Capacity Planning Model Research Articles

Flexible capacity planning for engineering systems based on decision rules and differential evolution

Abstract This paper proposes a novel approach to managing capacity planning flexibility in engineering systems based on decision rules and differential evolution (DE) algorithm. A multi-stage capacity expansion problem is formalized based on a decision rule representation. The formalized model can analyze multiple sources of uncertainty simultaneously (e.g., changes in demand and material price) and explicitly consider design principles like economies of scale and learning effect. An evolutionary decision rule optimization framework based on the DE algorithm is proposed to automatically optimize the decision rule for capacity planning. Compared to traditional capacity planning models (e.g., stochastic programming), this work is able to offer a flexible capacity plan under uncertainty assumptions at the system design stage. The output of our approach is a decision rule that provides thresholds/conditions for decision-makers to adaptively adjust decisions at the system deployment stage. In the application of capacity planning for a waste-to-energy system, experimental results show that the decision rules generated by our approach offer a 20% improvement in terms of expected net present value, compared to a fixed capacity plan generated by stochastic programming.

Capacity planning with demand uncertainty for outpatient clinics

In this study, we develop a capacity planning model to determine the required number of physicians for an outpatient system with patient reentry. First-visit (FV) patients are assumed to arrive randomly to the system. After their first appointment, the FV patient may require additional appointments, and will then become a re-visit (RV) patient; after each appointment an RV patient will require subsequent visits with a given probability. The system must achieve a set of targets on the appointment lead-times for both FV and RV patients. Furthermore, the system must have sufficient capacity to assure that a given percentile of FV patients is admitted. We develop a deterministic model that finds the required capacity over a finite horizon. We establish the tractability of the deterministic model and show that it provides a reasonable approximation to the stochastic model. We also demonstrate the value from knowing the demands in terms of the required resources. These conclusions are numerically illustrated using real data from the Urology outpatient clinic of the studied hospital.

Optimal capacity planning of MG with multi‐energy coordinated scheduling under uncertainties considered

Multi-objective optimisation of capacity planning for a grid-connected Microgrid (MG) with multi-energy demands is developed in this study. The optimal multi-energy coordinated scheduling, in which uncertainties of renewable energy generation and electricity demand are described by uncertainty intervals, is considered within the capacity planning model. To solve the capacity planning problem while considering the optimal scheduling with uncertainties, a two-level optimisation approach is proposed by integrating interval mixed integer linear programming for scheduling into the multi-objective particle swarm optimisation for capacity planning. The multi-objective addresses the economic issue by minimising the worst-case optimal annual costs of device investments, operation and maintenance, and pollutant emission treatment, as well as the reliability concern by minimising the worst-case optimal annual costs of energy outage. Several MG cases studies are shown to demonstrate the proposed method.

Wait Times and Bed Capacity for Hematopoietic Cell Transplantation in Ontario: A System Dynamics Model

Objectives: Cancer Care Ontario (CCO) is the provincial governmental organization responsible for planning hematopoietic cell transplantation (HCT) services in Ontario, Canada. The objective of this project is to develop a capacity planning model to investigate the effects on wait times of adding extra bed capacity for allogeneic transplant (ALLOHCT) in HCT centers.Approach: A high-level process flow diagram was generated to understand patient flow at a 6-bed HCT unit within a hospital in Ontario and validated through consultation. This flow diagram was used to construct a system dynamics model to simulate patient flow. The model was parameterized with data from CCO, Discharge Abstract Database, and with hospital and clinical expert input. The effects at six months were projected for five scenarios: 1) current state; 2) increase bed capacity by 1 bed; or 3) increase bed capacity by 2 beds; 4) increasing patient demand by 20 patients per year; 5) combination of scenarios 3 and 4. Provincial clinical consensus established a benchmark wait time of 42 days for ALLOHCT from ready to transplant to the transplant date. In addition, the estimated number of beds required to reduce the wait times to the provincial benchmark within 1 year was calculated.Results:The addition of 1 ALLOHCT bed resulted in a reduction of 22% and 11% to the ALLOHCT wait times and wait lists, respectively. The addition of 2 beds resulted in a reduction of 38% and 22% to the wait times and wait lists, respectively. If the demand increases by 20 patients per year, the addition of 2 beds resulted in a reduction of 16% in the wait times and while the wait list may experience a brief reduction, after 6 months, the wait list size will have increased by 9% as a result of the increased demand. In order to reduce the wait times to the provincial benchmark within 1 year, an additional 8 beds are needed.Considerations: Concurrent planning for additional health human resources (physicians, nurses, etc…) needs to be done to ensure the additional beds are adequately staffed. This model also only considers the effects of adding beds within 1 year. There may be instances where bed space cannot be immediately opened and new capital is required. Additionally, the demand for ALLOHCT continues to increase, which in turn drives up the number of arrivals to the queue. A multi-year model will be built to account for timing of bed openings and increasing demand for ALLOHCT.Conclusion:Using a system dynamics model, we are able to quantify the relationship between ALLOHCT bed capacity and wait times at an HCT center. This model can be used to estimate the ALLOHCT bed requirements for sites in other jurisdictions where ALLOHCT demand and wait time benchmarks are known. DisclosuresKouroukis:Janssen: Research Funding; Karyopharm: Research Funding.

Traffic demand forecasting and flexible planning in airport capacity expansions: Lessons from the Madrid-Barajas new terminal area master plan

Liberalization and constant but irregular traffic growth have made the airport sector hard to recognize compared to some decades ago. The new paradigm for airport management calls for flexible capacity planning models. However, airport infrastructure investments are cumbersome and almost never efficient in the short run. It is difficult to assess the right time for and right size of such an investment. Traffic demand forecasts become of critical importance in the planning phase. This case study performs an ex post evaluation of the demand forecasts included in the Madrid-Barajas master plan, in view of the airport's last capacity expansion in 2006. We deduct that a more robust (and neutral) forecasting methodology, coupled with a more flexible approach to planning in general, could have led to a more efficient and economically sound infrastructure project.

Impact of Operational Flexibility on Electricity Generation Planning With Renewable and Carbon Targets

Recent work on operational flexibility—a power system’s ability to respond to variations in demand and supply—has focused on the impact of large penetration of renewable generation on existing power systems. Operational flexibility is equally important for long-term capacity expansion planning. Future systems with larger shares of renewable generation, and/or carbon emission limits, will require flexible generation mixes; yet, flexibility is rarely fully considered in capacity planning models because of the computational demands of including mixed integer unit commitment within capacity expansion. We present a computationally efficient unit commitment/maintenance/capacity planning formulation that includes the critical operating constraints. An example of capacity planning for a Texas-like system in 2035 with hypothetical RPS and carbon policies shows how considering flexibility results in different capacity and energy mixes and emissions, and that the omission of flexibility can lead to a system that is unable to simultaneously meet demand, carbon, and RPS requirements.

Simulation-based Production Planning and Execution Control for Reconfigurable Assembly Cells

In order to meet the continuously changing market conditions and achieve economy of scale, a current trend in the automotive industry is the application of modular reconfigurable assembly systems. Although they offer efficient solution to meet the customers needs, the management of these systems is often a challenging issue, as the continuous advance in the assembly technology introduces new requirements in production planning and control activities. In the paper, a novel approach is introduced that enables the faster introduction of modular assembly cells in the daily production by offering a flexible platform for evaluating the system performance considering dynamic logistics and production environment. The method is aimed at evaluating different modular cell configurations with discrete-event simulation, applying automated model building and centralized simulation model control. Besides, the simulation is linked with the production and capacity planning model of the system in order to implement a cyclic workflow to plan the production and evaluate the system performance in a proactive way, before releasing the plan to the production. The method and the implemented workflow are evaluated within a real case study from the automotive industry.

Leveraging Slack Capacity in IaaS Contract Cloud Services

This paper develops pricing and capacity planning models for a cloud provider taking into account the trade-off between service reliability and capacity utilization. The provider's objectives are to optimally allocate resources, price different types of cloud services, and ultimately extract the maximum revenue from the market. We first analyze two basic types of cloud services: contract and on-demand. We then propose hybrid models in which the two basic services simultaneously exist. For each hybrid model, we determine an optimal capacity planning strategy, optimal reservation fee, optimal per-usage fee, and corresponding service reliability. We conduct experiments to study how optimal solutions change with respect to critical system parameters such as service rate, market size, and service reliability. Our work provides a framework for resource allocation, service provision, and strategic planning in cloud computing practice.

An agent-based multimodal simulation model for capacity planning of a cross-border transit facility

Cross-border transit facilities constitute major public investment, and thus must serve the long-term needs of the communities, such as providing access to schools and businesses, contributing to a shared regional culture and lifestyle, fostering international trade, and supporting jobs for the region’s residents. Numerous studies have been conducted to evaluate the economic implications of vehicular flow delays at border crossings, however none of the studies focused on assessing cross-border flow of bus passengers and pedestrians. Since pedestrians are considered to be autonomous, intelligent, and perceptive, it is a challenging task to predict pedestrian movement and behavior in comparison to vehicular flows which follow a specific set of traffic rules. This paper presents a multiagent based multimodal simulation model to evaluate the capacity and performance of a cross-border transit facility. The significance of this research is the use of dynamic mode choice functionality in the model, which allows an individual person to make instantaneous choices between available modes of transportation. The scope of interest of the paper is limited to simulating access interface, circulation areas, ancillary and processing facilities. The developed model was calibrated to ensure realistic performance, and validated against specific performance criteria such as throughput per processing facility. In order to demonstrate the applicability of the developed simulation model, capacity and operational planning of a pedestrian transit facility was performed. The relative performance of alternative design or configuration was evaluated using the level of service criteria. Lastly, the effectiveness of each proposed capacity or operational improvement strategy was compared to the “do-nothing” scenario.

Special Issue on Production Planning and Scheduling

This issue focuses on production planning and scheduling for production system and the related problems that have arisen in these areas in the last half century as digital computer systems developed. These problems relate to production management, production planning, shop floor control, product design and process planning. In the first stage of production planning and scheduling systems R&D, optimization is a key issue that has been widely discussed and many theories and optimization algorithms proposed. Rule-based methods are discussed as potential solutions to these problems. With rapid advances in computer and information processing technologies and performance, tremendous progress has been made in the areas of production systems such as production planning, production scheduling, advances production systems (APS), enterprise resource planning (ERP), just-in time (JIT) processes, the theory of constraint (TOC), product data management (PDM) and computer-aided design / manufacturing / engineering (CAD/CAM/CAE). This special issue addresses the latest research advances, applications, and case studies in production planning and scheduling covering such as decentralized and autonomous production systems, distributed simulation models, robust capacity planning models, wireless sensor networks for production systems and applications to automotive component and steel production. We hope that learning about these advances will enable readers to share their own experience and knowledge in technology, new developments and the potential applications of production planning and scheduling methods and solutions.

A simulation model for capacity planning in community care

Sustainable health care requires the building of sufficient capacity in order to ensure that patients receive the right care in a timely fashion. Often the efficient use of available capacity at one level (ie, acute care) is hindered by insufficient capacity at a downstream level (ie, long-term care (LTC)). This paper provides a simulation that helps determine the necessary downstream capacity in LTC in order to maintain smooth patient flow out of the hospitals in the region while still maintaining wait times within a target for those accessing LTC directly from the community. The model is complicated by multiple demand classes, client preferences, competing performance metrics, clients transferring between servers (ie, LTC facilities), significant wait time-dependent reneging and non-homogeneous servers. We provide policy recommendations for capacity planning in the region both for LTC and for supportive housing.

Taming Replication Latency of Big Data Events with Capacity Planning

Ensuring low replication latency of database events is business-critical but challenging with big data. A proposed capacity-planning model helps achieve this goal by forecasting future traffic rates, predicting replication latency, and determining required replication capacity. The Web extra at http://youtu.be/ZupPlrS8dGA is a video of in which author Zhenyun Zhuang demonstrates Naarad, an open-source performance analysis tool (https://github.com/linkedin/naarad) written in python that analyzes various metrics (gc, sar, Jmeter etc), evaluates SLAs and generates a user friendly report to aid in performance analysis and investigations.

Modelling ramp-up curves to reflect learning: improving capacity planning in secondary pharmaceutical production

The experience gained during production ramp-up leads to an increase of the effective production capacity over time. However, full utilisation of production capacity is not always possible during ramp-up. In such cases, the experience gained and hence the available effective capacity are overestimated. We develop a new method, which captures ramp-up as a function of the cumulative production volume to better reflect the experience gained while producing the new product. The use of the more accurate and computationally effective approach is demonstrated for the case of secondary pharmaceutical production. Due to its regulatory framework, this industry cannot fully exploit available capacities during ramp-up. We develop a capacity planning model for a new pharmaceutical drug, which determines the number and location of new production lines and the build-up of inventory such that product availability at market launch is ensured. Our MILP model is applied to a real industry case study using three empirically observed ramp-up curves to demonstrate its value as decision support tool. We demonstrate the superiority of our volume-dependent method over the traditional time-dependent ramp-up functions and derive managerial insights into the selection of ramp-up function and the value of shortening ramp-ups.

Combined scheduling and capacity planning of electricity-based ammonia production to integrate renewable energies

Economic assessment of energy-related processes needs to adapt to the development of large-scale integration of renewable energies into the energy system. Flexible electrochemical processes, such as the electrolysis of water to produce hydrogen, are foreseen as cornerstones to renewable energy systems. These types of technologies require the current methods of energy storage scheduling and capacity planning to incorporate their distinct non-linear characteristics in order to be able to fully assess their economic impact. A combined scheduling and capacity planning model for an innovative, flexible electricity-to-hydrogen-to-ammonia plant is derived in this paper. A heuristic is presented, which is able to translate the depicted, non-convex and mixed-integer problem into a set of convex and continuous non-linear problems. These can be solved with commercially available solvers. The global optimum of the original problem is encircled by the heuristic, and, as the numerical illustration with German electricity market data of 2013 shows, can be narrowed down and approximated very well. The results show, that it is not only meaningfulness, but also feasible to solve a combined scheduling and capacity problem on a convex non-linear basis for this and similar new process concepts. Application to other hydrogen based concepts is straightforward and to other, non-linear chemical processes generally possible.

Business Analytics Assists Transitioning Traditional Medicine to Telemedicine at Virtual Radiologic

Virtual Radiologic (vRad), the largest teleradiology company in the United States, faces the difficult problem of matching more than 400 radiologists with time-varying and seasonal demand. In addition to the constraints that traditional medical facilities face, vRad is subject to supply and demand requirements that are unique to the teleradiology business environment. In this paper, we present a forecasting and capacity-planning model that more accurately assesses demand and plans system capacity to provide better service to vRad’s customers. We discuss the underlying reasons for improvement and quantify the impact on vRad’s entire system. We explain managerial insights that will help both vRad and other companies in the service sector with similar service-response requirements and demand patterns. We also highlight the implementation challenges our teams faced.

A MINLP technique for optimal placement of multiple DG units in distribution systems

Loss reduction is one of the prime objectives for planning of distributed generation (DG). To improve performance of distribution systems, optimal placement of distributed generator is critically important, as DG benefits are site and size specific. Optimal placement of multiple DG units is non-convex and non-linear optimization problem. The methodology proposed in this paper solves Mixed Integer Non-Linear Programming (MINLP) formulation for loss minimization. The proposed methodology simplifies the problem by dividing it in two phases, namely Siting Planning Model (SPM) and Capacity Planning Model (CPM) thereby reducing the search space and computational time. The SPM model selects the candidate buses based on Combined Loss Sensitivity (CLS). In CPM, optimal locations and sizes are obtained by integrating Sequential Quadratic Programming (SQP) and Branch and Bound (BAB) algorithm for MINLP problem. The performance of the proposed method is tested on IEEE 33-bus and IEEE 69-bus distribution system for placement of single and multiple DG units capable of delivering either real or both real and reactive power. The obtained results are then compared with three basic classes of optimization methods, viz., Exhaustive Load Flow (ELF), Improved Analytical (IA) and Particle Swarm Optimization (PSO). The key advantage of the proposed method is that it simultaneously considers placement of multiple DG units giving better optimal solution in reasonably less computational time.

Assessment of projected temperature impacts from climate change on the U.S. electric power sector using the Integrated Planning Model®

This study analyzes the potential impacts of changes in temperature due to climate change on the U.S. power sector, measuring the energy, environmental, and economic impacts of power system changes due to temperature changes under two emissions trajectories—with and without emissions mitigation. It estimates the impact of temperature change on heating and cooling degree days, electricity demand, and generating unit output and efficiency. These effects are then integrated into a dispatch and capacity planning model to estimate impacts on investment decisions, emissions, system costs, and power prices for 32 U.S. regions. Without mitigation actions, total annual electricity production costs in 2050 are projected to increase 14% ($51 billion) because of greater cooling demand as compared to a control scenario without future temperature changes. For a scenario with global emissions mitigation, including a reduction in U.S. power sector emissions of 36% below 2005 levels in 2050, the increase in total annual electricity production costs is approximately the same as the increase in system costs to satisfy the increased demand associated with unmitigated rising temperatures.

Workforce management: Risk-based financial planning and capacity provisioning

We propose a principled risk-based framework that enables organizations or complex systems to jointly perform financial planning and human-resource management functions in an environment characterized by uncertainty in demand and supply and by other exogenous factors. This framework consists of a set of risk-based capacity planning models and methods that, first, allow for a rigorous quantification of how the financial metrics of an institution depend on its resource provisioning decisions. Second, they provide the capability to make the best decisions to achieve the financial objectives of the institution. Consequently, these models and methods enable interactions between financial and operational planning processes. Decisions including, but not limited to, investments, capacity adjustments, and demand prioritization as well as product or service pricing can all be evaluated quantitatively within this framework, and an overall optimal strategy and plan can be produced with user-specified objectives. These models and methods are based on analysis and optimization algorithms developed for large-scale stochastic loss networks.

Capacity and production planning for a hybrid system with manufacturing and remanufacturing facilities

This study proposes an integrated model for capacity and production planning in a hybrid production system with manufacturing and remanufacturing facilities. The manufacturing facility produces new products from raw materials, while the remanufacturing facility produces remanufactured products by disassembling, reprocessing and reassembling end-of-use/life products. The problem is to determine capacity requirements and production quantities at manufacturing and remanufacturing facilities, together with subcontracting quantities, to satisfy the demands over a given planning horizon. The objective is to minimize the sum of shutdown, production, inventory holding and subcontracting costs. In particular, this study considers the budget constraint that restricts the cost required to change manufacturing and remanufacturing capacities. An integer programming model is developed to represent the integrated problem mathematically, and then, due to the problem complexity, two linear programming relaxation–based heuristics, each of which fixes the binary variables using a systematic method, are suggested. Computational experiments were done on a number of test instances, and the test results show that the heuristics give near-optimal solutions.

Attractiveness-Based Airline Network Models with Embedded Spill and Recapture

Purpose: In airline revenue management, the modeling of the spill and recapture effects is essential for an accurate estimation of the passenger flow and the revenue in a flight network. However, as most current approaches toward spill and recapture involve either non-linearity or a tremendous amount of additional variables, it is computationally intractable to apply those techniques to the classical network design and capacity planning models. Design/methodology: We present a new framework that incorporates the spill and recapture effects, where the spill from an itinerary is recaptured by other itineraries based on their attractiveness. The presented framework distributes the accepted demand of an itinerary according to the currently available itineraries, without adding extra variables for the recaptured spill. Due to its compactness, we integrate the framework with the classical capacity planning and network design models. Findings: Our preliminary computational study shows an increase of 1.07% in profitability anda better utilization of the network capacity, on a medium-size North American airline provided by Sabre Airline Solutions. Originality/value: Our investigation leads to a holistic model that tackles the network design and capacity planning simultaneously with an accurate modeling of the spill and re- capture effects.Furthermore, the presented framework for spill and recapture is versatile and can be easily applied to other disciplines such as the hospitality industry and product line design (PLD) problems.