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𝗣𝗗𝗙 | On Jan 1, , Horst Tempelmeier and others published Günther, H.-O. und H. Tempelmeier, Produktion und Logistik - Supply Chain. , Horst Tempelmeier · Material-Logistik: Modelle und Algorithmen für die Produktionsplanung und -steuerung und das Supply Chain Management ; mit. Einkauf und Logistik e.V., a federal association for purchasing and logistics) in which .. According to GÜNTHER and TEMPELMEIER capacity planning can be separated Günther, H.-O., Tempelmeier, H. (): Produktion und Logistik, Springer: Berlin . Calculating_Technical_Capacity_pdf, date

Produktion Und Logistik Tempelmeier Pdf

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Tutorium SS Produktion und Logistik (Studentensicht).pdf. 1. Tutorium SS Produktion und Logistik (Studenten).pdf. Uploaded by Sanchez Diego Günther & Planning Process [12] [29] [21] Tempelmeier Horizon [20] . We hope that our [20] Günther H.O. & Tempelmeier H., , Produktion and Logistik. months ended march 31, http:// – G€unther HO, Tempelmeier H () Produktion und Logistik.

The solutions of these conflicts of objectives are usually achieved through contractual agreements framework agreements. For decentralized planning and control, an important consequence can be derived from their framework agreements: The supply chain is a non-hierarchical decision-making framework in which the decisions to be made as well as the communication mechanisms between the involved parties need to be modeled Logistics Journal: Not Reviewed ISSN Page 3 4 Figure 3.

It is a use case well-suited for the study and development of a decentralized planning and control system for logistic networks, as the agricultural activities involved are heavily dependent on tightly controlled logistics processes. The major logistical challenge when harvesting silage maize is the sheer volume of chopped plants produced in the field.

With yield rates of several hundred tons per hour and no bunker on-board of the forage harvester, it is essential to provide a transport vehicle for overloading the harvested crops at any time. Otherwise the process comes to a complete halt, which is costly and problematic, due to the short and weather-dependent time windows of the harvesting season. The goals in SOFiA regarding the process of silage maize harvesting are twofold: Firstly we are concerned with digitizing the contract between a farmer and an agricultural contractor.

Secondly, we aim to utilize the smart objects for the cooperative control of the involved machines that is adapting the transport vehicles schedule, the harvester s production rate, as well as the working rate of the compactor vehicle. By deploying smart object technology for decentralized decision making on board of agricultural machines, it is possible to address problems when and where they arise.

This is particularly beneficial, since agricultural processes are often located in rural areas lacking broad band connectivity, which makes data transfer to and from a centralized planning and control architecture brittle if not impossible. Next, we will present our approach towards the decentralized planning and control of logistic chains.

For brevity, we hereby concentrate on the use case of supply chain management. The agricultural use case is based on the same principles and architecture. Here, a supply chain is modeled as a network of nodes shipper, hubs of the logistics service provider, recipient and edges routes between the nodes cf. In the network the parts will be transported in transport equipment curtain trailers according to the transport order from one shipper to one recipient.

Different means of transport truck, ship, train move the transportation equipment. The means of transport can be changed at the hubs. Therefore, the transport order can be divided into so-called route section orders. Every route section is operated by one means of transport truck, ship, train. The transport demand is determined by the production and distribution planning of the shipper and the production and procurement planning of the recipient.

This results in three classes of OTD-Net models: models for the shipper, models for the logistics service provider or individual hubs of the logistics service provider LSP and models for the recipient.

Figure 5 shows the conceptual interfaces of the models with local systems of the parties involved in the supply chain. These models perform the following planning and control tasks: In LSP models, the planning of incoming and outgoing transports per hub is made.

In addition to the transport capacities of the different means of transport, handling resources and available empty transport equipment are also considered.

Apart from a standard route for a transport section from one hub to the next, a LSP can also choose alternative transport route sections to react to events breakdown of a machine, missed ships and trains, but also changes of the plan at the shipper or recipient.

A shipper is the source start location and time for a transportation request and provides resources for the loading and, if needed, the buffering of transportation equipment. A recipient is the sink location and time for a transportation request and provides resources for the unloading and, if needed, the buffering of transportation equipment.

Recipients can submit a transport demand plan to the LSP based on their procurement planning if the recipient is the customer and inform the LSP about incoming transport goods delivery advice.

Transport orders, planned receipts and issues of transport goods are the main results of the planning process on the shipper, LSP and recipient side respectively. A route section order specifies the transport of transport equipment from a start to a target node shipper, hub, recipient. For the order control, the start location and a collection window as well as a target location and a delivery window are relevant.

In coordination with the respective means of transport, the Smart Object of the transport equipment can permanently monitor its order status and inform the next hub about expected delays cf. The cloud-based component connects the multiple stakeholders involved in a supply chain via a smart contracting service. This service bridges the gap between financial and material flows in the supply chain. It also provides security, fraud tolerance and serves as the enabling connector to run smart payment methods automated and invoice-independent transactions between smart objects and smart finance services based on smart contracts.

The service is based on a Blockchain, since the underlying technology is not only a perfect place to store cryptocurrencies like bitcoins, but also an appropriate solution to save and share contract relevant data in a secure and tamper resistant way [WFN16].

A private Blockchain guarantees a shared and trusted ledger of transactions that every consortium partner can inspect, but no one can control or change a later point in time [SSU16]. If a certified partner or a qualified system puts value in it, the decrypted data will be stored irreversibly. The smart contract service is able to analyze, monitor and verify all these events in terms of examining whether all contract components and requirements are fulfilled or not. Additionally, the smart contract is empowered to automatically trigger a financial transaction after an on-time delivery with no discrepancies.

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To connect the smart contract service with the payment cloud write-and-read-permissions are assigned by open source technologies such as the MultiChain. Based on this technology the partners of the SOFiAproject design smart processes in logistics and farming. The smart contract services, which are implemented as web Figure 6. Underlying the principles of the Blockchain each stream stores its data on various servers to secure the inviolability of important process data.

The extent of the permissions given to the contracting parties is defined by the business community and varies between full access on data, reading-permission only or the complete refusal of all information.

This procedure ensures that only a verified party can add data or change status. It also simplifies the traceability in case of data abuse. When a contract component or requirement is fulfilled the DCU transfers the information of completion to one of the related Blockchains.

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Subsequently, the smart contract triggers the determined payment cf. As well as the payment cloud, further smart B2B services like financing or insurance service can be integrated easily into the cloud-based system. These systems provide the initial plans on how to execute the negotiated logistical services - either by using a manual disposition process or utilizing an OTD-Net based simulation of the supply chain to come up with a suitable set of actions and schedules for all involved parties and objects.

Figure 8. To enable the on-going tracking of service contract components directly on-board the transport vehicle or cargo units, it is important that the DCU is able to understand the process chain and the associated tasks for this particular unit.

It is essential that a DCU can measure its own progress, in order to report to the smart contracting system, as well as to re-plan its own activities to better fulfill its assigned tasks.

To implement process monitoring and self-organization, consider the DCU as an agent and the logistical network as a multiagent system [Woo09]. This reflects in the DCU s underlying architecture concept, depicted in Fig. Each DCU is equipped with a formal model of the supply chain and maintains knowledge about the process in knowledge bases using this model. We use semantic web technology standards e. To capture process data, we based our model on an existing ontological conceptualization of logistical processes [DF13].

This model consists of three interconnected ontologies: The logistics core ontology LogiCo defines the basic vocabulary for describing movable resources involved in a supply chains, e. The logistics service ontology LogiServ describes logistical activities, e. Finally, the transport ontology LogiTrans explicitly describes the communication between a customer and a LSP for organizing logistical transport.

It provides a transport request which is specified by the customer.

It denotes the loading and unloading locations, the required delivery times, as well as the cargo and its properties. As a response the provider issues a transport plan detailing how the request will be handled. We use this conceptualization to capture the individual tasks for every logistical object, as provided by the ERP system in the DCU s internal memory.

However, the model was not designed to include information about an on-going logistics process, hence we extended the model to also capture the current state of the task. We update this state continuously using sensor data input e. For transportation tasks, for example, we measure the remaining travel time and match it against the planned time of arrival. If the DCU detects any major deviations from the initial plan, we extract data from the process model to generate a set of simulation models and use the latter to determine possible alternative activities, e.

Nevertheless, in- tegrated planning methods using this sequential approach provide better results re- garding defined indicators then traditional sequential planning approaches Chandra and Fisher , Chen and Vairaktarakis , Pundoor and Chen There is another group of integrated planning methods beside this sequential approaches to solve the integrated problem.

These recently developed planning methods are metaheuristics applying an improvement procedure on a complete so- lution for a simultaneous modification of production and transportation plan in or- der to improve the total costs.

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For example, Boudia and Prins present a memetic algorithm with a dynamic population management using a genetic algorithm and a local search method to improve an initial solution by allowing elementary changes moves Boudia and Prins Each single solution comprises a tuple of lists, matrices and vectors representing e. Archetti et al. Further- more, an increased integration could lead to an improved integrated planning re- sult regarding defined indicators.

Thus, this research is motivated to fill the gap identified by development of an integrated planning approach for the operational level considering capacity constraints in production and distribution as well as dy- namics and economic aspects.

This research is not about production planning and scheduling or transportation planning methods but on the integration of this sepa- rate planning problems. A concept of this integrated planning method is presented in the following chapter. The aim is to ensure an improved coordination of the single planning problems to achieve an increased supply chain performance as well as an increased competitiveness and an improved acceptance of the integrated planning result within the supply chain.

In order to improve the integration we first cluster already known orders using a backward scheduling and a selection criteria like the delivery date. For the backward scheduling the already known production time and the transport time of the orders are used.

By using e. Each cluster contains orders with a defined range of remaining time until delivery date and thus with a different priority. This range depends on the cluster criteria, which has to be defined previously. After this clustering step we start a planning step with the cluster having the highest priority and containing all urgent orders with the lowest remaining time until delivery date.

The planning step comprises a seperate planning of production and transport using already known planning methods from the respective fields. For production planning dispatching rules or shifting-bottleneck-heuristics are applicable. The sweep-heuristics or nearest-neighbour-rule for example could be used for generating transport plans. At the end of the planning step there is a seperated and initial production plan and a transportation plan for all orders of the first cluster and we go on with an optimization step.

In the optimization step we combine the two initial planning results. Therefore, sorting and evolutionary algorithms as well as permutation based rules and local search algorithms could be used. The aim is to put similar orders together and to reduce transportation costs or setup times. The optimization step is equal to the integration step of existing integrated planning methods.

At the end of the optimization step a so called cluster plan is available. After the optimization step of the first cluster an initial integrated production and transport plan is available.

It can be fixed for this cluster because of the 5 highest order priority. The further successive proceeding of the planning and the optimization step for the cluster with a lower priority is the same as described for the first cluster.

After finishing the optimization step of each cluster the integration of planning results into the integrated plan is necessary. In the integrated plan, which contains fixed slots form clusters with higher priorities, unused production and transportation slots are available. These slots can be used for insertation of the cluster plan and completion of the integrated plan.

The procedure ends after insertation of cluster plan n in the integrated production and transport plan. By this way the integrated plan will be completed step by step based on the results of previous cluster and integration plans. The operating principle of the dscribed integrated production and outbound distribution planning method is depicted in Figure 1.

Order pool Backward scheduling and clustering Clustering result: Integration Cluster Operating principle of the integrated planning method In each cluster we combine production and transportation issues and restrictions and use these result as a basis for the integration of the subsequent cluster.

Thus, the integration level of this conceptual integrated method is on a distinct higher level than the integration level of existing planning methods.

In order to transfer this concept into a detailled integrated planning method which is applicable in practical problems we identified a number of relevant research questions.


The first focus of our future investigations is on clustering. The goal is to identify appropriate clustering methods and to define cluster boundaries depending on the order structure and additional production and transportation 6 restrictions. A large number of cluster reduces the problem complexity within one cluster and thus the CPU time but there is also a reduction of the optimization potential.

The formulation of an objective function for the optimization step is the second goal of our future resaerch. This objective function is necessary to decide if a feasible improvement in the optimization step will have a positive effect for the cluster plan and for the target indicators of the integrated plan as well.

The design of the objective function represents planning assumptions in the supply chain. Therefore, weighting factors as well as economic and process indicators have to be defined and included. The most important focus of future investigations is the development respectively the selection of methods which will be used for the planning and optimization steps. For evaluation the developed integrated method will be implemented in existing modells from the literature and additionally in a model of a use case deduced from a practical use case.

The modell from the literature will be used to check the functionality. Furthermore, there will be a comparision with existing integrated planning methods and with production or transport oriented sequential planning processes.

Especially transport oriented planning methods as presented in Scholz-Reiter et al. As a result of a literature review it can be stated that exisiting integrated planning methods focus on specific scenarios or are on a very generic level with a low integration of respective sub- problems.

This was the motivation for the development of the provided concept for an integrated planning method with a distinct higher integration. We presented the operating principles as a concept for an integrated planning method, which will be detailled in the future.

A number of relevant research questions were identified and described. This approach is promising because of the high integration and its probably higher acceptance at the planning departments of the supply chain partners.Product architectural-SC detailed due to the need Furthermore, our paper has a more focused review scope for cross-hierarchical abstraction in the trade-off model.

Chen reviews integrated planning approaches only on the operational level Chen Next, we will present our approach towards the decentralized planning and control of logistic chains. This small Huang et al. Our trade-off quality. For decentralized planning and control, an important consequence can be derived from their framework agreements: The supply chain is a non-hierarchical decision-making framework in which the decisions to be made as well as the communication mechanisms between the involved parties need to be modeled Logistics Journal: Not Reviewed ISSN Page 3 4 Figure 3.

The service is based on a Blockchain, since the underlying technology is not only a perfect place to store cryptocurrencies like bitcoins, but also an appropriate solution to save and share contract relevant data in a secure and tamper resistant way [WFN16].

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