Hartmut Stadtler

Supply Chain Management and Advanced Planning

A ceramic fiber mat attached to the interior wall or surface of a high temperature chamber or furnace or adapted to overlie an intermediate insulating member positioned between the mat and a furnace wall, the fibers in the mat lying in planes generally perpendicular to the wall, the mat constituting an improved insulation for the wall where the interior of the chamber or furnace will be operating at temperatures in excess of 1600 DEG F.

Supply chain management and advanced planning––basics, overview and challenges

Literature on supply chain management (SCM) covers several disciplines and is growing rapidly. This paper firstly aims at extracting the essence of SCM and advanced planning in the form of two conceptual frameworks: The house of SCM and the supply chain planning matrix. As an illustration, contributions to this feature issue will then be assigned to the building blocks of the house of SCM or to the modules covering the supply chain planning matrix. Secondly, focusing on software for advanced planning, we outline its main shortcomings and present latest research results for their resolution. 2004 Elsevier B.V. All rights reserved.

Negotiation-based collaborative planning between supply chains partners

It is often proposed that operations planning in supply chains can be organized in terms of a hierarchical planning system. However, the hierarchical approach assumes a single, centralized planning task for synchronizing operations across the supply chain. As central coordination can usually be realized only for isolated parts of an overall supply chain, the question arises whether there are alternative ways of coordination. In this paper we propose a non-hierarchical, negotiation-based scheme which can be used to synchronize plans between two independent supply chain partners linked by material flows. Assuming that plans are generated based upon mathematical programming models, we show how modified versions of these models can be utilized for evaluating material orders or supplies proposed by the supply chain partner and for generating counter-proposals. Resulting is an iterative, negotiation-like process which establishes and subsequently improves a consistent overall plan. Computational tests suggest that the scheme comes close to optimal results as obtained by central coordination.

Supply Chain Management — An Overview

What is the essence of Supply Chain Management (SCM)? How does it relate to Advanced Planning? In which sense are the underlying planning concepts “advanced”? What are the origins of SCM? These as well as related questions will be answered in this chapter.

A framework for collaborative planning and state-of-the-art

Inter-organizational supply chain management incurs the challenge to align the activities of all members which contribute to the value creation of a product or service offered to customers. In general, a supply chain faces the “problem” of information asymmetry, members having their own objectives and constraints which may be in conflict with those of the other members. Still, activities have to be aligned in such a way that the supply chain as a whole stays or becomes competitive while each member wins by cooperating. A number of collaborative planning schemes have been put forward in the last two decades with different assumptions and different areas of application. This paper intends to provide a framework and an overview on the state-of-the-art of collaborative planning. The criteria of the framework will allow us to position existing concepts and to identify areas where more research is needed. The focus of the literature reviewed here will be on model-based decision support at the operational planning level.

The Capacitated Lot-Sizing Problem with Linked Lot Sizes

In this paper a new mixed integer programming (MIP) model formulation and its incorporation into a time-oriented decomposition heuristic for the capacitated lot-sizing problem with linked lot sizes (CLSPL) is proposed. The solution approach is based on an extended model formulation and valid inequalities to yield a tight formulation. Extensive computational tests prove the capability of this approach and show a superior solution quality with respect to other solution algorithms published so far.

A one-dimensional cutting stock problem in the aluminium industry and its solution

Abstract When producing window frames made out of aluminium one faces the decision problem to determine how many aluminium profiles of a given standard length have to be bought to fulfill production requirements. As results of the currently implemented First Fit Decreasing (FFD) heuristic were regarded unsatisfactorily, a new code—based on the column generation procedure of Gilmore and Gomory—has been developed and supplemented by a one-pass branching up procedure to achieve integrality. Algorithmic details about the integrality achievement phase as well as results obtained when solving real world cutting stock problems on a personal computer are provided.

Negotiation-based collaborative planning in divergent two-tier supply chains

Advanced Planning Systems are based on the principles of hierarchical planning, which—at least at the top level—grounds on centralized planning. However, central coordination requires access to all relevant information and the power to impose planning results on all organizational units. In consequence it can be realized only for parts of an inter-organizational supply chain, and the question arises whether there exist alternate ways to achieve coordination. In this paper we describe a non-hierarchical, negotiation-based process, which can be used to synchronize plans between independent partners of a two-tier supply chain consisting of one supplier and several buyers. Assuming that all partners generate plans based upon mathematical programming—as in most Advanced Planning Systems at the master planning level—we show how modified versions of these models can be utilized to support the negotiation process by evaluating given purchasing orders or supplies and by generating counter-proposals. Resulting is an iterative, negotiation-like scheme, which establishes and subsequently improves a consistent overall plan based on a limited exchange of information between the supply chain partners.

Production Planning and Scheduling

Assuming that the master plan has been generated, we can now derive detailed plans for the different plants and production units. In the following we will describe the underlying decision situation (Sect. 10.1) and outline how to proceed from a model to a solution (Sect. 10.2). Some of these steps will be presented in greater detail, namely model building (Sect. 10.3) and updating a production schedule (Sect. 10.4). Whether Production Planning and Scheduling should be done by a single planning level or by a two-level planning hierarchy largely depends on the production type of the shop floor. This issue will be discussed together with limitations of solution methods in Sect. 10.5.

Reformulations of the shortest route model for dynamic multi-item multi-level capacitated lotsizing

The shortest route representation of the dynamic multi-item multi-level capacitated lotsizing problem is appealing due to the tight bound provided by its Linear Programming (LP) relaxation. However, it suffers from two main drawbacks: Firstly, even solving the LP relaxation of problem instances with up to 16 time periods and 40 items with standard mathematical programming software might require a prohibitive amount of computer time. Secondly, the quadratic growth of the number of variables with the number of periods restricts the solution of problem instances with many periods. Both drawbacks will be addressed in this paper by proposing reformulations of the original shortest route model. Especially we will introduce a model formulation which allows theuser to find a tradeoff between model size and tightness of the lower bound obtained by the LP relaxation by specifying the number oflook ahead periods τ. Furthermore, we will provide an iterative procedure for determining those look ahead periods which result in the tightest LP relaxation. Theoretical insights as well as computational results are provided, too.ZusammenfassungDas dynamische mehrstufige Mehrprodukt-Losgrößenproblem läßt sich als gemischt-ganzzahliges Optimierungsproblem mit einem Kürzeste-Wege-Modell abbilden. Ein besonderer Vorteil dieser Modellformulierung liegt in der sehr scharfen unteren Schranke der zugehörigen LP-Relaxation. Als nachteilig erweist sich jedoch einerseits der unverhältnismäßig hohe Rechenaufwand zur Lösung der LP-Relaxation mit Standardsoftware der mathematischen Programmierung sowie andererseits die mit der Periodenanzahl quadratisch wachsende Variablenanzahl, die die Lösbarkeit von Probleminstanzen mit vielen Perioden stark einschränkt. Zur Behebung der genannten Nachteile wird in dem vorliegenden Aufsatz eine Modifikation des ursprünglichen Kürzesten-Wege-Modells vorgeschlagen. Diese neue Modellformulierung erlaubt es demAnwender einen Kompromiß zwischen der Modellgröße und der Schärfe der zugehörigen LP-Relaxation zu finden. Hierzu ist einVorschauhorizont τ vom Anwender zu definieren. Zusätzlich wird ein iteratives Verfahren beschrieben, das den minimalen Vorschauhorizont τ bestimmt, bei dem die schärfste untere Schranke erreicht wird. Neben theoretischen Erkenntnissen werden auch die Ergebnisse umfangreicher Testrechnungen vorgestellt.