Energy Efficiency Improvement through Optimal Batch Sizing in Job Shop

The increasing customization of products with greater variances and smaller lot sizes, has motivated manufacturers to adopt highly dynamic production planning. The production plans not only need to adapt to the production system state changes rapidly but also need to adopt energy reduction schemes to satisfy key sustainability performance indicators. The dilemma from industry point of view is to tackle multi-faceted problem of optimising economic and environmental performance. This research aims to overcome the multi-faceted objectives of small and medium-sized enterprises (SME’s) by providing a simulation-optimisation platform that creates the best possible production plans for optimum results. The applicability of the proposed framework is demonstrated through a real-life job-shop environment with the focus on optimisation of energy as well as job tardiness.

JOB SHOP SCHEDULING CONSIDERING MAKESPAN, PENALTIES OF MACHINE IDLING, AND JOB OUT OF TIME

In this study, the job shop scheduling problem is analysed with respect to numerous objectives, and a multi-objective model is developed to optimise the makespan, maximum job earliness, maximum job tardiness, maximum machine idle time, total machine idling penalties, total job untimed penalties, and total penalties (multi-objective) using the genetic algorithm.

Non-dominated Sorting Genetic Algorithms for a Multi-objective Resource Constraint Project Scheduling Problem

The resource constraint project scheduling problem (RCPSP) has attracted growing attention since the last decades. Precedence constraints are considered as well as resources with limited capacities. During the project, the same resource can be required by several in-process jobs and it is compulsory to ensure that the consumptions do not exceed the limited capacities. In this paper, several criteria are involved, namely makespan, total job tardiness, and workload balancing level. Our problem is firstly solved by the non-dominated sorting genetic algorithm-II (NSGAII) as well as the recently proposed NSGAIII. Giving emphasis to the selection procedure, we apply both the traditional Pareto dominance and the less documented Lorenz dominance into the niching mechanism of NSGAIII. Hence, we adopt and modify L-NSGAII to our problem and propose L-NSGAIII by integrating the notion of Lorenz dominance. Our methods are tested by 1350 randomly generated instances, considering problems with 30–150 jobs and different configurations of resources and due dates. Hypervolume and C-metric are considered to evaluate the results. The Lorenz dominance leads the population more toward the ideal point. As experiments show, it allows improving the original NSGA approach.

Due window assignment and scheduling on parallel machines: a FPTAS for a bottleneck criterion

A fully polynomial time approximation scheme (FPTAS) with run time is developed for a problem which combines common due window assignment and scheduling n jobs on m identical parallel machines. The problem criterion is bottleneck (min-max) such that the maximum cost, which includes job earliness, job tardiness and due window size costs, is minimized.

Manufacturing Work Study and System Analysis of the NIFOR Digester Screw Press

It is generally assumed that the probability of the customer placing an order is always one, implying that customers will place orders regardless of producer quoted lead times and customer lead time expectations. As the job shops increasingly compete on the basis of the delivery speed and reputation, the relative performance of quoted versus actual realized lead times will have a strong effect on whether the customer will place future orders or not. A work study and manufacturing system analysis was conducted on the NIFOR digester screw press to study the techniques of job shop scheduling. Incessant delay in manufacturing process, job tardiness and unsatisfactory customers feedback had led to this research work. The work involves critical examination of all the resources and factors which affect the efficiency of the job shop. Machines set up times and run times were collected at each machine centre for all the components produced during the period of evaluation. The data collected were used as input data for the Gantt chart to evaluate the total actual times spent in performing each task. The work provides useful information for the development of an improved method of job scheduling for effective machine utilization so as to minimize the total time required for completion of all manufacturing tasks and reduce job tardiness.