Showing papers on "Job shop published in 2006"
TL;DR: A hybrid particle swarm optimization (PSO) for the job shop problem (JSP) is proposed and the computational results show that the modified PSO performs better than the original design, and that the hybrid PSO is better than other traditional metaheuristics.
307 citations
TL;DR: A review of the literature related to the class of scheduling problems that involve sequence-dependent setup times (costs), an important consideration in many practical applications, can be found in this paper.
Abstract: This paper reviews the literature related to the class of scheduling problems that involve sequence-dependent setup times (costs), an important consideration in many practical applications. It focuses on papers published within the last decade, addressing a variety of machine configurations including single machine, parallel machine, flow shop, and job shop systems and reviews the optimization and heuristic solution methods used for each category. Since lot sizing is so intimately related to scheduling, this paper reviews work that integrates these issues in relationship to each configuration. This paper provides a perspective of this line of research, gives conclusions, and discusses fertile research opportunities posed by this class of scheduling problems.
229 citations
TL;DR: This paper proposes a hybrid genetic algorithm to solve the flexible job shop scheduling problem with non-fixed availability constraints (fJSP-nfa) and defines two kinds of neighbourhood for the problem based on the concept of critical path.
Abstract: Most flexible job shop scheduling models assume that the machines are available all of the time. However, in most realistic situations, machines may be unavailable due to maintenances, pre-schedules and so on. In this paper, we study the flexible job shop scheduling problem with availability constraints. The availability constraints are non-fixed in that the completion time of the maintenance tasks is not fixed and has to be determined during the scheduling procedure. We then propose a hybrid genetic algorithm to solve the flexible job shop scheduling problem with non-fixed availability constraints (fJSP-nfa). The genetic algorithm uses an innovative representation method and applies genetic operations in phenotype space in order to enhance the inheritability. We also define two kinds of neighbourhood for the problem based on the concept of critical path. A local search procedure is then integrated under the framework of the genetic algorithm. Representative flexible job shop scheduling benchmark problems and fJSP-nfa problems are solved in order to test the effectiveness and efficiency of the suggested methodology.
171 citations
TL;DR: A brief review on job shop scheduling techniques in semiconductor manufacturing can be found in this paper, where the authors provide a brief overview of the problem, the techniques used and the researchers involved in solving this problem.
Abstract: This paper presents a brief review on job shop scheduling techniques in semiconductor manufacturing. The manufacturing environment in a semiconductor industry is considered a highly complex job shop, involving multiple types of work centers, large and changing varieties of products, sequence-dependent setup times, reentrant process flow, etc., in a dynamic scheduling environment. Due to the stubborn nature of the deterministic job shop scheduling problem itself, many of the solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem-specific strategies. In this paper, we seek to provide a brief overview of the problem, the techniques used and the researchers involved in solving this problem.
137 citations
TL;DR: This paper presents a similar PSO algorithm to solve JSSP, and it is obtained that the SPSOA is more clearly efficacious than standard GA for JSSP to minimize makespan.
121 citations
TL;DR: Empirical results indicate that the data mining approach coupled with the attribute selection scheme outperforms these methods.
121 citations
TL;DR: In this article, a genetic algorithm-based approach is developed to solve the problem of flexible job shop scheduling under resource constraints, which is an extension of classical job shop problems that permit an operation of each job to be processed by more than one machine.
Abstract: A flexible job-shop-scheduling problem is an extension of classical job-shop problems that permit an operation of each job to be processed by more than one machine. The research methodology is to assign operations to machines (assignment) and determine the processing order of jobs on machines (sequencing) such that the system objectives can be optimized. This problem can explore very well the common nature of many real manufacturing environments under resource constraints. A genetic algorithm-based approach is developed to solve the problem. Using the proposed approach, a resource-constrained operations-machines assignment problem and flexible job-shop scheduling problem can be solved iteratively. In this connection, the flexibility embedded in the flexible shop floor, which is important to today's manufacturers, can be quantified under different levels of resource availability.
105 citations
TL;DR: In this paper, the authors proposed an integrated formulation of the combined production and material handling scheduling problems, which is formulated as a mathematical programming model and as a constraint programming model which are compared for optimally solving a series of test problems.
104 citations
TL;DR: A big bucket model for multi-product multi-level production based on the general lot sizing and scheduling problem (GLSP) for single- level production is developed and minimises the lead-time of semi-finished goods and the throughput time of finished goods.
93 citations
TL;DR: This paper addresses no-wait or no-idle flow shop scheduling problems with deteriorating jobs, i.e., jobs whose processing times are an increasing function of their starting time, and shows that for the problems to minimize makespan or weighted sum of completion time, polynomial algorithms still exist, although these problems are more complicated than the classical ones.
Abstract: This paper addresses no-wait or no-idle flow shop scheduling problems with deteriorating jobs, i.e., jobs whose processing times are an increasing function of their starting time. A simple linear deterioration function is assumed and some dominating relationships between machines can be satisfied. It is shown that for the problems to minimize makespan or weighted sum of completion time, polynomial algorithms still exist, although these problems are more complicated than the classical ones. When the objective is to minimize maximum lateness or maximum tardiness, the solutions of a classical version may not hold.
91 citations
TL;DR: In this paper, an agent-based negotiation approach to integrate process planning and scheduling (IPPS) in a job shop kind of flexible manufacturing environment is presented. But this approach requires both part agents and machine agents to engage in bidding.
Abstract: This paper presents the development of an agent-based negotiation approach to integrate process planning and scheduling (IPPS) in a job shop kind of flexible manufacturing environment. The agent-based system comprises two types of agents, part agents and machine agents, to represent parts and machines respectively. For each part, all feasible manufacturing processes and routings are recorded as alternative process plans. Similarly, alternative machines for an operation are also considered. With regard to the scheduling requirements and the alternative process plans of a part, the proposed agent-based IPPS system aims to specify the process routing and to assign the manufacturing resources effectively. To establish task allocations, the part and machine agents have to engage in bidding. Bids are evaluated in accordance with a currency function which considers an agent's multi-objectives and IPPS parameters. A negotiation protocol is developed for negotiations between the part agents and the machine agents....
Journal Article•
TL;DR: In this article, the authors investigated the use of genetic programming in automatized synthesis of scheduling heuristics for single machine dynamic problem and job shop scheduling with bottleneck estimation, where the applied scheduling technique is priority scheduling where the next state of the system is determined based on priority values of certain system elements.
Abstract: This paper investigates the use of genetic programming in automatized synthesis of scheduling heuristics. The applied scheduling technique is priority scheduling, where the next state of the system is determined based on priority values of certain system elements. The evolved solutions are compared with existing scheduling heuristics for single machine dynamic problem and job shop scheduling with bottleneck estimation.
TL;DR: A heuristic algorithm is proposed to overcome the inefficiency of the branch-and-bound algorithm and to find a sequence that minimizes total completion time in a two-machine flow shop scheduling problem with deteriorating jobs.
TL;DR: This paper re-establishes that, even for two machines, the problem of minimizing the average job completion time is NP-hard in the strong sense.
Abstract: The concurrent open shop problem is a relaxation of the well known open job shop problem, where the components of a job can be processed in parallel by dedicated, component specific machines. Recently, the problem has attracted the attention of a number of researchers. In particular, Leung et al. (2005) show, contrary to the assertion in Wagneur and Sriskandarajah (1993), that the problem of minimizing the average job completion time is not necessarily strongly NP-hard. Their finding has thus once again opened up the question of the problem's complexity. This paper re-establishes that, even for two machines, the problem is NP-hard in the strong sense.
TL;DR: In this paper, the authors proposed a framework for integration of process planning with production scheduling in a job shop environment for axisymmetric components, based on the design specifications of incoming parts, feasible process plans are generated taking into account the real-time shop floor status and availability of machine tools.
Abstract: Today’s manufacturing systems are striving for an integrated manufacturing environment. To achieve truly computer-integrated manufacturing systems (CIMS), the integration of process planning and production scheduling is essential. This paper proposes a framework for integration of process planning with production scheduling in a job shop environment for axisymmetric components. Based on the design specifications of incoming parts, feasible process plans are generated taking into account the real time shop floor status and availability of machine tools. The scheduling strategy prioritizes the machine tools based on cost considerations.
Posted Content•
TL;DR: The proposed S GSs significantly improve previously best-known results on a set of hard benchmark instances and show how the proposed SGSs can be used within single-pass and multi-pass priority rule based heuristics.
Abstract: We consider the job-shop problem with sequence-dependent setup times. We focus on the formal definition of schedule generation schemes (SGSs) based on the semi-active, active, and non-delay schedule categories. We study dominance properties of the sets of schedules obtainable with each SGS. We show how the proposed SGSs can be used within single-pass and multi-pass priority rule based heuristics. We study several priority rules for the problem and provide a comparative computational analysis of the different SGSs on sets of instances taken from the literature. The proposed SGSs significantly improve previously best-known results on a set of hard benchmark instances.
TL;DR: To capture all the dynamic situations of the warehousing processes, the problem is formulated as a generalized dynamic job shop sequencing model and a heuristic solution concept for a real warehouse of a steel supply chain is developed.
TL;DR: This paper focuses on the timetabling problem and takes advantage of the symmetry of the problem in order to suggest a new timetabling procedure, and suggests embedding this timetabling into a recent metaheuristic named complete local search with memory.
TL;DR: In this article, the authors focus on refining a Workload Control (WLC) concept to improve the applicability of the approach to the shop characteristics found in practice, which is a two-stage process leading to significant conceptual refinements to a key WLC methodology.
Abstract: The paper focuses on refining a Workload Control (WLC) concept to improve the applicability of the approach to the shop characteristics found in practice. This is a two-stage process leading to significant conceptual refinements to a key WLC methodology. The first stage focuses on the development of a Decision Support System (DSS) based on a WLC concept designed for Make-To-Order (MTO) companies. Refinements made include changes to the backwards scheduling procedure and the way in which jobs are released onto the shop floor. The second stage focuses on the process of implementation. Using a case study of a MTO company, the paper describes the strategy taken to overcome a number of prerequisites to the successful implementation of a Production Planning and Control (PPC) concept. Issues addressed include grouping machines and determining capacities. This case study adds to the available literature by looking specifically at implementing WLC from the customer enquiry stage, while the case study experience also provides further refinements to the WLC concept.
TL;DR: This paper considers Nowicki and Smutnicki's i-TSAB tabu search algorithm, which represents the current state-of-the-art for the makespan-minimization form of the classical job-shop scheduling problem, and identifies those components of i- TSAB that enable it to achieve state- of- the-art performance levels.
TL;DR: It is demonstrated that under certain conditions frequent updates of lead times will lead to uncontrolled production system with erratic and very long lead times.
TL;DR: A fast tabu search algorithm is implemented for the no-wait job shop problem with a makespan objective and is extensively tested on known benchmark instances and compares favorably to the best existing algorithms.
Abstract: This paper deals with the no-wait job shop problem with a makespan objective We present some new theoretical properties on the complexity of subproblems associated with a well-known decomposition approach Justified by the complexity results, we implement a fast tabu search algorithm for the problem at hand It is extensively tested on known benchmark instances and compares favorably to the best existing algorithms for the no-wait job shop as well as the no-wait flow shop
TL;DR: In this article, the authors investigate an intelligent system that selects dispatching rules to apply locally for each machine in a job shop, based on a statistical characterization of the job mix.
Abstract: This paper investigates an intelligent system that selects dispatching rules to apply locally for each machine in a job shop. Randomly generated problems are scheduled using optimal permutations of three different dispatching rules on five machines. A neural network is then trained to associate between a statistical characterization of the job mix in each of these problems, with the best combination of dispatching rules to use. Once trained, the neural network is able to recommend for new problems a dispatching rule to use on each machine. Two networks are trained separately for minimizing makespan and the mean flowtime in the job shop. Test results show that the combinations of dispatching rules suggested by the trained networks produce better results, for both objectives, than the alternative of using a single rule common to all machines.
TL;DR: This paper studies a new scheduling problem that arises in a disruption environment, and focuses on the two-machine case to demonstrate some major properties, and hopes that these properties can provide insights for solving other general problems, such as multiple (more than two) machine scheduling and machine scheduling in other configurations under disruption.
Abstract: Effective logistics scheduling requires synchronization of manufacturing and delivery to optimize customer service at minimum total cost In this paper, we study a new scheduling problem that arises in a disruption environment Such a problem occurs when a disruption unexpectedly happens, and consequently, some machines become unavailable for certain periods Jobs that are assigned to the disrupted machines and have not yet been processed can either be moved to other available machines for processing, which may involve additional transportation time and cost, or can be processed by the same machine after the disruption Our goal is to reschedule jobs so that an objective function, including the original cost function, and possibly transportation costs and disruption cost caused by deviating from the originally planned completion times, is minimized In this paper, we focus on the two-machine case to demonstrate some major properties, and hope that these properties can provide insights for solving other general problems, such as multiple (more than two) machine scheduling and machine scheduling in other configurations (job shop or flow shop) under disruption We study problems with different related costs In each problem, we either provide a polynomial algorithm to solve the problem optimally, or show its NP-hardness If the problem is NP-hard in the ordinary sense, we also present a pseudo-polynomial algorithm to solve the problem optimally
TL;DR: It is proved that the routing open-shop problem is NP-hard even on a two-node network with two machines, and even onA two- node network withTwo jobs and m machines.
TL;DR: A branch and bound algorithm for obtaining the optimal coloring of a special mixed graph with the criterion of minimizing the sum of maximal colors is shown to determine a schedule for minimizing total completion time for processing jobs in a job shop.
01 Jan 2006
TL;DR: In this paper, a methodology to group tasks and assets into several clusters (decision makers, command cells) is proposed, which employs concepts from group technology and genetic algorithms (GAs) to minimize the weighted total workload, measured in terms of intra-DM and inter-DM coordination workloads.
Abstract: A key concept in congruent organizational design is the so-called strategic grouping, which involves the aggregation of task functions, positions, and assets into units. Group technology (GT) has emerged as a manufacturing philosophy for improving productivity in batch production systems, while retaining the flexibility of a job shop production. In this paper, a methodology [nested genetic algorithm (NGA)] to group tasks and assets into several clusters [decision makers (DMs), command cells] is proposed; this methodology employs concepts from GT and genetic algorithms (GAs) to minimize the weighted total workload, measured in terms of intra-DM and inter-DM coordination workloads. The numerical results show that the proposed NGA approach obtains a near-optimal layout of the organization, i.e., the assignment of platforms to tasks and the patterns of coordination achieve a nice tradeoff between inter-DM and intra-DM coordination workload.
TL;DR: In this paper, two heuristic algorithms, priority dispatching rules algorithm (PDRA) and simulated annealing algorithm (SAA), are proposed to derive optimal solutions for FMS scheduling problems.
Abstract: Flexible manufacturing cells (FMCs) are now common place in many manufacturing companies, due to their numerous advantages such as the production of a wide range of part types with short lead times, low work-in-progress, economical production of small batches and high resource utilization. Part and tool flows, two major dynamic entities, are the key factors and their management plays an important role in the operation of a FMC. The theme of this paper is to a generate joint operation - tool schedule in a FMC consisting of several machines and a common tool magazine (CTM). To achieve this aim, the jobs and tools must be jointly sequenced and scheduled in a tool constrained environment. Two heuristic algorithms, priority dispatching rules algorithm (PDRA) and simulated annealing algorithm (SAA) are proposed to derive optimal solutions. PDRA, are the most frequently applied heuristics for solving job shop/combinatorial scheduling problems in practice because of their ease of implementation and their low complexity, when compared with excel algorithms. SAA that belong to search categories, which are emerging along with the high computational capability of computers, can be used for FMS scheduling problems. Both adopt the Giffler & Thompson procedure for active feasible schedule generation. The performance of these two algorithms is compared with makespan and computational time. The analysis reveals that the SAA based heuristic provides an optimal or near optimal solution with reasonable computational time.
TL;DR: This study considers the machine scheduling problem with limited waiting time constraints, and examines the machine environment of the open-shop, job- shop, flow-shop and permutation flow-Shop, and uses makespan as a measure performance.
Abstract: This study considers the machine scheduling problem with limited waiting time constraints. We examine the machine environment of the open-shop, job-shop, flow-shop, and permutation flow-shop, and uses makespan as a measure performance. Eight mixed binary integer programming models are developed to optimally solve these problems.
01 Jan 2006
TL;DR: In this article, a heuristic to control integrated multi-product multi-machine production-inventory systems with job shop routings and stochastic arrival, set-up and processing times is presented.
Abstract: Factory Design.- Dilemmas in factory design: paradox and paradigm.- Unreliable Production Lines.- Lean buffering in serial production lines with non-exponential machines.- Analysis of flow lines with Cox-2-distributed processing times and limited buffer capacity.- Performance evaluation of production lines with finite buffer capacity producing two different products.- Automated flow lines with shared buffer.- Integrated quality and quantity modeling of a production line.- Stochastic cyclic flow lines with blocking: Markovian models.- Queueing Network Models of Manufacturing Systems.- Performance analysis of multi-server tandem queues with finite buffers and blocking.- An analytical method for the performance evaluation of echelon kanban control systems.- Closed loop two-echelon repairable item systems.- A heuristic to control integrated multi-product multi-machine production-inventory systems with job shop routings and stochastic arrival, set-up and processing times.- Performance analysis of parallel identical machines with a generalized shortest queue arrival mechanism.- A review and comparison of hybrid and pull-type production control strategies.- Stochastic Production Planning and Assembly.- Planning order releases for an assembly system with random operation times.- A multiperiod stochastic production planning and sourcing problem with service level constraints.