Climate-induced interannual variability of marine primary and export production in three global coupled climate carbon cycle models

Fully coupled climate carbon cycle models are sophisticated tools that are used to predict future climate change and its impact on the land and ocean carbon cycles. These models should be able to adequately represent natural variability, requiring model validation by observations. The present study focuses on the ocean carbon cycle component, in particular the spatial and temporal variability in net primary productivity (PP) and export production (EP) of particulate organic carbon (POC). Results from three coupled climate carbon cycle models (IPSL, MPIM, NCAR) are compared with observation-based estimates derived from satellite measurements of ocean colour and results from inverse modelling (data assimilation). Satellite observations of ocean colour have shown that temporal variability of PP on the global scale is largely dominated by the permanently stratified, low-latitude ocean (Behrenfeld et al., 2006) with stronger stratification (higher sea surface temperature; SST) being associated with negative PP anomalies. Results from all three coupled models confirm the role of the low-latitude, permanently stratified ocean for anomalies in globally integrated PP, but only one model (IPSL) also reproduces the inverse relationship between stratification (SST) and PP. An adequate representation of iron and macronutrient co-limitation of phytoplankton growth in the tropical ocean has shown to be the crucial mechanism determining the capability of the models to reproduce observed interactions between climate and PP.

Scheduling the production of rolling ingots: Industrial context, model, and solution method

We study a real-world scheduling problem arising in the context of a rolling ingots production. First we review the production process and discuss peculiarities that have to be observed when scheduling a given set of production orders on the production facilities. We then show how to model this scheduling problem using prescribed time lags between operations, different kinds of resources, and sequence-dependent changeovers. A branch-and-bound solution procedure is presented in the second part. The basic principle is to relax the resource constraints by assuming infinite resource availability. Resulting resource conflicts are then stepwise resolved by introducing precedence relationships among operations competing for the same resources. The algorithm has been implemented as a beam search heuristic enumerating alternative sets of precedence relationships.

Advanced production scheduling for batch plants in process industries

An Advanced Planning System (APS) offers support at all planning levels along the supply chain while observing limited resources. We consider an APS for process industries (e.g. chemical and pharmaceutical industries) consisting of the modules network design (for long–term decisions), supply network planning (for medium–term decisions), and detailed production scheduling (for short–term decisions). For each module, we outline the decision problem, discuss the specifi cs of process industries, and review state–of–the–art solution approaches. For the module detailed production scheduling, a new solution approach is proposed in the case of batch production, which can solve much larger practical problems than the methods known thus far. The new approach decomposes detailed production scheduling for batch production into batching and batch scheduling. The batching problem converts the primary requirements for products into individual batches, where the work load is to be minimized. We formulate the batching problem as a nonlinear mixed–integer program and transform it into a linear mixed–binary program of moderate size, which can be solved by standard software. The batch scheduling problem allocates the batches to scarce resources such as processing units, workers, and intermediate storage facilities, where some regular objective function like the makespan is to be minimized. The batch scheduling problem is modelled as a resource–constrained project scheduling problem, which can be solved by an efficient truncated branch–and–bound algorithm developed recently. The performance of the new solution procedures for batching and batch scheduling is demonstrated by solving several instances of a case study from process industries.

Batch scheduling in process industries: an application of resource-constrained project scheduling

The paper deals with batch scheduling problems in process industries where final products arise from several successive chemical or physical transformations of raw materials using multi–purpose equipment. In batch production mode, the total requirements of intermediate and final products are partitioned into batches. The production start of a batch at a given level requires the availability of all input products. We consider the problem of scheduling the production of given batches such that the makespan is minimized. Constraints like minimum and maximum time lags between successive production levels, sequence–dependent facility setup times, finite intermediate storages, production breaks, and time–varying manpower contribute to the complexity of this problem. We propose a new solution approach using models and methods of resource–constrained project scheduling, which (approximately) solves problems of industrial size within a reasonable amount of time.

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.