An application of a cocitation-analysis method to find further research possibilities on the area of scheduling problems

In this article we will give firstly a classification scheme of scheduling problems and their solving methods. The main aspects under examination are the following: machine and secondary resources, constraints, objective functions, uncertainty, mathematical models and adapted solution methods. In a second part, based on this scheme, we will examine a corpus of 60 main articles (1015 citation links were recorded in total) in scheduling literature from 1977 to 2009. The main purpose is to discover the underlying themes within the literature and to examine how they have evolved. To identify documents likely to be closely related, we are going to use the cocitation-based method of Greene et al. (2008). Our aim is to build a base of articles in order to extract the much developed research themes and find the less examined ones as well, and then try to discuss the reasons of the poorly investigation of some areas

On-line planning and scheduling: an application to controlling modular printers

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge

CleanET: enabling timing validation for complex automotive systems

Timing validation for automotive systems occurs in late integration stages when it is hard to control how the instances of software tasks overlap in time. To make things worse, in complex software systems, like those for autonomous driving, tasks schedule has a strong event-driven nature, which further complicates relating those task-overlapping scenarios (TOS) captured during the software timing budgeting and those observed during validation phases. This paper proposes CleanET, an approach to derive the dilation factor r caused due to the simultaneous execution of multiple tasks. To that end, CleanET builds on the captured TOS during testing and predicts how tasks execution time react under untested TOS (e.g. full overlap), hence acting as a mean of robust testing. CleanET also provides additional evidence for certification about the derived timing budgets for every task. We apply CleanET to a commercial autonomous driving framework, Apollo, where task measurements can only be reasonably collected under 'arbitrary' TOS. Our results show that CleanET successfully derives the dilation factor and allows assessing whether execution times for the different tasks adhere to their respective deadlines for unobserved scenarios.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015- 65316-P, the SuPerCom European Research Council (ERC) project under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 772773), and the HiPEAC Network of Excellence. MINECO partially supported Jaume Abella under Ramon y Cajal postdoctoral fellowship (RYC-2013-14717).Peer ReviewedPostprint (author's final draft

Genetic Algorithms in Supply Chain Scheduling of Ready-Mixed Concrete

The coordination of just-in-time production and transportation in a network of partially independent facilities to guarantee timely delivery to distributed customers is one of the most challenging aspects of supply chain management. From the theoretical perspective, the timely production/distribution can be viewed as a hybrid combination of planning, scheduling and routing problem, each notoriously affected by nearly prohibitive combinatorial complexity. From a practical viewpoint, the problem calls for a trade-off between risks and profits. This paper focuses on the ready-made concrete delivery: in addition to the mentioned complexity, strict time-constraints forbid both earliness and lateness of the supply. After developing a detailed model of the considered problem, we propose a novel meta-heuristic approach based on a hybrid genetic algorithm combined with constructive heuristics. A detailed case study derived from industrial data is used to illustrate the potential of the proposed approach

Critical path analysis type scheduling in a finite capacity environment

In order to cope with more realistic production scenarios, scheduling theory has been increasingly considering assembly job shops. Such an effort has raised synchronization of operations and components as a major scheduling issue. Most effective priority rules designed for assembly shops have incorporated measures to improve coordination when scheduling assembly structures. However, by assuming a forward loading, the priority rules designed by these studies schedule all operations as soon as possible, which often leads to an increase of the workin- progress level. This study is based on the assumption that synchronization may be improved by sequencing rules that incorporate measures to cope with the complexity of product structures. Moreover, this study favours the idea that, in order to improve synchronization and, consequently, reduce waiting time, backward loading should be considered as well. By recognizing that assembly shop structures are intrinsically networks, this study investigates the feasibility of adopting the Critical Path Method as a sequencing rule for assembly shop. Furthermore, since a Critical Path type scheduling requires a precise determination of production capacity, this study also includes Finite Capacity as a requisite for developing feasible schedules. In order to test the above assumptions, a proven and effective sequencing rule is selected to act as a benchmark and a simulation model is developed. The simulation results from several experiments showed significant reduction on the waiting time performance measure due to the adoption of the proposed critical path type priority rule. Finally, a heuristic procedure is proposed as a guideline for designing scheduling systems which incorporate Critical Path based rules and Finite Capacity approach