Dispatch Rules Research Articles

Регулирование стока ирригационным водохранилищем с учетом данных о штормовых осадках по метеостанциям речного бассейна

The scientific paper is devoted to development the methodology of the rules for control of water reservoirs in the south of the European territory of Russia (ETR), the relevance of that has determined by necessary for more reliable and efficient of river flow regulation. The studies has based on the data of synchronous observations at the region weather stations and of corresponding runoff to the Krasnodar reservoir, it provides rice fields. A correlation analysis between rainfall at weather stations and the corresponding flood volumes have gave possibility to evaluate their rate of dependence. So a predictor weather stations were chosen, which allowed to select decisions in respect to control by water resource of the reservoir. The analysis of traditional and improved rules for control of the reservoir has carried out using a simulation model of its functioning. It has based on the balance formula for water reservoir, taking into account the logical conditions of the traditional dispatch rules for control by reservoir, which designed to compensate for water deficits in rice fields and to be safe during catastrophic floods. The model makes it possible to change the rules of water storage management taking into account short-term forecasts of precipitations and runoff for modeling according to five-day period (pentad) discreteness. The obtained results confirmed the hypothesis about the possibility of reducing the likelihood of emergencies during the catastrophe of catastrophic floods with the stability of the planned water consumption of the irrigation system.

Genetic Programming with Delayed Routing for Multiobjective Dynamic Flexible Job Shop Scheduling.

Dynamic Flexible Job Shop Scheduling (DFJSS) is an important and challenging problem, and can have multiple conflicting objectives. Genetic Programming Hyper-Heuristic (GPHH) is a promising approach to fast respond to the dynamic and unpredictable events in DFJSS. A GPHH algorithm evolves dispatching rules (DRs) that are used to make decisions during the scheduling process (i.e., the so-called heuristic template). In DFJSS, there are two kinds of scheduling decisions: the routing decision that allocates each operation to a machine to process it, and the sequencing decision that selects the next job to be processed by each idle machine. The traditional heuristic template makes both routing and sequencing decisions in a non-delay manner, which may have limitations in handling the dynamic environment. In this article, we propose a novel heuristic template that delays the routing decisions rather than making them immediately. This way, all the decisions can be made under the latest and most accurate information. We propose three different delayed routing strategies, and automatically evolve the rules in the heuristic template by GPHH. We evaluate the newly proposed GPHH with Delayed Routing (GPHH-DR) on a multiobjective DFJSS that optimises the energy efficiency and mean tardiness. The experimental results show that GPHH-DR significantly outperformed the state-of-the-art GPHH methods. We further demonstrated the efficacy of the proposed heuristic template with delayed routing, which suggests the importance of delaying the routing decisions.

Penerapan Non Delay Schedule Dengan Mempertimbangkan Machine Availability Constraint untuk Meminimasi Makespan pada PT Autotech Perkasa Mandiri, Cikarang, Jawa Barat

One of the efforts to increase production efficiency is by implementing the right scheduling. PT Autotech Perkasa Mandiri is the job shop production system. Currently implements a heuristic method with a priority dispatch rule, namely the Earliest Due Date (EDD). The EDD rule has the highest priority for the job with the earliest due date to be processed on a machine. This study is to purpose a more optimal scheduling by comparing the makespan of the current schedule with the proposed one. Previous research is the non delay schedule without considering machine availability constraints. In this study, machine availability constraints are taken into account, thus, they are more realistic when applied to the actual problem. The algorithm used is a non delay schedule with working time window (WT-NSA). Machine availability constraints that are taken into account are lunch-break hours (12: 00-13: 00) and shift changes (17: 00-08: 00). From the analysis, it is obtained that makespan from non delay schedule is 23 hours 6 minutes, while makespan from WT-NSA is 26 hours 6 minutes, and makespan from EDD schedule is 38 hours 54 minutes. Non-delay schedule has the smallest makespan, which is 23 hours 6 minutes, but WT-NSA is more realistic when applied to the real problem.

Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction

In this paper, we address the dynamic single-machine scheduling problem for minimisation of total weighted tardiness by learning of dispatching rules (DRs) from schedules. We propose a decision-tree-based approach called Generation of Rules Automatically with Feature construction and Tree-based learning (GRAFT) in order to extract dispatching rules from existing or good schedules. GRAFT consists of two phases: learning a DR from schedules, and improving the DR with feature-construction-based genetic programming. With respect to the process of learning DRs from schedules, we present an approach for transforming schedules into training data containing underlying scheduling decisions and generating a decision-tree-based DR. Thereafter, the second phase improves the learned DR by feature-construction-based genetic programming so as to minimise the average total weighted tardiness. We conducted experiments to verify the performance of the proposed approach, and the results showed that it outperforms the existing dispatching rules. Moreover, the proposed algorithm is effective in terms of extracting scheduling insights in such understandable formats as IF–THEN rules from existing schedules and improving DRs by grafting a new branch with a discovered attribute into a decision tree.

Identical parallel machine scheduling with assurance of maximum waiting time for an emergency job

Currently, customer satisfaction is playing an increasingly vital role in both manufacturing and service industries. Assuring an acceptable waiting time to customers is considered as an effective approach to improve customer satisfaction. In this study, an identical parallel machine scheduling problem assuring the maximum waiting time for an emergency job which arrives at any time is investigated. A mixed integer programming model is formulated, based on which a variant formulation is generated. The formulations are further enhanced by various techniques, which forms two formulation-based methods. Two objectives, makespan and total completion time, are considered separately. Regarding the makespan, the worst-case approximation ratios of the classical heuristic rules are deduced. For the total completion time, efficient bounds are provided and the NP-hardness of the problem is proved. Heuristic methods based on the classical dispatch rules are developed, for both the cases. Extensive computational experiments are conducted, based on which the performances of the formulation-based methods and heuristics are compared, the relationship between the objective values and the assured maximum waiting time for an emergency job is explored, and a few observations and managerial insights are obtained.

Wind power integration and emission reduction via coal power retrofits in China's quota-based dispatch system: a case study of Jilin Province.

As China expands the renewable share in its power system, coal power is regulated to balance load by adjusting output to meet system demand at all times. In 2016, the Chinese government required existing coal fleets to conduct flexibility retrofits to accommodate more renewable power. We conduct this study to evaluate how coal power retrofits could affect wind integration and emissions at a system level. By constructing a balancing model that respects China's unique quota-based dispatch rules, we use real data from provincial dispatch center of Jilin Province to compare wind power integration and system emissions before and after coal power retrofits. We find that coal power retrofits can significantly reduce wind curtailment from 31 to 7% in our base simulation model. However, emission reductions achieved from coal power retrofits are subject to diminishing marginal returns. We suggest policy efforts to proceed with the strategy of coal power retrofits but carefully evaluate both economic and environmental costs associated with different technologies to reduce pollution and implement from the least expensive ones.

Ranking dispatching rules in multi-objective dynamic flow shop scheduling: a multi-faceted perspective

In this paper, we present a multi-faceted approach for ranking dispatching rules (DRs) in multi-objective dynamic flow shop scheduling systems using data envelopment analysis (DEA). The merits of the proposed DEA-based approach stem in its ability to (1) integrate explicitly, under the same DEA framework, desirable and undesirable performance criteria of DRs without a priori normalisation or aggregation; (2) guarantee that the best DR preserves its benchmarking status regardless of the production scenario; (3) circumvent potential occurrence of multiple efficient DRs through embedding ordered weighted averaging (OWA) under DEA cross evaluation to produce aggregate ranking scores for the DRs. The evaluation of the new ranking approach is conducted using 18 data instances of 20 DRs each. The results reveal that, whatever the OWA optimism level, the preferred DR shifts away from the Shortest Processing Time (SPT) rule to the Cost Over Time (COVERT) rule as due-date tightness becomes relaxed, which appears consistent with known performance expectations of these DRs under such settings. To demonstrate a possible implementation of these results to support decision making in operations scheduling, we present a basic adaptive rule that switches automatically between the preferred rules based on real-time due-date tightness and machine utilisation levels.

Present status of pumped hydro storage operations to mitigate renewable energy fluctuations in Japan

This paper focuses on pumped hydro energy storage (PHES) plants’ current operations after electricity system reforms and variable renewable energy (VRE) installations in Japan. PHES plants have historically been developed to create electricity demand at night in order to operate base load power plants, such as nuclear power plants, in stable conditions. Therefore, many PHES plants are located midway between nuclear power plants and large demand areas. However, all nuclear power plants had to - at least temporarily - shut down after the Great East Japan Earthquake followed by a nuclear accident at Fukushima Daiichi in 2011, and renewable energy power plants have been deployed rapidly after the introduction of a feed-in-tariff (FIT) scheme. Therefore, PHES plants are being used to mitigate fluctuations of VRE, especially in areas where renewable energy has been significantly installed. The daily highest capacity ratio of PHES plants in Kyushu area has recorded three times higher than it in the other areas where the past operating mode is still conducted. But those operations on PHES plants are simply followed as a dispatch rule of the Organization for Cross- regional Coordination of Transmission Operators (OCCTO), market-based operations have not been conducted enough yet. The market design shall be changed to harmonize VRE installation and PHES plants’ operations are necessary to make the transition from the past operating mode of PHES plants across Japan.

Improving genetic algorithm performance by population initialisation with dispatching rules

Scheduling is an important process that is present in many real world scenarios where it is essential to obtain the best possible results. The performance and execution time of algorithms that are used for solving scheduling problems are constantly improved. Although metaheuristic methods by themselves already obtain good results, many studies focus on improving their performance. One way of improvement is to generate an initial population consisting of individuals with better quality. For that purpose a variety of methods can be designed. The benefit of scheduling problems is that dispatching rules (DRs), which are simple heuristics that provide good solutions for scheduling problems in a small amount of time, can be used for that purpose. The goal of this paper is to analyse whether the performance of genetic algorithms can be improved by using such simple heuristics for initialising the starting population of the algorithm. For that purpose both manual and different kinds of automatically designed DRs were used to initialise the starting population of a genetic algorithm. In case of the manually designed DRs, all existing DRs for the unrelated machines environment were used, whereas the automatically designed DRs were generated by using genetic programming. The obtained results clearly demonstrate that using populations initialised by DRs leads to a significantly better performance of the genetic algorithm, especially when using automatically designed DRs. Furthermore, it is also evident that such a population initialisation strategy also improves the convergence speed of the algorithm, since it allows it to obtain significantly better results in the same amount of time. Additionally, the DRs have almost no influence on the execution speed of the genetic algorithm since they construct the schedule in time which is negligible when compared to the execution of the genetic algorithm. Based on the obtained results it can be concluded that initialising individuals by using DRs significantly improves both the convergence and performance of genetic algorithm, without the need of having to manually design new complicated initialisation procedures and without increasing the execution time of the genetic algorithm.

Effect of nature-inspired algorithms and hybrid dispatching rules on the performance of automatic guided vehicles in the flexible manufacturing system

The application of nature-inspired algorithms and priority hybrid dispatching rules for simultaneous scheduling and dispatching of automatic guided vehicles (AGVs) are observed to be highly significant for the best utilization of the flexible manufacturing system (FMS) facility. The earlier studies on the use of AGVs in the FMS have generally focused on minimizing the complexity of AGV operations by optimizing their material handling schedule and their routing in different types of FMS configurations. However, this is achieved only by using an appropriate optimizing algorithm and dispatching rule under different FMS operating conditions. The aim of this paper is to use the simulation methodology so as to compare and analyze the combined effect of four experimental factors, namely four types of priority hybrid dispatching rules, three different nature-inspired algorithms, two levels of loading/unloading times and two levels of machine failures, on the different performance parameters of the FMS. The performance parameters of the FMS analyzed are simultaneous minimization in distance travel and backtracking of AGV, the total production rate of the FMS, mean AGV utilization and mean work center utilization in the FMS. Additionally, in order to find the mean and interaction effect of the experimental factors on the aforementioned performance parameters of the FMS, an analysis of variance (ANOVA) is also carried out. From the results, it is observed that the interaction between experimental factors, namely nature-inspired algorithms and loading–unloading time, has a significant effect on the performance measures, namely simultaneous reduction in distance travel and backtracking, mean work center utilization (%) and total production rate of the FMS. The interaction between aforesaid experimental factors has no significant effect on mean AGV utilization (%) in the FMS. However, the interaction between priority hybrid dispatching rules and loading/unloading times is found to have significant effect on the mean work center utilization (%) in the FMS facility.

The role of intelligent generation control algorithms in optimizing battery energy storage systems size in microgrids: A case study from Western Australia

Battery energy storage systems can play a substantial role in maintaining low-cost operation in microgrids, and therefore finding their optimal size is a key element of microgrids’ planning and design. This paper explores the optimal sizing options for batteries in microgrids that include wind turbines, solar photovoltaics, synchronous machines and a grid connection supply under various types of retail tariff schemes. The optimal size of batteries is hypothesized to be significantly related to the intelligent control rules applied to dispatch the microgrid sources. This problem can be formulated as a mixed linear integer problem and can be solved using linear/non-linear solvers depending on the complexity of the generation control plan. The main objective of this work is to apply online intelligent adaptation mechanism to tune the economic generation control (dispatch) rules of the microgrid. This tuning objectives are maintaining secure operation, maximizing profitable utilization of batteries and managing their charging life-cycles. While sizing options exploration has been formulated as a linear programming based optimization problem, Fuzzy-Logic is proposed to control the charging/discharging time and quantity for batteries. For the sake of performance comparison, various optimization techniques, i.e., Particle Swarm Optimization, Genetic Algorithm and Flower Pollination Algorithm are applied to perform the economic dispatch calculation. As a case study, a commercial type load connected to the 22 kV distribution network in south Western Australia was used in the testing and validation if the results of the proposed sizing method. The operation condition data was obtained from Western Power the distribution and transmission company in south Western Australia, the Australian Bureau Of Meteorology (BOM) and the Australian Energy Market Operator (AEMO). The results showed that employing intelligent batteries in operation can reduce the annual generation cost of microgrids. However, the decision on selecting the size of batteries depends heavily on the amount of upfront investment cost. The simulation results showed that the intelligence added to batteries’ control could achieve 6.5%, 7.6% and 11.5% of the annual generation cost in the Islanded, Grid-connected with no-export and Grid-connected with export operating modes respectively. Also, intelligent batteries operation control was proven to minimize their payback time to 2.8, 2.7 and 2.7 years in the Islanded, Grid-connected with no-export and Grid-connected with export operating modes respectively.

Scheduling Post-Disaster Repairs in Electricity Distribution Networks

Natural disasters, such as hurricanes, earthquakes, and large wind or ice storms, typically cause damages to a large number of components in electricity distribution networks. Since power cannot be restored until these damages are repaired, strategically scheduling the repairs by available crews could reduce the harm done to the affected community. Considering the radial structure of many distribution networks, we model this repair and restoration process as a scheduling problem with soft precedence constraints. As a benchmark, we first formulate this problem as a time-indexed integer linear program (LP) with valid inequalities. Three approximation algorithms with performance guarantees are then proposed to solve this problem: first, an LP-based list scheduling algorithm, second, a single to multi-crew repair schedule conversion algorithm, and third, a dispatch rule based on $\rho$ -factors which can be interpreted as component importance measures. Numerical results validate the effectiveness of the proposed methods.

The Strategies for Improving Energy Efficiency of Power System with Increasing Share of Wind Power in China

Coal-fired power generation will dominate the electricity supply in China in the foreseeable future. Coal fired power units can play a crucial role in integrating intermittent wind energy and improving the overall energy efficiency of the power system. The integration benefits of wind power, along with the gains of high load rates of coal fired units, should be fully taken into account. An optimal model combining wind power and coal fired units is built to analyze the operational flexibility of coal fired units and the integration of wind power. Taking the coal fired units in North China Power Grid as an example, the dispatch costs and benefits are examined under the energy efficiency dispatch mode, in comparison with those under the fair dispatch rules and the installed capacity. The results show that increasing the flexibility of the power system under the energy efficiency dispatch mode may be the best choice for the power system with the high share of coal fired units to integrate more wind power, and that the units delivering flexibility services are financially influenced. The results also indicate that a certain amount of wind power curtailment may be reasonable, and that rational penalty rate and fees for the curtailment of wind power may help to optimize the operation of the power system and integrate more wind power. Based on these results, policy and strategy recommendations are proposed to promote the flexibility of coal fired units and change their operation mode and their dispatch mode in the power system.

On Modelling and Simulating Open Pit Mine Through Stochastic Timed Petri Nets

A structured and robust methodology of modeling and simulation can be available through modular stochastic timed Petri nets, including experiments that allow designers to simulate the processes involved in ore production on well-founded estimates. This prerogative guides an experimental research based on real data from an Brazilian open pit mine operation. Three basic simple modules allows to achieve complex models for a real mine. The difference between simulated and measured average productivities are small when compared to an analytic model for the bottleneck and a model implemented in discrete event system language SIMAN, which also validates the simple truck dispatch rule proposed in this paper. As results of the experiment, we derived a valid simulation structure for the open-pit mining process using Petri nets. It was obtained a behavioral evaluation of the efficiency of the structure according to variations in the probability distribution function.

Balancing multiple objectives with anarchic manufacturing

Balancing multiple conflicting objectives is a problem faced by all manufacturers, with on-time performance and cash position as two important examples. This study compares the effect of changing demand and priority of conflicting objectives within conventional, centralised, hierarchical systems and anarchic distributed systems, via agent-based simulation and modelling. The anarchic manufacturing system, based on free-market principles, delegates decision making authority and autonomy to the lowest possible level. Its proposed inherent flexibility and self-healing characteristics can accommodate complex scenarios. Decision-makers within the hierarchical system selected an appropriate dispatch rule to improve performance against objectives within its span of control. The anarchic system is shown to trade-off on-time and cash performance as respective objectives change priority, while the hierarchical systems do not. This warrants further investigation for distributed and anarchic systems as a system able to flexibly deal with complexity that also reacts to balance multiple objectives is highly desirable.

Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm

Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO2 emission, are seldom considered in the traditional production scheduling problems. Recently, the energy-related scheduling problem has been paid increasingly more attention by researchers. In this paper, an energy-efficient job shop scheduling problem (EJSP) is investigated with the objective of minimizing the sum of the energy consumption cost and the completion-time cost. As the classical JSP is well known as a non-deterministic polynomial-time hard (NP-hard) problem, an improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem. The improvement is performed using dispatching rules (DR), a nonlinear convergence factor (NCF), and a mutation operation (MO). The DR is used to enhance the initial solution quality and overcome the drawbacks of the random population. The NCF is adopted to balance the abilities of exploration and exploitation of the algorithm. The MO is employed to reduce the possibility of falling into local optimum to avoid the premature convergence. To validate the effectiveness of the proposed algorithm, extensive simulations have been performed in the experiment section. The computational data demonstrate the promising advantages of the proposed IWOA for the energy-efficient job shop scheduling problem.

Emergency Medical Service Response: Analyzing Vehicle Dispatching Rules

In an era of information and advanced computing power, emergency medical services (EMS) still rely on rudimentary vehicle dispatching and reallocation rules. In many countries, road conditions such as traffic or road blocks, exact vehicle positions, and demand prediction are valuable information that is not considered when locating and dispatching emergency vehicles. Within this context, this paper presents an investigation of different EMS vehicle dispatching rules by comparing them using various metrics and frameworks. An intelligent dispatching algorithm is proposed, and survival metrics are introduced to compare the new concepts with the classic ones. This work shows that the closest idle vehicle rule (classic dispatching rule) is far from optimal and even a random dispatching of vehicles can outperform it. The proposed intelligent algorithm has the best performance in all the tested situations where resources are adequate. If resources are scarce, especially during peaks in demand, dispatching delays will occur, degrading the system’s performance. In this case, no conclusion could be drawn as to which rule might be the best option. Nevertheless, it draws attention to the need for research focused on managing dispatch delays by prioritizing the waiting calls that inflict the higher penalty on the system performance. Finally, the authors conclude that the use of real traffic information introduces a considerable gain to the EMS response performance.

A modified Genetic Algorithm approach to minimize total weighted tardiness with stochastic rework and reprocessing times

Abstract Scheduling challenges are typical with electronics manufacturing services (EMS) providers. The rework and reprocessing of failed electronics components consume more time in the production line, causing jobs to miss their due dates. A mathematical model and a Modified Shortest Total Estimated Processing Time (MSTEPT) Algorithm to minimize the Total Weighted Tardiness (TWT) are proposed in this research. This research then develops a novel modified Genetic Algorithm approach to solve the scheduling problem with stochastic rework and reprocessing time. While the Genetic Algorithm as a methodology to solve scheduling problems has been developed in earlier research articles, the existing set of genes in the chromosomes of a regular Genetic Algorithm would not be able of handle jobs waiting to undergo reprocessing. The modified Genetic Algorithm in this research introduces the concept of priority genes, specifically encoded to handle jobs waiting to be reprocessed after they have been reworked. Experimental results indicate that the proposed modified GA outperforms the best of different commonly used dispatch rules, in terms of solution quality. For small-to-medium-sized job shops, the proposed algorithm outperforms optimal results from CPLEX® optimal solver, as well as those from the MSTEPT algorithm.

Integrative Programming of Production and Preventive Maintenance Tasks in an Environment of Identical Parallel Machines: Proposal of a Construction Heuristic

Abstract The article presents a constructive heuristic for the integrated programming of production orders and preventive maintenance (PM) interventions in an environment of identical parallel machines, aimed at minimizing the completion time of the last scheduled job (makespan). Unlike other proposals found in the literature, the one here considers that the time between preventive interventions is not previously known, but instead, this is assumed as one of the decision variables of the system in the beginning of each of these interventions; in addition, the proposal also considers the possibility that not all jobs have the same release time. The design of the heuristic algorithm is based on the general approach of the Longest Processing Time dispatch rule, with a slight modification in its conception to consider the random character of the equipment failures and their impact on the execution time of the tasks. Consequently, a simple dispatch rule called rj - LPT was also proposed to comply with one of the steps of the heuristic, as well as an algorithm to determine the minimum dimension for the makespan, which constitutes the basis of the stop criterion linked to the initial solution.

A Hybrid Genetic Algorithm for Solving the Unrelated Parallel Machine Scheduling Problem with Sequence Dependent Setup Times

The parallel machine scheduling problem is a well known combinatorial optimization problem with several applications in computer science, telecommunications and operations management. Several studies have shown that exact approaches for this problem are not useful in practical situations due to the high computational costs involved. This paper describes a hybrid genetic algorithm for solving the unrelated parallel machine scheduling problem with sequence dependent setup times. Computational results with simulated data are presented and discussed. A case study on the granite industry is presented.The proposed approach outperformed three traditional dispatch rules presented in the current literature.