Rolling horizon

Venkataraman, R., Frequency of Replanning in a Rolling Horizon Master Production Schedule for a Process Industry Environment A Case Study, Production and Operations Management, Vol. 5, pp. 255-265, 1996. 

Most of the literature on pipeline transportation considers customer demands for the products at the depots to be served at the end of the planning horizon. An exception is proposed in Cafaro and Cerda (2008) where customer demands at the depots are associated with due-dates such that backorder costs are incorporated in the objective function. Moreover, the work proposes a rolling horizon model for updating and re-scheduling previously determined schedules according to updated demand characteristics. 

For a similar rolling horizon approach for pipeline operations planning see Cafaro and Cerda (2008). 

To generate plans for inter-site rail transport and turnover proce.sses, the MC-RTP model is used in a rolling horizon environment where the individual MC-RTP instances are solved by CPLEX 12.3. The planning horizon is set to seven days. The re-planning interval is set to three days such that flovi decisions are fixed for three days. The general struc-... 

MRP serves as a tool to make production quantity decision. However, MRP assumes deterministic demands subject to changes in different periods. MRP is a push system. The example above assumes a static MRP that has a fixed planning horizon, 6 weeks. In reality an MRP needs to be run each period to manipulate productions decisions. Rolling horizon approach implements only the first-period decision of A -period problem [3]. When using rolling horizon approach, number of periods should be long enough to make the first-period decision constant. 

Disjunctive programming model and a rolling horizon algorithm for optimal multiperiod capacity expansion in a multiproduct batch plant... 

Garcfa-Ayala, G., Rios-Mercado, R.Z., and Chacdn-Mondragdn, O.L. (2012) A disjunctive programming model and a rolling horizon algorithm for optimal multiperiod capacity expansion in a multiproduct batch plant. Computers Chemical Engineering, 46,29-38. 

A commercial heavy vehicle is used during several years. It is coherent to choose a rolling horizon method for such a system. 

Section 2 develops the rolling horizon methods. Section 3 deals with degradation models. Section 4 presents the suggested method. Section 5 finally analyses the different parameters of the proposed method. 

Rolling horizon methods use properties of finite-and infinite-horizon methods. The maintenance decisions are based on a long-term plan but they are updated according to short-term information. 

The individual optimal maintenance date i,-, and penalty function hi(At) are calculated according to the failure probability function F,(t). This probability function F, (i) is derived from the failure rate function Li(t). This function X,(i) is determined by a survival analysis of component i. It is based on the time-to-failure of a component population and can not be computed for one particular component (Singpurwalla 1995). Although the classical rolling horizon method provides a dynamic maintenance planning to the ciu-rent system (according to the short-term information), this optimization is based on a priori information concerning the reliability properties of components. 

Figure 7. Introduction of degradation models in rolling-horizon method.

In this section, we suggest a method to provide an optimized, dynamic and well-suited maintenance planning to a multi-components system like a commercial heavy vehicle. This method uses degradation models with a maintenance optimization using a rolling horizon. 

We want to determine the type of component, for which our method is better than the classical rolling horizon method. 

We compare the maintenance costs per time unit of the system in the same conditions with the classical rolling horizon method and our adaptive method. 

When the corrective replacement is expensive, our method can save cost compared with the classical rolling horizon method (Fig. 10). Monitoring the degradation level of components provides more useful information to avoid the component failures and save on the maintenance cost. 

The method is based on maintenance optimization methods using a rolling horizon and on degradation models. The optimization method aims at grouping several maintenance operations at the same date to reduce the maintenance cost of the system. The degradation models estimate the future degradation level and the reliabihty characteristics of each component. This customization supplies information for the optimization method to adapt and to optimize the maintenance planning to the current system. 

Because of many uncertainties, the reality always deviates from the plan. We must control it. If the difference is too obvious, the plan must be amended. Planning on a rolling horizon basis is a interactive method of planning-controlling—revising [15, 16]. The planning period in this chapter (e.g. year, month or week) is divided... 

F. 4.9 Adjustment strategy for planning on a rolling horizon basis... 

Warehouse management was implemented in three DCs, two return centers, and several stores, to integrate inventory with other systems. It was initially focused on the DC-to-store channel and was then integrated into the catalog and internet channels. The replenishment procedure calculates daily orders in response to actual sales and updates inventory positions. Each inventory item is forecast weekly on a rolling horizon basis, and order projections are provided to the DCs and vendors. Additional capabilities include system-generated seasonal profiles, demand alerts, purchase order alerts, and order frequency optimization. 

To get the quantity discount, DO may order 10,000 bottles over the last week even though it expects to sell only 3,000. hi this case, cycle inventory in the supply chain goes up in spite of the fact that there is no lot-size-based quantity discount. The situation in which orders peak toward the end of a financial horizon is referred to as the hockey stick phenomenon because demand increases dramatically toward the end of a period, similar to the way a hockey stick bends upward toward its end This phenomenon has been observed in many industries. One possible solution is to base the volume discounts on a rolhng horizon. For example, each week the manufacturer may offer DO the volume discount based on sales over the past 12 weeks. Such a rolling horizon dampens the hockey stick phenomenon by making each week the last week in some 12-week horizon. 

Methods that used a rolling horizon approach... 

Fig. 9.2 Rolling horizon strategy Detailed Scheduling Equations...

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