21 resultados para Mixed integer non-linear programming (MINLP)


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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.

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In process industries, make-and-pack production is used to produce food and beverages, chemicals, and metal products, among others. This type of production process allows the fabrication of a wide range of products in relatively small amounts using the same equipment. In this article, we consider a real-world production process (cf. Honkomp et al. 2000. The curse of reality – why process scheduling optimization problems are diffcult in practice. Computers & Chemical Engineering, 24, 323–328.) comprising sequence-dependent changeover times, multipurpose storage units with limited capacities, quarantine times, batch splitting, partial equipment connectivity, and transfer times. The planning problem consists of computing a production schedule such that a given demand of packed products is fulfilled, all technological constraints are satisfied, and the production makespan is minimised. None of the models in the literature covers all of the technological constraints that occur in such make-and-pack production processes. To close this gap, we develop an efficient mixed-integer linear programming model that is based on a continuous time domain and general-precedence variables. We propose novel types of symmetry-breaking constraints and a preprocessing procedure to improve the model performance. In an experimental analysis, we show that small- and moderate-sized instances can be solved to optimality within short CPU times.

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Despite the impact of red blood cell (RBC) Life-spans in some disease areas such as diabetes or anemia of chronic kidney disease, there is no consensus on how to quantitatively best describe the process. Several models have been proposed to explain the elimination process of RBCs: random destruction process, homogeneous life-span model, or a series of 4-transit compartment model. The aim of this work was to explore the different models that have been proposed in literature, and modifications to those. The impact of choosing the right model on future outcomes prediction--in the above mentioned areas--was also investigated. Both data from indirect (clinical data) and direct life-span measurement (biotin-labeled data) methods were analyzed using non-linear mixed effects models. Analysis showed that: (1) predictions from non-steady state data will depend on the RBC model chosen; (2) the transit compartment model, which considers variation in life-span in the RBC population, better describes RBC survival data than the random destruction or homogenous life-span models; and (3) the additional incorporation of random destruction patterns, although improving the description of the RBC survival data, does not appear to provide a marked improvement when describing clinical data.

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The execution of a project requires resources that are generally scarce. Classical approaches to resource allocation assume that the usage of these resources by an individual project activity is constant during the execution of that activity; in practice, however, the project manager may vary resource usage over time within prescribed bounds. This variation gives rise to the project scheduling problem which consists in allocating the scarce resources to the project activities over time such that the project duration is minimized, the total number of resource units allocated equals the prescribed work content of each activity, and various work-content-related constraints are met. We formulate this problem for the first time as a mixed-integer linear program. Our computational results for a standard test set from the literature indicate that this model outperforms the state-of-the-art solution methods for this problem.

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This paper deals with an event-bus tour booked by Bollywood film fans. During the tour, the participants visit selected locations of famous Bollywood films at various sites in Switzerland. Moreover, the tour includes stops for lunch and shopping. Each day, up to five buses operate the tour; for organizational reasons, two or more buses cannot stay at the same location simultaneously. The planning problem is how to compute a feasible schedule for each bus such that the total waiting time (primary objective) and the total travel time (secondary objective) are minimized. We formulate this problem as a mixed-integer linear program, and we report on computational results obtained with the Gurobi solver.

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AIMS γ-Hydroxybutyrate (GHB) is used as a treatment for narcolepsy and alcohol withdrawal and as recreational substance. Nevertheless, there are limited data on the pharmacokinetics and pharmacokinetic-pharmacodynamic relationship of GHB in humans. We characterized the pharmacokinetic profile and exposure-psychotropic effect relationship of GHB in humans. METHODS Two oral doses of GHB (25 and 35 mg/kg) were administered to 32 healthy male subjects (16 for each dose) using a randomized, placebo-controlled, cross-over design. RESULTS Maximal concentrations of GHB were (geometric mean and 95%CI): 218 (176-270) nmol/ml and 453 (374-549) nmol/ml for the 25 and 35 mg/kg GHB doses, respectively. The elimination half-lives (mean ± SD) were 36 ± 9 and 39 ± 7 min and the AUC∞ values (geometric mean and 95%CI) were 15,747 (12,854-19,290) and 40,113 (33,093-48,622) nmol∙min/ml for the 20 and 35 mg/kg GHB doses, respectively. Thus, plasma GHB exposure (AUC0-∞ ) rose disproportionally (+40%) with the higher dose. γ-Hydroxybutyrate produced mixed stimulant-sedative effects, with a dose-dependent increase in sedation and dizziness. It did not alter heart rate or blood pressure. A close relationship between plasma GHB exposure and its psychotropic effects was found, with higher GHB concentrations associated with higher subjective stimulation, sedation, and dizziness. No clockwise hysteresis was observed in the GHB concentration effect plot over time (i.e., no acute pharmacological tolerance). CONCLUSION Evidence was found of a non-linear dose-exposure relationship (i.e., no dose proportionality) at moderate doses of GHB. The effects of GHB on consciousness were closely linked to its plasma exposure and exhibited no acute tolerance. This article is protected by copyright. All rights reserved.