Planning horizon

Environmental factors should also be incorporated into a company s technical or research and development program. Since the planning horizons for new projects may now extend to 5 to 10 years, R D programs can be designed for ecific projects. These may include new process modifications or end-of-pipe control technologies. 

Estimate the cash-flow profile for each alternative. The cash-flow profile should include the costs and revenues if they differ, for the alternative being considered during each period in the planning horizon. For public projects, revenues may be replaced by estimates of benefits for the public as a whole. If revenues can be assumed to be constant for all alternatives, only costs in each period are estimated. Cash-flow profiles should be specific to each alternative. We shall denote revenues for an alternative x in period t as B (t,x), and costs as C (t,x). By convention, cash flows are usually assumed to occur at the end of the time period, and initial expenditures to occur at the beginning of the planning horizon, that IS, m year 0. 

Calculations of life-cycle costs, net benefits, or other measures of economic performance are commonly performed on electronic calculators, commercial spreadsheet software, or by hand. The calculation approach for life-cycle costs is to first compute the net cost amount in each period for each alternative, C(t,x). The life-cycle cost (LCC) of each alternative is then calculated as the sum of the discounted values of C(t,x) over the entire planning horizon ... 

Typical batch sizes are 200, 500 and 1000 kg, where within this study the largest batch size was not considered, since these products are produced in a regular fashion. The production is run in a two-shift operation, where two workers are responsible for the smaller batch sizes (200 and 500 kg). The bottling plants operate in continuous three-shift operation. On the weekend the production is shut down. Up to now, the scheduling of the production process is done manually with a planning horizon of one week. 

To get a better idea of the complexity of a real application scenario in these industries it makes sense to, once, exemplarily depict the planning processes in a typical production of active pharmaceutical ingredients (API production). Most pharmaceutical companies are looking at planning scenarios in which several hundred individual resources or facilities have to be accounted for, with demands and orders for some thousand final products. The planning horizon is often set to 2-5 years. Next to single equipment, there are facility pools, with one pool consisting of several individual units. 

Number of resources 14 Number of products 22 Number of quants 7000 Planning horizon 5 years... 

Fig. 4.12 The difference before and after the change of the network. The first scheduling result shows a start solution before the change of the network, the second after the change. The planning horizon necessary to take all processes is reduced from about 20 years to 2.5 years.

Planning horizon three years with stepwise less constraints on capacities when moving to the future. 

Every link in the product flow network can have a validity interval. Validity intervals may be automatically extended to add missing validity intervals that are necessary to cover the whole planning horizon. 

In this example the validity is crucial for the choice of alternative BOMs. Validity intervals are none intersecting. The most left and right interval (may be the same if only one exists) are extended to cover the parts of the planning horizon where no specific recipes are defined. 

This step is repeated until the available time is totally consumed. At this point one has found the optimal total amount to produce for each product within the planning horizon. Of course the procedure above does not match the actual sequence of production and does not recommend the split of the total amount into several lots. This is done by further steps which are somewhat more technical and not described here. 

For SNP relevant resources, however, integration issues arise due to different time profiles used by SNP, which is based on a periodic time profile with, in the scenario at hand, monthly time buckets, and PP/DS, which utilizes a continuous time representation of the planning horizon. This means that the time-continuous resource capacity profiles used in PP/DS, which are usually synchronized with the... 

A direct result of the different levels of detail of recipes is that the planned production orders created by planning in SNP and PP/DS are incongruent. Based on the planning horizon concepts, SNP planned orders can be manually converted to PP/DS planned orders as soon as they move into the overlapping part of the SNP and PP/DS planning horizons. An automated conversion process takes place for SNP planned orders which move into the part of the planning horizon which exclusively belongs to PP/DS. 

At the beginning of the planning horizon variables Kjt need to be initialized. This can be done by constraints (11.12). 

For some devices (resources) one wishes to know their future allocation at any point in time over a given time interval (planning horizon). For this purpose one can use a resource allocation list that lists all operations from process orders that use the resource. The resource allocation list is visualized by bar graphs (Gantt chart, original version defined around from 1900 by Henry Laurence Gantt). 

The processes can be further systematized and compared by process level, decision supported, time buckets, planning horizon, frequency and granularity (s. table 5). 

R6 - Price uncertainty consideration in planning Spot demand quantity and prices are uncertain in commodity business specifically in the considered planning horizon of 3-12 months. Since price is the main buying criterion in commodity business, mid-term demand quantity is mainly influenced by the price level. If spot demand quantity and prices depend on each other, uncertainty can be limited to one parameter while the other parameter is kept constant. The spot demand price is considered as uncertain in this problem leading to different turnover scenarios for the same sales quantity. Lababidi et al. (2004) presenting a petrochemical case that had to consider uncertainty in market prices and raw material costs in supply chain optimization. 

Future capital costs considered in the objective function rely on future capital values - in this scope future inventory values. The planning of future inventory values in all future periods and in all network locations is a complex task. As described in the requirements, future inventory value is determined by the future product values of the products on stock. These product values change, if the included material costs of the product change, which is regularly the case due to volatile raw material prices. The task now is to calculate the future inventory value throughout the value chain network and product steps considering the raw material price forecast for the planning horizon. The problem is illustrated in fig. 57. 

These equations have to be adapted for the final planning period with no distribution demand existing for the following period out-of the planning horizon. It is assumed that the ranges are applied on the distribution demand for the final period. Hence, they remain stable compared to the period before the last period. 

Starting period and ending period flexible definition of the planning horizon with starting and ending period within the defined like a month. 

The model areas have limited complexity considering single indices such as products or resources. Complexity increases combining basis indices like transportation lanes and products. A planning horizon of 12 periods will lead later to optimization models with more than 10,000 decision variables already for this limited scope. 

The basis plan evaluation analyzes optimization volumes and value results across the considered planning horizon of 12 periods. First, solution times for the continuous products and the campaign problem are shown in table 31. 

Inventories in the first months are relatively high compared to the minimum inventory index due to higher actual inventories at the beginning of the planning horizon as shown in fig. 79. 

Subsequently, inventories are dropped down to minimum with a slight increase in the third quarter of the planning horizon. The inventory increase is mainly driven by raw material procurement decisions in combination with production quantities. 

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