the optimal contracts tend to be flat-price sales contracts. Second, when the seller has a relatively high discount rate compared to the buyer but production costs or production capacity are high, the optimal contracts are sales contracts with volume discounts. Third, if the buyer's discount rate is high relative to the seller's, then the optimal contracts tend to be volume-dependent options contracts and can involve both volume discounts and volume premia. However, when the seller's production capacity is sufficiently low, it is possible to observe flat-price option contracts. Furthermore, we provide links between production and spot market characteristics, contract design, and efficiency.National Science Foundation (U.S.) (contract CMMI-0758069)National Science Foundation (U.S.) (contract DMI-0245352

    Scheduling periodic tasks in a hard real-time environment

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    We consider a real-time scheduling problem that occurs in the design of software-based aircraft control. The goal is to distribute tasks aui=(ci,pi) au_i=(c_i,p_i) on a minimum number of identical machines and to compute offsets aia_i for the tasks such that no collision occurs. A task aui au_i releases a job of running time cic_i at each time ai+kcdotpi,kinmathbbN0a_i + kcdot p_i,k in mathbb{N}_0 and a collision occurs if two jobs are simultaneously active on the same machine. We shed some light on the complexity and approximability landscape of this problem. Although the problem cannot be approximated within a factor of n1−varepsilonn^{1-varepsilon} for any varepsilon>0varepsilon>0, an interesting restriction is much more tractable: If the periods are dividing (for each i,ji,j one has pi∣pjp_i | p_j or pj∣pip_j | p_i), the problem allows for a better structured representation of solutions, which leads to a 2-approximation. This result is tight, even asymptotically
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