scholarly journals A Metaheuristic Method for the Task Assignment Problem in Continuous-Casting Production

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Hongtao Hu ◽  
Yiwei Wu ◽  
Tingsong Wang
Keyword(s):  
Continuous Casting ◽  
Molten Steel ◽  
Assignment Problem ◽  
Programming Model ◽  
Task Assignment ◽  
Solution Algorithm ◽  
Mixed Integer ◽  
Computational Time ◽  
Solution Quality ◽  
Task Assignment Problem

The steelmaking and continuous-casting (SCC) process in integrated iron and steel enterprises can be described as two stages: the upstream stage and downstream stage. Raw materials are transformed into molten steel in the upstream stage, while the downstream stage is responsible for transforming molten steel which is released at regular intervals and has a limited time for being turned into slabs. This article focuses on the task assignment problem in the downstream stage within the given information resulting from the upstream stage. This problem is formulated as a nonlinear mixed-integer programming model aimed at minimizing total tardiness within the resource constraints and time windows constraints for the tasks. An improved solution algorithm based on particle swam optimization is developed to efficiently solve the proposed model. Finally, computational experiments are implemented to evaluate the performance of the solution algorithm in terms of solution quality and computational time.

scholarly journals A Time-Efficient Co-Operative Path Planning Model Combined with Task Assignment for Multi-Agent Systems

Robotics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 35 ◽  
Author(s):  
Sumana Biswas ◽  
Sreenatha G. Anavatti ◽  
Matthew A. Garratt
Keyword(s):  
Path Planning ◽  
Assignment Problem ◽  
Task Assignment ◽  
Computational Time ◽  
Search Model ◽  
Nearest Neighbour ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Task Assignment Problem

Dealing with uncertainties along with high-efficiency planning for task assignment problem is still challenging, especially for multi-agent systems. In this paper, two frameworks—Compromise View model and the Nearest-Neighbour Search model—are analyzed and compared for co-operative path planning combined with task assignment of a multi-agent system in dynamic environments. Both frameworks are capable of dynamically controlling a number of autonomous agents to accomplish multiple tasks at different locations. Furthermore, these two models are capable of dealing with dynamically changing environments. In both approaches, the Particle Swarm Optimization-based method is applied for path planning. The path planning approach combined with the obstacle avoidance strategy is integrated with the task assignment problem. In one framework, the Compromise View model is used for completing the tasks and a combination of clustering method with the Nearest-Neighbour Search model is used to assign tasks to the other framework. The frameworks are compared in terms of computational time and the resulting path length. Results indicate that the Nearest-Neighbour Search model is much faster than the Compromise View model. However, the Nearest-Neighbour Search model generates longer paths to accomplish the mission. By following the Nearest-Neighbour Search approach, agents can successfully accomplish their mission, even under uncertainties such as malfunction of individual agents. The Nearest-Neighbour Search framework is highly effective due to its reactive structure. As per requirements, to save time, after completing its own tasks, one agent can complete the remaining tasks of other agents. The simulation results show that the Nearest-Neighbour Search model is an effective and robust way of solving co-operative path planning combined with task assignment problems.

scholarly journals Multi-Manned Assembly Line Balancing: Workforce Synchronization for Big Data Sets through Simulated Annealing

2021 ◽  
Vol 11 (6) ◽  
pp. 2523
Author(s):  
Francesco Pilati ◽  
Emilio Ferrari ◽  
Mauro Gamberi ◽  
Silvia Margelli
Keyword(s):  
Simulated Annealing ◽  
Assembly Line ◽  
Simulated Annealing Algorithm ◽  
Programming Model ◽  
Assembly Line Balancing ◽  
Task Assignment ◽  
Computation Time ◽  
Mixed Integer ◽  
Solution Quality ◽  
Line Design

The assembly of large and complex products such as cars, trucks, and white goods typically involves a huge amount of production resources such as workers, pieces of equipment, and layout areas. In this context, multi-manned workstations commonly characterize these assembly lines. The simultaneous operators’ activity in the same assembly station suggests considering compatibility/incompatibility between the different mounting positions, equipment sharing, and worker cooperation. The management of all these aspects significantly increases the balancing problem complexity due to the determination of the start/end times of each task. This paper proposes a new mixed-integer programming model to simultaneously optimize the line efficiency, the line length, and the workload smoothness. A customized procedure based on a simulated annealing algorithm is developed to effectively solve this problem. The aforementioned procedure is applied to the balancing of the real assembly line of European sports car manufacturers distinguished by 665 tasks and numerous synchronization constraints. The experimental results present remarkable performances obtained by the proposed procedure both in terms of solution quality and computation time. The proposed approach is the practical reference for efficient multi-manned assembly line design, task assignment, equipment allocation, and mounting position management in the considered industrial fields.

Modeling and scheduling hybrid open shops for makespan minimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kennedy Anderson Guimarães de Araújo ◽  
Tiberius Oliveira e Bonates ◽  
Bruno de Athayde Prata
Keyword(s):  
Programming Model ◽  
Computing Time ◽  
Iterated Local Search ◽  
Mixed Integer ◽  
Scheduling Problems ◽  
Priority Rules ◽  
Production Flow ◽  
Solution Quality ◽  
Content Type ◽  
Constructive Heuristics

Purpose This study aims to address the hybrid open shop problem (HOSP) with respect to the minimization of the overall finishing time or makespan. In the HOSP, we have to process n jobs in stages without preemption. Each job must be processed once in every stage, there is a set of mk identical machines in stage k and the production flow is immaterial. Design/methodology/approach Computational experiments carried out on a set of randomly generated instances showed that the minimal idleness heuristic (MIH) priority rule outperforms the longest processing time (LPT) rule proposed in the literature and the other proposed constructive methods on most instances. Findings The proposed mathematical model outperformed the existing model in the literature with respect to computing time, for small-sized instances, and solution quality within a time limit, for medium- and large-sized instances. The authors’ hybrid iterated local search (ILS) improved the solutions of the MIH rule, drastically outperforming the models on large-sized instances with respect to solution quality. Originality/value The authors formalize the HOSP, as well as argue its NP-hardness, and propose a mixed integer linear programming model to solve it. The authors propose several priority rules – constructive heuristics based on priority measures – for finding feasible solutions for the problem, consisting of adaptations of classical priority rules for scheduling problems. The authors also propose a hybrid ILS for improving the priority rules solutions.

A solution method for treatment scheduling in rehabilitation hospitals with real-life requirements

2018 ◽  
Vol 30 (4) ◽  
pp. 367-386 ◽  
Author(s):  
Liyang Xiao ◽  
Mahjoub Dridi ◽  
Amir Hajjam El Hassani ◽  
Wanlong Lin ◽  
Hongying Fei
Keyword(s):  
Waiting Time ◽  
Programming Model ◽  
Search Algorithm ◽  
Real Life ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Mixed Integer ◽  
Solution Quality ◽  
Improved Cuckoo Search Algorithm ◽  
Rehabilitation Hospitals

Abstract In this study, we aim to minimize the total waiting time between successive treatments for inpatients in rehabilitation hospitals (departments) during a working day. Firstly, the daily treatment scheduling problem is formulated as a mixed-integer linear programming model, taking into consideration real-life requirements, and is solved by Gurobi, a commercial solver. Then, an improved cuckoo search algorithm is developed to obtain good quality solutions quickly for large-sized problems. Our methods are demonstrated with data collected from a medium-sized rehabilitation hospital in China. The numerical results indicate that the improved cuckoo search algorithm outperforms the real schedules applied in the targeted hospital with regard to the total waiting time of inpatients. Gurobi can construct schedules without waits for all the tested dataset though its efficiency is quite low. Three sets of numerical experiments are executed to compare the improved cuckoo search algorithm with Gurobi in terms of solution quality, effectiveness and capability to solve large instances.

Quadratic Convex Reformulation for Solving Task Assignment Problem with Continuous Hopfield Network

2021 ◽  
Author(s):  
Youssef Hami ◽  
Chakir Loqman
Keyword(s):  
Assignment Problem ◽  
Optimal Allocation ◽  
Task Assignment ◽  
Hopfield Network ◽  
Quadratic Program ◽  
Convex Objective Function ◽  
Calculation Results ◽  
Quadratic Convex Reformulation ◽  
Task Assignment Problem ◽  
Theoretical Results

This research is an optimal allocation of tasks to processors in order to minimize the total costs of execution and communication. This problem is called the Task Assignment Problem (TAP) with nonuniform communication costs. To solve the latter, the first step concerns the formulation of the problem by an equivalent zero-one quadratic program with a convex objective function using a convexification technique, based on the smallest eigenvalue. The second step concerns the application of the Continuous Hopfield Network (CHN) to solve the obtained problem. The calculation results are presented for the instances from the literature, compared to solutions obtained both the CPLEX solver and by the heuristic genetic algorithm, and show an improvement in the results obtained by applying only the CHN algorithm. We can see that the proposed approach evaluates the efficiency of the theoretical results and achieves the optimal solutions in a short calculation time.

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