Integrated Optimization Approach Research Articles

Stochastic integer programming for multi-disciplinary outpatient clinic planning

Scheduling appointments in a multi-disciplinary clinic is complex, since coordination between disciplines is required. The design of a blueprint schedule for a multi-disciplinary clinic with open access requirements requires an integrated optimization approach, in which all appointment schedules are jointly optimized. As this currently is an open question in the literature, our research is the first to address this problem. This research is motivated by a Dutch hospital, which uses a multi-disciplinary cancer clinic to communicate the diagnosis and to explain the treatment plan to their patients. Furthermore, also regular patients are seen by the clinicians. All involved clinicians therefore require a blueprint schedule, in which multiple patient types can be scheduled. We design these blueprint schedules by optimizing the patient waiting time, clinician idle time, and clinician overtime. As scheduling decisions at multiple time intervals are involved, and patient routing is stochastic, we model this system as a stochastic integer program. The stochastic integer program is adapted for and solved with a sample average approximation approach. Numerical experiments evaluate the performance of the sample average approximation approach. We test the suitability of the approach for the hospital’s problem at hand, compare our results with the current hospital schedules, and present the associated savings. Using this approach, robust blueprint schedules can be found for a multi-disciplinary clinic of the Dutch hospital.

Integrated scheduling of preventive maintenance and renewal projects for multi-unit systems with grouping and balancing

This paper studies preventive maintenance and renewal scheduling for multi-unit systems. We develop an integrated optimization method to schedule preventive maintenance and renewal projects by grouping them and simultaneously finding the optimal balance between them. Grouping and balancing are resource utilization techniques to reduce the costs of maintenance without reducing the level of maintenance. We next model the problem as a pure integer linear programming formulation to minimize the costs of maintenance and renewal projects and their relevant preparatory works and downtime costs over the planning horizon. We use a numerical experiment with sensitivity analysis to illustrate the model, and the potential cost reductions that using such an integrated model may lead to. Applications of this model arise in the maintenance of railways, roadways, electricity distribution networks, and distributed pipeline assets. Due to the complexity of the model, two heuristic algorithms based on decomposition approach with problem-specific cutting planes are applied to tackle the problem. Experimental results show that our integrated optimization approach performs well in reducing the cost of preparatory works through proper scheduling. Finally, a case study for the maintenance of railway track shows that using our integrated approach reduces the maintenance and renewal costs by up to 14% as compared with the spreadsheet based approach used currently at the operation.

Cost-benefit analysis for regenerative energy storage in metro

PurposeRegenerative braking is an efficient energy saving technology in urban rail system, in which the recovery energy from braking trains is collected by some equipments and released to accelerating trains when needed. However, the high cost and low lifetime of storage devices prevent the widespread use of this technology. The purpose of this paper is to conduct thorough cost-benefit analysis to facilitate China’s urban rail companies to make decisions on the use of such technology.Design/methodology/approachTo evaluate the benefit from regenerative energy storage, the authors formulate an improved integrated scheduling and speed control model to calculate the net energy consumption associated with different energy recovery rates and then define the benefit as the amount of energy saving arising from the usage of storage equipments. With the frequent charge/discharge operations on storage equipments, the energy recovery rate generally decreases which lowers the benefit, but the maintenance cost increases. By trading-off benefit and cost, the authors derive the optimal scrapping time, the maximum profit and the profitability condition for storage devices.FindingsSimulation studies based on the Beijing Metro Yizhuang Line of China are given. The results show that compared with the current timetable and speed profile, the integrated scheduling and speed control approach with energy recovery rate of 0.5 can reduce the net energy consumption by 12.69 per cent; the net energy consumption can be well approximated as a linear function of energy recovery rate; and the maximum profit and the optimal scrapping time on regenerative energy storage devices are both positively related to the electricity price. The allowance proportion and the number of service trains such that busy lines with higher electricity price or allowance proportion have advantages to use the regenerative energy storage devices.Research limitations/implicationsIn this work, a linear energy recovery rate and a linear maintenance cost are used in the cost-benefit analysis process. In future research, the more accurate expressions on energy recovery rate and maintenance cost should be considered if more data on recovery rate and maintenance cost can be gathered.Originality/valueThe main values of this paper are to develop the integrated optimization approaches for train scheduling and speed control and, on this basis, make thorough cost-benefit analysis for regenerative energy storage to improve the operations management of urban rail transit.

Optimal water management in macroscopic systems under economic penalty scenarios

An integrated optimization approach to assess the sustainability of water management strategies in a macroscopic system is proposed. Those strategies include alternative water sources, such as rainwater harvesting, and the design of distributed water treatment systems. To deal with the economic challenges inherent to wastewater treatment, an economic penalization scheme is presented as an alternative that can achieve better cost‐effectiveness and pollution abatement than traditional command and control practices. The proposed approach results in an MINLP multiperiod model, which has been solved through the GAMS®modeling environment. The solution of our case‐study allows finding the minimum investment to meet the desired environmental goals with respect to freshwater consumption and pollution abatement. Results include the number, size, and location of rainwater storage devices as well as treatment technologies, the total amount of recycled wastewater, and the total amount of fines charged to the users for violation of environmental regulations. © 2017 American Institute of Chemical EngineersAIChE J, 63: 3419–3441, 2017

A Multi-Objective Multi-State Degraded System to Optimize Maintenance/Repair Costs and System Availability

In this study, a multi-state degraded system is studied, where status of system is degrading over time continuously. As time progresses, system may either deteriorate gradually and go to lower performance state or it may fail suddenly. If the system fails, some repairs are carried out to restore the system to the previous state. When the inspections revealed that the system has reached its last acceptable state, a PM is carried out to restore the system to the higher performance states. The goal is to find the optimal PM level so that the mean availability of the system is maximized and the total cost of the system is minimized. In this regard, Markov process is employed to represent different states of system. An integrated optimization approach is also suggested based on the desirability function statistical approach. The suggested aggregation method is robust to the potential dependency between the total cost and the mean availability. It also ensures that both objective functions fall in decision maker’s acceptable region. In order to show the efficiency of the proposed approach, a numerical example is presented and analyzed.

Integrated model of refining and petrochemical plant for enterprise-wide optimization

Abstract In this paper, a novel integrated optimization approach which couples the up-stream refinery and the down-stream ethylene plant is proposed. When the material balances of the intermediate products between the two complex processes are considered, the potential of increasing the overall margin can be explored. A multi-period enterprise-wide mixed-integer nonlinear programming (MINLP) model is formulated to optimize the production planning of the processing units in the refinery and the ethylene plant simultaneously. Due to the model complexity, the Lagrangian algorithm is applied to decompose the integrated mathematical model into an MILP problem for the refinery and a small-scale MINLP problem for the ethylene plant. The performance of the proposed model was investigated on industrial examples to illustrate the economic potential and trade-offs involved in the enterprise-wide network. Results show that the integrated approach gains improvement in overall profit compared with the traditional sequential approach.

An approach for making optimal decisions in building energy efficiency retrofit projects

The study presents an optimization-based decision support approach to determine the optimal energy efficiency retrofit options in existing buildings. The main challenge encountered in the decision-making process of building retrofit projects is the selection of an optimal set of solutions within a wide range of solution space according to multiple criteria. In order to overcome this problem, this study uses an integrated optimization approach by combining both the variance-based sensitivity analysis and optimization methods to maximize energy savings and optimize financial returns in building energy efficiency retrofit projects. The proposed approach was applied to an existing public school building in Izmir, Turkey that represents hot-humid climate to test its validity and performance. The optimizations were performed and results were obtained for three different scenarios whose content was determined based on project objectives and constraints fulfilled by a survey conducted by building users. The case study results indicate that the proposed approach is effective in defining the application order of retrofit options by identifying the most influential parameters for building energy efficiency and finding out the optimal retrofit solutions per multiple criteria.

The Buffer Allocation Problem: A Joint Classification and Review of Decision Problems, Solution Approaches, and Test Instances

Flow production lines with finite buffer capacities are used in practice for mass production, e.g., in the automotive and food industries. The decision regarding the allocation of buffer capacities to mitigate throughput losses from stochastic processing times and unreliable stations is known as the Buffer Allocation Problem (BAP).We provide a review that jointly considers the detailed characteristics of the flow lines, the objective function, and the constraints of the different versions of the BAP. Moreover, a new classification scheme for solution approaches is presented that differentiates between explicit solutions, integrated optimization approaches, and iterative optimization approaches. Characteristics of test instances derived from real cases and test instances used in multiple references are discussed.

Parameters optimization of hybrid fiber laser-arc butt welding on 316L stainless steel using Kriging model and GA

It is of great significance to select appropriate welding process parameters for obtaining optimal weld geometry in hybrid laser-arc welding. An integrated optimization approach by combining Kriging model and GA is proposed to optimize process parameters. A four-factor, five-level experiment using Taguchi L25 is conducted considering laser power (P), welding current (A), distance between laser and arc (D) and traveling speed (V). Kriging model is adopted to approximate the relationship between process parameters and weld geometry, namely depth of penetration (DP), bead width (BW) and bead reinforcement (BR). The constructed Kriging model was used for parameters optimization by GA to maximize DP, minimize BW and ensure BR at a desired value. The effects of process parameters on weld geometry are analyzed. Microstructure and micro-hardness are also discussed. Verification experiments demonstrate that the obtained optimum values are in good agreement with experimental results.

Design of a sensitive air quality monitoring network using an integrated optimization approach

A new method is developed to design a multi-objective and multi-pollutant sensitive air quality monitoring network (AQMN) for an industrial district. A dispersion model is employed to estimate the ground level concentration of the air pollutants emitted from different emission sources. The primary objective of AQMN is providing the maximum information about the pollutant with respect to (1) maximum coverage area, (2) maximum detection of violations over ambient air standards and (3) sensitivity of monitoring stations to emission sources. Ant Colony Optimization algorithm (ACO) and Genetic Algorithm (GA) are adopted as the optimization tools to identify the optimal configuration of the monitoring network. The comparison between the results of ACO and GA shows that the performance of both algorithms is acceptable in finding the optimal configuration of AQMN. The application of the method to a network of existing refinery stacks indicates that three stations are suitable to cover the study area. The sensitivity of the three optimal station locations to emission sources is investigated and a database including the sensitivity of stations to each source is created.

An integrated optimisation approach to airport ground operations to foster sustainability in the aviation sector

With increasing air traffic, rising fuel costs and tighter environmental targets, efficient airport ground operations are one of the key aspects towards sustainable air transportation. This complex system includes elements such as ground movement, runway scheduling and ground services. Previously, these problems were treated in isolation since information, such as landing time, pushback time and aircraft ground position, are held by different stakeholders with sometimes conflicting interests and, normally, are not shared. However, as these problems are interconnected, solutions as a result of isolated optimisation may achieve the objective of one problem but fail in the objective of the other one, missing the global optimum eventually. Potentially more energy and economic costs are thus required. In order to apply a more systematic and holistic view, this paper introduces a multi-objective integrated optimisation problem incorporating the newly proposed Active Routing concept. Built with systematic perspectives, this new model combines several elements: scheduling and routing of aircraft, 4-Dimensional Trajectory (4DT) optimisation, runway scheduling and airport bus scheduling. A holistic economic optimisation framework is also included to support the decision maker to select the economically optimal solution from a Pareto front of technically optimal solutions. To solve this problem, a multi-objective genetic algorithm is adopted and tested on real data from an international hub airport. Preliminary results show that the proposed approach is able to provide a systematic framework so that airport efficiency, environmental assessment and economic analysis could all be explicitly optimised.

Optimizing Fuel Treatments to Reduce Wildland Fire Risk

Fuel treatments have been widely used as an effective fire management tool to mitigate catastrophic wildland fire risk in forested landscapes. Fire research efforts of the last two decades have significantly advanced fire behavior modeling and fuel treatment effects analysis, but integrated fuel treatment planning and optimization models have yet to be extensively developed and used, mainly due to the complexity of the planning problem. This paper describes the problem complexity in terms of essential considerations when deciding where, when, and how to perform fuel treatments. Previous studies published in mainstream peer-reviewed journals are summarized and identified by their unique contributions, assumptions, and simplifications. Only a handful studies assessed fuel treatment effects in spatial and temporal contexts and incorporated them into the optimization framework. Most of these existing studies introduced optimization approaches as proof of concept with limited applications. It is hoped that future studies will build on these previous efforts and develop more efficient and integrated optimization approaches that can address multiple concerns simultaneously while producing effective fuel treatment plans for field implementation.

An Integrated Evaluation Approach for Modelling and Optimization of Surface Grinding Process Parameters

This paper presents an application of a Response surface methodology (RSM) for modeling and non-dominated sorting genetic algorithm-II (NSGA-II) for multi-objective optimization of a surface grinding process. The proposed methodology models the material removal rate (MRR) and surface roughness in terms of the three prominent machining parameters using RSM and developed models are used for optimization. As the chosen machining performances are conflict in nature, the problem under consideration is formulated as a multi-objective optimization problem. An efficient evolutionary optimization algorithm, NSGA-II is then applied to obtain the Pareto optimal front of solutions.

Energy minimization in dynamic train scheduling and control for metro rail operations

Since the passenger demands change frequently in daily metro rail operations, the headway, cycle time, timetable and speed profile for trains should be adjusted correspondingly to satisfy the passenger demands while minimizing energy consumption. In order to solve this problem, we propose a dynamic train scheduling and control framework. First, we forecast the passenger demand, and determine the headway and cycle time for the next cycle. Then we optimize the reference timetable and speed profile for trains at the next cycle subject to the headway and cycle time constraints. Finally, the automatic train control system is used to operate trains with real-life conditions based on the reference timetable and speed profile. In this paper, we focus on the optimization of the timetable and speed profile. Generally speaking, the former distributes the cycle time to different stations and inter-stations under the headway constraint, and the latter controls the trains’ speeds at inter-stations to reduce the consumption on tractive energy and increase the storage on regenerative energy. In order to achieve a global optimality on energy saving, we formulate an integrated energy-efficient timetable and speed profile optimization model, which is transformed to a convex optimization problem by using the linear approximation method. We use the Kuhn–Tucker conditions to solve the optimal solution and present some numerical experiments based on the actual operation data of Beijing Metro Yizhuang Line of China, which shows that the integrated approach can reduce the net energy consumption around 11% than the practical timetable. Furthermore, with given passenger demand sequence at off-peak hours, the dynamic scheduling and integrated optimization approach with adaptive cycle time can reduce the net energy consumption around 7% than the static scheduling and integrated optimization approach with fixed cycle time.

Discrete Time Formulation for the Integration of Scheduling and Dynamic Optimization

We propose a model-based optimization approach for the integration of production scheduling and dynamic process operation for general continuous/batch processes. The method introduces a discrete time formulation for simultaneous optimization of scheduling and operating decisions. The process is described by the resource task network (RTN) representation coupled with detailed first-principles process dynamic models. General complications in scheduling and control can be fully represented in this modeling framework, such as customer orders, transfer policies, and requirements on product quality and process safety. The scheduling and operation layers are linked with the task history state variables in the state space RTN model. A tailored generalized Benders decomposition (GBD) algorithm is applied to efficiently solve the resulting large nonconvex mixed-integer nonlinear program by exploring the particular model structure. We apply the integrated optimization approach to a polymerization process with two pa...

Multiperiod Planning Model for Integrated Optimization of a Refinery Production and Utility System

In this work, a novel integrated optimization approach that couples the production system and the utility system of a refinery is proposed to explore the potential for increasing the overall refinery margin and reducing the economic losses due to energy waste compared with the traditional approach in which the two systems are optimized sequentially. A multiperiod refinery-wide mixed-integer nonlinear programming (MINLP) model is formulated to optimize the production planning of the processing unit and the operational planning of the utility equipment simultaneously. The energy consumption and generation model of the processing unit is introduced to correlate the two systems in terms of energy by linking the amounts of energy consumed and generated with the unit throughput, product properties, and operation mode. A material balance of fuel oil and fuel gas is considered to integrate the two systems in terms of materials to improve energy utilization in a multiperiod planning site. A real industrial example...

A Strategy for Reliability-Based Multidisciplinary Design Optimization of Wind Turbine Using BLISS and PMA

Performance of wind turbines can be negatively affected by uncertainties. Uncertainty-based multi-disciplinary design optimization (UMDO) techniques have been successfully applied in the aerospace industry and given the similarities to wind turbine design problem, application of UMDO techniques is an opportunity to improve wind turbine design. However, the major challenges of UMDO, namely computational complexity and organizational complexity caused by both time-consuming disciplinary analysis models and UMDO algorithms, still greatly hamper its usage in wind engineering. In recent years, there is a surge of research aiming at solving these problems. The purpose of this paper is to review these approaches and with the gathered information, a strategy with bi-level integrated system synthesis (BLISS) and performance measurement approach (PMA) for a reliability-based multidisciplinary design optimization of a wind turbine is proposed.

Enhanced lipase recovery through RSM integrated differential evolutionary approach from the fermented biomass

The aim of this work was to apply a modeling integrated optimisation approach for a complex, highly nonlinear system for an extracellular lipase extraction process. The model was developed using mutation, crossover and selection variables of Differential Evolution (DE) based on central composite design of Response Surface Methodology. The experimentally validated model was optimized by DE, a robust evolutionary optimization tool. A maximum lipase activity of 134.13 U/gds (more than 36.28 U/gds compared to one variable at a time approach) was observed with the DE-stated optimum values of 25.01% dimethyl sulfoxide concentration, 40 mM buffer, 128.52 min soaking time and 35oC with the DE control parameters, namely number of population, generations, crossover operator and scaling factor as 20, 50, 0.5 and 0.25, respectively. The use of DE approach improved the optimization capability and decision speed, resulting in an improved yield of 36.28 U/gds compared to the one variable at a time approach for the extracellular lipase activity under the non-optimized conditions. The developed mathematical model and optimization were generic in nature, which seemed to be useful for the scale-up studies of maximum recovery of lipase from the fermented biomass.

Optimal Design of an N-Stage Nonlinear Transmission Line Based on Genetic Algorithm and LTspice

In this paper, we discuss a theoretical optimal design of a nonlinear transmission line using a genetic algorithm (GA) to achieve peak pulse power. The technique is based on an integrated optimization approach that uses a GA coded in Matlab and the circuit simulation program LTspice. A bidirectional interface is created that allowed information to be rapidly transferred between the circuit simulator and GA with real-time monitoring of the entire design process, including peak power and elemental values. The designed process shows a peak voltage enhancement of 318% while eliminating the tedious trial and error process usually required. It is important to note that this result is achieved using only six L-C segments and a much greater voltage increase is likely as the number of segments is increased.

A robust optimization model for agile and build-to-order supply chain planning under uncertainties

Supply chain planning as one of the most important processes within the supply chain management concept, has a great impact on firms’ success or failure. This paper considers a supply chain planning problem of an agile manufacturing company operating in a build-to-order environment under various kinds of uncertainty. An integrated optimization approach of procurement, production and distribution costs associated with the supply chain members has been taken into account. A robust optimization scenario-based approach is used to absorb the influence of uncertain parameters and variables. The formulation is a robust optimization model with the objective of minimizing the expected total supply chain cost while maintaining customer service level. The developed multi-product, multi-period, multi-echelon robust mixed-integer linear programming model is then solved using the CPLEX optimization studio and guidance related to future areas of research is given.