Showing papers on "Job shop published in 2009"
01 Jan 2009
TL;DR: A simulation model is presented to solve the multi-objective flexible job shop scheduling problem using Matlab, a special mathematical computation language, and the results obtained have shown that the proposed approach is a feasible and effective approach.
Abstract: Flexible job shop schedule is very important in both fields of combinatorial optimization and production management. In this paper, a simulation model is presented to solve the multi-objective flexible job shop scheduling problem. The proposed model has been coded by Matlab which is a special mathematical computation language. After modeling the pending problem, the model is validated by five representative instances based on practical data. The results obtained from the computational study have shown that the proposed approach is a feasible and effective approach for the multi-objective flexible job shop scheduling problem.
124 citations
TL;DR: Inspired by the proposed SBP algorithm, feasibility satisfaction procedure (FSP) algorithm is developed to solve and analyse the BPMJSS problem, by an alternative graph model that is an extension of the classical disjunctive graph models.
124 citations
TL;DR: A hybrid framework integrating a heuristic and a genetic algorithm is utilized for job-shop scheduling to minimize weighted tardiness and is found to be superior to a well-recognized heuristic improvement procedure (lead-time iterations).
108 citations
TL;DR: This paper describes how to integrate simulation into genetic algorithm to the dynamic scheduling of a flexible job shop with machines that suffer stochastic breakdowns and reveals that the relative performance of the algorithm can be affected by changing the levels of the breakdown parameters.
Abstract: Much of the research on operations scheduling problems has ignored dynamic events in real-world environments where there are complex constraints and a variety of unexpected disruptions. Besides, while most scheduling problems which have been discussed in the literature assume that machines are incessantly available, in most real life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to a scheduled preventive maintenance where the periods of unavailability are determined in advance (deterministic unavailability). This paper describes how we can integrate simulation into genetic algorithm to the dynamic scheduling of a flexible job shop with machines that suffer stochastic breakdowns. The objectives are the minimization of two criteria, expected makespan and expected mean tardiness. An overview of the flexible job shops and scheduling under the stochastic unavailability of machines are presented. Subsequently, the details of integrating simulation into genetic algorithm are described and implemented. Consequently, problems of various sizes are used to test the performance of the proposed algorithm. The results obtained reveal that the relative performance of the algorithm for both abovementioned objectives can be affected by changing the levels of the breakdown parameters.
106 citations
TL;DR: In this article, a novel parallel quantum genetic algorithm (NPQGA) is proposed for the stochastic job shop scheduling problem with the objective of minimizing the expected value of makespan, where the processing times are subjected to independent normal distributions.
105 citations
TL;DR: In this paper, the authors investigated how lean production control principles can be used in a make-to-order job shop, where volume is typically low and there is high variety.
Abstract: Due to the success of lean manufacturing, many companies are interested in implementing a lean production control system. Lean production control principles include the levelling of production, the use of pull mechanisms and takt time control. These principles have mainly been applied in high volume flow shop environments where orders move through the production system in one direction in a limited number of identifiable routing sequences. This article investigates how lean production control principles can be used in a make-to-order job shop, where volume is typically low and there is high variety. We show how production levelling, constant work in process, first in first out and takt time can be integrated in a lean production control system. A case study is presented to illustrate the design and phased implementation of the system in a typical dual resource constrained production environment. The case study demonstrates that lean production control principles can be successfully implemented in a high-variety/low-volume context. Implementation led to a reduction in flow times and an increase in the service level achieved, with on-time delivery performance improving from 55 to 80%.
103 citations
TL;DR: In this paper, a unified representation model for integrated process planning and scheduling (IPPS) has been developed based on this model, a modern evolutionary algorithm, i.e., particle swarm optimisation (PSO) algorithm has been employed to optimise the IPPS problem.
Abstract: Traditionally, process planning and scheduling are two independent essential functions in a job shop manufacturing environment In this paper, a unified representation model for integrated process planning and scheduling (IPPS) has been developed Based on this model, a modern evolutionary algorithm, ie the particle swarm optimisation (PSO) algorithm has been employed to optimise the IPPS problem To explore the search space comprehensively, and to avoid being trapped into local optima, the PSO algorithm has been enhanced with new operators to improve its performance and different criteria, such as makespan, total job tardiness and balanced level of machine utilisation, have been used to evaluate the job performance To improve the flexibility and agility, a re-planning method has been developed to address the conditions of machine breakdown and new order arrival Case studies have been used to a verify the performance and efficiency of the modified PSO algorithm under different criteria A comparison has been made between the result of the modified PSO algorithm and those of the genetic algorithm (GA) and the simulated annealing (SA) algorithm respectively, and different characteristics of the three algorithms are indicated Case studies show that the developed PSO can generate satisfactory results in optimising the IPPS problem
99 citations
TL;DR: This linear formulation differs from the previously published ones as it takes into account the maximum number of jobs allowed in the system, limited input/output buffer capacities, empty vehicle trips and no-move-ahead trips simultaneously.
88 citations
TL;DR: The proposed VNS obviates the notorious myopic behavior of local search-based metaheuristic algorithms by the means of several systematic insertion neighborhood search structures and is readily intelligible yet is a robust solution technique for the problem of SDST JSS.
87 citations
TL;DR: In this paper, a flexible job shop scheduling problem with a new approach, overlapping in operations, is discussed, in which embedded operations of each job can be performed due to overlap considerations in which each operation may be overlapped with the others.
85 citations
TL;DR: In this article, the authors proposed a hybrid job shop scheduling problem with time window constraints, fixed operations, maintenance activities and periods of section unavailability for scheduling additional train services (SATS).
Abstract: In this paper techniques for scheduling additional train services (SATS) are considered as is train scheduling involving general time window constraints, fixed operations, maintenance activities and periods of section unavailability. The SATS problem is important because additional services must often be given access to the railway and subsequently integrated into current timetables. The SATS problem therefore considers the competition for railway infrastructure between new services and existing services belonging to the same or different operators. The SATS problem is characterised as a hybrid job shop scheduling problem with time window constraints. To solve this problem constructive algorithm and meta-heuristic scheduling techniques that operate upon a disjunctive graph model of train operations are utilised. From numerical investigations the proposed framework and associated techniques are tested and shown to be effective.
TL;DR: In this paper, the authors proposed two techniques that are easy to understand and code, yet simplistically adaptable to any other machine-scheduling problems, such as job shop scheduling with sequencedependent setup times and preventive maintenance policies.
Abstract: Although many researchers have proposed different techniques to integrate production scheduling and preventive maintenance, these techniques have some drawbacks. For example, some of them are so intricate that one cannot easily implement them, or some strongly exploit specific features of the original studied problem that one cannot apply them to other problems. We hereby propose two techniques that are easy to understand and code, yet simplistically adaptable to any other machine-scheduling problems. This paper investigates job shop scheduling with sequence-dependent setup times and preventive maintenance policies. The optimization criterion is to minimize makespan. Four metaheuristics based on simulated annealing and genetic algorithms as well as adaptations of two metaheuristics in the literature are employed to solve the problem. The performances of the proposed algorithms are evaluated by comparing their solutions through two benchmarks based on Taillard’s instances.
TL;DR: In this paper, the authors identify appropriate application domains of ant colony optimization (ACO) in the area of dynamic job shop scheduling problem and test ACO in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems.
Abstract: The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.
TL;DR: In this paper, the authors deal with hybrid flow shop scheduling problems in which there are sequence-dependent setup times (SDSTs), and machines suffer stochastic breakdowns, to optimise objectives based on the expected makespan.
Abstract: Much of the research on operations scheduling problems has either ignored setup times or assumed that setup times on each machine are independent of the job sequence. Furthermore, most scheduling problems that have been discussed in the literature are under the assumption that machines are continuously available. Nevertheless, in most real-life industries a machine can be unavailable for many reasons, such as unanticipated breakdowns (stochastic unavailability), or due to scheduled preventive maintenance where the periods of unavailability are known in advance (deterministic unavailability). This paper deals with hybrid flow shop scheduling problems in which there are sequence-dependent setup times (SDSTs), and machines suffer stochastic breakdowns, to optimise objectives based on the expected makespan. With the increase in manufacturing complexity, conventional scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. An immune algorithm (IA) can be used to tackle ...
TL;DR: A hybrid genetic algorithm (HGA) is proposed to solve a no-wait job shop problem with the objective of minimizing total completion time and the experimental results show that this hybrids can accelerate the convergence and improve solution quality.
Abstract: A no-wait job shop (NWJS) describes a situation where every job has its own processing sequence with the constraint that no waiting time is allowed between operations within any job. A NWJS problem with the objective of minimizing total completion time is a NP-hard problem and this paper proposes a hybrid genetic algorithm (HGA) to solve this complex problem. A genetic operation is defined by cutting out a section of genes from a chromosome and treated as a subproblem. This subproblem is then transformed into an asymmetric traveling salesman problem (ATSP) and solved with a heuristic algorithm. Subsequently, this section with new sequence is put back to replace the original section of chromosome. The incorporation of this problem-specific genetic operator is responsible for the hybrid adjective. By doing so, the course of the search of the proposed genetic algorithm is set to more profitable regions in the solution space. The experimental results show that this hybrid genetic algorithm can accelerate the convergence and improve solution quality as well.
TL;DR: A holonic control architecture and implementing issues for agile job shop assembly with networked intelligent robots, based on the dynamic simulation of material processing and transportation, is described and two solutions for production planning are proposed.
TL;DR: A generalization of the Blocking Job Shop problem which takes into account transfer operations between machines and sequence-dependent setup times is studied, and a neighborhood for local search is developed, based on which a tabu search algorithm is devised.
TL;DR: These exact hybrid methods based on integer linear programming (ILP) and constraint programming (CP) for an integrated employee timetabling and job-shop scheduling problem outperform pure CP for employee cost minimization while it is not the case for makespan minimization.
TL;DR: In this article, a genetic algorithm is used to solve the problem of job shop setup times where the setup times are sequence dependent under minimization of the maximum completion time or makespan.
Abstract: In this work we consider job shop problems where the setup times are sequence dependent under minimisation of the maximum completion time or makespan. We present a genetic algorithm to solve the problem. The genetic algorithm is hybridised with a diversification mechanism, namely the restart phase, and a simple form of local search to enrich the algorithm. Various operators and parameters of the genetic algorithm are reviewed to calibrate the algorithm by means of the Taguchi method. For the evaluation of the proposed hybrid algorithm, it is compared against existing algorithms through a benchmark. All the results demonstrate that our hybrid genetic algorithm is very effective for the problem.
TL;DR: A simple card-based system for job shop control, the Cobacabana (control of balance by card- based navigation) system, based on the concept of workload control (WLC), which has already proven its value in job shops.
TL;DR: It is revealed that the opposite occurs for a strongly NP-hard problem, which requires sequencing n jobs through an m machine flow shop so as to minimize the makespan, and for larger values of n, the heuristics become more frequently optimal as n increases.
TL;DR: This paper focuses on solving the lot streaming problem in a job shop environment, where consistent sublots are considered and a constructive multi-level neighbourhood is developed, which effectively connects three isolated neighbourhood functions of tabu search.
02 Apr 2009
TL;DR: A simple criterion to integrate machine availability constraints and scheduling decisions simultaneously is established and a hybrid meta-heuristic to tackle the given problem is proposed, called EMSA.
Abstract: In this paper, we explore job shop problems with two recently popular and realistic assumptions, sequence-dependent setup times and machine availability constraints to actualize the problem. The criterion is a minimization of total weighted tardiness. We establish a simple criterion to integrate machine availability constraints and scheduling decisions simultaneously. We propose a hybrid meta-heuristic to tackle the given problem. This meta-heuristic method, called EMSA, is a combination of two meta-heuristics: (1) Electromagnetic-like mechanism (EM); and (2) simulated annealing (SA). The hybridization is done to overcome some existing drawbacks of each of these two algorithms. To evaluate the proposed hybrid meta-heuristic method, we carry out a benchmark by which the proposed EMSA is compared with some existing algorithms as well as simulated annealing and electromagnetic-like mechanism alone in a fixed given computational time. All the related results and analysis obtained through the benchmark illustrate that our proposed EMSA is very effective and supersedes the foregoing algorithms.
TL;DR: In this article, an application of the global optimization technique called tabu search that is combined with the ant colony optimization technique to solve the job shop scheduling problems has been presented, where the neighborhoods are selected based on the strategies in the Ant Colony Optimization with dynamic tabu length strategies in Tabu search.
Abstract: The manufacturing industry continues to be a prime contributor and it requires an efficient schedule. Scheduling is the allocation of resources to activities over time and it is considered to be a major task done to improve shop-floor productivity. Job shop problem comes under this category and is combinatorial in nature. Research on optimization of the job shop problem is one of the most significant and promising areas of optimization. This paper presents an application of the global optimization technique called tabu search that is combined with the ant colony optimization technique to solve the job shop scheduling problems. The neighborhoods are selected based on the strategies in the ant colony optimization with dynamic tabu length strategies in the tabu search. The inspiring source of ant colony optimization is pheromone trail that has more influence in selecting the appropriate neighbors to improve the solution. The performance of the algorithm is tested using well-known benchmark problems and is also compared with other algorithms in the literature.
TL;DR: It is demonstrated that genetic algorithms (GA) can be used to produce solutions in times comparable to common heuristics but closer to optimal in a job shop environment that includes sequence-dependent setup times.
TL;DR: A simulation-based experimental study of scheduling rules for scheduling a dynamic job shop in which the setup times are sequence-dependent indicates that setup-oriented rules provide better performance than ordinary rules.
Abstract: This paper presents the salient aspects of a simulation-based experimental study of scheduling rules for scheduling a dynamic job shop in which the setup times are sequence-dependent. A discrete-event simulation model of the job shop system is developed for the purpose of experimentation. Seven scheduling rules from the literature are incorporated in the simulation model. Five new setup-oriented scheduling rules are proposed and implemented. Simulation experiments have been conducted under experimental conditions characterised by different setup time ratios. The simulation results are analysed using statistical significance tests. The results indicate that setup-oriented rules provide better performance than ordinary rules. The difference in performance between these two groups of rules increases with increase in shop load and setup time ratio. One of the proposed rules performs better for mean flow time and mean tardiness measures. Multiple linear regression based metamodels have been developed for the b...
TL;DR: A new heuristic algorithm is proposed that effectively combines the classical shifting bottleneck procedure (SBP) with a dynamic and adaptive neighborhood search procedure, based on a filter-and-fan (F&F) procedure.
TL;DR: In this article, the problem of demand management in a firm where the firm's historical delivery service level reputation influences the number of quotation requests from its potential customers is considered, and the authors demonstrate the use of simulation to test different demand management bidding and negotiation strategies for different market and firm scenarios.
Abstract: The research considers the problem of demand management in a firm where the firm's historical delivery service level reputation influences the number of quotation requests from its potential customers. Customers have a maximum and the firm has a minimum net price to due date tradeoff curve for each job. The demand management function bargains with the customer over price and promised due date. Bargaining finishes either with an agreed price and delivery date or with the customer refusing the firm's bid and placing the order elsewhere. The firm's objective is to maximize its long-term net revenue. The firm's demand management negotiation strategy guides this bidding process. The research demonstrates the use of simulation to test different demand management bidding and negotiation strategies for different market and firm scenarios.
The demonstration uses 16 scenarios to test the different demand management negotiation strategies with a model of a classical job shop in a classical market. The investigation examines finite scheduling-based due date estimation methods, as well as the more traditional parameter-based methods. This demonstration shows that it is possible to test different bidding policies, using a simulation model of a firm and its customers, and to obtain usable results.
TL;DR: In this paper, a multiple decision-making scheme is proposed to plan and control operations in a general job shop, and to improve delivery and workload related performance measures, and the performance of different decision rules changes when they are consid...
Abstract: There has been extensive research on workload and input–output control with the objective of improving manufacturing operations in job-shops. In this paper, a multiple decision-making scheme is proposed to plan and control operations in a general job-shop, and to improve delivery and workload related performance measures. The job-shop characteristics reinforce the need for designing a global system that controls both the jobs entering (order acceptance, due date setting and job release) and the work-in-process (dispatching), leading to an improvement of operational measures. Previous research has concentrated on scheduling a set of orders through the shop floor, according to some decision mechanism, in order to optimise some measure of performance (usually total lead time). This means that, since only a part of the decision-making system is being optimised, the resulting decision may be sub-optimal. In this paper it is shown that the performance of the different decision rules changes when they are consid...
TL;DR: Experimental results show that CLLM (complete local search with limited memory) outperforms all the existing effective algorithms for the considered problem with a little more computation time.