Quasi on-line scheduling procedures for flexible manufacturing systems †

This paper presents three quasi on-line scheduling procedures for FMSs consisting of work stations, transport devices, and operators. In the scheduling, different types of decisions are taken to perform a particular operation, i.e. the selection of (a) a work station, (b) a transport device and (c) an operator. Further, (d) the scheduling sequence of the operations has to be determined. The three developed procedures differ in the way these four decision problems are solved hierarchically. Several dispatching rules (SPT, SPT.TOT, SPT/ TOT and EFTA) are available to solve the last mentioned decision problem. Limited buffer capacities in an FMS may cause deadlock in the procedures as well as in practice. The scheduling procedures involve a buffer handling method to avoid deadlock. A case study is presented to demonstrate the three procedures and to show some of its properties. Based on simulation tests, some conclusions are drawn about the performance of the scheduling procedures and the various dispatching rules.     

OPAL: A multi-knowledge-based system for industrial job-shop scheduling†

A job-shop scheduling software currently under development is described, based on artificial intelligence programming techniques. The idea is 10 be able to make three kinds of knowledge cooperate in the derivation of a feasible schedule: theoretical knowledge (issued form scheduling theory) which achieves the management of time; empirical knowledge about priority rules and their influence on production objectives: and practical knowledge (provided by shop-floor managers) about technological constraints to be satisfied in a given application. The latter is usually not considered in pure operations research algorithms. The system is actually implemented in COMMON LISP and runs on a Texas Explorer LISP Machine and a SUN workstation. Computational results are reported.     

Design of a scheduling system for flexible manufacturing cells†

The objective of this paper is to describe an effective scheduling system for flexible manufacturing cells (FMC). Based on FMC characteristics, cell scheduling can be categorized for a dynamic, modified flow shop working in a real-time environment. A heuristic static cell scheduling methodology for minimizing mean flowtime is first proposed. This is then modified to allow dynamic cell scheduling to reflect the real-world situation of continuous job introduction to the cell. Computational results show that the proposed algorithms yield optimal or near optimal job sequences in a very short period of time, thus satisfying a real-time need of rapid computation.     

Single-machine earliness–tardiness scheduling with two competing agents and idle time

Two-agent scheduling has gained a lot of research attention recently. Two competing agents who have their own objective functions have to perform their respective set of jobs on one or more shared machines. This study considers a two-agent single-machine earliness and tardiness scheduling problem where jobs have distinct due dates and unforced idleness in between any two consecutive jobs is allowed. The objective is to minimize the total earliness and tardiness of jobs from one agent given that the maximum earliness–tardiness of jobs from the other agent cannot exceed an upper bound. In other words, each job from the second agent has a hard due window, whereas each job from the first agent will incur a penalty if completed either before or after its due date. Two mathematical models of the problem are presented, and several necessary optimality conditions are derived. By exploiting the established dominance properties, heuristic algorithms are developed for the problem. Finally, computational experiments are conducted to assess the models and heuristic procedures.     

Optimal short‐term scheduling of shunt compensators via a decomposition algorithm

Abstract

The short term (one day) scheduling of VAr controllers involves the setting of shunt compensators, EHV taps and generator voltages. A decomposition approach is presented to solve the problem of optimal short‐term scheduling of shunt compensators in this paper. The entire problem is decomposed into two levels: the master and the slave levels. The master level deals with minimization of the depreciation cost of compensators in order to reduce the control actions for compensators while satisfying operating constraints. The slave level treats minimization of capitalized MW losses while satisfying system security constraints by adjusting generator voltages and taps. These two levels interact through linear constraints in the iteration process. The IEEE 30‐bus and a practical 265‐bus systems, namely Taiwan Power System, are used to serve as samples to show the applicability of the presented approach.     

A hybrid neural network–genetic algorithm approach for permutation flow shop scheduling

The objective of this paper is to find a sequence of jobs for the permutation flow shop to minimise the makespan. The shop consists of 10 machines. A feed-forward back-propagation artificial neural network (ANN) is used to solve the problem. The network is trained with the optimal sequences for five-, six- and seven-job problems. This trained network is then used to solve a problem with a greater number of jobs. The sequence obtained using the neural network is used to generate the initial population for the genetic algorithm (GA) using the random insertion perturbation scheme (RIPS). The makespan of the sequence obtained by this approach (ANN-GA-RIPS) is compared with that obtained using GA starting with a random population (ANN-GA). It was found that the ANN-GA-RIPS approach performs better than ANN-GA starting with a random population. The results obtained are compared with those obtained using the Nawaz, Enscore and Ham (NEH) heuristic and upper bounds of Taillard's benchmark problems. ANN-GA-RIPS performs better than the NEH heuristic and the results are found to be within 5% of the upper bounds.     

A modified teaching–learning-based optimisation algorithm for bi-objective re-entrant hybrid flowshop scheduling

In this paper, a modified teaching–learning-based optimisation (mTLBO) algorithm is proposed to solve the re-entrant hybrid flowshop scheduling problem (RHFSP) with the makespan and the total tardiness criteria. Based on the simple job-based representation, a novel decoding method named equivalent due date-based permutation schedule is proposed to transfer an individual to a feasible schedule. At each generation, a number of superior individuals are selected as the teachers by the Pareto-based ranking phase. To enhance the exploitation ability in the promising area, the insertion-based local search is embedded in the search framework as the training phase for the TLBO. Due to the characteristics of the permutation-based discrete optimisation, the linear order crossover operator and the swap operator are adopted to imitate the interactions among the individuals in both the teaching phase and the learning phase. To store the non-dominated solutions explored during the search process, an external archive is used and updated when necessary. The influence of the parameter setting on the mTLBO in solving the RHFSP is investigated, and numerical tests with some benchmarking instances are carried out. The comparative results show that the proposed mTLBO outperforms the existing algorithms significantly.     

Minimising total completion time on single-machine scheduling with new integrated maintenance activities

A single-machine scheduling problem with new maintenance activities is examined in this paper. In the scheduling literature, it is often assumed that the interval between maintenance activities is fixed or within a specified time frame. However, this assumption may not hold true in many real-world situations, such as the maintenance activities in wafer manufacturing of semiconductor. Before the wafer manufacturing process starts, it is imperative that the wafers go through a number of cleaning operations to avoid contamination. Using a cleaning agent as the main material of wafer cleaning, the contamination will be dissolved and removed from wafer surface. In case of contamination being accumulated substantial and going beyond a permitted value, the cleaning agent is highly likely to damage the wafer surfaces. Thus, the interval between maintenance activities in the wafer manufacturing process is deemed irregular. The objective function of the proposed problem is to minimise total completion time. Addressing the problem, a binary integer programming model is formulated in this paper. Furthermore, with the research problem being NP-hard, a heuristic based on two special properties is proposed to address the problem. To evaluate and validate the proposed heuristic, a new lower bound is further developed. Extensive experiments have been conducted showing that the proposed heuristic efficiently yields a near-optimal solution with an average percentage error of 15.4 from lower bound.     

Single-item production–delivery scheduling problem with stage-dependent inventory costs and due-date considerations

This paper studies an integrated scheduling problem for a single-item, make-to-order supply chain system consisting of one supplier, one capacitated transporter and one customer. Specifically, we assume the existence in the production stage of an intermediate inventory that works as a buffer to balance the production rate and the transportation speed. Jobs are first processed on a single machine in the production stage, and then delivered to the pre-specified customer by a capacitated vehicle in the delivery stage. Each job has a due date specified by the customer, and must be delivered to the customer before its due date. Moreover, it is assumed that a job that is finished before its departure date or arrives at the customer before its due date will incur a stage-dependent corresponding inventory cost (WIP inventory, finished-good inventory or customer inventory cost). The objective is to find a coordinated production and delivery schedule such that the sum of setup, delivery and inventory costs is minimised. We formulate the problem as a nonlinear model in a general way and provide some properties. We then derive a precise instance from the general model and develop a heuristic algorithm for solving this precise instance. In order to evaluate the performance of the heuristic algorithm, we propose a simple branch-and-bound (B&B) approach for small-size problems, and a lower bound based on the Lagrangian relaxation method for large-size problems. Computational experiments show that the heuristic algorithm performs well on randomly generated problems.     

A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem

The permutation flow shop scheduling problem (PFSP) which is known to be NP-hard has been widely investigated in recent years. In this paper, an effective hybrid discrete biogeography-based optimization (HDBBO) algorithm is proposed for solving the PFSP with the objective to minimise the makespan. Opposition-based learning method and the NEH heuristic are utilised in the HDBBO to generate an initial population with certain quality and diversity. Moreover, a novel variable local search strategy is presented and incorporated within the biogeography-based optimization framework to improve the exploitation ability. Computational results on two typical benchmark suits and comparisons with some state-of-the-art algorithms are presented to show the effectiveness of the HDBBO scheme.     

Integrated process planning and scheduling – multi-agent system with two-stage ant colony optimisation algorithm

In this paper, a two-stage ant colony optimisation (ACO) algorithm is implemented in a multi-agent system (MAS) to accomplish integrated process planning and scheduling (IPPS) in the job shop type flexible manufacturing environments. Traditionally, process planning and scheduling functions are performed sequentially and the actual status of the production facilities is not considered in either process planning or scheduling. IPPS is to combine both the process planning and scheduling problems in the consideration, that is, the actual process plan and the schedule are determined dynamically in accordance with the order details and the status of the manufacturing system. The ACO algorithm can be applied to solve IPPS problems. An innovative two-stage ACO algorithm is introduced in this paper. In the first stage of the algorithm, instead of depositing pheromones on graph edges as in common ant algorithms, ants are directed to deposit pheromones at the nodes to select a set of more favourable processes. In the second stage, the set of nodes not selected in the first stage will be ignored, and pheromones will be deposited along the graph edges while the ants traverse the paths connecting the selected set of nodes.     

Resource–constrained project scheduling: a heuristic for the multi–mode case

This paper presents a heuristic solution procedure for a very general resource–constrained project scheduling problem. Here, multiple execution modes are available for the individual activities of the project. In addition, minimum as well as maximum time lags between different activities may be given. The objective is to determine a mode and a start time for each activity such that the temporal and resource constraints are met and the project duration is minimized. Project scheduling problems of this type occur e.g. in process industries. The heuristic is a multi–pass priority–rule method with backplanning which is based on an integration approach and embedded in random sampling. Its performance is evaluated within an experimental performance analysis for problem instances of real–life size with 100 activities and up to 5 modes per activity.

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Received: September 22, 2000 / Accepted: May 18, 2001     

Flexible job-shop scheduling problems with ‘AND’/‘OR’ precedence constraints

The purpose of this research is to solve flexible job-shop scheduling problems with ‘AND’/‘OR’ precedence constraints in the operations. We first formulate the problem as a Mixed-Integer Linear Program (MILP). The MILP can be used to compute optimal solutions for small-sized problems. We also developed a heuristic algorithm that can obtain a good solution for the problem regardless of its size. Moreover, we have developed a representation and schedule builder that always produces a legal and feasible solution for the problem, and developed genetic and tabu search algorithms based on the proposed schedule builder. The results of the computational experiments show that the developed meta-heuristics are very effective.     

Tabu-search simulation optimization approach for flow-shop scheduling with multiple processors — a case study

The flow shop with multiple processors (FSMP) environment is relatively common and has a variety of applications. The majority of academic authors solve the scheduling problem of FSMP using deterministic data that ignore the stochastic nature of a real-world problem. Discrete-event simulation can model a non-linear and stochastic problem and allows examination of the likely behaviour of a proposed manufacturing system under selected conditions. However, it does not provide a method for optimization. The present paper proposes to solve the FSMP scheduling problem by using a tabu-search simulation optimization approach. It features both the stochastically modelling capability of the discrete-event simulation and the efficient local-search algorithm of tabu search. A case study from a multilayer ceramic capacitor manufacturing illustrates the proposed solution methodology. Empirical results show promise for the practical application of the proposed methodologies. Future research opportunities are then addressed.     

Optimal age replacement scheduling for a random work system with random lead time

System maintenance and spare parts are two closely related logistics activities since maintenance generates the demand for spare parts. Most studies on integrated models of preventive replacement and inventory of spare parts have focused on age replacement scheduling, while random replacement policy, which is sensible and necessary in practice, is rarely discussed and applied. The purpose of this paper is to present a generalised age replacement policy for a system which works at random time and considers random lead time for replacement delivery. To model an imperfect maintenance action, we consider that the system undergoes minimal repairs at minor failures and corrective replacements at catastrophic failures. Before catastrophic failures, the system is replaced preventively at age T or at the completion of a working time, whichever occurs first. The main objective is to determine an optimal schedule of age replacement that minimises the mean cost rate function of the system in a finite time horizon. The existence and uniqueness of optimal replacement policy are derived analytically and computed numerically. It can be seen that the proposed model is a generalisation of the previous works in maintenance theory.     

The infrastructure of the timed EOPNs-based multiple-objective real-time scheduling system for 300 mm wafer fab

Modern semiconductor wafer fabrication systems are changing from 200?mm to 300?mm wafer processing, and with the dual promises of more chips per wafer and economy of scale, leading semiconductor manufacturers are attracted to developing and implementing 300?mm wafer fabs. However, in today's dynamic and competitive global market, a successful semiconductor manufacturer has to excel in multiple performance indices, such as manufacturing cycle time and on-time delivery, and simultaneously optimize these objectives to reach the best-compromised system achievement. To cope with this challenge, in this paper, the infrastructure of a timed EOPNs-based multiple-objective real-time scheduling system (MRSS) is proposed to tackle complex 300?mm wafer fabs. Four specific performance objectives pursued by contemporary semiconductor manufacturers are integrated into a priority-ranking algorithm, which can serve as the initial scheduling guidance, and then all wafer lots will be dynamically dispatched by the real-time state-dependent dispatching system. This dispatching control system is timed EOPN-based and adopts a heterarchical organization that leads to a better real-time performance and adaptability. As the foundation of real-time schedule, the timed EOPNs modelling approach is expounded in detail, and the prototype of the MRSS simulation system is also provided.     

A two‐phase approach for single machine scheduling problems: Minimizing the total absolute deviation

Abstract

This paper presents a heuristic for solving a single machine scheduling problem with the objective of minimizing the total absolute deviation. The job to be scheduled on the machine has a processing time, p_i , and a preferred due date, d_i . The total absolute deviation is defined as the sum of the earliness or tardiness of each job on a schedule 5. This problem is proved to be NP‐complete by Garey et al. [8]. As a result, we developed a two‐phase procedure to provide a near‐optimal solution to this problem. The two‐phase procedure includes the following steps: First, a greedy heuristic is applied to the set of jobs, N, to generate a “good” initial sequence. According to this initial sequence, we run Garey's local optimization algorithm to provide an initial schedule. Then, a pairwise switching algorithm is adopted to further reduce the total deviation of the schedule. The effectiveness of the two‐phase procedure is empirically evaluated and has been found to indicate that the solutions obtained from this heuristic procedure are often better than other heuristic approaches.     

A note on the paper ‘Demonstrating Johnson’s algorithm via resource constrained scheduling’

In this paper, we demonstrate that the relation between two jobs defined by min{a_i, b_j} ≤ min{b_i, a_j}, used in Johnson’s theorem, is not transitive. However, both the theorem and Johnson’s algorithm are correct.     

Distributed project scheduling with information sharing in supply chains: part I—an agent-based negotiation model

Distributed project scheduling problem (DPSP) in supply chains is concerned with configuration and scheduling of multiple projects in a network of independent and autonomous enterprises. Individual enterprises must collaborate with each other during two main stages: the configuration of a project—selection of contractors for performing project operations and the project scheduling—determining when the operations start. However, the collaboration is especially difficult because none of these enterprises holds the global information about the entire supply chain and all constituent enterprises. Instead, they have to capitalize whatever information is shared between them in order to solve their own local problems in a distributed and autonomous fashion. It is essential for the solution process to strike an overall balance between effectiveness and efficiency. The research, reported in this two-part paper, is aimed at proposing a negotiation-based algorithm for solving DPSP. Its emphasis is how to improve the convergence and quality of the solution by taking advantage of inter-enterprise information sharing especially the sharing of schedule flexibility information (SFI). The first part of this paper describes a new agent-based approach to DPSPs in supply chains while the second part will present detailed discussion on the theoretical and experimental analysis.     

Competitive two-agent scheduling problems to minimize the weighted combination of makespans in a two-machine open shop

In this article, a competitive two-agent scheduling problem in a two-machine open shop is studied. The objective is to minimize the weighted sum of the makespans of two competitive agents. A complexity proof is presented for minimizing the weighted combination of the makespan of each agent if the weight α belonging to agent B is arbitrary. Furthermore, two pseudo-polynomial-time algorithms using the largest alternate processing time (LAPT) rule are presented. Finally, two approximation algorithms are presented if the weight is equal to one. Additionally, another approximation algorithm is presented if the weight is larger than one.