Enhancing Manufacturing Planning and Control Systems Through Artificial Intelligence Techniques

Manufacturing planning and control systems are currently dominated by systems based upon Material Requirements Planning (MRP). MRP systems have a number of fundamental flaws. A potential alternative to MRP systems is suggested after research into the economic batch scheduling problem. Based on the ideas of economic batch scheduling, and enhanced through artificial intelligence techniques, an alternative approach to manufacturing planning and control is developed. A framework for future research on this alternative to MRP is presented

Constraint Programming for Scheduling

Our goal is to introduce the constraint programming (CP) approach within the context of scheduling. We start with an introduction to CP and its distinct technical vocabulary. We then present and illustrate a general algorithm for solving a CP problem with a simple scheduling example. Next, we review several published studies where CP has been used in scheduling problems so as to provide a feel for its applicability. We discuss the advantages of CP in modeling and solving certain types of scheduling problems. We then provide an illustration of the use of a commercial CP tool (OPL Studio) in modeling and designing a solution procedure for a classic problem in scheduling. We conclude with our speculations about the future of scheduling research using this approach

IS Journal Quality Assessment Using the Author Affiliation Index

Research productivity is one means by which academic units attain legitimacy within their institutional milieu and make their case for resources. Journal quality assessment is an important component for assessing faculty research productivity. We introduce the Author Affiliation Index (AAI), a simple method for assessing journal quality, to the IS domain. Essentially, the AAI of a journal is the percentage of academic authors publishing in that journal who are affiliated with a base set of high-quality academic institutions. Besides explaining the AAI, we demonstrate its use with a set of well-known IS journals, discuss its rankings vis-à-vis those resulting from other methods, and provide an example of how the basic AAI approach can be modified by changing the base school set that is used to define journal quality. The AAI has a number of advantages. First, it is a simple, low cost and transparent method for assessing any journal given a base school set. Second, it provides a consistent ranking of journals, particularly of those beyond the top consensus journals where less consistency is achieved with other measures. Third, it enables new journals to be rapidly assessed against more established ones without the lags or costs of other measures. The AAI provides another indicator of journal quality that is different from surveys and citation analyses

A Novel Approach to the Common Due-Date Problem on Single and Parallel Machines

This paper presents a novel idea for the general case of the Common Due-Date (CDD) scheduling problem. The problem is about scheduling a certain number of jobs on a single or parallel machines where all the jobs possess different processing times but a common due-date. The objective of the problem is to minimize the total penalty incurred due to earliness or tardiness of the job completions. This work presents exact polynomial algorithms for optimizing a given job sequence for single and identical parallel machines with the run-time complexities of $O(n \log n)$ for both cases, where $n$ is the number of jobs. Besides, we show that our approach for the parallel machine case is also suitable for non-identical parallel machines. We prove the optimality for the single machine case and the runtime complexities of both. Henceforth, we extend our approach to one particular dynamic case of the CDD and conclude the chapter with our results for the benchmark instances provided in the OR-library.Comment: Book Chapter 22 page

Parts verification for multi-level-dependent demand manufacturing systems: a recognition and classification structure

This research has developed and implemented a part recognition and classification structure to execute parts verification in a multi-level dependent demand manufacturing system. The part recognition algorithm enables the parent and child relationship between parts to be recognised in a finite-capacitated manufacturing system. This algorithm was developed using SIMAN simulation language and implemented in a multi-level dependent demand manufacturing simulation model. The part classification structure enables the modelling of a multi-level dependent demand manufacturing between parts to be carried out effectively. The part classification structure was programmed using Visual Basic Application (VBA) and was integrated to the work-to-list generated from a simulated MRP model. This part classification structure was then implemented in the multi-level dependent demand manufacturing simulation model. Two stages of implementation, namely parameterisation and execution, of the part recognition and classification structure were carried out. A real case study was used and five detail steps of execution were processed. Simulation experiments and MRP were run to verify and validate the part recognition and classification structure. The results led to the conclusion that implementation of the recognition and classification structure has effectively verified the correct parts and sub-assemblies used for the correct product and order. No parts and sub-assemblies shortages were found, and the quantity required was produced. The scheduled release for some orders was delayed due to overload of the required resources. When the loading is normal, all scheduled release timing is adhered to. The recognition and classification structure has a robust design; hence it can be easily adapted to new systems parameter to study a different or more complex case

Simulation based energy-resource efficient manufacturing integrated with in-process virtual management

Supported by the EU 7th Framework ICT Programme under EuroEnergest Project (Contract No. 288102)