Resilience Analysis of Service Oriented Collaboration Process Management systems

Collaborative business process management allows for the automated coordination of processes involving human and computer actors. In modern economies it is increasingly needed for this coordination to be not only within organizations but also to cross organizational boundaries. The dependence on the performance of other organizations should however be limited, and the control over the own processes is required from a competitiveness perspective. The main objective of this work is to propose an evaluation model for measuring a resilience of a Service Oriented Architecture (SOA) collaborative process management system. In this paper, we have proposed resilience analysis perspectives of SOA collaborative process systems, i.e. overall system perspective, individual process model perspective, individual process instance perspective, service perspective, and resource perspective. A collaborative incident and maintenance notification process system is reviewed for illustrating our resilience analysis. This research contributes to extend SOA collaborative business process management systems with resilience support, not only looking at quantification and identification of resilience factors, but also considering ways of improving the resilience of SOA collaborative process systems through measures at design and run-time

Exact and Approximate Analysis of Multi-Echelon, Multi-Indenture Spare Parts Systems with Commonality

no abstrac

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

Safety stock policies for multi-echelon production systems

nrpages: 32status: publishe

Protective stocks in multi-stage production systems

Uncertainty in MRP systems does exist in several forms, variability in demand from period to period, uncertainty in the supply from stage to stage due to the variability in the yields from each production batch, and uncertainty in the lead times. The goals of the paper are to develop theoretical models for determining optimal decisions in this uncertain environment, to develop a computationally tractable heuristic and to examine how safety stock and safety time can arise. Finally, our methodology is compared with MRP practice. © 1984 Taylor and Francis Ltd.nrpages: 46status: publishe

Naval Wholesale Inventory Optimization

The article of record as published may be found at https://doi.org/10.1007/978-3-030-28565-4_15The U.S. Naval Supply Systems Command (NAVSUP), Weapon Systems Support, manages an inventory of approximately 400,000 maritime and aviation line items valued at over $20 billion. This work describes NAVSUP’s Wholesale Inventory Optimization Model (WIOM), which helps NAVSUP’s planners establish inventory levels. Under certain assumptions, WIOM determines optimal reorder points (ROPs) to minimize expected shortfalls from fill rate targets and deviations from legacy solutions. Each item’s demand is modeled probabilistically, and negative expected deviations from target fill rates are penalized with nonlinear terms (conveniently approximated by piecewise linear functions). WIOM’s solution obeys a budget constraint. The optimal ROPs and related expected safety stock levels are used by NAVSUP’s Enterprise Resource Planning system to trigger requisitions for procurement and/or repair of items based on forecasted demand. WIOM solves cases with up to 20,000 simultaneous items using both a direct method and Lagrangian relaxation. In particular, this proves to be more efficient in certain cases that would otherwise take many hours to produce a solution