Supply chain optimization, as a key determinant of strategic resources mobility along the value-added chain, allows each participant in the global network to capitalize on its particular strategic ...competency. Simulation is widely used to test the impact on supply chain performance for the strategic level decisions, such as the number of plants, the modes of transport, or the relocation of warehouses. However, the complexity of supply chain optimization problem and the stochastic nature of simulation cause the unaffordable computational load; the evaluation of a large number of alternatives for supply chain optimization is in a class of NP-hard problem and the number of simulation replications is required for accurately evaluating the performance of each alternative. The objective of the present work is to propose hybrid algorithm with the application of the nested partitioning (NP) method and the optimal computing budget allocation (OCBA) method to reduce the computational load, hence, to improve the efficiency of supply chain optimization via discrete event simulation. The NP method is a global sampling strategy that is continuously adapted via a partitioning of the feasible solution region. The number of candidate alternatives to be evaluated can be reduced by the application of NP. The OCBA method minimizes the number of samples (simulation replications) required to evaluate a particular alternative by allocating computing resources to potentially critical alternative. Carefully designed experiments show extensive numerical result to illustrate the benefits of the proposed approach.
► Simulation-based optimization approach for supply chain management. ► Solving the multi-location transshipment problem with supplier capacity. ► Transshipment literature: extend transshipment ...models to include supply capacity. ► Simulation literature: construct SAA-based algorithms for a non-trivial application.
We consider coordination among stocking locations through replenishment strategies that explicitly take into account lateral transshipments, i.e., transfer of a product among locations at the same echelon level. Our basic contribution is the incorporation of supply capacity into the traditional transshipment model. Our goal is to analyze the impact on system behavior and on stocking locations’ performance of the fact that the supplier may fail to fulfill all the replenishment orders. We therefore formulate the capacitated supply scenario as a network flow problem embedded in a stochastic optimization problem, which is solved through a sample average approximation method. We find that, depending on the production capacity, system behavior can vary drastically. Moreover, in a production-inventory system, we find evidence that either capacity flexibility (i.e., extra production) or transshipment flexibility (i.e., pooling) is required to maintain a desired level of service.
Transshipments, monitored movements of material at the same echelon of a supply chain, represent an effective pooling mechanism. Earlier papers dealing with transshipments either do not incorporate ...replenishment lead times into their analysis, or only provide a heuristic algorithm where optimality cannot be guaranteed beyond settings with two locations. This paper uses infinitesimal perturbation analysis by combining with a stochastic approximation method to examine the multi-location transshipment problem with positive replenishment lead times. It demonstrates the computation of optimal base stock quantities through sample path optimization. From a methodological perspective, this paper deploys a duality-based gradient computation method to improve computational efficiency. From an application perspective, it solves transshipment problems with non-negligible replenishment lead times. A numerical study illustrates the performance of the proposed approach.
In a manufacturing system, we need to capture collaborative processes among its components in order to clearly define supporting functions of a system. However, pervasive process modeling techniques, ...including IDEF3, Petri Nets, and UML, are not sufficient for modeling collaborative processes. Therefore, we have developed a novel modeling method referred to as collaborative process modeling (CPM) to describe collaborative processes. CPM models can be transformed into marked graph models so that we can use the analysis power of Petri Nets. In this paper, we first briefly discuss these process modeling techniques. Then, we illustrate the CPM method and transformation rules with illustrative examples. CPM allows us to develop collaborative process models, understand and facilitate the realization of collaboration, and verify models before moving onto development.
We consider the problem of feasibility determination in a stochastic setting. In particular, we wish to determine whether a system belongs to a given set Γ based on a performance measure estimated ...through Monte Carlo simulation. Our contribution is two-fold: (i) we characterize fractional allocations that are asymptotically optimal; and (ii) we provide an easily implementable algorithm, rooted in stochastic approximation theory, that results in sampling allocations that provably achieve in the limit the same performance as the optimal allocations. The finite-time behavior of the algorithm is also illustrated on two small examples.
We extend our earlier work by focusing on broadcasting opinions (one-to-many interactions) alongside narrowcasts (one-to-one interactions) in social media conversations taking explicitly into ...consideration the behavioral characteristics of agents and the properties of the underlying network. In particular, we construct a generalized model for the spread of influence through broadcast and narrowcast interactions on social media discussion sites, and implement an agent-based model to develop insights regarding the effects of broadcasting. Our preliminary experiments show that increased broadcasting (in terms of frequency, depth, and number of broadcasters) increases homogeneity in an evolving scale-free network.
Managing commodity procurement risk through hedging Kleindorfer, Paul; Yücesan, Enver
Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World,
12/2013
Conference Proceeding
Open access
The key to corporate value is in making good investments and in harvesting the cash flows from these investments through effective execution. The latter is improved through stability of plans. Cash ...flows, however, can be disrupted by movements in external factors such as exchange rates, commodity prices, potentially compromising the stability of plans and, in the worst case, undermining the company's ability to invest in otherwise good opportunities. Risk management is therefore directed at providing increased stability of plans, increased fidelity to strategic budgets, and, in the process, at understanding better the supply markets. The particular focus in this paper is on financial hedging tools designed to limit procurement exposure (i.e., control the maximum hedge-adjusted spend) within the context of highly volatile commodity markets.
The objective of inventory management models is to determine effective policies for managing the trade-off between customer satisfaction and the cost of service. These models have become increasingly ...sophisticated, incorporating many complicating factors that are relevant in practice such as demand uncertainty, finite supplier capacity, and yield losses. Curiously absent from these models are the financial constraints imposed by working capital requirements (WCR). In practice, many firms are self-financing; their ability to replenish their own inventories is directly affected not only by their current inventory levels, but also by their receivables and payables. In this paper, we analyze the materials management practices of a self-financing firm whose replenishment decisions are constrained by cash flows, which are updated periodically following purchases and sales in each period. In particular, we investigate the interaction between the financial and operational parameters as well as the impact of WCR constraints on the long-run average cost.
Breakthrough innovation has two key prerequisites: idea generation, collection of a large number of competing designs, and idea screening, efficient evaluation, and ranking of these designs to ...identify the best one(s). Open innovation has recently been modeled and analyzed as innovation contests, where many individuals or teams submit designs or prototypes to an innovating firm. Innovation tournaments increase the capacity of idea generation by enabling access to a broad pool of solvers while avoiding exorbitant costs. To deliver on their promise, however, such tournaments must be designed to enable effective screening of proposed ideas. In particular, given the large number of designs to be evaluated, tournaments must be efficient, favoring quick judgments based on imperfect information over extensive data collection. Through a simulation study, this article shows that contests may not necessarily be the best process for ranking innovation opportunities and selecting the best ones in an efficient way. Instead, we propose a ranking and selection approach that is based on ordinal optimization, which provides both efficiency and accuracy by dynamically allocating evaluation effort away from inferior designs onto promising ones. A numerical example quantifies the benefits. The proposed approach should therefore complement innovation tournaments' ability of idea generation with efficient idea screening.
The exciting developments in the World Wide Web (WWW) have revived interest in computer simulation for modeling, particularly for conceiving simulation languages and building model libraries that can ...be assembled and executed over the Internet, and for analysis, particularly for developing simulation optimization algorithms for parallel experimentation. This paper contributes to this second stream of research by introducing a framework for Optimization via Simulation (OvS) through Parallel Replicated Discrete Event Simulation (PRDES). In particular, we combine Nested Partitions (NP) and Extended Optimal Computing Budget Allocation (EOCBA) to provide an efficient framework for PRDES experiments. The number of candidate alternatives to be evaluated can be reduced by the application of NP. EOCBA, a modification of the Optimal Computing Budget Allocation (OCBA) for PRDES, minimizes the number of simulation replications required to evaluate a particular alternative by allocating computing resources to potentially critical alternatives. We deploy web services technologies based on the Java Axis and .NET framework to create a viable infrastructure for heterogeneous PRDES systems. This approach, which receives increasing attention under the banners of `grid computing' and `cloud computing,' further promotes reusability, scalability, and interoperability. Experimental results with a prototype implementation not only furnish a proof of concept but also illustrate significant gains in simulation efficiency with PRDES. The proposed concept and techniques can also be applied to simulation models that require coordination and interoperation in heterogeneous environments, such as decentralized supply chains.
