In the current paper, we present an optimization system solving multi objective production scheduling problems (MOOPPS). The identification of Pareto optimal alternatives or at least a close approximation of them is possible by a set of implemented metaheuristics. Necessary control parameters can easily be adjusted by the decision maker as the whole software is fully menu driven. This allows the comparison of different metaheuristic algorithms for the considered problem instances. Results are visualized by a graphical user interface showing the distribution of solutions in outcome space as well as their corresponding Gantt chart representation.
The identification of a most preferred solution from the set of efficient solutions is supported by a module based on the aspiration interactive method (AIM). The decision maker successively defines aspiration levels until a single solution is chosen.
After successfully competing in the finals in Ronneby, Sweden, the MOOPPS software has been awarded the European Academic Software Award 2002 (http://www.bth.se/llab/easa_2002.nsf)
Distributed computing, such as cloud computing, provides promising platforms to execute multiple workflows. Workflow scheduling plays an important role in multi-workflow execution with multi-objective requirements. Although there exist many multi-objective scheduling algorithms, they focus mainly on optimizing makespan and cost for a single workflow. There is a limited research on multi-objective optimization for multi-workflow scheduling. Considering multi-workflow scheduling, there is an additional key objective to maintain the fairness of workflows using the resources. To address such issue…
Observation scheduling problem for agile earth observation satellites (OSPFAS) plays a critical role in management of agile earth observation satellites (AEOSs). Active imaging enriches the extension of OSPFAS, we call the novel problem as observation scheduling problem for AEOS with variable image duration (OSWVID). A cumulative image quality and a detailed energy consumption is proposed to build OSWVID as a bi-objective optimization model. Three multi-objective memetic algorithms, PD+NSGA-II, LA+NSGA-II and ALNS+NSGA-II, are then designed to solve OSWVID. Considering the heuristic knowledge …
In the era of Industry 4.0, the focus is on the minimization of human element and maximizing the automation in almost all the industrial and manufacturing establishments. These establishments contain numerous processing systems, which can execute a number of tasks, in parallel with minimum number of human beings. This parallel execution of tasks is done in accordance to a scheduling policy. However, the minimization of human element beyond a certain point is difficult. In fact, the expertise and experience of a group of humans, called the experts, becomes imminent to design a fruitful scheduli…
East China University of Science and Technology（华东理工大学）；The Hong Kong Polytechnic University（香港理工大学）；Lulea University of Technology（吕勒奥理工大学）
In the fashion industry, order scheduling focuses on the assignment of production orders to appropriate production lines. In reality, before a new order can be put into production, a series of activities known as pre-production events need to be completed. In addition, in real production process, owing to various uncertainties, the daily production quantity of each order is not always as expected. In this research, by considering the pre-production events and the uncertainties in the daily production quantity, robust order scheduling problems in the fashion industry are investigated with the a…
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
Institute of AI for Health, Computational Health Center, Helmholtz Munich（亥姆霍兹慕尼黑人工智能健康研究所（计算健康中心））；Machine Learning Research Lab, Volkswagen Group（大众集团机器学习研究实验室）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Technical University Munich（慕尼黑工业大学）；U.S. FDA/CDRH（美国食品药品监督管理局医疗器械和辐射健康中心）；Applied Geology, GeoZentrum Nordbayern, Friedrich-Alexander-Universität（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学北巴伐利亚地理中心应用地质学系）
United Arab Emirates University（阿联酋大学）；College of Information Technology（信息技术学院）；National Water and Energy Center（国家水与能源中心）
Cloud computing enables remote execution of users tasks. The pervasive adoption of cloud computing in smart cities services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objecti…
A customized multi-objective evolutionary algorithm (MOEA) is proposed for the multi-objective flexible job shop scheduling problem (FJSP). It uses smart initialization approaches to enrich the first generated population, and proposes various crossover operators to create a better diversity of offspring. Especially, the MIP-EGO configurator, which can tune algorithm parameters, is adopted to automatically tune operator probabilities. Furthermore, different local search strategies are employed to explore the neighborhood for better solutions. In general, the algorithm enhancement strategy can b…
IIT Kharagpur（印度理工学院卡哈拉格普尔分校）；L3S Research Center（L3S研究中心）；IIT Delhi（印度理工学院德里分校）；MPI-SWS（马克斯·普朗克软件系统研究所）
Recently, almost all conferences have moved to virtual mode due to the pandemic-induced restrictions on travel and social gathering. Contrary to in-person conferences, virtual conferences face the challenge of efficiently scheduling talks, accounting for the availability of participants from different timezones and their interests in attending different talks. A natural objective for conference organizers is to maximize efficiency, e.g., total expected audience participation across all talks. However, we show that optimizing for efficiency alone can result in an unfair virtual conference sched…
Active-imaging agile earth observation satellite (AI-AEOS) is a new generation agile earth observation satellite (AEOS). With renewed capabilities in observation and active im-aging, AI-AEOS improves upon the observation capabilities of AEOS and provide additional ways to observe ground targets. This however makes the observation scheduling problem for these agile earth observation satellite more complex, especially when considering multi-strip ground targets. In this paper, we investigate the multi-strip observation scheduling problem for an active-image agile earth observation satellite (MOS…
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Max-Planck Institute for Software Systems（马克斯·普朗克软件系统研究所）
The (COVID-19) pandemic-induced restrictions on travel and social gatherings have prompted most conference organizers to move their events online. However, in contrast to physical conferences, virtual conferences face a challenge in efficiently scheduling talks, accounting for the availability of participants from different time-zones as well as their interests in attending different talks. In such settings, a natural objective for the conference organizers would be to maximize some global welfare measure, such as the total expected audience participation across all talks. However, we show tha…
Hunan University（湖南大学）；Hunan Key Laboratory of intelligent decision-making technology for emergency management（湖南省应急管理智能决策技术重点实验室）
The asynchronous development between the observation capability and the transition capability results in that an original image data (OID) formed by one-time observation cannot be completely transmitted in one transmit chance between the EOS and GS (named as a visible time window, VTW). It needs to segment the OID to several segmented image data (SID) and then transmits them in several VTWs, which enriches the extension of satellite image data downlink scheduling problem (SIDSP). We define the novel SIDSP as satellite image data downlink scheduling problem with family attribute (SIDSPWFA), in …
Refined Iterated Pareto Greedy for Energy-aware Hybrid Flowshop Scheduling with Blocking Constraints
Department of Computer Science（计算机科学系）；University College Cork（科尔克大学）；Process, Energy and Transport Engineering（过程、能源与运输工程）；Munster Technological University（穆斯林技术大学）
The Oven Scheduling Problem (OSP) is a new parallel batch scheduling problem that arises in the area of electronic component manufacturing. Jobs need to be scheduled to one of several ovens and may be processed simultaneously in one batch if they have compatible requirements. The scheduling of jobs must respect several constraints concerning eligibility and availability of ovens, release dates of jobs, setup times between batches as well as oven capacities. Running the ovens is highly energy-intensive and thus the main objective, besides finishing jobs on time, is to minimize the cumulative ba…
Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
University of Tennessee, Knoxville\ Ridge National Laboratory Oak Ridge, TN USA；Argonne National Laboratory Lemont, IL USA；University of Southern California Los Angeles, CA USA；Oak Ridge National Laboratory Oak Ridge, TN USA；University of Tennessee, Knoxville\ Ridge National Laboratory；Argonne National Laboratory；University of Southern California；Oak Ridge National Laboratory
Universidad de Alcalá（阿尔卡拉大学）；Federal University of Rio de Janeiro（里约热内卢联邦大学）；Federal Center of Technology Education of Minas Gerais（米纳斯吉拉斯联邦技术教育中心）
This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competiti…
University of Nottingham（诺丁汉大学）；University of Essex（埃塞克斯大学）
Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of local and global resources while respecting a range of constraints. A critical aspect of the benchmarks addressed in this paper is that the primary objective is to minimise the sum of the project completion times, with the usual makespan minimisation as a secondary objective. We observe that this leads to an expected different overall structure of goo…
University of Oxford（牛津大学）；Imperial College London（帝国理工学院）
Scheduling plays an important role in automated production. Its impact can be found in various fields such as the manufacturing industry, the service industry and the technology industry. A scheduling problem (NP-hard) is a task of finding a sequence of job assignments on a given set of machines with the goal of optimizing the objective defined. Methods such as Operation Research, Dispatching Rules, and Combinatorial Optimization have been applied to scheduling problems but no solution guarantees to find the optimal solution. The recent development of Reinforcement Learning has shown success i…
