Job-Shop Scheduling Theory: What Is Relevant?

McKay, Kenneth N.; Safayeni, Frank; Buzacott, John A.

doi:10.1287/inte.18.4.84

Cited by 183 publications

(68 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our proposed approach addresses many of the limitations of existing job-shop scheduling identified in [5] by providing a decision support collaborative framework for scheduling. It enables scheduling solutions to be evaluated by experts with different points of view incorporating their knowledge in the system.…”

Section: Resultsmentioning

confidence: 99%

“…Automated scheduling, whatever its nature whether heuristic or rulebased, may not produce realistic schedules in environments where contextual information is represented inadequately. Where objectives are complex and unstated and situations are dynamic and uncertain, domain experts address these issues [5][6][7][8] bringing to scheduling their inductive and pattern recognition abilities.…”

Section: Collaborative Schedulingmentioning

confidence: 99%

See 1 more Smart Citation

An Approach to Collaborative Scheduling Through Group Decision Support

Almeida¹,

Marreiros²

2006

J. Adv. Comput. Intell. Intell. Inform.

View full text Add to dashboard Cite

The model we present supporting collaborative scheduling in complex dynamic manufacturing environments, considers the interaction between an agentbased scheduling module (ASM) and a group decision support module (GDSM). The ASM outputs a set of candidate scheduling solutions, each generated based on specific criteria and/or by a particular method. Scheduling is a multicriteria decision problem in practice where different schedulers may agree on key objectives but differ greatly on their relative importance in any given situation. Interaction among scheduling actors is supported by the GDSM selecting a scheduling solution.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Collaborative Schedulingmentioning

confidence: 99%

An Approach to Collaborative Scheduling Through Group Decision Support

Almeida¹,

Marreiros²

2006

J. Adv. Comput. Intell. Intell. Inform.

View full text Add to dashboard Cite

show abstract

“…In fact, in many production scheduling domains, the management problem is more accurately described as a rescheduling problem rather than a scheduling problem. In this regard, as has been recently pointed out by several researchers (e.g., [85]), work in scheduling theory has been solving the wrong problem altogether. For the most part, OR research relcvant to managing executional uncertainty has focused on the development of local dispatch heuristics for dynamic decision-making (e.g., [111]).…”

Section: Existing Solutionsmentioning

confidence: 96%

Knowledge-based production management approaches, results and prospects

Smith

1992

Production Planning & Control

View full text Add to dashboard Cite

Puoc reonr,rr burden forgirs -oileton of ,nforrralo-s.' esil-ted to A-eaq I'onur oer rcwn,*."nCludmng the time tor reviewing instrucions. searcmhrnq eiift-ri data sources gathering and maintaining the oata needed. and con'pieting and reviewing h CC. A1BSTRACT WMaxirrnum,10woras) In this paper we provide an overview of research in the field of knowledge-based production management. We begin by examining the important sources of decision-making difficulty in practical production management domains, discussing the requirements implied by each with respect to the development of effective production management tools, and identifying the general opportunities in this regard provided by Al-based technology. We then categorize work in the field along several different dimensions, indicating the principal types of manufacturing domains that have received attention, the particular production management and control activities that have been emphasized, and the various perspectives that have emerged with respect to the tradeoff that must be made in practical production management contexts between predictive decision-making to optimize behavior and reactive decision-making to manage executional uncertainty. The bulk of the paper focuses on summarizing the dominant approaches to knowledge-based production management that have emerged. Here, we identify the general concepts, principles, and techniques that distinguish various paradigms, characterize the strengths and weaknesses of each paradigm from the standpoint of different production management requirements, and indicate the results that work within each paradigm has produced to date. Among the paradigms for knowledge-based production management considered are rule-based scheduling, simulation-based scheduling, constraint-based scheduling, fuzzy scheduling, planning and scheduling, iterative scheduling, and interactive scheduling. We also examine work aimed at integrating heterogeneous planning and scheduling methods (both Al and CIR Lngoj) ,Indi th', rn-nctinn nf z)-wn fcnr mth~ijirv prniutn m neni ind cicnirol, Finalig-we 'in'ev morn. SUBJECT TERMS AbstractOver the past decade, a large (and continually increasing) number of efforts (both research and development) have sought to investigate and exploit the use of Artificial Intelligence (AI) concepts and techniques in production management applications. In some cases, Al-based concepts have provided frameworks for making traditional Operations Research (OR) techniques more accessible and usable in practical production management settings. In others, novel concepts and techniques have been developed that offer new opportunities for more cost-effective factory performance. While this field of "knowledge-based" production management is still fairly young and the literature is still dominated by experimental research systems, results are nonetheless starting to have an impact in actual production environments. In recent years, several systems have made their way into operation, and many have been attributed with substan...

show abstract

“…There is a range of applications here, from simple closed-loop presentation of a canned solution which cannot be modified, through dynamic algorithms that can account for changing situations within a limited modelling framework, to fully interactive support systems which provide recommendations, along with a transparent set of reasoning, and allow for modification and interaction as the schedule is being built (Godin 1978, McKay et al 1988, Glassy 1991. While afterthe-fact modification is an improvement on the flexibility of batch scheduling, it is still short of fully interactive scheduling.…”

Section: Operational Efficiency and Effectivenessmentioning

confidence: 99%

In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations

Freed

Doerr

Chang

2007

International Journal of Production Research

View full text Add to dashboard Cite

Most manufacturing processes can benefit from an automated scheduling system. However, the design of a fast, computerised scheduling system that achieves high-quality results and requires minimal resources is a difficult undertaking. Efficient scheduling of a semiconductor device test facility requires an information system that provides good schedules quickly. Semiconductor device testing is the last stage of the long semiconductor manufacturing process, and therefore is subjected to customer service pressures. The cost of an off-the-shelf computerised scheduling system may be prohibitive for many companies. In addition, many companies are taken aback by other characteristics of off-the-shelf scheduling systems, such as code confidentiality, maintenance costs, and failure rates. We draw upon the literature and our field case to discuss some of the trade-offs between in-house development and off-the-shelf acquisition of software. We describe the in-house design and implementation of a scheduling decision support system for one device test facility. Using the design and implementation process of this system as a case study, we discuss how one facility uses in-house design of systems in a strategic way, as a competitive capability.

show abstract

Job-Shop Scheduling Theory: What Is Relevant?

Cited by 183 publications

References 11 publications

An Approach to Collaborative Scheduling Through Group Decision Support

An Approach to Collaborative Scheduling Through Group Decision Support

Knowledge-based production management approaches, results and prospects

In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations

Contact Info

Product

Resources

About