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

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

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

A Novel Approach to the Common Due-Date Problem on Single and Parallel Machines

This paper presents a novel idea for the general case of the Common Due-Date (CDD) scheduling problem. The problem is about scheduling a certain number of jobs on a single or parallel machines where all the jobs possess different processing times but a common due-date. The objective of the problem is to minimize the total penalty incurred due to earliness or tardiness of the job completions. This work presents exact polynomial algorithms for optimizing a given job sequence for single and identical parallel machines with the run-time complexities of $O(n \log n)$ for both cases, where $n$ is the number of jobs. Besides, we show that our approach for the parallel machine case is also suitable for non-identical parallel machines. We prove the optimality for the single machine case and the runtime complexities of both. Henceforth, we extend our approach to one particular dynamic case of the CDD and conclude the chapter with our results for the benchmark instances provided in the OR-library.Comment: Book Chapter 22 page

Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System

An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects

[EN] This paper addresses a sequence dependent setup times no-wait flowshop with learning and forgetting effects to minimize total flowtime. This problem is NP-hard and has never been considered before. A position-based learning and forgetting effects model is constructed. Processing times of operations change with the positions of corresponding jobs in a schedule. Objective increment properties are deduced and based on them three accelerated neighbourhood construction heuristics are presented. Because of the simplicity and excellent performance shown in flowshop scheduling problems, an iterated greedy heuristic is proposed. The proposed iterated greedy algorithm is compared with some existing algorithms for related problems on benchmark instances. Comprehensive computational and statistical tests show that the presented method obtains the best performance among the compared methods. (C) 2018 Elsevier Inc. All rights reserved.This work is supported by the National Natural Science Foundation of China (Nos. 61572127, 61272377), the Collaborative Innovation Center of Wireless Communications Technology and the Key Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 12KJA630001). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness(MINECO), under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards" with reference DPI2015-65895-R.Li, X.; Yang, Z.; Ruiz García, R.; Chen, T.; Sui, S. (2018). An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects. Information Sciences. 453:408-425. https://doi.org/10.1016/j.ins.2018.04.038S40842545

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Selected heuristic algorithms for solving job shop and flow shop scheduling problems

Importance of job shop and flow shop scheduling has increased to a high extent. Nowadays, each and every industry focuses largely on how to schedule their machine working, since it is an important factor which decides the net productivity. All manufacturing systems including flexible manufacturing system follow a planned schedule of machine operation depending on the demand criterion. With the increase in number of machines and jobs to be scheduled, complexity of the problem increases which demands the need of a proper scheduling technique. Here this thesis shows some of the essential methods of solving a job shop and flow shop scheduling problems. This thesis focuses on finding the most efficient way of scheduling in a flow shop environment with the help of heuristics algorithms that include Palmer’s algorithm, CDS algorithm and NEH algorithm. Comparison was also done between the various heuristics algorithms. For getting the optimum make span for job shop scheduling we have used branch and bound algorithm and shifting bottleneck algorithm. Basis input parameters are given in the problem which are then used for computing the make span. A C programming code was generated to find the optimum results of the scheduling problem

Serial-batch scheduling – the special case of laser-cutting machines

The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning