Mixed Integer Programming Models for the Synchronization of a Project Building Planning with Its Supply Chain

Mixed Integer Programming Models for the Synchronization of a Project Building Planning with Its Supply Chain
Author: Diana Samaranch Mir
Publisher:
Total Pages:
Release: 2012
Genre:
ISBN:

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A project consists of a set of activities coordinated by precedence relationships. When this activities have no needs of resources or the resources are unlimited, project management is easier and techniques like PERT(program evaluation and review technique)[1] or CPM (Critical Path Method)[1] can be used. The resource-constrained project scheduling problem (RCPSP) is one of the best known cumulative scheduling problems due to its combinatorial complexity, the interest of the operational research community, and to its numerous industrial applications. The basic RCPSP [2], [3] deals with scheduling activities subject to Finish-to- Start precedence constraints with zero time-lags and renewable resources under the minimum project completion time objective. The context of the present research is the industrialization of the process of thermal insulation of buildings by the outside. The idea is to manufacture in plants highly integrated big panels, that can be easily transported and then plugged on building faces. . The planning -issue we address here concerns- the possible synchronization of the project of a building renovation with the plant (or plants) that provide the panels. The novelty is to move from a "on the building site" make to order production, to a mass customization supply chain. Therefore, we are seeking for an homogenization of the renovation process to improve its efficiency in all technical senses but also organizational and economic. Objectives depend on the manager and his responsibilities in the supply chain. From a plant manager perspective, it can be the perfect synchronization of the plant production and the on-site assembly in order to avoid inventories. In the building project manager idea, it is usually to finalize the project by the minimal possible time allowed by the precedence relationships, and to minimize the cost of his on-site project. The differences with the classical RCPSP[4] is that, in this case, the resources to be considered are both renewable and non-renewable. Moreover the objective function is not only about to finish as early as possible, as issues such as the synchronization of the project become very important. Finally the issue is to provide a production planning that is compatible with the delivery of the project and the availability of resources both renewable and non-renewable. The former is the part of the problem on the supply chain perspective. The latter concerns the management of the load on nonrenewable resources, which is moved from the building site to the plants. Let consider the most general case we consider in this paper: one project of building with various suppliers with resource constraints. These external resources are independent. It means that every resource has one different supplier. But, it will be necessary to manage simultaneously the project on-site resources and partners-plant resources.

Mixed Integer Programming Models for Supply Chain Integrated Planning

Mixed Integer Programming Models for Supply Chain Integrated Planning
Author: Devender Mohan Gupta
Publisher:
Total Pages: 0
Release: 2003
Genre: Business logistics
ISBN:

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This thesis is intended to highlight the methodology of integrated planning for coordinating the supply chain in order to improve a system's overall performance. For that, a typical system is considered in which several products must be produced and distributed over several time periods. This work is done on a particular system that includes several suppliers, raw material processing plants, finishing plants and customers. Two main models have been developed based on different approaches, and they have been implemented in two different contexts. Various processes such as raw material procurement, capacity utilization, inventory, and distribution are integrated and optimized. More specifically, the following stages are considered: supply of raw material; production, at raw material processing plants, of product families; production, at finishing plants, of finished goods; and distribution of finished goods to customers. The finished goods are grouped into product families; setups are incurred at the raw material processing plants for product families, and at the finishing plants for individual products. Mixed Integer Program (MIP) formulations are utilized for optimizing the system. Test cases include both small sized and large sized problems. Analysis is done to gain insights into the workings of the models and systems like these in general. In the end, we have successfully demonstrated the utility of managing the supply chain effectively by integrating various processes along with the power and utility of MIP for representing such systems.

Introduction to Computational Optimization Models for Production Planning in a Supply Chain

Introduction to Computational Optimization Models for Production Planning in a Supply Chain
Author: Stefan Voß
Publisher: Springer Science & Business Media
Total Pages: 239
Release: 2013-06-05
Genre: Business & Economics
ISBN: 3540247645

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An easy-to-read introduction to the concepts associated with the creation of optimization models for production planning starts off this book. These concepts are then applied to well-known planning models, namely mrp and MRP II. From this foundation, fairly sophisticated models for supply chain management are developed. Another unique feature is that models are developed with an eye toward implementation. In fact, there is a chapter that provides explicit examples of implementation of the basic models using a variety of popular, commercially available modeling languages.

Supply Chain Management and Advanced Planning

Supply Chain Management and Advanced Planning
Author: Hartmut Stadtler
Publisher: Springer Science & Business Media
Total Pages: 369
Release: 2013-04-17
Genre: Business & Economics
ISBN: 3662042150

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Supply Chain Management concerns organizational aspects of integrating legally separated firms as well as coordinating materials and information flows within a production-distribution network. The book provides insights regarding the concepts underlying APS, with special emphasis given to modelling supply chains and successfully implementing APS in industry. Understanding is enhanced through the use of case studies as well as an introduction to the solution algorithms used.

Mixed-integer Linear Programming Model for Distributioni Network Optimization

Mixed-integer Linear Programming Model for Distributioni Network Optimization
Author: Mary Ann P. Perlas
Publisher:
Total Pages: 86
Release: 2011
Genre:
ISBN:

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Thsi study was conducted to develop a mathematical model that generates the optimum supply option supply option in delivering products from manufacturing source to customers. The model is based on a 4-tier distribution network where a single product sourced from a single supplier is distributed to several customers using multiple supply options, namely : (1) direct containerized shipment from plant (40' or 20' footer) ; (2) indirect shipment thru main distribution center ; (3) indirect shipment thru regional distributor supplied locally by main distribution center. Optimal solution was determined using mixed-integer linear model programmed and simulated in AIMMS (Advanced Integrated Multidimensional Modeling Software), commercial optimization software developed by Paragon-Decision Technology. Optimal solution was solved by minimizing total logistics cost in a given set of constraints which includes location-based and operational constraints inherent to distribution network in archipelagic setting. To assess the functionality and reliability of the proposed model, actal data from a fast-moving consumer goods company (FMCG) was used. Out of 24 distributors, the model proposed eleven (11) customers to be best suplied directly by plant, six (6) thru a regional distributor and the balance sutomers remain status quo via main distribution center. Implementing the recommended optimal solution would give the FMCG company 16% savings in current logistics cost and would free-up 85% of warehouse space allocated to current product volume ...