Improvements of An Integrated Approach for Lot Sizing and Detailed Scheduling

4 pagesInternational audienceIn a previous work, we developed an integrated approach for determining optimal feasible production plans, i.e. lot sizes for which there is at least one schedule in which lots are completed on time. Based on the limitations of this approach, we propose various improvements in di fferent parts. The most relevant improvements are summarized in this extended abstract

Smoothing Procedures for an Integrated Approach for Multi-Level Lot Sizing and Detailed Scheduling

International audienceThe integration of production planning and scheduling decisions represents an important challenge in production management. Typical Enterprise Resource Planning (ERP) systems, using Manufacturing Resource Planning (MRP-II) technique for capacity management, cannot guarantee feasible procurement and production plans at the operational level (short term planning horizon). In this paper we study the integrated multi-level lot-sizing and job-shop scheduling problem. We analyze various smoothing procedures that "repair" the relaxed capacity and Bill-Of-Materials (BOM) constraints in the Lagrangian heuristic of a recent approach

Combining Column Generation and Lagrangian Relaxation

Although the possibility to combine column generation and Lagrangian relaxation has been known for quite some time, it has only recently been exploited in algorithms. In this paper, we discuss ways of combining these techniques. We focus on solving the LP relaxation of the Dantzig-Wolfe master problem. In a first approach we apply Lagrangian relaxation directly to this extended formulation, i.e. no simplex method is used. In a second one, we use Lagrangian relaxation to generate new columns, that is Lagrangian relaxation is applied to the compact for-mulation. We will illustrate the ideas behind these algorithms with an application in Lot-sizing. To show the wide applicability of these techniques, we also discuss applications in integrated vehicle and crew scheduling, plant location and cutting stock problems.column generation;Lagrangean relaxation;cutting stock problem;lotsizing;vehicle and crew scheduling

Combining Column Generation and Lagrangian Relaxation

Although the possibility to combine column generation and Lagrangian relaxation has been known for quite some time, it has only recently been exploited in algorithms. In this paper, we discuss ways of combining these techniques. We focus on solving the LP relaxation of the Dantzig-Wolfe master problem. In a first approach we apply Lagrangian relaxation directly to this extended formulation, i.e. no simplex method is used. In a second one, we use Lagrangian relaxation to generate new columns, that is Lagrangian relaxation is applied to the compact for-mulation. We will illustrate the ideas behind these algorithms with an application in Lot-sizing. To show the wide applicability of these techniques, we also discuss applications in integrated vehicle and crew scheduling, plant location and cutting stock problems

Clips: a capacity and lead time integrated procedure for scheduling.

We propose a general procedure to address real life job shop scheduling problems. The shop typically produces a variety of products, each with its own arrival stream, its own route through the shop and a given customer due date. The procedure first determines the manufacturing lot sizes for each product. The objective is to minimize the expected lead time and therefore we model the production environment as a queueing network. Given these lead times, release dates are set dynamically. This in turn creates a time window for every manufacturing order in which the various operations have to be sequenced. The sequencing logic is based on a Extended Shifting Bottleneck Procedure. These three major decisions are next incorporated into a four phase hierarchical operational implementation scheme. A small numerical example is used to illustrate the methodology. The final objective however is to develop a procedure that is useful for large, real life shops. We therefore report on a real life application.Model; Models; Applications; Product; Scheduling;

Advanced resource planning as decision support module to ERP.

In this paper, we show that the planning and decision-support capabilities of the MPC (Manufacturing Planning and Control) system, which forms the core of any ERP (Enterprise Resource Planning) package, may be substantively enhanced by including a Decision Support Module (DSM) as an add-on at the midterm planning level. This DSM, called Advanced Resource Planning (ARP), serves as parameter setting process as well as tool for improving the structure of the ERP system itself. The ultimate goal of the DSM is to yield realistic information both for scheduling, sales and marketing, strategic and operational decision making and suppliers and customers.

From ERP to advanced resource planning: Improving the operational performance by getting the inputs right.

In this paper, we show that the planning and decision support capabilities of the MPC (Manufacturing Planning and Control) system, which forms the core of any ERP package, may be greatly enhanced by including an Advanced Resource Planning (ARP) module as an add-on at the midterm planning level. This ARP module enables to estimate the impact of variability, complexity and dynamic system behavior on key planning parameters. As such, it yields realistic information both for short-term planning purposes and for reliable lead time quotations. We show how dynamic behavior impacts the operational performance of a manufacturing system, and discuss the framework for incorporating the ARP module into the ERP system.Planning; Operational performance; Performance; International; Science;

Operations planning test bed under rolling horizons, multiproduct,multiechelon, multiprocess for capacitated production planning modelling with strokes

[EN] One of the problems when conducting research in mathematical programming models for operations planning is having an adequate database of experiments that can be used to verify advances and developments with enough factors to understand different consequences. This paper presents a test bed generator and instances database for a rolling horizons analysis for multiechelon planning, multiproduct with alternatives processes, multistroke, multicapacity with different stochastic demand patterns to be used with a stroke-like bill of materials considering production costs, setup, storage and delays for operations management. From the analysis of the operations planning obtained from this test bed, it is concluded that a product structure with an alternative process obtains the lowest total cost and the highest service level. In addition, decreasing seasonal demand could present a lower total cost than constant demand, but would generate a worse service level. 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