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

Methods for Scheduling Problems Considering Experience, Learning, and Forgetting Effects

[EN] Workers with different levels of experience and knowledge have different effects on job processing times. By taking into account 1) the sum-of-processing-time; 2) the job-position; and 3) the experience of workers, a more general learning model is introduced for scheduling problems. We show that this model generalizes existing ones and brings the consideration of learning and forgetting effects closer to reality. We demonstrate that some single machine scheduling problems are polynomially solvable under this general model. Considering the forgetting effect caused by the idle time on the second machine, we construct a learning-forgetting model for the two-machine permutation flow shop scheduling problem with makespan minimization. A branch-and-bound method and four heuristics are presented to find optimal and approximate solutions, respectively. The proposed heuristics are evaluated over a large number of randomly generated instances. Experimental results show that the proposed heuristics are effective and efficient.This work was supported in part by the National Natural Science Foundation of China under Grant 61572127 and Grant 61272377, in part by the Key Research and Development Program in Jiangsu Province under Grant BE2015728, in part by the Collaborative Innovation Center of Wireless Communications Technology and the Key Natural Science Fund for Colleges and Universities in Jiangsu Province under Grant 12KJA630001, and in part by the Collaborative Innovation Center of Wireless Communications Technology. The work of R. Ruiz was supported by the Spanish Ministry of Economy and Competitiveness through Project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" under Grant DPI2015-65895-R. This paper was recommended by Associate Editor A. Janiak.Li, X.; Jiang, Y.; Ruiz García, R. (2018). Methods for Scheduling Problems Considering Experience, Learning, and Forgetting Effects. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 48(5):743-754. https://doi.org/10.1109/TSMC.2016.2616158S74375448

Design choices for agent-based control of AGVs in the dough making process

In this paper we consider a multi-agent system (MAS) for the logistics control of Automatic Guided Vehicles (AGVs) that are used in the dough making process at an industrial bakery. Here, logistics control refers to constructing robust schedules for all transportation jobs. The paper discusses how alternative MAS designs can be developed and compared using cost, frequency of messages between agents, and computation time for evaluating control rules as performance indicators. Qualitative design guidelines turn out to be insufficient to select the best agent architecture. Therefore, we also use simulation to support decision making, where we use real-life data from the bakery to evaluate several alternative designs. We find that architectures in which line agents initiate allocation of transportation jobs, and AGV agents schedule multiple jobs in advance, perform best. We conclude by discussing the benefits of our MAS systems design approach for real-life applications

Stochastic single machine scheduling problem as a multi-stage dynamic random decision process

In this work, we study a stochastic single machine scheduling problem in which the features of learning effect on processing times, sequence-dependent setup times, and machine configuration selection are considered simultaneously. More precisely, the machine works under a set of configurations and requires stochastic sequence-dependent setup times to switch from one configuration to another. Also, the stochastic processing time of a job is a function of its position and the machine configuration. The objective is to find the sequence of jobs and choose a configuration to process each job to minimize the makespan. We first show that the proposed problem can be formulated through two-stage and multi-stage Stochastic Programming models, which are challenging from the computational point of view. Then, by looking at the problem as a multi-stage dynamic random decision process, a new deterministic approximation-based formulation is developed. The method first derives a mixed-integer non-linear model based on the concept of accessibility to all possible and available alternatives at each stage of the decision-making process. Then, to efficiently solve the problem, a new accessibility measure is defined to convert the model into the search of a shortest path throughout the stages. Extensive computational experiments are carried out on various sets of instances. We discuss and compare the results found by the resolution of plain stochastic models with those obtained by the deterministic approximation approach. Our approximation shows excellent performances both in terms of solution accuracy and computational time

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

Scheduling problems with the effects of deterioration and learning

Author name used in this publication: T. C. E. Cheng2006-2007 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe