In this paper, we demonstrate the computational consequences of making a simple assumption on production cost structures in capacitated lot-size problems. Our results indicate that our cost assumption of increased productivity over time has dramatic effects on the problem sizes which are solvable. Our experiments indicate that problems with more than 1000 products in more than 1000 time periods may be solved within reasonable time. The Lagrangian decomposition algorithm we use does of course not guarantee optimality, but our results indicate surprisingly narrow gaps for such large-scale cases - in most cases significantly outperforming CPLEX. We also demonstrate that general CLSP's can benefit greatly from applying our proposed heuristic.
The subject of this paper is a flow-shop based on a case study aimed at the optimisation of ordering production jobs in mechanical engineering, in order to minimize the overall processing time, the makespan. The production jobs are processed by machines, and each job is assigned to a certain machine for technological reasons. Before processing a job, the machine has to be adjusted; there is only one adjuster who adjusts all of the machines. This problem is treated as a hybrid two-stage flow-shop: the first stage of the job processing is represented by the machine adjustment for the respective job, and the second stage by the processing of the job itself on the adjusted machine. In other words, the job-processing consists of two tasks, where the first task is the machine adjustment for the job, and the second task is the job processing itself. A mathematical model is proposed, a heuristic method is formulated, and the NP hardness of the problem, called a "hybrid flow-shop with adjustment," is proved.
V posledních přibližně dvaceti letech byla pozornost v oblasti deskriptivní etiky soustředěna především na výzkum morálního usuzování a morálních intuic. V současnosti lze nalézt tři rozdílné přístupy týkající se této problematiky. Jedná se o heuristický přístup, teorii duálních procesů a teorii univerzální morální gramatiky. Všechny vycházejí z podobné empirické evidence kombinující poznatky evoluční biologie, morální psychologie a neuroetiky, ale ohledně povahy a spolehlivosti morálních intuic docházejí k odlišným závěrům. Cílem článku bude jednotlivé přístupy vzájemně porovnat a zhodnotit, nakolik jsou v souladu s dostupnou vědeckou evidencí. Závěr článku se bude věnovat důsledkům, které mají tato zjištění pro oblast morální epistemologie a normativní etiky. Cílem bude ukázat, že i přes rozdílné interpretace je možné dojít v otázce spolehlivosti morálních intuic k uspokojivému pragmatickému řešení, které bude v souladu s touto empirickou evidencí, nebude na ní však nutně závislé. and In the last twenty years, there has been an enormous growth of scientifi c research concerning the process of human moral reasoning and moral intuitions. In contemporary descriptive ethics, three dominant approaches can be found – heuristic approach, dual-process theory, and universal moral grammar. Each of these accounts is based on similar empirical evidence combining fi ndings from evolutionary biology, moral psychology, and neuroethics. Nevertheless, they come to diff erent conclusions about the reliability of moral intuitions. Th e aim of this paper is to critically investigate each of these approaches and compare them with recent scientifi c fi ndings. Last chapter addresses implications of these fi ndings for moral epistemology and normative ethics. Th e aim is to show that despite diff erent interpretations of available data, we can reach a satisfying pragmatical conclusion which would be in compliance with the empirical evidence, yet it would not necessarily depend on it.
Non linear and multidimensional models of an interdisciplinary nature are needed to study a broad spectrum of tasks related to design of mechanical engineering systems. Oversimplified and therefore inapplicable results are frequently obtained as the final result of model development. A human way of making decisions is not based on numbers but on common-sense reasoning. Qualitative modeling brings additional options in the modeling of complex systems. Qualitative variables are quantified using three values only - positive (increasing), zero (constant) and negative (decreasing). The classical quantitative tools cannot deal with such variables. Typical areas suitable for qualitative modeling are design related tasks with a large number of variables. A presented case study an optimization of a solar collector, is given in details. and Obsahuje seznam literatury
This paper proposes a specialized LP-algorithm for a sub problem arising in simple Profit maximising Lot-sizing. The setting involves a single (and multi) item production system with negligible set-up costs/times and limited production capacity. The producer faces a monopolistic market with given time-varying linear demand curves.