Showing papers on "Flow shop scheduling published in 2016"

A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0

Abstract: Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analy...

Automated Design of Production Scheduling Heuristics: A Review

Abstract: Hyper-heuristics have recently emerged as a powerful approach to automate the design of heuristics for a number of different problems. Production scheduling is a particularly popular application area for which a number of different hyper-heuristics have been developed and are shown to be effective, efficient, easy to implement, and reusable in different shop conditions. In particular, they seem to be a promising way to tackle highly dynamic and stochastic scheduling problems, an aspect that is specifically emphasized in this survey. Despite their success and the substantial number of papers in this area, there is currently no systematic discussion of the design choices and critical issues involved in the process of developing such approaches. This paper strives to fill this gap by summarizing the state-of-the-art approaches, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling. This paper also identifies challenges and open questions and highlights various directions for future work.

Delay-Aware Scheduling and Resource Optimization With Network Function Virtualization

Abstract: To accelerate the implementation of network functions/middle boxes and reduce the deployment cost, recently, the concept of network function virtualization (NFV) has emerged and become a topic of much interest attracting the attention of researchers from both industry and academia. Unlike the traditional implementation of network functions, a software-oriented approach for virtual network functions (VNFs) creates more flexible and dynamic network services to meet a more diversified demand. Software-oriented network functions bring along a series of research challenges, such as VNF management and orchestration, service chaining, VNF scheduling for low latency and efficient virtual network resource allocation with NFV infrastructure, among others. In this paper, we study the VNF scheduling problem and the corresponding resource optimization solutions. Here, the VNF scheduling problem is defined as a series of scheduling decisions for network services on network functions and activating the various VNFs to process the arriving traffic. We consider VNF transmission and processing delays and formulate the joint problem of VNF scheduling and traffic steering as a mixed integer linear program. Our objective is to minimize the makespan/latency of the overall VNFs’ schedule. Reducing the scheduling latency enables cloud operators to service (and admit) more customers, and cater to services with stringent delay requirements, thereby increasing operators’ revenues. Owing to the complexity of the problem, we develop a genetic algorithm-based method for solving the problem efficiently. Finally, the effectiveness of our heuristic algorithm is verified through numerical evaluation. We show that dynamically adjusting the bandwidths on virtual links connecting virtual machines, hosting the network functions, reduces the schedule makespan by 15%–20% in the simulated scenarios.

Parallel Machine Scheduling Under Time-of-Use Electricity Prices: New Models and Optimization Approaches

Abstract: The industrial sector is one of the largest energy consumers in the world. To alleviate the grid’s burden during peak hours, time-of-use (TOU) electricity pricing has been implemented in many countries around the globe to encourage manufacturers to shift their electricity usage from peak periods to off-peak periods. In this paper, we study the unrelated parallel machine scheduling problem under a TOU pricing scheme. The objective is to minimize the total electricity cost by appropriately scheduling the jobs such that the overall completion time does not exceed a predetermined production deadline. To solve this problem, two solution approaches are presented. The first approach models the problem with a new time-interval-based mixed integer linear programming formulation. In the second approach, we reformulate the problem using Dantzig–Wolfe decomposition and propose a column generation heuristic to solve it. Computational experiments are conducted under different TOU settings and the results confirm the effectiveness of the proposed methods. Based on the numerical results, we provide some practical suggestions for decision makers to help them in achieving a good balance between the productivity objective and the energy cost objective.

An Estimation of Distribution Algorithm-Based Memetic Algorithm for the Distributed Assembly Permutation Flow-Shop Scheduling Problem

Abstract: In this paper, an estimation of distribution algorithm (EDA)-based memetic algorithm (MA) is proposed for solving the distributed assembly permutation flow-shop scheduling problem (DAPFSP) with the objective to minimize the maximum completion time. A novel bi-vector-based method is proposed to represent a solution for the DAPFSP. In the searching phase of the EDA-based MA (EDAMA), the EDA-based exploration and the local-search-based exploitation are incorporated within the MA framework. For the EDA-based exploration phase, a probability model is built to describe the probability distribution of superior solutions. Besides, a novel selective-enhancing sampling mechanism is proposed for generating new solutions by sampling the probability model. For the local-search-based exploitation phase, the critical path of the DAPFSP is analyzed to avoid invalid searching operators. Based on the analysis, a critical-path-based local search strategy is proposed to further improve the potential solutions obtained in the EDA-based searching phase. Moreover, the effect of parameter setting is investigated based on the Taguchi method of design-of-experiment. Suitable parameter values are suggested for instances with different scales. Finally, numerical simulations based on 1710 benchmark instances are carried out. The experimental results and comparisons with existing algorithms show the effectiveness of the EDAMA in solving the DAPFSP. In addition, the best-known solutions of 181 instances are updated by the EDAMA.

Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives

Abstract: Flexible job-shop scheduling problem (FJSP) is a practically useful extension of the classical job shop scheduling problem. This paper proposes an effective discrete harmony search (DHS) algorithm to solve FJSP. The objectives are the weighted combination of two minimization criteria namely, the maximum of the completion time (Makespan) and the mean of earliness and tardiness. Firstly, we develop a new method for the initial machine assignment task. Some existing heuristics are also employed for initializing the harmony memory with discrete machine permutation for machine assignment and job permutation for operation sequencing. Secondly, we develop a new rule for the improvisation to produce a new harmony for FJSP incorporating machine assignment and operation sequencing. Thirdly, several local search methods are embedded to enhance the algorithm's local exploitation ability. Finally, extensive computational experiments are carried out using well-known benchmark instances. Computational results and comparisons show the efficiency and effectiveness of the proposed DHS algorithm for solving the FJSP with weighted combination of two objectives.

Dynamic Multiple-Period Reconfiguration of Real-Time Scheduling Based on Timed DES Supervisory Control

Abstract: Based on the supervisory control theory (SCT) of timed discrete-event systems (TDES), this study presents a dynamic reconfiguration technique for real-time scheduling of real-time systems running on uni-processors. A new formalism is developed to assign periodic tasks with multiple-periods. By implementing SCT, a real-time system (RTS) is dynamically reconfigured when its initial safe execution sequence set is empty. During the reconfiguration process, based on the multiple-periods, the supervisor proposes different safe execution sequences. Two real-world examples illustrate that the presented approach provides an increased number of safe execution sequences as compared with the earliest-deadline-first (EDF) scheduling algorithm.

Optimal One-Wafer Cyclic Scheduling and Buffer Space Configuration for Single-Arm Multicluster Tools With Linear Topology

Abstract: This work studies the scheduling problem of a single-arm multicluster tool with a linear topology and process-bound bottleneck individual tool. The objective is to find a one-wafer cyclic schedule such that the lower bound of cycle time is reached by optimally configuring spaces in buffering modules that link individual cluster tools. A Petri net (PN) model is developed to describe the dynamic behavior of the system by extending resource-oriented PNs such that a schedule can be parameterized by robots’ waiting time. Based on this model, conditions are presented under which a one-wafer cyclic schedule with the lower bound of cycle time can be found. With the derived conditions, an algorithm is developed to find such a schedule and optimally configure buffer spaces. The algorithm requires only simple calculation to set the robots’ waiting time and buffer size. Illustrative examples are presented to demonstrate the proposed method.

Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion

Abstract: This study addresses flexible job shop scheduling problem (FJSP) with two constraints, namely fuzzy processing time and new job insertion. The uncertainty of processing time and new job insertion are two scheduling related characteristics in remanufacturing. Fuzzy processing time is used to describe the uncertainty in processing time. Rescheduling operator is executed when new job(s) is (are) inserted into the schedule currently being executed. A two-stage artificial bee colony (TABC) algorithm with several improvements is proposed to solve FJSP with fuzzy processing time and new job insertion constraints. Also, several new solution generation methods and improvement strategies are proposed and compared with each other. The objective is to minimize maximum fuzzy completion time. Eight instances from remanufacturing are solved using the proposed TABC algorithm. The proposed improvement strategies are compared and discussed in detail. Two proposed ABC algorithms with the best performances are compared against seven existing algorithms over by five benchmark cases. The optimization results and comparisons show the competitiveness of the proposed TABC algorithm for solving FJSP.

A genetic algorithm for energy-efficiency in job-shop scheduling

Abstract: Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality.

Optimizing freshness of information: On minimum age link scheduling in wireless systems

Abstract: There is a growing interest in age of information, which is a newly introduced metric that measures the freshness of information in communication systems. We investigate the age of information in wireless networks and propose the novel approach of optimizing the scheduling strategy to deliver the information as timely as possible. We consider a set of links that share a common channel, each containing a number of packets with time stamps, and address the scheduling problem with the objective of minimizing the overall information age. We model this problem mathematically and prove it is NP-hard in general. Fundamental insights including tractable cases and optimality conditions are presented. An integer linear programming formulation is provided for performance benchmarking. Moreover, a steepest age decent algorithm with better scalability is developed. Numerical study shows that, by employing the optimal schedule, the overall information age is significantly reduced in comparison to other scheduling strategies.

Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Abstract: We build a novel model of the developed distributing scheduling by supplementing the reentrant characteristic into the model of distributed reentrant permutation flow shop scheduling (DRPFS).We determine the number of factory needs to use, factory assignment and sequence of job assigned to the factory.A novel multi-objective adaptive large neighborhood search (MOALNS) algorithm is developed.The various destroy and repair operators are presented. Factory management plays an important role in improving the productivity and quality of service in the production process. In particular, the distributed permutation flow shop scheduling problem with multiple factories is considered a priority factor in the factory automation. This study proposes a novel model of the developed distributed scheduling by supplementing the reentrant characteristic into the model of distributed reentrant permutation flow shop (DRPFS) scheduling. This problem is described as a given set of jobs with a number of reentrant layers is processed in the factories, which compromises a set of machines, with the same properties. The aim of the study is to determine the number of factory needs to be used, jobs assignment to certain factory and sequence of job assigned to the factory in order to simultaneously satisfy three objectives of minimizing makespan, total cost and average tardiness. To do this, a novel multi-objective adaptive large neighborhood search (MOALNS) algorithm is developed for finding the near optimal solutions based on the Pareto front. Various destroy and repair operators are presented to balance between intensification and diversification of searching process. The numerical examples of computational experiments are carried out to validate the proposed model. The analytical results on the performance of proposed algorithm are checked and compared with the existing methods to validate the effectiveness and robustness of the proposed potential algorithm in handling the DRPFS problem.

A survey of multi-factory scheduling

Abstract: Because of current globalization trend, production has shifted from the single factory production to multi-factory production network. To become competitive in today's rapidly changing market requirements, factories have shifted from a centralized to a more decentralized structure, in many areas of decision making including scheduling. In multi-factory production network, each factory can be considered as an individual entity which has different efficiency and is subject to different constraints, for example, machine advances, worker cost, tax, close to suppliers, and transportation facilities, etc. Since limited resources make scheduling an important decision in the production, for several decades, researchers focused on determining an efficient schedule to improve the productivity. The recent remarkable attention in distributed production management in both academia and the industry has demonstrated the significance of multi-factory scheduling. For the first time, this paper provides a review on the multi-factory machine scheduling. For this, first, the paper classifies and reviews the literature according to shop environments, including single machine, parallel machines, flowshop, job shop, and open shop. Then the reviewed literature is quantified and measured. At the end, the paper concludes by presenting some problems receiving less attention than the others and proposes several research opportunities in the field.

End-to-End Reliability-Aware Scheduling for Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) are gaining popularity as a flexible and economical alternative to field-bus installations for monitoring and control applications. For mission-critical applications, communication networks must provide end-to-end reliability guarantees, posing substantial challenges for WSN. Reliability can be improved by redundancy, and is often addressed on the MAC layer by resubmission of lost packets, usually applying slotted scheduling. Recently, researchers have proposed a strategy to optimally improve the reliability of a given schedule by repeating the most rewarding slots in a schedule incrementally until a deadline. This Incrementer can be used with most scheduling algorithms but has scalability issues which narrows its usability to offline calculations of schedules, for networks that are rather static. In this paper, we introduce SchedEx, a generic heuristic scheduling algorithm extension, which guarantees a user-defined end-to-end reliability. SchedEx produces competitive schedules to the existing approach, and it does that consistently more than an order of magnitude faster. The harsher the end-to-end reliability demand of the network, the better the SchedEx performs compared to the Incrementer. We further show that SchedEx has a more evenly distributed improvement impact on the scheduling algorithms, whereas the Incrementer favors schedules created by certain scheduling algorithms.

An Improved Artificial Bee Colony Algorithm for Solving Hybrid Flexible Flowshop With Dynamic Operation Skipping

Abstract: In this paper, we propose an improved discrete artificial bee colony (DABC) algorithm to solve the hybrid flexible flowshop scheduling problem with dynamic operation skipping features in molten iron systems. First, each solution is represented by a two-vector-based solution representation, and a dynamic encoding mechanism is developed. Second, a flexible decoding strategy is designed. Next, a right-shift strategy considering the problem characteristics is developed, which can clearly improve the solution quality. In addition, several skipping and scheduling neighborhood structures are presented to balance the exploration and exploitation ability. Finally, an enhanced local search is embedded in the proposed algorithm to further improve the exploitation ability. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed DABC algorithm is favorably compared against several presented algorithms, both in solution quality and efficiency.