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Showing papers on "Job shop scheduling published in 2019"


Journal ArticleDOI
TL;DR: The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.
Abstract: In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.

421 citations


Proceedings ArticleDOI
19 Aug 2019
TL;DR: Decima as discussed by the authors uses reinforcement learning and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective, such as minimizing average job completion time, and shows that RL techniques can generate highly-efficient policies automatically.
Abstract: Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems use simple, generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective, such as minimizing average job completion time. However, off-the-shelf RL techniques cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves average job completion time by at least 21% over hand-tuned scheduling heuristics, achieving up to 2x improvement during periods of high cluster load.

310 citations


Journal ArticleDOI
TL;DR: This paper explores the future research direction in SDS and discusses the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.
Abstract: Traditional job shop scheduling is concentrated on centralized scheduling or semi-distributed scheduling. Under the Industry 4.0, the scheduling should deal with a smart and distributed manufacturing system supported by novel and emerging manufacturing technologies such as mass customization, Cyber-Physics Systems, Digital Twin, and SMAC (Social, Mobile, Analytics, Cloud). The scheduling research needs to shift its focus to smart distributed scheduling modeling and optimization. In order to transferring traditional scheduling into smart distributed scheduling (SDS), we aim to answer two questions: (1) what traditional scheduling methods and techniques can be combined and reused in SDS and (2) what are new methods and techniques required for SDS. In this paper, we first review existing researches from over 120 papers and answer the first question and then we explore a future research direction in SDS and discuss the new techniques for developing future new JSP scheduling models and constructing a framework on solving the JSP problem under Industry 4.0.

308 citations


Journal ArticleDOI
TL;DR: A model-free approach based on deep reinforcement learning is proposed to determine the optimal strategy for charging strategy due to the existence of randomness in traffic conditions, user's commuting behavior, and the pricing process of the utility.
Abstract: Driven by the recent advances in electric vehicle (EV) technologies, EVs have become important for smart grid economy. When EVs participate in demand response program which has real-time pricing signals, the charging cost can be greatly reduced by taking full advantage of these pricing signals. However, it is challenging to determine an optimal charging strategy due to the existence of randomness in traffic conditions, user’s commuting behavior, and the pricing process of the utility. Conventional model-based approaches require a model of forecast on the uncertainty and optimization for the scheduling process. In this paper, we formulate this scheduling problem as a Markov Decision Process (MDP) with unknown transition probability. A model-free approach based on deep reinforcement learning is proposed to determine the optimal strategy for this problem. The proposed approach can adaptively learn the transition probability and does not require any system model information. The architecture of the proposed approach contains two networks: a representation network to extract discriminative features from the electricity prices and a Q network to approximate the optimal action-value function. Numerous experimental results demonstrate the effectiveness of the proposed approach.

277 citations


Journal ArticleDOI
TL;DR: This paper studies an extension of the well known permutation flowshop scheduling problem in which there is a set of identical factories, each one with a flowshop structure, and presents simple Iterated Greedy algorithms that have performed well in related problems.
Abstract: Large manufacturing firms operate more than one production center. As a result, in relation to scheduling problems, which factory manufactures which product is an important consideration. In this paper we study an extension of the well known permutation flowshop scheduling problem in which there is a set of identical factories, each one with a flowshop structure. The objective is to minimize the maximum completion time or makespan among all factories. The resulting problem is known as the distributed permutation flowshop and has attracted considerable interest over the last few years. Contrary to the recent trend in the scheduling literature, where complex nature-inspired or metaphor-based methods are often proposed, we present simple Iterated Greedy algorithms that have performed well in related problems. Improved initialization, construction and destruction procedures, along with a local search with a strong intensification are proposed. The result is a very effective algorithm with little problem-specific knowledge that is shown to provide demonstrably better solutions in a comprehensive and thorough computational and statistical campaign.

255 citations


Journal ArticleDOI
TL;DR: A novel thoughtful decomposition based on the technique of the Logic-Based Benders Decomposition is designed, which solves a relaxed master, with fewer constraints, and a subproblem, whose resolution allows the generation of cuts which will, iteratively, guide the master to tighten its search space.
Abstract: Multi-access edge computing (MEC) has recently emerged as a novel paradigm to facilitate access to advanced computing capabilities at the edge of the network, in close proximity to end devices, thereby enabling a rich variety of latency sensitive services demanded by various emerging industry verticals. Internet-of-Things (IoT) devices, being highly ubiquitous and connected, can offload their computational tasks to be processed by applications hosted on the MEC servers due to their limited battery, computing, and storage capacities. Such IoT applications providing services to offloaded tasks of IoT devices are hosted on edge servers with limited computing capabilities. Given the heterogeneity in the requirements of the offloaded tasks (different computing requirements, latency, and so on) and limited MEC capabilities, we jointly decide on the task offloading (tasks to application assignment) and scheduling (order of executing them), which yields a challenging problem of combinatorial nature. Furthermore, we jointly decide on the computing resource allocation for the hosted applications, and we refer this problem as the Dynamic Task Offloading and Scheduling problem, encompassing the three subproblems mentioned earlier. We mathematically formulate this problem, and owing to its complexity, we design a novel thoughtful decomposition based on the technique of the Logic-Based Benders Decomposition. This technique solves a relaxed master, with fewer constraints, and a subproblem, whose resolution allows the generation of cuts which will, iteratively, guide the master to tighten its search space. Ultimately, both the master and the sub-problem will converge to yield the optimal solution. We show that this technique offers several order of magnitude (more than 140 times) improvements in the run time for the studied instances. One other advantage of this method is its capability of providing solutions with performance guarantees. Finally, we use this method to highlight the insightful performance trends for different vertical industries as a function of multiple system parameters with a focus on the delay-sensitive use cases.

238 citations


Journal ArticleDOI
TL;DR: The mathematical model of FJSP is presented, the constraints in applications are summarized, and the encoding and decoding strategies for connecting the problem and algorithms are reviewed to give insight into future research directions.
Abstract: Flexible job shop scheduling problems ( FJSP ) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence ( SI ) and evolutionary algorithms ( EA ) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm ( GA ) and one newest imperialist competitive algorithm ( ICA ) with variables neighborhood search ( VNS ) for solving FJSP are presented. Finally, we summarize, discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.

221 citations


Journal ArticleDOI
TL;DR: A systematic review as well as classification of proposed scheduling techniques along with their advantages and limitations of cloud computing are provided.

220 citations


Journal ArticleDOI
TL;DR: This paper investigates the joint problem of partial offloading scheduling and resource allocation for MEC systems with multiple independent tasks, and proposes iterative algorithms for the joint issue of POSP.
Abstract: Mobile edge computing (MEC) is a promising technique to enhance computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling, and resource allocation for MEC systems is a challenging issue. In this paper, we investigate the joint problem of partial offloading scheduling and resource allocation for MEC systems with multiple independent tasks. A partial offloading scheduling and power allocation (POSP) problem in single-user MEC systems is formulated. The goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraint of the tasks. The execution delay of tasks running at both MEC and mobile device is considered. The energy consumption of both the task computing and task data transmission is considered as well. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Lagrangian dual decomposition. The task offloading decision and offloading scheduling problem, given the allocated transmission power, is solved in the upper level using flow shop scheduling theory or greedy strategy, and the suboptimal power allocation with the partial offloading decision is obtained in the lower level using convex optimization techniques. We propose iterative algorithms for the joint problem of POSP. Numerical results demonstrate that the proposed algorithms achieve near-optimal delay performance with a large energy consumption reduction.

210 citations


Journal ArticleDOI
TL;DR: It is proved that the task offloading scheduling problem is NP-hard, and centralized and distributed Greedy Maximal Scheduling algorithms are introduced to resolve the problem efficiently.
Abstract: Mobile Edge Cloud Computing (MECC) has becoming an attractive solution for augmenting the computing and storage capacity of Mobile Devices (MDs) by exploiting the available resources at the network edge. In this work, we consider computation offloading at the mobile edge cloud that is composed of a set of Wireless Devices (WDs), and each WD has an energy harvesting equipment to collect renewable energy from the environment. Moreover, multiple MDs intend to offload their tasks to the mobile edge cloud simultaneously. We first formulate the multi-user multi-task computation offloading problem for green MECC, and use Lyaponuv Optimization Approach to determine the energy harvesting policy: how much energy to be harvested at each WD; and the task offloading schedule: the set of computation offloading requests to be admitted into the mobile edge cloud, the set of WDs assigned to each admitted offloading request, and how much workload to be processed at the assigned WDs. We then prove that the task offloading scheduling problem is NP-hard, and introduce centralized and distributed Greedy Maximal Scheduling algorithms to resolve the problem efficiently. Performance bounds of the proposed schemes are also discussed. Extensive evaluations are conducted to test the performance of the proposed algorithms.

200 citations


Journal ArticleDOI
TL;DR: This paper studies the joint optimization of cost and makespan of scheduling workflows in IaaS clouds, and proposes a novel workflow scheduling scheme which closely integrates the fuzzy dominance sort mechanism with the list scheduling heuristic HEFT.

Journal ArticleDOI
TL;DR: A comprehensive computational campaign against the closely related and well performing algorithms in the literature is carried out and the results show that both the presented constructive heuristics and metaheuristics are very effective for solving the DPFSP with total flowtime criterion.
Abstract: Distributed permutation flowshop scheduling problem (DPFSP) has become a very active research area in recent years. However, minimizing total flowtime in DPFSP, a very relevant and meaningful objective for today's dynamic manufacturing environment, has not captured much attention so far. In this paper, we address the DPFSP with total flowtime criterion. To suit the needs of different CPU time demands and solution quality, we present three constructive heuristics and four metaheuristics. The constructive heuristics are based on the well-known LR and NEH heuristics. The metaheuristics are based on the high-performing frameworks of discrete artificial bee colony, scatter search, iterated local search, and iterated greedy, which have been applied with great success to closely related scheduling problems. We explore the problem-specific knowledge and accelerations to evaluate neighboring solutions for the considered problem. We introduce advanced and effective technologies like a referenced local search, a strategy to escape from local optima, and an enhanced intensive search method for the presented metaheuristics. A comprehensive computational campaign against the closely related and well performing algorithms in the literature is carried out. The results show that both the presented constructive heuristics and metaheuristics are very effective for solving the DPFSP with total flowtime criterion.

Journal ArticleDOI
TL;DR: This paper discretizes a novel and simple metaheuristic, named Jaya, resulting in DJaya, and improves it to solve FJRP for new job insertion arising from pump remanufacturing, and proposes five objective-oriented local search operators and four ensembles of them to improve the performance of DJaya.
Abstract: Rescheduling is a necessary procedure for a flexible job shop when newly arrived priority jobs must be inserted into an existing schedule. Instability measures the amount of change made to the existing schedule and is an important metrics to evaluate the quality of rescheduling solutions. This paper focuses on a flexible job-shop rescheduling problem (FJRP) for new job insertion. First, it formulates FJRP for new job insertion arising from pump remanufacturing. This paper deals with bi-objective FJRPs to minimize: 1) instability and 2) one of the following indices: a) makespan; b) total flow time; c) machine workload; and d) total machine workload. Next, it discretizes a novel and simple metaheuristic, named Jaya, resulting in DJaya and improves it to solve FJRP. Two simple heuristics are employed to initialize high-quality solutions. Finally, it proposes five objective-oriented local search operators and four ensembles of them to improve the performance of DJaya. Finally, it performs experiments on seven real-life cases with different scales from pump remanufacturing and compares DJaya with some state-of-the-art algorithms. The results show that DJaya is effective and efficient for solving the concerned FJRPs.

Journal ArticleDOI
TL;DR: The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.
Abstract: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing.

Journal ArticleDOI
TL;DR: This paper extends the classical DQN to address the decisions of multiple edge devices, and shows that the proposed method performs better than the other methods using only one dispatching rule.
Abstract: Manufacturing is involved with complex job shop scheduling problems (JSP). In smart factories, edge computing supports computing resources at the edge of production in a distributed way to reduce response time of making production decisions. However, most works on JSP did not consider edge computing. Therefore, this paper proposes a smart manufacturing factory framework based on edge computing, and further investigates the JSP under such a framework. With recent success of some AI applications, the deep Q network (DQN), which combines deep learning and reinforcement learning, has showed its great computing power to solve complex problems. Therefore, we adjust the DQN with an edge computing framework to solve the JSP. Different from the classical DQN with only one decision, this paper extends the DQN to address the decisions of multiple edge devices. Simulation results show that the proposed method performs better than the other methods using only one dispatching rule.

Journal ArticleDOI
TL;DR: A novel dynamic energy management system is developed to incorporate efficient management of energy storage system into MG real- time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price.
Abstract: This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system is developed to incorporate efficient management of energy storage system into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price. The developed dynamic energy management mechanism does not require long-term forecast and optimization or distribution knowledge of the uncertainty, but can still optimize the long-term operational costs of MGs. First, the real-time scheduling problem is modeled as a finite-horizon Markov decision process over a day. Then, approximate dynamic programming and deep recurrent neural network learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California independent system operator, a detailed simulation study is carried out to validate the effectiveness of the proposed method.

Journal ArticleDOI
TL;DR: This paper evaluates the scheduling problem for energy hub system consisting of wind turbine, combined heat and power units, auxiliary boilers, and energy storage devices via hybrid stochastic/information gap decision theory (IGDT) approach and optimizes energy hub scheduling problem in uncertain environment by mixed-integer nonlinear programming.
Abstract: This paper evaluates the scheduling problem for energy hub system consisting of wind turbine, combined heat and power units, auxiliary boilers, and energy storage devices via hybrid stochastic/information gap decision theory (IGDT) approach. Considering that energy hub plays an undeniable role as the coupling among various energy infrastructures, still it is essential to be investigated in both modeling and scheduling aspects. On the other hand, penetration of wind power generation is significantly increased in energy infrastructures in recent years. In response, this paper aims to focus on the hybrid stochastic/IGDT optimization method for the optimal scheduling of wind integrated energy hub considering the uncertainties of wind power generation, energy prices and energy demands explicitly in a way that not only global optimal solution can be reached, but also volume of computations can be lighten. In addition, by the proposed hybrid model, the energy hub operator can pursue two different strategies to face with price uncertainty, i.e., risk-seeker strategy and risk-averse strategy. This method optimizes energy hub scheduling problem in uncertain environment by mixed-integer nonlinear programming. This formulation is proposed to minimize the expected operation cost of energy hub where different energy demands of energy hub would be efficiently met. The forecast errors of uncertainties related to wind power generation and energy demands are modeled as a scenario, while an IGDT optimization approach is proposed to model electricity price uncertainty.

Journal ArticleDOI
01 Jul 2019
TL;DR: Computational results show that selection of genetic operation type has a great influence on the quality of solutions, and the proposed algorithm could generate better solutions compared to other developed algorithms in terms of computational times and objective values.
Abstract: Open-shop scheduling problem (OSSP) is a well-known topic with vast industrial applications which belongs to one of the most important issues in the field of engineering. OSSP is a kind of NP problems and has a wider solution space than other basic scheduling problems, i.e., Job-shop and flow-shop scheduling. Due to this fact, this problem has attracted many researchers over the past decades and numerous algorithms have been proposed for that. This paper investigates the effects of crossover and mutation operator selection in Genetic Algorithms (GA) for solving OSSP. The proposed algorithm, which is called EGA_OS, is evaluated and compared with other existing algorithms. Computational results show that selection of genetic operation type has a great influence on the quality of solutions, and the proposed algorithm could generate better solutions compared to other developed algorithms in terms of computational times and objective values.

Journal ArticleDOI
TL;DR: An evolutionary multiobjective robust scheduling algorithm is suggested, in which solutions obtained by a variant of single-objective heuristic are incorporated into population initialization and two novel crossover operators are proposed to take advantage of nondominated solutions.
Abstract: In various flow shop scheduling problems, it is very common that a machine suffers from breakdowns. Under this situation, a robust and stable suboptimal scheduling solution is of more practical interest than a global optimal solution that is sensitive to environmental changes. However, blocking lot-streaming flow shop (BLSFS) scheduling problems with machine breakdowns have not yet been well studied up to date. This paper presents, for the first time, a multiobjective model of the above problem including robustness and stability criteria. Based on this model, an evolutionary multiobjective robust scheduling algorithm is suggested, in which solutions obtained by a variant of single-objective heuristic are incorporated into population initialization and two novel crossover operators are proposed to take advantage of nondominated solutions. In addition, a rescheduling strategy based on the local search is presented to further reduce the negative influence resulted from machine breakdowns.The proposed algorithm is applied to 22 test sets, and compared with the state-of-the-art algorithms without machine breakdowns. Our empirical results demonstrate that the proposed algorithm can effectively tackle BLSFS scheduling problems in the presence of machine breakdowns by obtaining scheduling strategies that are robust and stable.

Journal ArticleDOI
TL;DR: A multi-objective optimization model with the objective of minimizing energy consumption and makespan is formulated for a flexible job shop scheduling problem with transportation constraints and an enhanced genetic algorithm is developed to solve the problem.
Abstract: Manufacturing enterprises nowadays face huge environmental challenges because of energy consumption and associated environmental impacts. One of the effective strategies to reduce energy consumption is by employing intelligent scheduling techniques. Production scheduling can have significant impact on energy saving in manufacturing system from the operation management point of view. Resource flexibility and complex constraints in flexible manufacturing system make production scheduling a complicated nonlinear programming problem. To this end, a multi-objective optimization model with the objective of minimizing energy consumption and makespan is formulated for a flexible job shop scheduling problem with transportation constraints. Then, an enhanced genetic algorithm is developed to solve the problem. Finally, comprehensive experiments are carried out to evaluate the performance of the proposed model and algorithm. The experimental results revealed that the proposed model and algorithm can solve the problem effectively and efficiently. This may provide a basis for the decision makers to consider energy-efficient scheduling in flexible manufacturing system.

Journal ArticleDOI
TL;DR: An alternative method for cloud task scheduling problem which aims to minimize makespan that required to schedule a number of tasks on different Virtual Machines (VMs) is presented and the proposed MSDE algorithm outperformed other algorithms according to the performance measures.
Abstract: This paper presents an alternative method for cloud task scheduling problem which aims to minimize makespan that required to schedule a number of tasks on different Virtual Machines (VMs). The proposed method is based on the improvement of the Moth Search Algorithm (MSA) using the Differential Evolution (DE). The MSA simulates the behavior of moths to fly towards the source of light in nature through using two concepts, the phototaxis and Levy flights that represent the exploration and exploitation ability respectively. However, the exploitation ability is still needed to be improved, therefore, the DE can be used as local search method. In order to evaluate the performance of the proposed MSDE algorithm, a set of three experimental series are performed. The first experiment aims to compare the traditional MSA and the proposed algorithm to solve a set of twenty global optimization problems. Meanwhile, in second and third experimental series the performance of the proposed algorithm to solve the cloud task scheduling problem is compared against other heuristic and meta-heuristic algorithms for synthetical and real trace data, respectively. The results of the two experimental series show that the proposed algorithm outperformed other algorithms according to the performance measures.

Journal ArticleDOI
TL;DR: This article proposes a joint collaborative caching and processing framework that supports Adaptive Bitrate (ABR)-video streaming in MEC networks and proposes practically efficient solutions, including a novel heuristic ABR-aware proactive cache placement algorithm when video popularity is available.
Abstract: Mobile-Edge Computing (MEC) is a promising paradigm that provides storage and computation resources at the network edge in order to support low-latency and computation-intensive mobile applications. In this article, we propose a joint collaborative caching and processing framework that supports Adaptive Bitrate (ABR)-video streaming in MEC networks. We formulate an Integer Linear Program (ILP) that determines the placement of video variants in the caches and the scheduling of video requests to the cache servers so as to minimize the expected delay cost of video retrieval. The considered problem is challenging due to its NP-completeness and to the lack of a-priori knowledge about video request arrivals. Our approach decomposes the original problem into a cache placement problem and a video request scheduling problem while preserving the interplay between the two. We then propose practically efficient solutions, including: (i) a novel heuristic ABR-aware proactive cache placement algorithm when video popularity is available, and (ii) an online low-complexity video request scheduling algorithm that performs very closely to the optimal solution. Simulation results show that our proposed solutions achieve significant increase in terms of cache hit ratio and decrease in backhaul traffic and content access delay compared to the traditional approaches.

Journal ArticleDOI
TL;DR: Issues in the context of urgent need for energy-conservation as well as the advent of globalized and multi-factory manufacture motivate the attempts to address a stochastic multi-objective distributed permutation flow shop scheduling problem by considering total tardiness constraint via minimizing the makespan and the total energy consumption.

Journal ArticleDOI
TL;DR: A two-phase meta-heuristic (TPM) based on imperialist competitive algorithm (ICA) and variable neighborhood search (VNS) is proposed and computational results show that TPM is a very competitive algorithm for the considered FJSP.
Abstract: Flexible job shop scheduling problem (FJSP) has been extensively considered; however, multiobjective FJSP with energy consumption threshold is seldom investigated, the goal of which is to minimize makespan and total tardiness under the constraint that total energy consumption does not exceed a given threshold. Energy constraint is not always met and the threshold is difficult to be decided in advance. These features make it more difficult to solve the problem. In this paper, a two-phase meta-heuristic (TPM) based on imperialist competitive algorithm (ICA) and variable neighborhood search (VNS) is proposed. In the first phase, the problem is converted into FJSP with makespan, total tardiness and total energy consumption and the new FJSP is solved by an ICA, which uses some new methods to build initial empires and do imperialist competition. In the second phase, new strategies are provided for comparing solutions and updating the nondominated set of the first phase and a VNS is used for the original problem. The current solution of VNS is periodically replaced with member of the set $\Omega $ to improve solution quality. An energy consumption threshold is obtained by optimization. Extensive experiments are conducted to test the performance of TPM finally. The computational results show that TPM is a very competitive algorithm for the considered FJSP.

Journal ArticleDOI
Yilin Fang1, Chao Peng1, Ping Lou1, Zude Zhou1, Jianmin Hu1, Junwei Yan1 
TL;DR: The architecture and working principle of the new job shop scheduling mode based on digital twin are introduced, and scheduling resource parameter updating methods and dynamic interactive scheduling strategies are proposed to achieve real-time and precise scheduling.
Abstract: Job shop scheduling always plays an important role in the manufacturing process and is one of the decisive factors influencing manufacturing efficiency. In the actual process of production scheduling, there exist some uncertain events, information asymmetry, and abnormal disturbance, which would cause the execution deviation and affect the efficiency and quality of scheduling execution. Traditional scheduling methods are not sufficient to solve the challenges well. Due to the rise of digital twin, which has the characters of virtual reality interaction, real-time mapping, and symbiotic evolution, a new job shop scheduling method based on digital twin is proposed to reduce the scheduling deviation. In this article, the architecture and working principle of the new job shop scheduling mode are introduced. Then, scheduling resource parameter updating methods and dynamic interactive scheduling strategies are proposed to achieve real-time and precise scheduling. Finally, a prototype system is designed to verify the validity of this new job shop scheduling mode.

Journal ArticleDOI
TL;DR: It is shown that mathematical programming and heuristics are frequently applied in the complex linear and combinatorial optimization problems.
Abstract: This paper provides a comprehensive survey of research on operating room planning and scheduling problems. Aiming to give a comprehensive classification on the studied problems, we review the literature from the perspectives of decision level, scheduling strategy, patient characteristics, problem setting, uncertainty, mathematical models, and solutions and methods. The papers are reviewed in diversified ways so as to obtain a detailed overview in this area, and the fields that need to be focused on are summarized. It shows that mathematical programming and heuristics are frequently applied in the complex linear and combinatorial optimization problems. Furthermore, future research trends and directions on operating room planning and scheduling are also identified.

Journal ArticleDOI
TL;DR: A hybrid task scheduling algorithm named FMPSO that is based on Fuzzy system and Modified Particle Swarm Optimization technique to enhance load balancing and cloud throughput and achieves the goal of minimizing the execution time and resource usage is proposed.

Journal ArticleDOI
TL;DR: An extension of the VRPD that is called VRPDERO, where drones may not only be launched and retrieved at vertices but also on some discrete points that are located on each arc, is proposed and some valid inequalities that enhance the performance of the MILP solvers are introduced.

Journal ArticleDOI
TL;DR: The aim of the addressed filtering problem is to design a recursive filter such that the filtering error covariance could be minimized by properly designing the filter gain at each time instant.
Abstract: This paper is concerned with the recursive filtering problem for a class of networked linear time-varying systems subject to the scheduling of the random access protocol (RAP). The communication between the sensor nodes and the remote filter is implemented via a shared network. For the purpose of preventing the data from collisions, only one sensor node is allowed to get access to the network at each time instant. The transmission order of sensor nodes is orchestrated by the RAP scheduling, under which the selected nodes obtaining access to the network could be characterized by a sequence of independent and identically-distributed variables. The aim of the addressed filtering problem is to design a recursive filter such that the filtering error covariance could be minimized by properly designing the filter gain at each time instant. The desired filter gain is calculated recursively by solving two Riccati-like difference equations. Furthermore, the boundedness issue of the corresponding filtering error covariance is investigated. Sufficient conditions are obtained to ensure the lower and upper bounds of the filtering error covariance. Two illustrative examples are given to demonstrate the correctness and effectiveness ofour developed recursive filtering approach.

Journal ArticleDOI
TL;DR: According to the numerical results, the use of drones can significantly reduce the makespan and the proposed VIEQ as well as the matheuristic approach have a significant contribution in solving the VRPD effectively.
Abstract: In this work, we are interested in studying the Vehicle Routing Problem with Drones (VRPD). Given a fleet of trucks, where each truck carries a given number of drones, the objective consists in designing feasible routes and drone operations such that all customers are served and minimal makespan is achieved. We formulate the VRPD as a Mixed Integer Linear Program (MILP), which can be solved by any standard MILP solver. Moreover, with the aim of improving the performance of solvers, we introduce several sets of valid inequalities (VIEQ). Due to limited performance of the solvers in addressing large instances, we propose a matheuristic approach that effectively exploits the problem structure of the VRPD. Integral to this approach, we propose the Drone Assignment and Scheduling Problem (DASP) that, given an existing routing of trucks, looks for an optimal assignment and schedule of drones such that the makespan is minimized. In this context, we propose two MILP formulations for the DASP. In order to evaluate the performance of a state-of-the-art solver in tackling the MILP formulation of the VRPD, the benefit of the proposed VIEQs, and the performance of the matheuristic, we carried out extensive computational experiments. According to the numerical results, the use of drones can significantly reduce the makespan and the proposed VIEQ as well as the matheuristic approach have a significant contribution in solving the VRPD effectively.