An approach to solve job shop scheduling problem

“A biotechnology device manufacturer needs to devise effective scheduling algorithms for its testing devices. A device is a configuration of machines, each of which performs a specific task, such as washing, reading and cleaning. These devices are used to test human samples to diagnose diseases like cholera, malaria etc. Each test is a job, which is to be processed on these machines for a specific amount of time. Every job has its own pre defined sequence. These samples are to be processed simultaneously on machines owing to constraint that as soon as one machine completes processing a sample, it should be immediately processed by another machine. This constraint is significantly known as no- wait constraint. Given a set of jobs the web application assigns an optimal start time for each job owing to no-wait constraint. This results in reducing the overall time taken to process the jobs, which is formally known as makespan. The main objective of the project is to minimize the makespan. The application is specific to laboratory platform, which helps them to test the samples in optimal time. The heuristic, which I have implemented, is designed with future advancements in mind. The application can be extended to test different heuristic procedures by keeping the time tabling intact. The development environment to be used in this project will require Microsoft Visual Studio, C#, ASP.NET, and other real time chart tools like Microsoft Silverlight.

Constructing Boolean Matrices with Restriction Enzymes as Row/Column Indicators in DNA Computing

The design and strategy to encode problems into DNA sequences for computation gives different advantages and limitations during extraction of their results. In this paper, we study the utilization of restriction enzymes as row/column indicators in the modeling and computing of Boolean matrices in DNA computing. We discuss the highlights, drawbacks and applicability of the restriction enzymes during the encoding of the problems in DNA computing

Artificial intelligence effectiveness in job shop environments

The aim of this paper is to define a new methodology that allows the comparison of the effectiveness among some of the major artificial intelligence techniques (random technique, taboo search, data mining, evolutionary algorithms). This methodology is applied in the sequencing production process in job shop environments, in a problem with N orders, and M machines, where each of the orders must pass through every machine regardless of its turn. These techniques are measured by the variables of total makespan time, total idle time, and machine utilization percentage. Initially, a theoretical review was conducted and showed the usefulness and effectiveness of artificial intelligence in the sequencing production processes. Subsequently and based on the experiments presented, the obtained results showed that these techniques have an effectiveness higher than 95%, with a confidence interval of 99.5% measured by the variables under study

Complete local search with limited memory algorithm for no-wait job shops to minimize makespan

In this paper, no-wait job shop problems with makespan minimization are considered. It is well known that these problems are strongly NP-hard. The problem is decomposed into the sequencing and the timetabling components. Shift timetabling is developed for the timetabling component. An effective method, CLLM (complete local search with limited memory), is presented by integrating with shift timetabling for the sequencing component. Experimental results show that CLLM outperforms all the existing effective algorithms for the considered problem with a little more computation time.No-wait Job shop Timetabling Sequencing