Storage problems in batch scheduling

This paper is concerned with the modelling of storage configurations for intermediate products in process industries. Those models form the basis of algorithms for scheduling chemical production plants. Different storage capacity settings (unlimited, finite, and no intermediate storage), storage homogeneity settings (dedicated and shared storage), and storage time settings (unlimited, finite, and no wait) are considered. We discuss a classification of storage constraints in batch scheduling and show how those constraints can be integrated into a general production scheduling model that is based on the concept of cumulative resources.

Implementation of the Convention on the Protection and Promotion of the Diversity of Cultural Expressions in the Digital Age: Challenges, Priority Actions and Recommendations

Report presented to the Intergovernmental Committee of the Convention on the Protection and Promotion of the Diversity of Cultural Expressions Seventh Ordinary Session, Paris, December 10-­‐13, 2013

A hybrid method for large-scale short-term scheduling of make-and-pack production processes

Due to the ongoing trend towards increased product variety, fast-moving consumer goods such as food and beverages, pharmaceuticals, and chemicals are typically manufactured through so-called make-and-pack processes. These processes consist of a make stage, a pack stage, and intermediate storage facilities that decouple these two stages. In operations scheduling, complex technological constraints must be considered, e.g., non-identical parallel processing units, sequence-dependent changeovers, batch splitting, no-wait restrictions, material transfer times, minimum storage times, and finite storage capacity. The short-term scheduling problem is to compute a production schedule such that a given demand for products is fulfilled, all technological constraints are met, and the production makespan is minimised. A production schedule typically comprises 500–1500 operations. Due to the problem size and complexity of the technological constraints, the performance of known mixed-integer linear programming (MILP) formulations and heuristic approaches is often insufficient. We present a hybrid method consisting of three phases. First, the set of operations is divided into several subsets. Second, these subsets are iteratively scheduled using a generic and flexible MILP formulation. Third, a novel critical path-based improvement procedure is applied to the resulting schedule. We develop several strategies for the integration of the MILP model into this heuristic framework. Using these strategies, high-quality feasible solutions to large-scale instances can be obtained within reasonable CPU times using standard optimisation software. We have applied the proposed hybrid method to a set of industrial problem instances and found that the method outperforms state-of-the-art methods.

Static replications with traffic light options

It is well known that sufficiently regular, one-dimensional payoff functions have an explicit static hedge by bonds, forward contracts, and options in a continuum of strikes. An easy and natural extension of the corresponding representation leads to static hedges based on the same instruments along with traffic light options, which have recently been introduced in the market. It is well known that the second strike derivative of non-discounted prices of vanilla options is related to the risk-neutral density of the underlying asset price in the corresponding absolutely continuous settings. Similar statements hold for traffic light options in sufficiently regular, bivariate settings.