Project scheduling under undertainty – survey and research potentials.

The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;

Dynamic scheduling in a multi-product manufacturing system

To remain competitive in global marketplace, manufacturing companies need to improve their operational practices. One of the methods to increase competitiveness in manufacturing is by implementing proper scheduling system. This is important to enable job orders to be completed on time, minimize waiting time and maximize utilization of equipment and machineries. The dynamics of real manufacturing system are very complex in nature. Schedules developed based on deterministic algorithms are unable to effectively deal with uncertainties in demand and capacity. Significant differences can be found between planned schedules and actual schedule implementation. This study attempted to develop a scheduling system that is able to react quickly and reliably for accommodating changes in product demand and manufacturing capacity. A case study, 6 by 6 job shop scheduling problem was adapted with uncertainty elements added to the data sets. A simulation model was designed and implemented using ARENA simulation package to generate various job shop scheduling scenarios. Their performances were evaluated using scheduling rules, namely, first-in-first-out (FIFO), earliest due date (EDD), and shortest processing time (SPT). An artificial neural network (ANN) model was developed and trained using various scheduling scenarios generated by ARENA simulation. The experimental results suggest that the ANN scheduling model can provided moderately reliable prediction results for limited scenarios when predicting the number completed jobs, maximum flowtime, average machine utilization, and average length of queue. This study has provided better understanding on the effects of changes in demand and capacity on the job shop schedules. Areas for further study includes: (i) Fine tune the proposed ANN scheduling model (ii) Consider more variety of job shop environment (iii) Incorporate an expert system for interpretation of results. The theoretical framework proposed in this study can be used as a basis for further investigation

Job-shop scheduling with an adaptive neural network and local search hybrid approach

This article is posted here with permission from IEEE - Copyright @ 2006 IEEEJob-shop scheduling is one of the most difficult production scheduling problems in industry. This paper proposes an adaptive neural network and local search hybrid approach for the job-shop scheduling problem. The adaptive neural network is constructed based on constraint satisfactions of job-shop scheduling and can adapt its structure and neuron connections during the solving process. The neural network is used to solve feasible schedules for the job-shop scheduling problem while the local search scheme aims to improve the performance by searching the neighbourhood of a given feasible schedule. The experimental study validates the proposed hybrid approach for job-shop scheduling regarding the quality of solutions and the computing speed

Production/maintenance cooperative scheduling using multi-agents and fuzzy logic

Within companies, production is directly concerned with the manufacturing schedule, but other services like sales, maintenance, purchasing or workforce management should also have an influence on this schedule. These services often have together a hierarchical relationship, i.e. the leading function (most of the time sales or production) generates constraints defining the framework within which the other functions have to satisfy their own objectives. We show how the multi-agent paradigm, often used in scheduling for its ability to distribute decision-making, can also provide a framework for making several functions cooperate in the schedule performance. Production and maintenance have been chosen as an example: having common resources (the machines), their activities are actually often conflicting. We show how to use a fuzzy logic in order to model the temporal degrees of freedom of the two functions, and show that this approach may allow one to obtain a schedule that provides a better compromise between the satisfaction of the respective objectives of the two functions

Solve scheduling problems with a fuzzy approach

Scheduling is a complex problem which occurs often in a manufacturing environment. Most of the available schedulers are based on a simulation approach using dispatching rules. These rules are often dedicated to the satisfaction of a single performance criterion, and are used whatever the characteristics of the system or the type of jobs, though scheduling is a multi-criteria problem. In this paper the scheduling problems are addressed using a non-classical approach supported by fuzzy control theory. The ability to deal with multi-variables makes fuzzy control a good alternative for the scheduling problems because it can easily make compromises among multi-criteria by properly combining elementary dispatching rules. These compromises can easily be adjusted in accordance with the objectives of the system and the characteristics of the jobs. The proposed method has been implemented and tested in a discrete event simulation environment. The results are presented in this pape

A Comparative Representation Approach to Modern Heuristic Search Methods in a Job Shop

The job shop problem is among the class of NP- hard combinatorial problems. This Research paper addresses the problem of static job shop scheduling on the job-based representation and the rule based representations. The popular search techniques like the genetic algorithm and simulated annealing are used for the determination of the objectives like minimizations of the makespan time and mean flow time. Various rules like the SPT, LPT, MWKR, and LWKR are used for the objective function to attain the results. The summary of results from this paper gives a conclusion that the genetic algorithm gives better results in the makespan time determination on both the job based representation and the rule based representation and the simulated annealing algorithm gives the better results in the mean flow time in both the representations

Adaptive fuzzy particle swarm optimization for flow-shop scheduling problem

Ovaj rad razmatra novi pristup problemu raspoređivanja u protočnoj proizvodnji korištenjem kombinacije neizrazite logike i optimizacije rojevima čestica u cilju postizanja sub-optimalnog rješenja. Predlaže se upotreba Tip-1 i Tip-2 modela neizrazite logike u kombinaciji s adaptivnim modelom rojeva čestica. Razvijeni model je uspoređen na standardiziranim testnim funkcijama za stohastičke algoritme (prvo jednokriterijske, a zatim višekriterijske postavljene funkcije cilja) kako bi se utvrdila njegova upotrebljivost na opće postavljenim problemima. Zatim je testiran na standardiziranim testnim zadacima za probleme protočne proizvodnje te konačno na dva praktična problema protočne proizvodnje (linije montaže i linije pakiranja). Rezultati ostvareni novim modelom su uspoređeni s konvencionalnim pravilima prioriteta te je pokazan kvantitativan i kvalitativan napredak primjenom hibrida neizrazite logike i rojeva čestica.This paper describes the application of a hybrid of fuzzy logic and swarm intelligence in order to achieve sub-optimal solutions for flow-shop scheduling problem. A novel adaptive approach with fuzzy particle swarm optimization is proposed. The developed model is tested with the standardized test functions and compared with selected stochastic algorithms (first with one objective functions and later with multi objective functions) to determine its applicability to general problems. Benchmark examples were utilized to evaluate the approach and determine the optimal number of the algorithm evaluations. Finally, the proposed model is applied on two practical problems of flow production problems (assembly lines and packaging lines). The results achieved were compared with the conventional priority rules and the effectiveness of the application of hybrid fuzzy logic and adaptive particle swarm optimization algorithm was demonstrated

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques