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Im Gutenberg-Produktionsmodell werden drei Anpassungsformen an alternative Beschäftigungen (Produktquantitäten) unterschieden: die zeitliche, die intensi-tätsmäßige und die quantitative Anpassung (vgl. ). Zeitliche Anpassung bedeutet, dass bei konstanter Anzahl der eingesetzten Potenzialfakto-ren eines Typs (Aggregate) und konstanter Intensität (Produktionsgeschwindig-keit) die Einsatzzeit der Aggregate an die zu erzeugende Produktquantität angepasst wird. Intensitätsmäßige Anpassung liegt vor, wenn bei fester Anzahl der eingesetzten Aggregate gleichen Typs und gegebener Einsatzzeit die Intensität der Potenzialfaktoren in Abhängigkeit der Produktquantität variiert wird. Von quantitativer Anpassung wird schließlich gesprochen, wenn bei vorgegebener Intensität und Einsatzzeit die Anzahl der eingesetzten Aggregate variiert wird (vgl. z. B. ). Es handelt sich dabei urn kurzfristige Anpassungsformen, da die Anpassung an alternative Produktquantitäten auf der Basis der zur Verfügung stehenden Potenzialfaktoren erfolgt, d. h. diese Anpassungsformen beziehen sich ausschließlich auf im Betrieb vorhandene Aggregate und sind daher sofort realisierbar (vgl. ). Die zeitliche, intensitätsmäßige und quantitative Anpassung bestimmen die Leistungsabgaben der vorhandenen Potenzialfaktoren und damit sowohl die resultierenden Faktorverbräuche als auch die damit verbunden Kosten. In der betrieblichen Praxis werden diese Anpassungsformen i. d. R. kombiniert. Es stellt sich somit das Problem, die drei Anpassungsformen derart zu gestalten, dass die gewünschte (vorgegebene) Produktquantität effizient erzeugt wird.

Since the advent of just-in-time driven production planning and control at the Toyota manufacturing plants, the just-in-time paradigm has considered wide-spread consideration within production and operations management (cf., e.g., Schniederjans [] and Cheng and Podolski []). While it was first employed for the high-volume-production of goods only, later there has been considerable research in the area of low-volume, make-to-order manufacturing (cf., e.g., Baker and Scudder [], Neumann et al. [], and Rachamadugu []). Agrawal et al. [] considered a practical scheduling problem at Westinghouse ESG, where a number of customer-specific products have to be assembled subject to technological precedence and capacity constraints. The authors developed a MIP-formulation and — in the face of the NP-hardness of the problem — a ‘lead time evaluation and scheduling algorithm’ with acronym LETSA.

In the process industries, final products arise from chemical and physical transformations of materials on processing units. In batch production mode, the total requirements for intermediate and final products are divided into individual batches. To produce a batch, at first the input materials are loaded into a processing unit. Then a transformation process, called a , is performed, and finally the output products are unloaded from the processing unit. Typically, a plant is operated in batch production mode when a large number of different products are processed on multi-purpose equipment. That is why we consider multi-purpose processing units, which can operate different tasks. Symmetrically, a task may be executed on different processing units, in which case the duration of the task may depend on the processing unit used. For a practical example of a multi-purpose batch production plant we refer to the case study presented by Kallrath (2002).

Labor costs become more and more important for a company’s success. This holds especially true for high-wage countries like Germany and for labor-intensive service industries like such as call centers or casinos. Efficient planning and allocation of personnel resources is therefore essential and quite a lot of models and methods for this so-called “workforce scheduling problem” have been developed thus far. Nevertheless, real problems are very different, complex, and specific. There exists no standard procedure to find optimal — or at least suboptimal — solutions (if the complexity of the problem is too great for exact algorithms). The formulation of the problem has to take into account the unique situation of a company and subsequently an appropriate solution method has to be adapted.

This paper is dedicated to Prof. Dr. Klaus Neumann on the occasion of his entrance into the retirement.

Production planning and inventory control is facing challenging risk management problems if it is confronted with uncertainties from both the demand and the process side. By analyzing the respective planning problems with methods of stochastic inventory control it is possible to gain remarkably deep insights into the way how optimal reorder and safety stock management responds to joint demand and yield risks. These insights can be exploited to assess and improve the simple type of risk management rules employed in MRP systems to cope with uncertainties in demand and production yield.

The hub & spoke network design problem is a strategic logistics planning problem with applications for airlines, telecommunication companies, computer networks, postal services, and trucking companies, for example. Basically, the problem in all these applications is that for a given set = 1,... , of nodes (airports, computers, post offices, depots, ...) goods must be transported between possibly every pair of nodes. Direct connections between every pair of nodes would result in ( − 1) linkages which is impractically high and economically non-profitable. Consider, for instance, an airline that serves several airports worldwide. Offering nonstop flights between every pair of airports would require a huge amount of planes and crews and many empty seats on board could be observed for many connections. In such settings, it turns out to be reasonable to install one or more so-called hub locations where direct links are then available to hub nodes as indicated in figure 1 where nodes 3, 6, and 9 are assumed to be hubs. Transporting goods from, say, node 1 to node 11, can then be done via hubs 3 and 6.

In this paper we consider one-buyer, one-seller, finite horizon, multi-period inventory models in which the economic order quantity is integrated with the economic production quantity (EOQ-EPQ in short). We introduce the Stackelberg equilibrium framework in which the objective is to maximize the vendor’s total benefit subject to the minimum total cost that the buyer is willing to incur. Some existence results, optimality conditions and the optimal replenishment policy under the Stackelberg equilibrium concept are obtained and a numerical algorithm is presented to find the optimal replenishment policy in practice.

Die wenigsten Autofahrer werden einen Bezug zwischen Operations Research — oder kurz OR — und ihrem Navigationssystem herstellen. Genauso wenig denkt man beim Studium der Reiseroute, die man sich gerade im Internet errechnet hat, an Begriffe wie „Minimalgerüst“ und „kürzeste Wege“. Und was hat die pünktliche Auslieferung der bestellten Möbel mit dem Knoten-orientierten Tourenplanungsproblem zu tun?

It is almost common knowledge in modern societies that regulate many important economic and social processes. Nevertheless, this topic has not received adequate attention in the past neither from empirically oriented nor from theoretical economists. It is the main purpose of this paper to analyze this problem using an analytical model of strategy adaptation in populations with a given social structure (network). In most studies on evolutionary strategy adaptation it is assumed that members of a population are randomly matched with each other member of the population. Seemingly, this does not give a realistic scenario of strategy adaptation in most modern societies where members of a population are matched only with members of their reference groups like families, colleagues at work, etc. However, these groups are typically not isolated from each other. They are interrelated by individual connections, which makes the strategy adaptation problem of a single individual in the population a non-trivial strategic decision problem.