Combining time and position dependent effects on a single machine subject to rate-modifying activities

We introduce a general model for single machine scheduling problems, in which the actual processing times of jobs are subject to a combination of positional and time-dependent effects, that are job-independent but additionally depend on certain activities that modify the processing rate of the machine, such as, maintenance. We focus on minimizing two classical objectives: the makespan and the sum of the completion times. The traditional classification accepted in this area of scheduling is based on the distinction between the learning and deterioration effects on one hand, and between the positional effects and the start-time dependent effects on the other hand. Our results show that in the framework of the introduced model such a classification is not necessary, as long as the effects are job-independent. The model introduced in this paper covers most of the previously known models. The solution algorithms are developed within the same general framework and their running times are no worse than those available earlier for problems with less general effects

Single machine scheduling with time-dependent linear deterioration and rate-modifying maintenance

We study single machine scheduling problems with linear time-dependent deterioration effects and maintenance activities. Maintenance periods (MPs) are included into the schedule, so that the machine, that gets worse during the processing, can be restored to a better state. We deal with a job-independent version of the deterioration effects, that is, all jobs share a common deterioration rate. However, we introduce a novel extension to such models and allow the deterioration rates to change after every MP. We study several versions of this generalized problem and design a range of polynomial-time solution algorithms that enable the decision-maker to determine possible sequences of jobs and MPs in the schedule, so that the makespan objective can be minimized. We show that all problems reduce to a linear assignment problem with a product matrix and can be solved by methods very similar to those used for solving problems with positional effects

Parallel-Machine Scheduling Problems with Past-Sequence-Dependent Delivery Times and Aging Maintenance

We consider parallel-machine scheduling problems with past-sequence-dependent (psd) delivery times and aging maintenance. The delivery time is proportional to the waiting time in the system. Each machine has an aging maintenance activity. We develop polynomial algorithms to three versions of the problem to minimize the total absolute deviation of job completion times, the total load, and the total completion time

Condition Based Maintenance Optimization of Multi-Equipment Manufacturing Systems by Combining Discrete Event Simulation and Multiobjective Evolutionary Algorithms

Modern industrial engineers are continually faced with the challenge of meeting increasing demands for high quality products while using a reduced amount of resources. Since systems used in the production of goods and deliveries of services constitute the vast portion of capital in most industries, maintenance of such systems is crucial (Oyarbide-Zubillaga, Goti, & Sánchez 2008). Several studies compiled by Mjema (2002) show that maintenance costs represent from 3 to 40 % out of the total product cost (with an average value of a 28%). Within maintenance, the Condition-Based Maintenance (CBM) techniques are very important. Nevertheless, and comparing it to the Preventive Maintenance (PM) optimization problem, relatively few papers related to CBM have been developed: According to Aven (1996), one of the reasons to justify this fact is that CBM models are usually by its nature rather sophisticated compared to the more traditional replacement models. Within this maintenance strategy, Das & Sarkar (1999) distinguish two CBM subtypes, On-Condition Maintenance (OCM) and Condition Monitoring (CMT). OCM is based on periodic inspections, while CMT performs a continuous monitoring on the hardware through instrumentation. Considering the described context, this paper focuses on the problem of CMT optimisation in a manufacturing environment, with the objective of determining the optimal CMT deterioration levels beyond which PM activities should be applied under cost and profit criteria in a multi-equipment system. The initiative considers the interaction of production, work in process material, quality and maintenance aspects. In this work the suitability of discrete event simulation to model or modify complex system models is combined with the aptitude that multiobjective evolutionary algorithms have shown to deal with multiobjective problems to develop a maintenance management and optimisation approach. An application case where the activities applied on a system that produces hubcaps for the car maker industry is performed, showing the quantitative benefits of adopting the detailed approach

A single-machine scheduling problem with multiple unavailability constraints: A mathematical model and an enhanced variable neighborhood search approach

AbstractThis research focuses on a scheduling problem with multiple unavailability periods and distinct due dates. The objective is to minimize the sum of maximum earliness and tardiness of jobs. In order to optimize the problem exactly a mathematical model is proposed. However due to computational difficulties for large instances of the considered problem a modified variable neighborhood search (VNS) is developed. In basic VNS, the searching process to achieve to global optimum or near global optimum solution is totally random, and it is known as one of the weaknesses of this algorithm. To tackle this weakness, a VNS algorithm is combined with a knowledge module. In the proposed VNS, knowledge module extracts the knowledge of good solution and save them in memory and feed it back to the algorithm during the search process. Computational results show that the proposed algorithm is efficient and effective

Maintenance Strategies to Reduce Downtime Due to Machine Positional Errors

Manufacturing strives to reduce waste and increase Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime competently and similarly identify the machines’ performance. Total productive maintenance (TPM) is a maintenance program that involves concepts for maintaining plant and equipment effectively. OEE is a powerful metric of manufacturing performance incorporating measures of the utilisation, yield and efficiency of a given process, machine or manufacturing line. It supports TPM initiatives by accurately tracking progress towards achieving “perfect production.” This paper presents a review of maintenance management methodologies and their application to positional error calibration decision-making. The purpose of this review is to evaluate the contribution of maintenance strategies, in particular TPM, towards improving manufacturing performance, and how they could be applied to reduce downtime due to inaccuracy of the machine. This is to find a balance between predictive calibration, on-machine checking and lost production due to inaccuracy. This work redefines the role of maintenance management techniques and develops a framework to support the process of implementing a predictive calibration program as a prime method to supporting the change of philosophy for machine tool calibration decision making. Keywords—maintenance strategies, down time, OEE, TPM, decision making, predictive calibration

Single-Machine Scheduling with Aging Effects and Optional Maintenance Activity Considerations

This paper explores a single-machine scheduling with aging effects and the problem regarding optional maintenance activity assignment. The jobs’ processing time is assumed to follow a power position-dependent aging model. The optional maintenance activity refers to the situation in which the maintenance activity can be scheduled immediately after processing of any job has been completed except for the last job and the duration of maintenance activity can be of any value from zero to a fixed time interval. A recovery function is proposed to reflect the efficiency of the machine or worker which is improved. The objective of this study is to decide whether and when to implement the maintenance activity into the job sequence, how long the duration of maintenance activity is, and how to schedule so as to minimize the makespan. Once the duration of maintenance activity is known, we introduce an efficient solution for this problem. In addition, when the maintenance activity is completely performed, we showed that the optimal policy is to schedule the maintenance activity in the middle of the task sequence and optimally solved it by lower order algorithm. Finally, we extend the problem to the case of multiple maintenance activities which are completely performed. Hence, the problem is regarded as polynomial time solvable

Smart Sustainable Manufacturing Systems

With the advent of disruptive digital technologies, companies are facing unprecedented challenges and opportunities. Advanced manufacturing systems are of paramount importance in making key enabling technologies and new products more competitive, affordable, and accessible, as well as for fostering their economic and social impact. The manufacturing industry also serves as an innovator for sustainability since automation coupled with advanced manufacturing technologies have helped manufacturing practices transition into the circular economy. To that end, this Special Issue of the journal Applied Sciences, devoted to the broad field of Smart Sustainable Manufacturing Systems, explores recent research into the concepts, methods, tools, and applications for smart sustainable manufacturing, in order to advance and promote the development of modern and intelligent manufacturing systems. In light of the above, this Special Issue is a collection of the latest research on relevant topics and addresses the current challenging issues associated with the introduction of smart sustainable manufacturing systems. Various topics have been addressed in this Special Issue, which focuses on the design of sustainable production systems and factories; industrial big data analytics and cyberphysical systems; intelligent maintenance approaches and technologies for increased operating life of production systems; zero-defect manufacturing strategies, tools and methods towards online production management; and connected smart factories

The Integration of Maintenance Decisions and Flow Shop Scheduling

In the conventional production and service scheduling problems, it is assumed that the machines can continuously process the jobs and the information is complete and certain. However, in practice the machines must stop for preventive or corrective maintenance, and the information available to the planners can be both incomplete and uncertain. In this dissertation, the integration of maintenance decisions and production scheduling is studied in a permutation flow shop setting. Several variations of the problem are modeled as (stochastic) mixed-integer programs. In these models, some technical nuances are considered that increase the practicality of the models: having various types of maintenance, combining maintenance activities, and the impact of maintenance on the processing times of the production jobs. The solution methodologies involve studying the solution space of the problems, genetic algorithms, stochastic optimization, multi-objective optimization, and extensive computational experiments. The application of the problems and managerial implications are demonstrated through a case study in the earthmoving operations in construction projects