Scheduling Methodology Research Articles

Water use assessment in muskmelon by the Penman–Monteith “one-step” approach

The use of the “one-step” approach of Penman–Monteith (P–M) to assess crop water use has its limit in the lack of user friendly methodologies for a daily assessment of crop canopy resistance, r c. The model proposed by Monteith [Monteith, J.L., 1965. Evaporation and environment. In: Fogg, G.E. (Ed.), The State and Movement of Water in Living Organism. Soc. Exp. Biol. Symp. 19, 205–234] to estimate r c ( r c = 100/LAI), although it is simple, requires the knowledge of LAI value during growing cycle. This work aims to propose an easy-to-use methodology for irrigation scheduling, to assess the values LAI gets during the growing cycle and thus for canopy resistance assessment. In muskmelon crop, grown with and without plastic film mulching, canopy resistance was measured by P–M formula inversion, clarifying the unknown resistivity term, being crop evapotranspiration known by lysimeter-based measurements. Measured canopy resistance was compared with the one estimated by Monteith model (1965), both in its original version and in the form we simplified in terms of LAI value during crop cycle. Thus, we compared the evapotranspiration assessed by the “one-step” use of P–M equation, with those estimated by the classical “two-step” approach, using crop coefficients, and with that directly measured by lysimeters. In particular in the mulched crop the “one-step” approach of P–M overestimates water use only by 4%, while the “two steps” approach underestimates water use by 17%. Despite both the methodologies proposed for LAI calculation and the Monteith model to assess canopy resistance extremely simplify more complex processes, they were able to give a good accuracy to assess muskmelon water use by the P–M “one-step” equation. Comparing the “one-step” and the more used “two-step” approaches it came out that, despite the latter is better correlated to the data measured by a lysimeter, it does not achieve a more accurate assessment compared to the “one-step” approach; in particular, in the mulched crop, the “two-step” approach significantly underestimates water use, while its estimation is reliable with the “one-step” approach.

A NOVEL METHODOLOGY FOR IRRIGATION SCHEDULING USING PLANT-BASED MEASUREMENTS AND MATHEMATICAL MODELLING

Because water is a scarce resource with often intense competition between different users, the search for an optimal irrigation strategy remains an important research objective. In the present study, a new methodology for irrigation scheduling is developed. The methodology combines the use of biosensors for monitoring plant responses with a mathematical model. The model incorporates a mechanistic description of the dynamic water transport in plants and it allows distinction between radial stem growth and the water-related shrinkage and swelling of the stem. Continuously measured stem sap flow rates are used as input for the model, while model calibration is performed using stem diameter variation data measured with a linear variable displacement transducer. The calibrated model is also able to simulate the stem water potential which is continuously compared to a critical threshold value for determination of the exact timing of the irrigation. The quantity of water to be supplied is obtained by integrating the measured sap flow pattern over time. For this novel methodology to be useful in practice, software was developed as well. This software not only enables the acquisition of the data and real-time visualisation of the plant health status, but also allows real-time simulation and calibration of the mathematical model and, finally, the irrigation scheduling itself. The new methodology allows true plant-dependent irrigation control by predicting the optimal timing for irrigation and the exact amount of water required by the plant.

Scheduling methodology for conditional execution of kernels onto multicontext reconfigurable architectures

The authors present a scheduling methodology for conditional execution of kernels onto single instruction stream/multiple data stream multicontext reconfigurable architectures. Data flow graphs are used to describe the target applications in which some kernels are conditionally executed depending on runtime conditions. Immediately after testing a condition the next kernel to be processed is known and its configurations and input data can be loaded, producing a computation stall while these transfers are performed. A compilation-time kernel scheduling is proposed to handle conditional branches (CBs) by determining a kernel sequence that minimises these computation stalls reducing the application latency. Target applications are firstly partitioned taking into account the presence of CBs, and then kernels are ordered for execution and mapped onto the reconfigurable system. Experimental results obtained for interactive and synthetic applications demonstrate the effectiveness of the proposal.

Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell

A fuzzy logic based methodology for generating the sequence of part movements in a multi-product batch processing through a computerized machine cell is presented in this paper. A number of production objectives are taken into account. Two fuzzy based strategies: fuzzy-job and fuzzy-machine are proposed and their performance is compared to two well known dispatching rules such as SPT (Shortest Processing Time) and WEED (Weighted Earliest Due Date). The sequencing algorithm was implemented on a standard personnel computer and the scheduler was interfaced to a robot controller for implementing loading and unloading strategy within the cell. The proposed fuzzy-based methodologies especially fuzzy-job shows a superior performance compared to the traditional dispatching rules considered.

A reinforcement learning framework for utility-based scheduling in resource-constrained systems

This paper presents a general methodology for online scheduling of parallel jobs onto multi-processor servers in a soft real-time environment, where the final utility of each job decreases with the job completion time. A solution approach is presented where each server uses Reinforcement Learning for tuning its own value function, which predicts the average future utility per time step obtained from completed jobs based on the dynamically observed state information. The server then selects jobs from its job queue, possibly preempting some currently running jobs and “squeezing” some jobs into fewer CPUs than they ideally require to maximize the value of the resulting server state. The experimental results demonstrate the feasibility and benefits of the proposed approach.

A Service-Curve Framework for Packet Scheduling With Switch Configuration Delays

In modern packet switches, technology limitations may introduce switch configuration delays that are non-negligible compared with the time required to transmit a single packet. In this paper, we propose a methodology for scheduling of packets, in the context of these technology limitations. If the total tolerable delay through a packet switch is at least on the order of the switch configuration delay, we show that a near 100% utilization of the communication links is possible, while providing strict quality of service guarantees. The main idea is to increase the quantum with which data is scheduled and switched to beyond that of a single packet. This also decreases the rate at which scheduling need to be made, and hence decreases the implementation complexity. The quality of service guarantees we consider are in terms of a service curve. Specifically, we present a framework for the provision of service curves while coping with non-negligible switch configuration delays.

Task assignment and scheduling in a constrained manufacturing system using GA

Driven by open global competition and rapidly changing technology, manufacturing organisations come across continuous change and significant amount of uncertainty. With increasing trend in customers' demand for a greater variety, high quality and competitive cost, traditional manufacturing approaches face threats to remain competitive. Flexible Manufacturing Systems (FMS) have brought in significant benefits to manufacturing sectors. The ability of FMS to flex to both internal and external changes gives rise to improvement in throughput, product quality, information flows, reliability and other strategic advantages. However, the constraints with the individual candidates of FMS must be considered while assigning tasks and making schedules. This makes the scheduling problem complex. Appropriate scheduling methodology can reap better results. In the present work, Genetic Algorithm (GA) is utilised for optimisation of scheduling of FMS. This approach is used to determine the best available combination of resources for processing the product and the method has been shown through a suitable example.

Towards holistic project scheduling using critical chain methodology enhanced with PERT buffering

Since the publication of Goldratt's Critical Chain (1997), Critical Chain Methodology (CCM) for project scheduling has received much attention both in the popular press and in academic journals. Key reasons for the enthusiasm for the methodology are that it takes a holistic, or integrated, approach to project scheduling and it is simple to apply. The CCM approach is designed for situations where task durations are probabilistic and where resources are constrained. However, CCM provides no specific method for determining appropriate project buffer sizing based on task uncertainty and no method for resolving resource constraint. We suggest the simple addition of a heuristic approach to Resource Constrained Project Scheduling (RCPS) to fill the resource constraint resolution gap and the addition of an enhanced Program Evaluation and Review Technique (PERT) method for project buffer sizing.

Optimal production scheduling for the dairy industry

The increasing variety of products offered by the food industry has helped the industry to respond to market trends, but at the same time has resulted in a more complex production process, which requires flexibility and an efficient coordination of existing resources. Especially in industrial yogurt production, there is a wide variety of products that differ in features like fat content, the whey used to produce the mixture, the flavor, the size of the container or the language on the label. The great diversification and the special features that characterize yogurt production lines (satisfaction of multiple due dates, variable processing times, sequence-dependent setup times and costs and monitoring of inventory levels), render generic scheduling methodologies impractical for real-world applications. In this work we present a customized Mixed Integer Linear Programming (MILP) model for optimizing yogurt packaging lines that consist of multiple parallel machines. The model is characterized by parsimony in the utilization of binary variables and necessitates the use of only a small pre-determined number of time periods. The efficiency of the proposed model is illustrated through its application to the yogurt production plant of a leading dairy product manufacturing company in Greece.

Feedback scheduling

Embedded computing systems today increasingly feature resource constraints and workload variability, which lead to uncertainty in resource availability. This raises great challenges to software design and programming in multitasking environments. In this paper, the emerging methodology of feedback scheduling is introduced to address these challenges. As a closed-loop approach to resource management, feedback scheduling promises to enhance the flexibility and resource efficiency of various software programs through dynamically distributing available resources among concurrent tasks based on feedback information about the actual usage of the resources. With emphasis on the behavioral design of feedback schedulers, we describe a general framework of feedback scheduling in the context of real-time control applications. A simple yet illustrative feedback scheduling algorithm is given. From a programming perspective, we describe how to modify the implementation of control tasks to facilitate the application of feedback scheduling. An event-driven paradigm that combines time-triggered and event-triggered approaches is proposed for programming of the feedback scheduler. Simulation results argue that the proposed event-driven paradigm yields better performance than time-triggered paradigm in dynamic environments where the workload varies irregularly and unpredictably.

Energy-aware scheduling and simulation methodologies for parallel security processors with multiple voltage domains

Dynamic voltage scaling (DVS) and power gating (PG) have become mainstream technologies for low-power optimization in recent years. One issue that remains to be solved is integrating these techniques in correlated domains operating with multiple voltages. This article addresses the problem of power-aware task scheduling on a scalable cryptographic processor that is designed as a heterogeneous and distributed system-on-a-chip, with the aim of effectively integrating DVS, PG, and the scheduling of resources in multiple voltage domains (MVD) to achieve low energy consumption. Our approach uses an analytic model as the basis for estimating the performance and energy requirements between different domains and addressing the scheduling issues for correlated resources in systems. We also present the results of performance and energy simulations from transaction-level models of our security processors in a variety of system configurations. The prototype experiments show that our proposed methods yield significant energy reductions. The proposed techniques will be useful for implementing DVS and PG in domains with multiple correlated resources.

Application of predictive control scheduling method to real-time periodic control tasks overrun

Based on the abort strategy of fixed periods, a novel predictive control scheduling methodology was proposed to efficiently solve overrun problems. By applying the latest control value in the prediction sequences to the control objective, the new strategy was expected to optimize the control system for better performance and yet guarantee the schedulability of all tasks under overrun. The schedulability of the real-time systems with p-period overruns was analyzed, and the corresponding stability criteria was given as well. The simulation results show that the new approach can improve the performance of control system compared to that of conventional abort strategy, it can reduce the overshoot and adjust time as well as ensure the schedulability and stability.

Integrated Placement and Skew Optimization for Rotary Clocking

The clock distribution network is a key component of any synchronous VLSI design. High power dissipation and pressure volume temperature-induced variations in clock skew have started playing an increasingly important role in limiting the performance of the clock network. Rotary clocking is a novel technique which employs unterminated rings formed by differential transmission lines to save power and reduce skew variability. Despite its appealing advantages, rotary clocking requires flip-flop locations to match predesigned clock skew on rotary clock rings. This requirement poses a difficult chicken-and-egg problem which prevents its wide application. In this paper, we propose an integrated placement and skew scheduling methodology to break this hurdle, making rotary clocking compatible with practical design flows. A network flow based flip-flop assignment algorithm and a cost-driven skew optimization algorithm are developed. We also present an integer linear programming formulation that minimizes maximum capacitance loaded at any of the rotary rings, thereby maximizing the operating frequency. Experimental results on benchmark circuits show that our method can reduce the tapping cost (measured as the total length of the wire segments connecting the rotary rings to the clock sinks) for rotary clocking by 33%-53%

General Methodology Combining Engineering Optimization of Primary HVAC&R Plants with Decision Analysis Methods—Part I: Deterministic Analysis

This paper is the first of a two-part sequence that proposes a general methodology for dynamic scheduling and optimal control of complex primary HVAC&R plants, which combines engineering analyses within a practical decision analysis framework by modeling risk attitudes of the operator. The methodology involves a computationally efficient, deterministic engineering optimization phase for scheduling and controlling primary systems over the planning horizon, followed by a systematic and comprehensive stochastic sensitivity and decision analysis phase, where various sources of uncertainties are evaluated along with alternative non-optimal but risk-averse operating strategies. This paper describes the deterministic component of the analysis methodology, which essentially involves the development of response surface models for different combinations of system configurations to be used for static optimization and then using them in conjunction with the modified Dijkstra's algorithm for dynamic scheduling and optimal control under different operating conditions and pricing signals. The proposed methodology is illustrated for a semi-real hybrid cooling plant operated under two different pricing schemes: real-time pricing and time-of-use with electricity demand. We feel that the general methodology framework proposed sacrifices very little in accuracy while being much more efficient computationally than the more complicated optimization methods proposed in thegeneral literature. Moreover, this approach is suitable for online implementation, and it is also general enough to be relevant to other energy systems.

General Methodology Combining Engineering Optimization of Primary HVAC&R Plants with Decision Analysis Methods—Part II: Uncertainty and Decision Analysis

A companion paper (Jiang and Reddy 2007) presents a general and computationally efficient methodology for dynamic scheduling and optimal control of complex primary HVAC&R plants using a deterministic engineering optimization approach. The objective of this paper is to complement the previous work by proposing a methodology by which the robustness of the optimal deterministic strategy to various sources of uncertainties can be evaluated against non-optimal but risk averse alternatives within a formal decision analysis framework. This specifically involves performing a sensitivity analysis on the effect of various stochastic factors that impact primary HVAC&R plant optimization, such as the uncertainty in load prediction and the uncertainties associated with various component models of the equipment. This is achieved through Monte Carlo simulations on the deterministic outcome, which allow additional attributes, such as its variability and the probability of insufficient cooling, to be determined along with the minimum operating cost. The entire analysis is then repeated for a specific non-optimal but risk-averse operating strategy. Finally, a formal decision analysis model using linear multi-attribute utility functions is suggested for comparing both these strategies in a framework that explicitly models the risk perception of the plant operator in terms of the three attributes. The methodology is demonstrated using the same illustrative case study as the companion paper.

Exact and heuristic methodologies for scheduling in hospitals: problems, formulations and algorithms

This text summarizes the PhD thesis defended by the author in January 2006 under the supervision of Professor Erik Demeulemeester at the Katholieke Universiteit Leuven. The thesis is written in English and is available from the author’s website (http://www.econ.kuleuven.be/jeroen.belien). In this research we propose a number of exact and heuristic algorithms for various scheduling problems encountered in hospitals. The emphasis lies on the design of new methodologies as well as on the applicability of the algorithms in real-life environments. The main contributions include a new decomposition approach for a particular class of staff scheduling problems, an extensive study of master surgery scheduling algorithms that aim at leveling the resultant bed occupancy and an innovative method for integrating nurse and surgery scheduling.

Deadlock-free scheduling and control of flexible manufacturing cells using automata theory

This paper presents a novel method for the scheduling and control of flexible manufacturing cells (FMCs). The approach employs automata, augmented by time labels proposed herein, for the modeling of machines, transportation devices, buffers, precedence constraints, and part routes. Ramadge-Wonham's supervisory-control theory is then used to synthesize a deadlock-free controller that is also capable of keeping track of time. For a given set of parts to be processed by the cell, A/sup */ search algorithm is subsequently employed using a proposed heuristic function. Three different production configurations are considered: Case 1) each part has a unique route; Case 2) parts may have multiple routes, but same devices in each route; and Case 3) parts may have multiple routes with different devices. The proposed approach yields optimal deadlock-free schedules for the first two cases. For Case 3, our simulations have yielded effective solutions but in practice, optimal deadlock-free schedules may not be obtainable without sacrificing computational time efficiency. One such nontime-efficient method is included in this paper. The proposed approach is illustrated through three typical manufacturing-cell simulation examples; the first adopted from a Petri-net-based scheduling paper, the second adopted from a mathematical-programming-based scheduling paper, and the third, a new example that deals with a more complex FMC scenario where parts have multiple routes for their production. These and other simulations clearly demonstrate the effectiveness of the proposed automata-based scheduling methodology.

Using Interval Alignment policies for efficient production control of supply chain systems

This paper discusses a class of Interval Alignment (IA) policies, which result in the development of a new methodology for detailed scheduling in supply chain management systems. In the past few decades, as the use of takt times and the Just-in-Time (JiT) philosophy in lean manufacturing have become prevalent, much effort has been put into removing variability from the flow of material in systems. It is shown that the proposed IA policies, by adding intermediate delays to the system, effectively smoothen flow of parts, improve performance, and decrease average Work-in-Process (WiP) and system lead times. Also discussed are applications of IA policies in work cell design in supply chain systems and the implementation of IA policies using kanban controls.

A Sequential Approach for Gas Turbine Power Plant Preventative Maintenance Scheduling

Traditionally, gas turbine power plant preventive maintenance schedules are set with constant intervals based on recommendations from the equipment suppliers. Preventive maintenance is based on fleet-wide experience as a guideline as long as individual unit experience is not available. In reality, the operating conditions for each gas turbine may vary from site to site and from unit to unit. Furthermore, the gas turbine is a repairable deteriorating system, and preventive maintenance usually restores only part of its performance. This suggests a gas turbine needs more frequent inspection and maintenance as it ages. A unit-specific sequential preventive maintenance approach is therefore needed for gas turbine power plant preventive maintenance scheduling. Traditionally, the optimization criteria for preventive maintenance scheduling is usually cost based. However, in the deregulated electric power market, a profit-based optimization approach is expected to be more effective than the cost-based approach. In such an approach, power plant performance, reliability, and the market dynamics are considered in a joint fashion. In this paper, a novel idea that economic factors drive maintenance frequency and expense to more frequent repairs and greater expense as equipment ages is introduced, and a profit-based unit-specific sequential preventive maintenance scheduling methodology is developed. To demonstrate the feasibility of the proposed approach, a conceptual level study is performed using a base load combined cycle power plant with a single gas turbine unit.

Printed circuit board scheduling in an openshop manufacturing environment

Printed circuit boards are used in a myriad of commercial and consumer products in today’s marketplace. In this paper, we address the problem of inserting/placing components on printed circuit boards using automated insertion/placement machines. The motivation to examine this problem stems from joint work with a major electronics manufacturer who not only introduced us to the manufacturing environment but also provided data for testing and validating our component allocation and scheduling methodology. The results of our analysis can be used to trade-off operational savings of reduced cycle times versus tactical costs of reorganizing the equipment on the shop floor. Further, we also are able to develop and validate a three step hierarchical scheduling methodology for use in similar manufacturing environments.