Optimal Scheduling Approach Research Articles

A distributionally robust optimization approach for stochastic elective surgery scheduling with limited intensive care unit capacity

In this paper, we study the decision process of assigning elective surgery patients to available surgical blocks in multiple operating rooms (OR) under random surgery durations, random postoperative length-of-stay in the intensive care unit (ICU), and limited capacity of ICU. The probability distributions of random parameters are assumed to be ambiguous, and only the mean values and ranges are known. We propose a distributionally robust elective surgery scheduling (DRESS) model that seeks optimal surgery scheduling decisions to minimize the cost of performing and postponing surgeries and the worst-case expected costs associated with overtime and idle time of ORs and lack of ICU capacity (which causes premature discharges or transfers). We evaluate the worst-case over a family of distributions characterized by the known mean values and ranges of random parameters. We leverage the separability of DRESS formulation in deriving an exact mixed-integer nonlinear programming reformulation. We linearize and derive a family of symmetry breaking inequalities to improve the solvability of the reformulation using an adapted column-and-constraint generation algorithm. Finally, we conduct extensive numerical experiments that demonstrate the superior performance of our DR approach as compared to the stochastic programming approach, and provide insights into DRESS.

Lossless data transmission for Internet of things application over wireless multimedia sensor networks using enhanced and optimal path scheduling approach to maximizing the quality of service

AbstractThe congestion of packet forwarding between a source and destination is challenging on downlink transmission in the entire file (ex. Audio and Video). Whenever file is been uploaded to the server, a user requests for file where server transmits it without knowledge of user's bandwidth, which is a major, cause of packet loss or time duration in the receiver end. To accumulate the better solution, Enhanced and Optimal Path Scheduling Approach (EOPSA) designs to find optimal path scheduling for multimedia data transmission in multimedia sensor network over cloud server using IoT devices. EOPSA studied the multisource video‐on‐demand streaming in multimedia sensor networks. The method introduced a heuristic distributed protocol to find optimal route for multimedia data transmissions. Efficient way to identify the bandwidth before the transmission ensures link establishment between sender and receiver. Here, the capture of bandwidth helps to check user's system capability to forward requested media data. Based on experiment evaluation, EOPSA improves 0.20 packet delivery ratio, 130 throughput, 0.20 second average delay and 14 communication overhead for 15, 25, 50, 75, and 100 nodes compared than conventional methods.

Optimal scheduling of power transformers preventive maintenance with Bayesian statistical learning and influence diagrams

Transformer preventive maintenance scheduling—as an integral part of the power utility company’s asset–management process—if optimized properly affords considerable savings in maintenance costs and results in the increased reliability of transformer stations. However, situation is exacerbated by the considerable uncertainty involved in the assessment of the transformer health status, not necessarily associated with its age, as well as in the projected asset replacement and maintenance costs. There is also uncertainty inherent in the transformer tests as such. This paper proposes a novel transformer optimal preventive maintenance scheduling approach, with the use of the Bayesian statistical learning and a generalization of the Bayesian networks, for decision-making based on the expected utility hypothesis. Proposed approach, in essence, manages risk associated with taking different preventive actions by leveraging uncertain information regarding the transformer health and the expected costs of these actions. In this way, each prospective decision has a monetary value attached to it, which depends on the particular circumstances defined by the power utility company. Time-related effects are introduced into the decision-making process by the Bayesian updating of the prior belief concerning the transformer health with new information. Solving a decision problem amounts to determining an (optimal) strategy that maximizes the expected utility for the decision maker. On top of that, Bayesian approach, uniquely, offers explicit account for the full uncertainty associated with every step of the decision-making process. The proposed approach, and its advantages, will be demonstrated on a fleet of power transformers. It will be shown that model can predict with a 90% accuracy and a high confidence the transformer health index class from a small dataset. It will also be shown that it can predict with high expected profits which transformers in the dataset would benefit the most from the early intervention (recuperating around 25% of the projected costs and retaining over 90% of the projected savings).

Data-Driven Suboptimal Scheduling of Switched Systems.

In this paper, a data-driven optimal scheduling approach is investigated for continuous-time switched systems with unknown subsystems and infinite-horizon cost functions. Firstly, a policy iteration (PI) based algorithm is proposed to approximate the optimal switching policy online quickly for known switched systems. Secondly, a data-driven PI-based algorithm is proposed online solely from the system state data for switched systems with unknown subsystems. Approximation functions are brought in and their weight vectors can be achieved step by step through different data in the algorithm. Then the weight vectors are employed to approximate the switching policy and the cost function. The convergence and the performance are analyzed. Finally, the simulation results of two examples validate the effectiveness of the proposed approaches.

A bio-inspired scheduling approach for machines and automated guided vehicles in flexible manufacturing system using hormone secretion principle

The product quality and production efficiency of a flexible manufacturing system have improved effectively by introducing the computer management and the material transportation system. The flexible manufacturing system performance greatly depends on the performance of the material transportation system. As a mobile robot controlled by a central controller, an automated guided vehicle has a strong ability for material transportation. This article studies a dynamic scheduling problem in a shop floor, where machines and automated guided vehicles run at a specified speed and specifies a mathematical model for the dynamic scheduling problem with the goal of makespan minimization. Meanwhile, inspired by the hormone secretion principle of the endocrine system, a bio-inspired scheduling optimization approach is developed to solve the dynamic scheduling problem in the flexible manufacturing system. To verify its practical application, the bio-inspired scheduling optimization approach and other scheduling approaches are tested, and the results illustrate that the bio-inspired scheduling optimization approach has better scheduling performance as well as optimizes the quality of integrated and real-time scheduling of machines and automated guided vehicles.

A WOA-Based Optimization Approach for Task Scheduling in Cloud Computing Systems

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this article, for the first time, we apply the latest metaheuristics whale optimization algorithm (WOA) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called I mproved W OA for C loud task scheduling (IWC) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks.

A Lagrangean decomposition optimization approach for long-term planning, scheduling and control

A decomposition strategy to address the simultaneous solution of large-scale planning, scheduling and control problems (PSC) is proposed in this work. Long-term PSC problems are hard to solve because of the large number of both discrete and continuous decision variables embedded in such optimization formulations. Improved optimal decisions can be realized by taking into account natural interactions present in PSC problems. This is the main justification for a simultaneous solution approach of such optimization problems, although this consideration strongly increases the computational solution of PSC problems. In this work, the integrated PSC problem is reformulated using Lagrangean Decomposition, resulting a model decomposed into planning and scheduling (PS) and control (C) subproblems. In the case study, the proposed solution strategy was applied to a multiproduct CSTR represented by the model of Hicks and Ray, which presents strong nonlinear behaviour, over planning horizons of three, four, eight, twelve, and sixteen periods lasting one week each. Moreover, the PSC model incorporates a non-linear model predictive control (NMPC) scheme in order to realize dynamic transitions which are as smooth as possible. The results were compared, in terms of the optimal profit and the CPU time consumed, against those produced by the direct solution of the problem (without using a decomposition strategy) for the specific case of three planning periods, showing a significant reduction in the computational effort.

Stochastic Optimization Approaches for Elective Surgery Scheduling and Downstream Capacity Planning: Models, Challenges, and Opportunities

Elective surgery patients receive surgery in the operating room (OR) and then recover in one or more subsequent downstream recovery units for several consecutive hours or days after surgery. Indeed, upstream scheduling that focuses on OR alone or a resource-constrained scheduling approach that fails to account for the inherent uncertainty in surgery durations and postoperative downstream recovery times yield sub-optimal or infeasible schedules and, consequently, higher cost and reduced quality of care. However, modeling such uncertainties at multiple levels is challenging, especially with limited reliable data on the random parameters in the models. Moreover, sequencing of surgical and recovery activities, and the multiple conflicting objectives of all parties involved (including management, clinicians, patients), lead to a class of complex combinatorial and multi- criteria stochastic optimization problems. In this review, we focus on stochastic optimization (SO) approaches for elective surgery scheduling and downstream capacity planning. We describe the art of formulating and solving such a class of stochastic resource-constrained scheduling problems, provide an analysis of existing SO approaches and their challenges, and highlight areas of opportunity for developing tractable, implementable, and data-driven approaches that might be applicable within and outside healthcare operations, particularly where multiple entities/jobs share the same downstream limited resources.

An Optimal Generation Scheduling Approach Based on Linear Relaxation and Mixed Integer Programming

This paper proposes an optimal generation scheduling approach based on linear relaxation and mixed integer programming, which is used to solve the generation dispatch problem. The quadratic transmission loss constraint of each transmission line is converted into linear constraints by using the linear relaxation and mixed integer programming technique. Consequently, the original optimal generation scheduling problem is formulated as a quadratic programming or mixed integer quadratic programming problem that can be solved by commercial optimization solver. In order to improve the efficiency of algorithm, this paper further analyses the generation scheduling model and deletes the redundant variables and constraints. Three test systems, including IEEE 30-node system, IEEE 118-node system, and Polish 2746-node system, are employed to examine the effectiveness of the proposed method. The comparative results obtained by the proposed method, quadratically constrained quadratic programming method (QCQP), and solving constraint integer programs solver (SCIP) verify the effectiveness of the proposed method in solving the optimal generation scheduling problem.

A Deep-Reinforcement-Learning-Based Optimization Approach for Real-Time Scheduling in Cloud Manufacturing

Resource scheduling problems (RSPs) in cloud manufacturing (CMfg) often manifest as dynamic scheduling problems in which scheduling strategies depend on real-time environments and demands. Generally, multiple resources in the CMfg scheduling process cause difficulties in system modeling. To solve this problem, we propose Sharer, a deep reinforcement learning (DRL)-based method that converts scheduling problems with multiple resources into one learning target and learns effective strategies automatically. Our preliminary results show that Sharer is comparable to the latest heuristics, adapts to different conditions, converges quickly, and subsequently learns wise strategies.

Optimal Energy Management Integrating Plug in Hybrid Vehicle Under Load and Renewable Uncertainties

This article introduces a robust optimal week-ahead generation scheduling approach that takes into account plug in hybrid electric vehicles (PHEVs) considering uncertainty in loads, renewable energy resources, and PHEV charging behavior. Due to the complexity of the scheduling process there is crucial need for a reliable optimal algorithm. The proposed approach can be applied in energy management platforms of decarbonized eco-friendly power systems. Generation scheduling is modeled as a multi-objective optimization problem: (a) minimize generation production cost and (b) minimize emission costs. The focal concern is to (a) handle the scheduling of renewable energy resources against their volatilities, (b) integrating PHEVs with uncertainties related to their state of charge, and (c) stochastic load behavior over a whole week. Two heuristic-based algorithms are used to solve the optimization problem, namely Water Cycle Algorithm and Gravitational Search Algorithm The proposed scheduling approach is implemented in MATLAB Ⓡ Platform, and is tested using two different microgrids sizes, 3 generator, and 10 generator unit systems integrating the effect of week days profile, renewable energy intermittency and different PHEV state of charges using the IEEE Reliability Test System (RTS) data. The results show promising performance of GSA over the WCA in the energy management studies integrating three different types of sources; thermal units, Renewable Energy Resources (RERs), and the PHEVs.

The Study of Linear Programming Approach for Optimal Scheduling of Work in A Corporation With different Models

In the present research paper, two types of models are discussed for linear programming problems. Models are determining the work of the employee in day-to-day life. The first is Staff Scheduling for Bhopal-Dewas Corridor Toll Plaza and second is worker scheduling for Aqua sure Aquagurad Water Purifiers drinking water Company(Rukmani Beverages Pvt. Ltd.)

Distributed Optimal Dispatching of Multi-Entity Distribution Network With Demand Response and Edge Computing

With large-scale penetrations of distributed generation (DG) and flexible loads, there will be multiple entities in traditional distribution network, such as distribution network operator (DNO), DG owner, and prosumers. Aiming at the collaborative optimization problem of distribution network among multiple entities, a distributed optimal scheduling approach of distribution network considering demand response and edge computing is proposed in this paper. Firstly, the virtual region decomposition method is proposed to divide the original distribution network into multiple regions according to different entities, and the bi-level optimization framework based on edge computing is constructed. Secondly, the optimal models of DNO, DG owner, and prosumers are established respectively, and the distributed optimal scheduling approach of distribution network with collaboration of control center and edge nodes is proposed. Then, the KKT conditions are adopted to realize the transformation of optimal models of DG owner and prosumers. Finally, the proposed distributed optimization scheduling approach is verified based on the modified IEEE33-node system. The results show that the proposed distributed optimal scheduling method can achieve better collaborative optimization among different entities in distribution network compared with the centralized optimization method.

An interval-prediction based robust optimization approach for energy-hub operation scheduling considering flexible ramping products

The energy-hub (EH) technique provides a new-type conceptual framework for the interaction, coupling, conversion of different forms of energies to achieve the improvement of energy usage efficiency. In this paper, an optimal economic dispatching model for a residential-level EH in the energy market considering the flexible ramping product (FRP) is proposed. As distinct from existing studies, in this work, we assume that the EH not only involves bidding in the energy market but may also get profits by providing the FRP to the grid. The discussed problem is formulated as a two-stage robust optimization (RO) model, wherein the first-stage is dedicated to minimizing the operation cost of the EH by optimizing its bidding strategy (energy and flexible ramping trading quantities) in the DA market, while the second-stage aims to determine the optimal re-dispatch/corrective schedule of the EH (including adjusted output of EH components, bidding and flexible ramping amount) in the RT market to achieve the energy balancing with the least cost, considering the worst-case realization of uncertainties. In practice, since the selection of uncertainty interval has an important impact on the efficacy of the RO solution, we propose an interval prediction-based approach combining the long short-term memory (LSTM) recurrent neural network and bootstrapping technique to determine the uncertainty interval used in our RO formulation. Compared with the conventional empirical-based interval-setting, the proposed prediction-based technique is useful to reduce the conservative level of RO solutions by deep learning the value of historical data and reflecting the real error distribution of point forecast results in its estimation. The effectiveness of the proposed methodology is examined based on a residential EH case, and the obtained results confirm the validity of the proposed approach in actual implementations.

A distributionally robust optimization approach for outpatient colonoscopy scheduling

We consider the outpatient colonoscopy scheduling problem, recognizing the impact of pre-procedure bowel preparation (prep) quality on the variability in colonoscopy duration. Data from a large academic medical center indicates that colonoscopy durations are bimodal, i.e., depending on the prep quality they can follow two different probability distributions, one for those with adequate prep and the other for those with inadequate prep. We therefore define a distributionally robust outpatient colonoscopy scheduling (DROCS) problem that seeks optimal appointment sequence and schedule to minimize the worst-case weighted expected sum of patient waiting, provider idling, and provider overtime, where the worst-case is taken over an ambiguity set (a family of distributions) characterized through the known mean and support of the prep quality and durations. We derive an equivalent mixed-integer linear programming formulation to solve DROCS. Finally, we present a case study based on extensive numerical experiments in which we draw several managerial insights into colonoscopy scheduling.

A novel approach for multi-objective optimal scheduling of large-scale EV fleets in a smart distribution grid considering realistic and stochastic modeling framework

The ever-increasing number of grid-connected electric vehicles (EVs) has led to emerging new opportunities and threats in electrical distribution systems (DS). Developing a realistic model of EV interaction with the DS, as well as developing a strategy to optimally manage these interactions in line with distribution system operators (DSOs) intentions, are the most important prerequisites for gaining from this phenomenon especially in modern smart distribution systems (SDS). In this paper, a comprehensive model describing the electric vehicle integration to an SDS is presented by considering the real-world data from EV manufacturers and DSOs. Moreover, a novel energy management strategy (EMS) based on the multi-objective optimization problem (MOOP) is developed to fulfill the operational objectives of DSO and EV owner, including peak load shaving, loss minimization, and EV owner profit maximization. In this regard, an innovative dimension reduction approach is presented, to make it feasible to apply the heuristic optimization methods to a MOOP with a large number of decision variables. Thanks to this method, the improved electromagnetism like algorithm (IEMA) is employed to perform the multi-objective energy scheduling for a large-scale EV fleet. In addition, a novel method is devised for estimating the optimal hosting capacity of an SDS in adopting EVs without the need for sophisticated computations. The presented method is applied to the modified IEEE-33 bus test system. Obtained results reveal that employment of a realistic model concludes to more accurate results than a simplified model. In addition, the efficiency of the proposed EMS in satisfying EV owner and DSO objectives are approved by analyzing obtained computation results.

Bi-objective optimization approach for energy aware scheduling considering electricity cost and preventive maintenance using genetic algorithm

Abstract In light of various environmental issues regarding electricity generation and usage, several approaches have been suggested to decrease energy usage. One well-known method on the supplier side is time-of-use (TOU) pricing, through which demand on the customer side is controlled by adjusting electricity prices. Several studies have investigated the scheduling problem in such an environment to efficiently handle energy usage from the customer perspective, and have addressed its effectiveness. However, in most energy-aware scheduling problems considering TOU pricing, machine failure is not considered. This is a significant assumption because it implies that machines are available at all times. In this study, we examined a single machine scheduling problem that reflects preventive maintenance under TOU pricing. To solve this problem, a bi-objective mixed-integer non-linear programming model is designed, and a hybrid multi-objective genetic algorithm (HMOGA) is proposed to handle medium- and large-sized problems. In addition, α - and P-improvement methods are proposed to efficiently generate a better Pareto frontier. The performance of the algorithm is compared with that of a non-dominated sorting genetic algorithm (NSGA)-2 to demonstrate the effectiveness of the proposed HMOGA. Numerical experiments showed that the HMOGA yields better outcomes than the NSGA-2 and faster than Baron solver. The major contribution of this study lies in building a trade-off relationship between the total electricity cost and machine unavailability in a TOU pricing environment.

Cost and makespan-aware workflow scheduling in hybrid clouds

Abstract Benefiting from rich resources and virtualization technologies, hybrid cloud has emerged as a promising solution to processing large-scale scientific workflow applications for users in a pay-as-you-go manner. However, considering the complexity of resource configuration and deployment in hybrid clouds, existing workflow scheduling strategies designed for traditional distributed computing systems are limited and powerless. Therefore, for profit-driven infrastructure-as-a-service (IaaS) cloud providers, minimizing makespan and monetary cost of scheduling scientific workflows is an imperative concern. In this paper, we propose two efficient workflow scheduling approaches for hybrid clouds that both consider makespan and monetary cost. Specifically, we first propose a single-objective workflow scheduling optimization approach called DCOH (deadline-constrained cost optimization for hybrid clouds) for minimizing the monetary cost of scheduling workflows under deadline constraint. Based on DCOH, we further propose a multi-objective workflow scheduling optimization approach called MOH (multi-objective optimization for hybrid clouds) for optimizing makespan and monetary cost of scheduling workflows simultaneously. Extensive simulation experiments have been conducted to validate the effectiveness of DCOH and MOH. Simulation results show that our DCOH approach can reduce up to 100.0% monetary cost for users as compared to the competing algorithms under the same deadline constraint and our MOH approach can achieve better cost-makespan trade-off solutions as compared to the competing algorithms.

A Robust Transmission Scheduling Approach for Internet of Things Sensing Service with Energy Harvesting.

Maximizing the utility under energy constraint is critical in an Internet of Things (IoT) sensing service, in which each sensor harvests energy from the ambient environment and uses it for sensing and transmitting the measurements to an application server. Such a sensor is required to maximize its utility under the harvested energy constraint, i.e., perform sensing and transmission at the highest rate allowed by the harvested energy constraint. Most existing works assumed a sophisticated model for harvested energy, but neglected the fact that the harvested energy is random in reality. Considering the randomness of the harvested energy, we focus on the transmission scheduling issue and present a robust transmission scheduling optimization approach that is able to provide robustness against randomness. We firstly formulate the transmission scheduling optimization problem subject to energy constraints with random harvested energy. We then introduce a flexible model to profile the harvested energy so that the constraints with random harvested energy are transformed into linear constraints. Finally, the transmission scheduling optimization problem can be solved traditionally. The experimental results demonstrate that the proposed approach is capable of providing a good trade-off between service flexibility and robustness.

Mixed-fleet single-terminal bus scheduling problem: Modelling, solution scheme and potential applications

Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators. We propose a Mixed Integer Linear Program (MILP) to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, coupled with an ad-hoc decomposition scheme aimed at enhancing the scalability of the proposed MILP. Two case studies arising from the PT network of the city of Luxembourg are employed in order to validate the model; sensitivity analysis to fleet design parameters is performed, specifically in terms of fleet size and fleet composition. Conclusions point to the fact that careful modelling and handling of mixed-fleet conditions are necessary to achieve operational savings, and that marginal savings gradually reduce as more conventional buses are replaced by their electric counterparts. We believe the methodology proposed may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions.