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

Single machine scheduling with general positional deterioration and rate-modifying maintenance

We present polynomial-time algorithms for single machine problems with generalized positional deterioration effects and machine maintenance. The decisions should be taken regarding possible sequences of jobs and on the number of maintenance activities to be included into a schedule in order to minimize the overall makespan. We deal with general non-decreasing functions to represent deterioration rates of job processing times. Another novel extension of existing models is our assumption that a maintenance activity does not necessarily fully restore the machine to its original perfect state. In the resulting schedules, the jobs are split into groups, a particular group to be sequenced after a particular maintenance period, and the actual processing time of a job is affected by the group that job is placed into and its position within the group

A note on optimization in deteriorating systems using scheduling problems with the aging effect and resource allocation models

AbstractThis paper concerns scheduling problems with the aging effect and additional resource allocation. A measurable result of the aging phenomenon is that the time required to perform a job increases whereas the additional resource allocation allows one to decrease it. As an example of a deteriorating system that can be described and optimized by the application of the models and algorithms considered, we choose the pickling process, where cleaning of metal items decreases the efficiency of the pickling (cleaning) bath (i.e., one containing an active substance), whereas heating it up can improve the efficiency. In particular, we focus on the optimization problems for such systems and model them as single-machine scheduling problems with job processing times dependent on the fatigue of a machine and on the allocation of additional resources. The objectives considered are the minimization of time criteria (the maximum completion time and the maximum lateness) under a given resource consumption as well as the minimization of the resource consumption under given time criteria. The computational complexity of the problems is determined and solution properties are proved. On the basis of these, we construct optimal polynomial time algorithms for some cases of the problems considered

Comparison of new metaheuristics, for the solution of an integrated jobs-maintenance scheduling problem

This paper presents and compares new metaheuristics to solve an integrated jobs-maintenance scheduling problem, on a single machine subjected to aging and failures. The problem, introduced by Zammori et al. (2014), was originally solved using the Modified Harmony Search (MHS) metaheuristic. However, an extensive numerical analysis brought to light some structural limits of the MHS, as the analysis revealed that the MHS is outperformed by the simpler Simulated Annealing by Ishibuchi et al. (1995). Aiming to solve the problem in a more effective way, we integrated the MHS with local minima escaping procedures and we also developed a new Cuckoo Search metaheuristic, based on an innovative Levy Flight. A thorough comparison confirmed the superiority of the newly developed Cuckoo Search, which is capable to find better solutions in a smaller amount of time. This an important result, both for academics and practitioners, since the integrated job-maintenance scheduling problem has a high operational relevance, but it is known to be extremely hard to be solved, especially in a reasonable amount of time. Also, the developed Cuckoo Search has been designed in an extremely flexible way and it can be easily readapted and applied to a wide range of combinatorial problems. (C) 2018 Elsevier Ltd. All rights reserved

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

Approximation schemes for scheduling on a single machine subject to cumulative deterioration and maintenance

We consider a scheduling problem on a single machine to minimize the makespan. The processing conditions are subject to cumulative deterioration, but can be restored by a single maintenance. We link the problem to the Subset-sum problem (if the duration of maintenance is constant) and to the Half-Product Problem (if the duration of maintenance depends on its start time). For both versions of the problem, we adapt the existing fully polynomial-time approximation schemes to our problems by handling the additive constants

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