Start Times Of Tasks Research Articles

An improved ant colony optimization algorithm for two-sided U-type assembly line balancing problems

Aiming at the improvement of production system, a new method of two-sided U-type assembly line balance is proposed. Define the partition of a two-sided U-type assembly line and derive the formula for the task start time. The multi-objective model of two-sided U-type assembly line balance is established by minimizing a smoothing index, the number of positions and stations. Considering the dual influence of heuristic factors and pheromone information, the improved ant colony optimization algorithm is proposed. Combined with 37 examples, the improved ant colony optimization algorithm is used to solve the balancing problem of a two-sided straight line and a two-sided U-type assembly line, respectively. Compared with the results of other algorithms, it verifies that the model is reasonable and the improved ant colony optimization algorithm is effective. The balance effect of a two-sided U-type assembly line is better than that of a two-sided straight line, which means the length of the assembly line is shortened.

Consensus-Based Decentralized Task Allocation for Multi-Agent Systems and Simultaneous Multi-Agent Tasks

This letter proposes a novel consensus-based timetable algorithm (CBTA) to solve the decentralized simultaneous multi-agent task allocation problem. Due to the limited capability of each agent, multiple agents may be required to perform a task simultaneously. A key challenge is how to meet the requirements and minimize the average start time of all tasks. The proposed CBTA aims to minimize the start time of each task to minimize the average start time of all tasks indirectly, it iterates between a timetable construction phase and a consensus phase. New tasks are included in the timetable of each agent by comparing the estimated start time of tasks placed by its own and other agents during the timetable construction phase. Then in the consensus phase, agents share their timetables with a communication network, and conflicts among their timetables are eliminated according to a consensus rule. Extensive simulation results show that the average start time of tasks of the proposed CBTA is nearly the same as the consensus-based bundle algorithm (CBBA) when performing single-agent tasks, and it is much less than the consensus-based grouping algorithm (CBGA) when performing multi-agent tasks with various communication network topologies.

Task sequencing in heterogeneous device for improved offloading decision using optimization technique

Edge Computing (EC) has made decisions regarding offloading tasks. Also, it might be a challenging task for a gadget of the multi-hop cause of competing network models due to the limited bandwidth available. The literature provides several studies on the issue of the multi-hop cloud environment. Moreover, partial offloading of multi-hop computing and dynamic network management leads to network problems. And inefficient performance measured in terms of task completion time has not been factored into current studies. To reduce the average finishing time of all jobs, this article proposed joint multi-task complete unloading and a network that supports the scheduling issue. Several variable decision factors such as the partial systematic ratio of offloading, Offloading remote devices, task start time, path of routing & dynamic network start time were optimized by the formulated problem. To manage tasks, we use the features inherent in multiple processors with various CPU frequencies. Under the restrictions of power & latency, we determine the appropriate quantity of task data to be handled immediately or distributed to the preferred nodes on the clouds remotely. Our objective is to solve the issue of optimization of task scheduling on a limited quasi-quadratic function. We offer an Improved Offloading and Heuristic Flow Optimization (IOHFO) solution effectively to solve these problems by using semi-definite relaxation. Finally, a simulator can be used to evaluate our proposed unloading system to reduce the (optimal) cost of unloading profiles for various parameters.

A decision model on human-robot collaborative routing for automatic logistics

This paper studies an integrated human-robot routing problem arising in collaborative order picking systems. Travelling routes of pickers and robots interact with each other and simultaneously determine the start time of tasks. A mixed-integer programming model is proposed which aims to minimize the total task completion time by optimizing both pickers’ and robots’ routes. Moreover, a variable neighborhood search and dynamic programming -based heuristic algorithm is developed for solving the problem in large-scale environments. Numerical experiments are conducted to prove the necessity of the proposed model and also validate the efficiency of the proposed heuristic algorithm. The results indicate the methodology can yield solutions with smaller optimality gap within a reasonable period of time, in comparison with an intuitive but common decision rule. A sensitivity analysis is also conducted to derive a number of managerial implications regarding the configuration of pickers and robots and robots’ speed setting.

A heuristic approach to integrate train timetabling, platforming, and railway network maintenance scheduling decisions

Train timetabling, platforming, and network maintenance scheduling are three highly interdependent problems that are crucial in the planning of railway operations, and each is normally addressed separately. In this paper, we simultaneously optimize these problems for a high-speed railway network that is comprised of multiple railway lines and stations. We model the railway network on a mesoscopic level and formulate a 0–1 binary integer programming model that minimizes the total train weighted running cost and any deviation from ideal maintenance task start times. A heuristic procedure, which dynamically updates the available time windows for each of the trains, is used to control the number of train paths in the mathematical model. The mathematical model is repeatedly solved, and at each iteration we gradually modify the set of train paths available. Four different strategies to modify train time windows are used in the train path modification step and their selection depends on the solution to the mathematical model. Computational results for three networks of different sizes conclusively demonstrate that there is not only benefit in integrating these problems, with improvements of as much as 30%, but also that the proposed solution approach is highly effective. Compared to the commercial solver CPLEX, the proposed approach is able to more quickly find better quality solutions within a given time limit.

Multi-Hop Multi-Task Partial Computation Offloading in Collaborative Edge Computing

Collaborative edge computing (CEC) is a recent popular paradigm where different edge devices collaborate by sharing data and computation resources. One of the fundamental issues in CEC is to make task offloading decision. However, it is a challenging problem to solve as tasks can be offloaded to a device at multi-hop distance leading to conflicting network flows due to limited bandwidth constraint. There are some works on multi-hop computation offloading problem in the literature. However, existing works have not jointly considered multi-hop partial computation offloading and network flow scheduling that can cause network congestion and inefficient performance in terms of completion time. This article formulates the joint multi-task partial computation offloading and network flow scheduling problem to minimize the average completion time of all tasks. The formulated problem optimizes several dependent decision variables including partial offloading ratio, remote offloading device, start time of tasks, routing path, and start time of network flows. The problem is formulated as an MINLP optimization problem and shown to be NP-hard. We propose a joint partial offloading and flow scheduling heuristic (JPOFH) that decides partial offloading ratio by considering both waiting times at the devices and start time of network flows. We also do the relaxation of formulated MINLP problem to an LP problem using McCormick envelope to give a lower bound solution. Performance comparison done using simulation shows that JPOFH leads to up to 32 percent improvement in average completion time compared to benchmark solutions which do not make a joint decision.

A Truthful Online Mechanism for Collaborative Computation Offloading in Mobile Edge Computing

Collaborative computation offloading in mobile edge computing where edge users offload tasks opportunistically to resourceful neighboring mobile devices (MDs), offers a promising solution to satisfy low-latency requirements. However, most existing works assume that those MDs volunteer to help edge users without an incentive mechanism. In this article, we propose an auction-based incentive mechanism, where users and MDs participate in the system dynamically. Our auction mechanism runs in the online fashion and optimizes the long-term system welfare without knowledge of future information, e.g., task start time, task length, resource demand, and valuation, etc. We prove that the proposed online mechanism achieves the desired properties, including individual rationality, truthfulness, and computational tractability. Moreover, the theoretical competitive ratio shows that our online mechanism achieves near-optimal long-term social welfare close to the offline optimum. Extensive experiments based on real-world traces demonstrate the efficiency of the proposed online mechanism.

Towards a comprehensive model for track allocation and roll-time scheduling at marshalling yards

This paper considers multi-stage train formation with mixed usage tracks at a marshalling yard without departure yard. A novel integer programming model for scheduling shunting tasks as well as allocating arrival yard tracks and classification bowl tracks is presented. By taking a comprehensive view of the marshalling yard operations, more effective schedules can be found, and a variety of characteristics can be optimised, including shunting work effort, number or cost of tracks, and shunting task start times. Two different objective functions are evaluated: minimising work effort in terms of wagon pull-backs and minimising track costs. A procedure for finding a hot-start solution with few wagon pull-backs is also presented. The proposed model is tested on real data from Sävenäs marshalling yard in Sweden. The results show that the method is able to return an optimal schedule for a planning period of 4 days if the hot-start solution is optimal or the remaining problem is tractable for the heuristics in CPLEX.

Agent-based fuzzy constraint-directed negotiation mechanism for distributed job shop scheduling

This paper presents an agent-based fuzzy constraint-directed negotiation (AFCN) mechanism to solve distributed job shop scheduling problems (JSSPs). The scheduling problem is modelled as a set of fuzzy constraint satisfaction problems (FCSPs), interlinked by inter-agent constraints. Each FCSP represents the perspective of the participants and is governed by autonomous agents. The novelty of the proposed AFCN is to bring the concept of a fuzzy membership function to represent the imprecise preferences of task start time for job and resource agents. This added information sharing is crucial for the effectiveness of distributed coordination. It not only can speed up the convergence, but also enforce a global consistency through iterative exchange of offers and counter-offers. The AFCN mechanism can also flexibly adopt different negotiation strategies, such as competitive, win–win, and collaborative strategies, for different production environments. The experimental results demonstrate that the proposed model can provide not only high-quality and cost-effective job shop scheduling (i.e., comparable to that of centralized methods) but also superior performance in terms of the makespan and average flow time compared with other negotiation models for agent-based manufacturing scheduling. As a result, the proposed AFCN mechanism is flexible and useful for distributed manufacturing scheduling with unforeseen disturbances.

A clustering based coscheduling strategy for efficient scientific workflow execution in cloud computing

SUMMARYDue to its advantages of cost‐effectiveness, on‐demand provisioning and easy for sharing, cloud computing has grown in popularity with the research community for deploying scientific applications such as workflows. Although such interests continue growing and scientific workflows are widely deployed in collaborative cloud environments that consist of a number of data centers, there is an urgent need for exploiting strategies which can place application datasets across globally distributed data centers and schedule tasks according to the data layout to reduce both latency and makespan for workflow execution. In this paper, by utilizing dependencies among datasets and tasks, we propose an efficient data and task coscheduling strategy that can place input datasets in a load balance way and meanwhile, group the mostly related datasets and tasks together. Moreover, data staging is used to overlap task execution with data transmission in order to shorten the start time of tasks. We build a simulation environment on Tianhe supercomputer for evaluating the proposed strategy and run simulations by random and realistic workflows. The results demonstrate that the proposed strategy can effectively improve scheduling performance while reducing the total volume of data transfer across data centers. Concurrency and Computation: Practice and Experience, 2013.© 2013 Wiley Periodicals, Inc.

A cumulative not-first/not-last filtering algorithm in O(n 2log(n))

In cumulative and disjunctive constraint-based scheduling, the resource constraint is enforced by several filtering rules. Among these rules, we have (extended) edge-finding and not-first/not-last rules. The not-first/not-last rule detects tasks that cannot run first/last relatively to a set of tasks and prunes their time bounds. In this paper, it is presented a sound O (n2 log n) algorithm for the cumulative not-first/not-last rule where n is the number of tasks. This algorithm reaches the same fix point as previous not-first/not-last algorithms, although it may take additional iterations to do so. The worst case complexity of this new algorithm for the maximal adjustment is the same as our previous complete O (n2|H| log n) not-first/not-last algorithm [7] where |H| is the maximum between the number of distinct earliest completion and latest start times of tasks. But, experimental results on benchmarks from the Project Scheduling Problem Library (PSPLib) and the Baptiste and Le Pape data set (BL) suggest that the new not-first/not-last algorithm has a substantially reduced runtime. Furthermore, the results demonstrate that in practice the new algorithm rarely requires more propagations than previous not-first/not-last algorithms.

Inactivation of the putamen selectively impairs reward history-based action selection

Behavioral decisions and actions are directed to achieve specific goals and to obtain rewards and escape punishments. Previous studies involving the recording of neuronal activity suggest the involvement of the cerebral cortex, basal ganglia, and midbrain dopamine system in these processes. The value signal of the action options is represented in the striatum, updated by reward prediction errors, and used for selecting higher-value actions. However, it remains unclear whether dysfunction of the striatum leads to impairment of value-based action selection. The present study examined the effect of inactivation of the putamen via local injection of the GABAA receptor agonist muscimol in monkeys engaged in a manual reward-based multi-step choice task. The monkeys first searched a reward target from three alternatives, based on the previous one or two choices and their outcomes, and obtained a large reward; they then earned an additional reward by choosing the last rewarded target. Inactivation of the putamen impaired the ability of monkeys to make optimal choices during third trial in which they were required to choose a target different from those selected in the two previous trials by updating the values of the three options. The monkeys normally changed options if the last choice resulted in small reward (lose-shift) and stayed with the last choice if it resulted in large reward (win-stay). Task start time and movement time during individual trials became longer after putamen inactivation. But monkeys could control the motivation level depending on the reward value of individual trial types before and after putamen inactivation. These results support a view that the putamen is involved selectively and critically in neuronal circuits for reward history-based action selection.

Multi-Mode Resource-Constrained Scheduling for Complex Product Design Tasks

In order to obtain the optimal scheduling plan which includes the reasonable task start time and resource allocation for complex product design project, a multi-mode resource-constrained scheduling method for complex product design tasks was proposed. In the method, human resource was separated out from the technology resource aiming at characteristics of product design process. The mathematics model was built based on the idea of multi-mode resource-constrained scheduling problem. To solve the model, designer-task-resource matching degree matrix was calculated and genetic algorithm was used. Finally, a case study was presented to validate the method.

A station-oriented enumerative algorithm for two-sided assembly line balancing

In this paper, a station-oriented enumerative algorithm for two-sided assembly lines balancing (TALB) is proposed. First, the time transfer function is defined and combined with the precedence relation to compute the earliest and the latest start time of tasks. With the direction and cycle time constraints, a station-oriented procedure based on the start time is designed to assign tasks, starting from the left station to the rightstation of the position. Some unsuitable position assignments would be finally removed by checking the precedence constraints among the assigned tasks. The proposed algorithm is integrated with the Hoffmann heuristic to develop a system for solving TALB problems. The test is performed on the well-known benchmark set of problem instances. Experimental results demonstrate that the proposed procedure is efficient.

New General Continuous-Time State−Task Network Formulation for Short-Term Scheduling of Multipurpose Batch Plants

A new continuous-time MILP model for the short-term scheduling of multipurpose batch plants is presented. The proposed model relies on the state-task network (STN) approach and addresses the general problem of batch scheduling, accounting for resource (utility) constraints, variable batch sizes and processing times, various storage policies (UIS, FIS, NIS, ZW), batch mixing/splitting, and sequence-dependent changeover times. The key features of the proposed model are the following: (a) a continuous-time representation is used, common for all units; (b) assignment constraints are expressed using binary variables that are defined only for tasks, not for units; (c) start times of tasks are eliminated, so that time-matching constraints are used only for the finish times of tasks; and (d) a new class of valid inequalities that improves the LP relaxation is added to the MILP formulation. Compared to other general continuous time STN formulations, the proposed model is faster. Compared to event-driven formulations, it is more general, as it accounts for resources other than equipment and gives solutions in comparable computational times. The application of the model is illustrated through four example problems.

Dynamic Dispatching of Cyclic Real-Time Tasks with RelativeTiming Constraints

In some hard real-time systems, relative timing constraints may be imposed on task executions, in addition to the release time and deadline constraints. Relative timing constraints such as separation or relative deadline constraints may be given between start or finish times of tasks (Gerber et al., 1995; Han and Lin, 1989; Han et al., 1992; Han and Lin, 1992; Han et al., 1996). One approach in real-time scheduling is to find a total order on a set of N tasks in a scheduling window, and cyclically use this order at run time to execute tasks. However, in the presence of relative timing constraints, if the task execution times are nondeterministic with defined lower and upper bounds, it is not always possible to statically assign task start times at pre-runtime for a given task ordering (Gerber et al., 1995). We develop a technique called dynamic cyclic dispatching as an extension of a parametric dispatching mechanism in (Gerber et al., 1995). An ordered set of N tasks is assumed to be given in a scheduling window and this schedule(ordering) is cyclically repeated at runtime in consecutive scheduling windows. Relative timing constraints between tasks may be defined across scheduling window boundaries as well as within one scheduling window. A task set is defined to be dispatchable if there exists any way in which the tasks can be dispatched with all their timing constraints satisfied. An off-line algorithm is presented to check the dispatchability of a task set and to obtain parametric lower and upper bound functions for task start times if the task set is dispatchable. These parametric bound functions are evaluated at runtime to obtain a valid time interval during which a task can be started. The complexity of this off-line component is shown to be O(n^2N^3) where n is the number of tasks in a scheduling window that have relative timing constraints with tasks in the next scheduling window. An online algorithm can evaluate these bounds in O(N) time. Unlike static approaches which assign fixed start times to tasks in the scheduling window, our approach allows us to flexibly manage the slack times at runtime without sacrificing the dispatchability of tasks. Also, a wider class of relative timing constraints can be imposed to the task set compared to the traditional approaches.

A duplication heuristic for static scheduling of tasks on distributed memory multiprocessors

Abstract A task duplication heuristic, DSH, was proposed in [11]. The underlying concept of the task duplication heuristic is duplicating some tasks on processors such that the earliest start time of tasks on processors can be reduced, that is, tasks on processors can be executed sooner. This leads to a better scheduling length. In this paper, we propose a more general task duplication heuristic, bottom‐up top‐down duplication heuristic (BTDH), for static scheduling of directed‐acyclic graphs (DAGs) on distributed memory multiprocessors. The key difference between BTDH and DSH is the method used for duplicating tasks. BTDH allows tasks to be duplicated on processors even though the duplication of tasks will temporarily increase the earliest start time of some tasks. DSH only allows those duplications which will reduce the earliest start time of tasks. Simulation results show that, for coarse‐grain DAGs, the scheduling length of BTDH is almost the same as the scheduling length of DSH. However, for medium‐g...

Crew Requirements Definition System Demonstration

The crew requirements definition system (CRDS) is a computer-based methodology designed to minimize the time required to accomplishment any set of tasks while using the fewest resources. It enables analysts and researchers to study in a timely and cost effective manner the effects of varying crew size, task start times (and hence task sequencing), and task allocation to crewmembers or equipment items during the performance of designated missions without the need to observe crews actually performing their duties. The CRDS is programmed in C-language and is designed to be used on an “XT” or faster class of personal computer. The basis of the system is several automated PERT, GANTT, and critical path method calculations. In addition, the system produces other automated calculations and summaries to aid the user. The user should have some knowledge of these operations research techniques to use the system effectively. Also needed is an understanding of the tasks to be performed, the personnel and equipment items available to perform the tasks, each task's duration, and any requirements for task sequencing. The U.S. Army Research Institute for the Behavioral and Social Sciences (ARI) developed the CRDS for the Force Design Directorate at the U.S. Army Combined Arms Combat Development Activity. However, the system is useful in any military or civilian situations in which there is a need to design and evaluate alternative small unit organizational structures. The system can be used whenever the user has some knowledge, or is willing to venture some guesstimates, of the tasks that need to be performed and the capabilities of various assets to perform those tasks.