Nội dung chính
- 1 Xem Which of the following decisions is normally addressed in an operations strategy? 2024
- 2 Biomass and biofuel supply chain modeling and optimization
- 3 Logistics Strategic Decisions
- 4 Operational planning in biofuel supply chain under uncertainty
- 5 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering
- 6 Basics of Decision-Making in Design and Management of Biomass-Based Production Chains
- 7 Optimization in Natural Gas Network Planning
- 8 Key issue, challenges, and status quo of models for biofuel supply chain design
- 9 22nd European Symposium on Computer Aided Process Engineering
- 10 Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design
- 11 Cryptology
Xem Which of the following decisions is normally addressed in an operations strategy? 2024
Biomass and biofuel supply chain modeling and optimization
D. Yue, F. You, in
Biomass Supply Chains for Bioenergy and Biorefining, 2016
7.2.2.2 Operational decisions
Operational decisions are those
decisions that are adjusted more frequently in correspondence to the current external and internal conditions, which usually have impacts for no longer than a year or even a day. Due to the large number of activities involved in the BSC, optimization models for operational decisions vary significantly in scale, complexity, and formulation. Regarding the biomass acquisition activities, operational decisions involve the allocation of operators to harvesting machines, arrangement of working shifts,
assignment of harvesting operation areas, and logistics on biomass hauling to storage. Regarding the inventory management activities, one needs to regularly determine the quantity to replenish from the upstream, deliver to the downstream, utilize for production of biofuel products, and keep in the storage units. Regarding the biofuel production activities, major operational decisions involve the coordination of various material flows and
scheduling of multiple processing tasks with continuous/batch equipment units in order to guarantee the timely delivery of requested products both in quantity and quality. In addition, in cases where the quality and water content of biomass feedstocks vary significantly, optimization of the operational parameters (eg, temperature, pressure, flow rate) and real-time control trajectory would be required.
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URL: https://www.sciencedirect.com/science/article/pii/B9781782423669000071
Logistics
Strategic Decisions
Maryam SteadieSeifi, in Logistics Operations and Management, 2011
3.4 Logistics Decisions
Logistics is a part of a
firm’s corporate strategy, but planning a logistics system has its own definitions, components, rules, and so on.
According to the planning horizon, logistics decisions are traditionally classified as strategic, tactical, and operational [6]. Logistics decisions are generally made hierarchically, in an iterative manner from the strategic to the tactical and the operational (Figure 3.2). But because this chapter is about
logistics strategic decision making and planning, we describe these three logistics decisions in reverse order.
Figure 3.2. How logistics decisions are interrelated.
3.4.1 Operational Decisions
Operational decisions are made in real time on a daily or weekly basis, so their scope is narrow. Decisions such as vehicle loading or dispatching, shipment, and warehouse routines are among the many types of operational decisions. These kinds of decisions are based on lots of detailed data and usually made by supervisors.
3.4.2
Tactical Decisions
Tactical decisions are made on a longer-term basis, whether monthly, quarterly, or even annually. Production planning, transportation planning, and resource planning are the best known types of logistics tactical decisions. These decisions are often made by middle managers or logistics engineers and often with disaggregated data.
3.4.3
Strategic Decisions
As mentioned earlier in this chapter, strategic decisions are business objectives and mission statements, as well as marketing and customer-service strategies. Therefore, they are long-term kinds of decisions made over one or more years. These decisions are made by executive administrators, top managers, and stockholders. The data at hand for such decisions are often imprecise, incomplete, and need forecasts.
Strategic
decisions are made to optimize three main objectives [6]:
1.
Capital reduction (the level of investment, which depends on owned equipment and inventories)
2.
Cost reduction (the total cost of transportation and storage)
3.
Service-level improvement (customer satisfaction and order cycle time)
According to Stock and Lambert, “Strategic plans provide direction and control for tactical plans and daily operations” [4].
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URL: https://www.sciencedirect.com/science/article/pii/B9780123852021000037
Operational planning in biofuel supply chain under uncertainty
Mir Saman Pishvaee, … Samira Bairamzadeh, in
Biomass to Biofuel Supply Chain Design and Planning Under Uncertainty, 2021
9.3 Conclusions
Operational
decisions in the supply chain are typically made weekly, daily, or even hourly, based on the type and characteristics of the chain. Unlike the strategic decisions, operational decisions can be altered and revised more frequently with regard to different internal and external factors influencing the performance of the supply chain. This chapter presents the operational decisions that are made at different stages of biomass-to-biofuel supply chains and discusses the most recognized types of
operational uncertainties threatening the biomass procurement stage.
In the light of growing biomass demand for producing biofuels, biomass harvest scheduling and planning models have received considerable attention in recent years. These models deal with the allocation of harvest equipment to corn fields to perform harvesting operations. In order to address harvest scheduling problem, a short-term corn stover harvest planning model is proposed in this
chapter that determines the number of balers required for stover harvest from several corn fields and assigns a sequence of fields to each baler, ensuring that stover harvest in each field is done within its allowable time window. The proposed model aims to maximize the total profit which is equal to revenues from selling stover to biorefineries minus the costs of harvesting. To hedge against uncertainty in the selling price of stover, the harvest scheduling model is extended to a data-driven
robust optimization version that is able to capture the complexity of the uncertainty data and to improve the robustness of the harvest schedule without imposing a significant cost. Finally, the performance of the proposed model is evaluated through a harvest scheduling problem aiming to complete the harvest of 15 corn fields within the planning horizon of 20 days. The results show that the proposed data-driven scheduling model returns more profit that the conventional robust optimization models
while ensuring the same level of protection against uncertainty.
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URL: https://www.sciencedirect.com/science/article/pii/B978012820640900009X
12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering
Marc Kalliski, … Sebastian Engell, in
Computer Aided Chemical Engineering, 2015
Abstract
Operational decisions in the day-to-day business of chemical production plants can have a significant impact on the resource efficiency, but this is
usually not transparent to managers and operators because of complex interactions in the plants and the lack of real-time resource efficiency indicators (RTREI). As a first step towards real-time decision support to improve resource efficiency, this contribution presents principles for the definition of resource efficiency indicators that capture the performance of a plant on shorter time scales. Guided by these principles, individual RTREI for a specific plant can be chosen. To assess the
indicators, an evaluation framework was developed that helps to identify which indicators are capable of reflecting the total resource efficiency best. To demonstrate the approach, indicators for an ethylene oxide production unit that is operated at INEOS in Cologne are presented and compared to the currently used energy efficiency indicator (EnPI), showing that the new indicators are better suited to capture the operational efficiency of the plant.
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URL: https://www.sciencedirect.com/science/article/pii/B9780444635761500194
Basics of Decision-Making in Design and Management of Biomass-Based Production Chains
Şebnem Yılmaz Balaman, in
Decision-Making for Biomass-Based Production Chains, 2019
6.1.3 Operational Decisions
Operational decisions are short-term
decisions that are made generally weekly, daily, or hourly, focusing mainly on the details of operations, day-to-day resource allocation, details of inventory control, and delivery routing, to ensure the efficiency of operations and an optimized flow of products along the biomass-based production chains. Operational decisions can be revised and adjusted frequently, due to the dynamic structure of internal and external conditions of supply chains and associated activities.
Operational
decisions are made to execute the short-term processes with the aim of achieving the long- and medium-term goals that the strategic and tactical level decisions have adopted. Given the constraints established by the configuration and medium-term planning policies, the goal during the operational level is to guarantee the continuous operation of the facilities in the supply chain and to maximize benefit from short-term activities. These activities include:
•
daily and weekly forecasting to estimate the end users’ demand,
•
setting the due date when demand is to be met and reviewing the variations between lead time and due date,
•
daily and weekly production planning and scheduling,
•
allocation of a shipment of
biomass sources or products to a particular transportation mode and vehicle,
•
daily and weekly delivery schedules and routes, fleet management,
•
monitoring shortage of supply and backlogs, planning additional activities in daily and weekly basis to meet the shortages,
•
daily and weekly inventory control and replenishment,
•
organizing the labor and working shifts.
The operational level plans at the biomass acquisition stage involve, allocation of the operators and other resources (such as fuel) to harvesting machines, assignment of the harvesting operation areas and setting due dates to harvest each area, determining the way of conveying the harvested biomass to storage, and preprocessing units with the
corresponding routes. The detailed inventory planning policies determine the frequency of inventory replenishment with the supply of biomass from the upstream of the supply chain and frequency of delivery from the storage to the downstream of the chain. Besides the frequencies, the quantity of the biomass and bio-products to be delivered to and from storage can be determined on a daily and weekly basis. These decisions may be more challenging if multiple types of biomass sources are utilized. In
addition, the seasonal variations in biomass acquisition and supply often require more comprehensive methodologies with uncertainty consideration for operational level decision-making.
At daily and weekly production planning and scheduling, the main operational decisions comprise scheduling of multiple tasks for production operations, allocation of operators to the operations, and conversion and preprocessing equipment, the coordination of material
requirement and flow, with the aim of ensuring the timely production and delivery of demanded products in prespecified quantity and quality. Although MRP decisions are considered among tactical level decisions, in operational level, daily and weekly reviews of the medium-term MRP decisions should be conducted so as to monitor the unexpected fluctuations in demand, material supply, or operational conditions, and to revise the decisions to accommodate these fluctuations and minimize their negative
impact on the production and distribution systems. The values of operational parameters for conversion processes, such as pressure, temperature, and flow rate, significantly depend on the feedstock characteristics (e.g., moisture content, total solid content, calorific value). To capture the operational level problems in production processes, the physical and thermodynamic properties of biomass should be exploited by different methods (e.g., dynamic modeling, simulation, advanced control
algorithms).
Operational decisions in the biomass-based production chains impact the timeliness, efficiency and cost-effectiveness in material flow, conversion/pretreatment operations, and storage. The tools and methodologies developed, to be used in making the above-mentioned detailed short-term decisions in generic supply chains, can also be adapted to biomass-based production chains, such as enterprise resource
planning (ERP) and MRP tools, demand forecasting models based on time series, material allocation procedures, service level measures, and information flow diagrams.
Another decision that is frequently revised, due to the variations in the lead time as a result of production and operational changes, is the due date that demand is to be met. In this scope, tardiness and earliness measures are used in scheduling. Tardiness defines executing operations with a
delay, whereas earliness is a measure of completing operations before the due date. Tardiness may be caused as a result of problems of availability of raw materials or equipment, or the dependency of the jobs or processes on each other. In biomass-based production systems, both tardiness and earliness may be undesirable in terms of inventory costs and customer satisfaction. Earliness involves additional expenses for the storage of bio-products and may be a reason of degradation in certain types
of biomass. Tardiness may result in stock outs and backlogs and therefore, delay in the fulfillment of customers’ demand, which leads to loss of commercial standing.
Demand forecasting aims to estimate future demand of a product through historical data and experiences. Appropriate demand forecasting techniques help in gathering information on current and potential markets of a business, to make strong decisions about products and target consumer groups, so
as to determine pricing and capacity expansion strategies. Also, demand forecasting has a significant influence on inventory management, allowing optimized inventory levels and turnover rates, and minimized inventory holding costs. Efficient demand forecasting and management enhances allocating a sufficient amount of resources to production activities, that is, increasing required labor and other resources to keep production processes running smoothly and accommodate the increase in demand
during peak periods. In addition, the future cash flow can be estimated more reliably and businesses can give more accurate budgeting decisions to cover the operational and other expenses.
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URL: https://www.sciencedirect.com/science/article/pii/B9780128142783000066
Optimization in Natural Gas Network Planning
Maryam Hamedi, … Gholamreza Esmaeilian, in
Logistics Operations and Management, 2011
Network Operation
Some operational decisions should be taken into account for the network to ensure that the demand
for natural gas is met. At high pressures of natural gas, the operation cost of the network is determined based on the operation of compressors because of the significant percentage of running costs of compressor stations in the total budget of companies. In low and medium pressures, an optimal operational cost is achieved through leakage reduction by optimizing the nodal pressures [9]. In general, the operating cost belonging to the natural gas network normally takes up more
than 60% of the total cost of the pipeline [5]. Therefore, operational decisions have a significant effect on the network performances. Given the fact that the amount of natural gas in the pipeline system is set by compressor stations and that the cost associated with the operation of compressor stations, including turning them on and off, the most critical operational decision in a natural gas network is selecting compressors. This important decision, which is influenced by the
compressors’ capacity and the energy required to turn the compressor units on and off, significantly affects total natural gas operation cost. Another critical factor on the performance of the natural gas network is starting or stopping compressors because of their different outputs [5]. Therefore, efficient operation of the complex networks of natural gas can substantially reduce airborne emissions, increase safety, and decrease the daily
operating cost [3].
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URL: https://www.sciencedirect.com/science/article/pii/B9780123852021000190
Key issue, challenges, and status quo of models for biofuel supply chain design
Kai Lan, … Yuan Yao, in
Biofuels for a More Sustainable Future, 2020
3.2 Tactical and operational decisions
Tactical and operational decisions are the medium- and short-term decisions which can be
alternated annually, weekly, or even daily (Awudu and Zhang, 2012). They are made under the constrained structure of strategic decisions that are typically made before investigating tactical and operational decisions (De Meyer et al., 2014). Compared with strategic decisions, tactical and operational decisions are typically made at a smaller scale(e.g., biorefinery level or process level). Tactical and operational decisions may include the following aspects:
•
Production planning determines detailed design and operations of unit processes included in BSC, such as supplying biomass and other raw materials (Zhang and Hu, 2013), process design (Tong et al., 2013; Kazemzadeh and Hu, 2013), and scheduling (Sharma et al., 2013; Beamon, 1998).
•
Inventory planning
determines the quantity and timing of materials or goods in stock (Cachon and Fisher, 2000; Stadtler, 2005; Min and Zhou, 2002) which needs to be aligned with production capacity, fuel distribution, and biomass supply (Tong et al., 2013; You and Wang, 2011; Azadeh et al., 2014). The storage contains raw materials for manufacturing, intermediate productions, and final product for distribution.
•
Logistic management refers to managing and implementing the sufficient and effective flows of materials (e.g., raw materials, intermediates, and products), goods, and information from original suppliers to end users (Bowersox, 1997; Lambert and Cooper, 2000; Sokhansanj et al., 2006).
•
Fleet management decides the movements of materials between different
BSC stages (Awudu and Zhang, 2012). Fleet management plays a crucial role in BSC as it directly affects the robustness of the transportation network (Ravula et al., 2008; Eriksson and Björheden, 1989; Van Wassenhove and Pedraza Martinez, 2012; Thomas and Griffin, 1996).
Depending on the predetermined strategic decisions and the objectives of BSC, different tactical and
operational decisions mentioned were considered in previous BSC cases (see Table 10.2). Zhang and Hu (2013) built two models to optimize the strategic decisions of facility locations and capacity, then tactical and operational decisions such as monthly biorefinery production planning and inventory control were investigated and determined. Some studies developed optimization approaches to make strategic and tactical decisions simultaneously. For example, Lin et al.
(2014) established a model to optimize the large-scale biomass-to-ethanol SC where the strategic (e.g., farm and facility locations and capacities) and tactical decisions (e.g., biomass production planning, plant operating schedules, and inventory control) were optimized simultaneously. An et al. (2011b) established a model considering multiple types of lignocellulosic biomass and the material flows in the BSC. This model could be used for both strategic and tactical
decisions including facility locations and capacities, technology types, production plans, transportation strategies, and storage amount. Ekşioǧlu et al. (2009) integrated the long-term decisions (e.g., capacity, location, and the number of biorefineries) and mid-term logistic decisions (e.g., biomass supply) (Ekşioǧlu et al., 2009). As a BSC usually has a large number of components, determining tactical and operational decisions without considering the uncertainty
related to each component may lead to poor performance of the whole SC (Awudu and Zhang, 2012). Different approaches (e.g., stochastic programming and fuzzy logic) have been developed to model and address uncertainties in BSC design, which are further discussed in the following sections.
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URL: https://www.sciencedirect.com/science/article/pii/B9780128155813000105
22nd European Symposium on Computer Aided Process Engineering
Javier Silvente, … Antonio Espuña, in
Computer Aided Chemical Engineering, 2012
Abstract
Tactical and Operational decision making requires considering a large amount of data, which must be properly stored and interpreted. In this context,
an on-line information system has been developed to allow the integration of real time reactive tools, which can constitute a useful operator support in the event of process and/or scenario disturbances. The use of this integrated system as a training tool over a simulated Supply Chain scenario has proved to help the Process Systems Engineering students to improve the information comprehension capabilities, as well as to enhance the way they apply their knowledge to plant and process
optimization.
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URL: https://www.sciencedirect.com/science/article/pii/B9780444595201501391
Proceedings of the 9th International Conference on Foundations of Computer-Aided Process Design
Yaling Nie, … Jie Li, in
Computer Aided Chemical Engineering, 2019
Optimal solutions for the 1st stage decisions
The optimal operational
decisions for subsystems in one period (year) are solved in the 1st stage. As example results for subsystems, Figure 3 shows the monthly optimal irrigation schedules in one year for the crop mix subsystem, and Figure 4 shows the optimal land allocation and assessment performance from the FEWS metric. Figure 5 shows the monthly optimal power generation for the power generation subsystem based on the dynamic power demand. Figure
6 is another example of the optimal land allocation and assessment for the power generation subsystem. The radar maps of the proposed FEWs metric shows that the solutions by maximizing the metric FEWs can give balanced designs for decision-making in each subsystem.
Figure 3. 1st stage decisions in one year: optimal irrigation schedules for the crop mix subsystem
Figure 4. 1st stage decisions in one year: optimal solutions and assessment for the crop mix subsystem. All the land grids have same scales. (a) Optimal land allocation for
crop mix; (b) solution assessment based on the FEW metric
Figure 5. 1st stage decisions in one
year: optimal power generation (Black line: Power Demand)
Figure 6. 1st stage decisions in one
year: optimal solutions and assessment in the power generation subsystem. (a) Optimal land allocation for power plants. 1 Optimal name capacity: Gas.ST.CT-50 MW, Gas.ST.Air- 100 Capacity, Gas.GT.NA – 50 MW, Solar.PV.NA -100 MW; (b) solution assessment based on the FEW metric
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URL: https://www.sciencedirect.com/science/article/pii/B9780128185971500333
Cryptology
Don E. Gordon, in
Electronic Warfare, 1981
TWO VITAL DECISIONS
Two critically important operational decisions were made during this period by the British Admiralty. First, an agreement was reached between
the Code and Cipher School and the Admiralty that all signals which were intercepted–encrypted or otherwise–would be sent to the Code and Cipher School by the Navy and that all information derived by the Code and Cipher School would be provided directly to Naval Intelligence after exploitation. The Code and Cipher School would not determine what information they thought the Admiralty needed, they would provide all information. This decision was an outgrowth of a
situation referred to as the “Pirate Submarine Case” in which the lack of coordination–that is, a charitable evaluation–between the Code and Cipher School and Naval Intelligence resulted in wrong conclusions being formed because neither party shared all the information each possessed. A two-man Naval Intelligence Division team was granted authority to coordinate all naval intelligence, regardless of its source, and was provided full responsibility for analysis and evaluation. The team activated
an operational intelligence center which became a principal function of the Naval Intelligence Division and referred to by its abbreviation, the “OIC.”15
Secondly, in 1938, the OIC was permitted to disseminate intelligence evaluation and analysis under the authority of the Director of the Naval Intelligence Division directly to authorities having a need to know–both inside and outside the Admiralty to ships at sea, and to the
Commander-in-Chief of the Navy. They could do this without needing special authority from the Chief of Naval Operations, as had been the case in the past. Intelligence was at last recognized as a principal staff function. As will be seen in the next chapter, during the chase of the Bismarck, despite the wisdom demonstrated in reaching these two decisions, the Admiralty had overlooked a circumstance not foreseen by those unfamiliar with the
traditions of the sea. The commander of the Home Fleet could refuse to accept intelligence analysis provided by the OIC.16
Nevertheless, these two decisions, born of British experience during WW I, probably allowed the British Navy to accomplish a significant jump of perhaps two to three years, vital years, ahead of the German Navy. The dissemination of Ultra material was markedly different in the Navy using the system just described
than the system used by Allied armies and air forces in which a special Ultra distribution system using Special Liaison Units (SLU)–described thoroughly by Winterbotham–were used. The Navy system has stood the test of time; the SLU system became too cumbersome for use on the modern battlefield. This was not an issue of security–both systems used one-time encryption pads exclusively to protect Ultra–it was an issue of disseminating intelligence directly from the intelligence center to the
commander without an intermediary, it was the Reformation.
At the beginning of WW II, the OIC had placed emphasis on increasing the number of radio intercept and high-frequency radio direction finding stations, on the design of a secure Ultra dissemination system, and on other cryptographic techniques to protect friendly communications. By 30 October 1940, the Admiralty expected the OIC to use intelligence to compensate for a shortage of combat power needed
to accomplish strategic objectives and tasks, and to serve as a multiplier of combat power.17 During this period, 1926 to 1939, however, a very secret attempt was being conducted by the Polish General Staff to cryptanalyze the newly introduced [1926] German electro-mechanical Enigma enciphering machine. The problem was that between 1926 and 1932 the branches of the German Military gradually introduced the Enigma and the British were unable to break the cipher
system.18
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URL: https://www.sciencedirect.com/science/article/pii/B9781483197227500066
What are the 4 operations strategies?
What are strategic operations decisions?
What type of decisions are operational decisions?
What are the operations strategies?
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