Optimization Algorithms in Project Scheduling

Scheduling, or planning in a general perspective, is the backbone of project management; thus, the successful implementation of project scheduling is a key factor to projects’ success. Due to its complexity and challenging nature, scheduling has become one of the most famous research topics within the operational research context, and it has been widely researched in practical applications within various industries, especially manufacturing, construction, and computer engineering. Accordingly, the literature is rich with many implementations of different optimization algorithms and their extensions within the project scheduling problem (PSP) analysis field. This study is intended to exhibit the general modelling of the PSP, and to survey the implementations of various optimization algorithms adopted for solving the different types of the PSP

Theoretical and Computational Research in Various Scheduling Models

Nine manuscripts were published in this Special Issue on “Theoretical and Computational Research in Various Scheduling Models, 2021” of the MDPI Mathematics journal, covering a wide range of topics connected to the theory and applications of various scheduling models and their extensions/generalizations. These topics include a road network maintenance project, cost reduction of the subcontracted resources, a variant of the relocation problem, a network of activities with generally distributed durations through a Markov chain, idea on how to improve the return loading rate problem by integrating the sub-tour reversal approach with the method of the theory of constraints, an extended solution method for optimizing the bi-objective no-idle permutation flowshop scheduling problem, the burn-in (B/I) procedure, the Pareto-scheduling problem with two competing agents, and three preemptive Pareto-scheduling problems with two competing agents, among others. We hope that the book will be of interest to those working in the area of various scheduling problems and provide a bridge to facilitate the interaction between researchers and practitioners in scheduling questions. Although discrete mathematics is a common method to solve scheduling problems, the further development of this method is limited due to the lack of general principles, which poses a major challenge in this research field

Evaluation of the quantiles and superquantiles of the makespan in interval valued activity networks

This paper deals with the evaluation of quantile-based risk measures for the makespan in scheduling problems represented as temporal networks with uncer tainties on the activity durations. More specifically, for each activity only the interval for its possible duration values is known in advance to both the sched uler and the risk analyst. Given a feasible schedule, we calculate the quantiles and the superquantiles of the makespan which are of interest as risk indicators in various applications. To this aim we propose and test a set of novel algorithms to determine rapid and accurate numerical estimations based on the calculation of theoretically proven lower and upper bounds. An extensive experimental campaign compu tationally shows the validity of the proposed methods, and allows to highlight their performances through the comparison with respect to the state-of-the-art algorithms

Deterministic Assembly Scheduling Problems: A Review and Classification of Concurrent-Type Scheduling Models and Solution Procedures

Many activities in industry and services require the scheduling of tasks that can be concurrently executed, the most clear example being perhaps the assembly of products carried out in manufacturing. Although numerous scientific contributions have been produced on this area over the last decades, the wide extension of the problems covered and the lack of a unified approach have lead to a situation where the state of the art in the field is unclear, which in turn hinders new research and makes translating the scientific knowledge into practice difficult. In this paper we propose a unified notation for assembly scheduling models that encompass all concurrent-type scheduling problems. Using this notation, the existing contributions are reviewed and classified into a single framework, so a comprehensive, unified picture of the field is obtained. In addition, a number of conclusions regarding the state of the art in the topic are presented, as well as some opportunities for future research.Ministerio de Ciencia e Innovación español DPI2016-80750-

An Adaptive Scheduling Algorithm for Dynamic Jobs for Dealing with the Flexible Job Shop Scheduling Problem

Modern manufacturing systems build on an effective scheduling scheme that makes full use of the system resource to increase the production, in which an important aspect is how to minimize the makespan for a certain production task (i.e., the time that elapses from the start of work to the end) in order to achieve the economic profit. This can be a difficult problem, especially when the production flow is complicated and production tasks may suddenly change. As a consequence, exact approaches are not able to schedule the production in a short time. In this paper, an adaptive scheduling algorithm is proposed to address the makespan minimization in the dynamic job shop scheduling problem. Instead of a linear order, the directed acyclic graph is used to represent the complex precedence constraints among operations in jobs. Inspired by the heterogeneous earliest finish time (HEFT) algorithm, the adaptive scheduling algorithm can make some fast adaptations on the fly to accommodate new jobs which continuously arrive in a manufacturing system. The performance of the proposed adaptive HEFT algorithm is compared with other state-of-the-art algorithms and further heuristic methods for minimizing the makespan. Extensive experimental results demonstrate the high efficiency of the proposed approach

Demystifying reinforcement learning approaches for production scheduling

Recent years has seen a sharp rise in interest pertaining to Reinforcement Learning (RL) approaches for production scheduling. This is because RL is seen as a an advantageous compromise between the two most typical scheduling solution approaches, namely priority rules and exact approaches. However, there are many variations of both production scheduling problems and RL solutions. Additionally, the RL production scheduling literature is characterized by a lack of standardization, which leads to the field being shrouded in mysticism. The burden of showcasing the exact situations where RL outshines other approaches still lies with the research community. To pave the way towards this goal, we make the following four contributions to the scientific community, aiding in the process of RL demystification. First, we develop a standardization framework for RL scheduling approaches using a comprehensive literature review as a conduit. Secondly, we design and implement FabricatioRL, an open-source benchmarking simulation framework for production scheduling covering a vast array of scheduling problems and ensuring experiment reproducibility. Thirdly, we create a set of baseline scheduling algorithms sharing some of the RL advantages. The set of RL-competitive algorithms consists of a Constraint Programming (CP) meta-heuristic developed by us, CP3, and two simulation-based approaches namely a novel approach we call Simulation Search and Monte Carlo Tree Search. Fourth and finally, we use FabricatioRL to build two benchmarking instances for two popular stochastic production scheduling problems, and run fully reproducible experiments on them, pitting Double Deep Q Networks (DDQN) and AlphaGo Zero (AZ) against the chosen baselines and priority rules. Our results show that AZ manages to marginally outperform priority rules and DDQN, but fails to outperform our competitive baselines

A Survey on Cost and Profit Oriented Assembly Line Balancing

http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2014/media/files/0866.pdfInternational audienceProblems, approaches and analytical models on assembly line balancing that deal explicitly with cost and profit oriented objectives are analysed. This survey paper serves to identify and work on open problems that have wide practical applications. The conclusions derived might give insights in developing decision support systems (DSS) in planning profitable or cost efficient assembly lines

Scheduling Stochastic Multi-Stage Jobs to Elastic Hybrid Cloud Resources

[EN] We consider a special workflow scheduling problem in a hybrid-cloud-based workflow management system in which tasks are linearly dependent, compute-intensive, stochastic, deadline-constrained and executed on elastic and distributed cloud resources. This kind of problems closely resemble many real-time and workflow-based applications. Three optimization objectives are explored: number, usage time and utilization of rented VMs. An iterated heuristic framework is presented to schedule jobs event by event which mainly consists of job collecting and event scheduling. Two job collecting strategies are proposed and two timetabling methods are developed. The proposed methods are calibrated through detailed designs of experiments and sound statistical techniques. With the calibrated components and parameters, the proposed algorithm is compared to existing methods for related problems. Experimental results show that the proposal is robust and effective for the problems under study.This work is sponsored by the National Natural Science Foundations of China (Nos. 71401079, 61572127, 61472192), the National Key Research and Development Program of China (No. 2017YFB1400801) and the Collaborative Innovation Center of Wireless Communications Technology. Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD-Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds.Zhu, J.; Li, X.; Ruiz García, R.; Xu, X. (2018). Scheduling Stochastic Multi-Stage Jobs to Elastic Hybrid Cloud Resources. IEEE Transactions on Parallel and Distributed Systems. 29(6):1401-1415. https://doi.org/10.1109/TPDS.2018.2793254S1401141529

Optimisation heuristics for solving technician and task scheduling problems

Motivated by an underlying industrial demand, solving intractable technician and task scheduling problems through the use of heuristic and metaheuristic approaches have long been an active research area within the academic community. Many solution methodologies, proposed in the literature, have either been developed to solve a particular variant of the technician and task scheduling problem or are only appropriate for a specific scale of the problem. The motivation of this research is to find general-purpose heuristic approaches that can solve variants of technician and task scheduling problems, at scale, balancing time efficiency and solution quality. The unique challenges include finding heuristics that are robust, easily adapted to deal with extra constraints, and scalable, to solve problems that are indicative of the real world. The research presented in this thesis describes three heuristic methodologies that have been designed and implemented: (1) the intelligent decision heuristic (which considers multiple team configuration scenarios and job allocations simultaneously), (2) the look ahead heuristic (characterised by its ability to consider the impact of allocation decisions on subsequent stages of the scheduling process), and (3) the greedy randomized heuristic (which has a flexible allocation approach and is computationally efficient). Datasets used to test the three heuristic methodologies include real world problem instances, instances from the literature, problem instances extended from the literature to include extra constraints, and, finally, instances created using a data generator. The datasets used include a broad array of real world constraints (skill requirements, teaming, priority, precedence, unavailable days, outsourcing, time windows, and location) on a range of problem sizes (5-2500 jobs) to thoroughly investigate the scalability and robustness of the heuristics. The key findings presented are that the constraints a problem features and the size of the problem heavily influence the design and behaviour of the solution approach used. The contributions of this research are; benchmark datasets indicative of the real world in terms of both constraints included and problem size, the data generators developed which enable the creation of data to investigate certain problem aspects, mathematical formulation of the multi period technician routing and scheduling problem, and, finally, the heuristics developed which have proved to be robust and scalable solution methodologies