scholarly journals Task Scheduling Model of Double-Deep Multi-Tier Shuttle System

Processes ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 604 ◽  
Author(s):  
Yanyan Wang ◽  
Rongxu Zhang ◽  
Hui Liu ◽  
Xiaoqing Zhang ◽  
Ziwei Liu
Keyword(s):  
Task Scheduling ◽  
Production Scheduling ◽  
Current System ◽  
Storage System ◽  
Complex Structure ◽  
Vertical Movement ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
High Flexibility ◽  
Shuttle System

As a new type of part-to-picker storage system, the double-deep multi-tier shuttle system has been developed rapidly in the e-commerce industry because of its high flexibility, large storage capacity, and robustness. The system consists of a multi-tier shuttle sub-system that controls horizontal movement and a lift sub-system that manages vertical movement. The combination of shuttles and lifts, instead of a stacker crane in conventional automated storage and retrieval system, undertakes inbound/outbound tasks. Because of the complex structure and numerous equipment of the system, task scheduling has become a major difficulty in the outbound operation of the double-deep multi-tier shuttle system. Figuring out methods to improve the overall efficiency of task scheduling operations is the focus of current system application enterprises. This paper introduces the task scheduling problem for the shuttle system. Inspired from workshop production scheduling problems, we minimize the total time of a batch of retrieval tasks as the objective function, applying the modified Simulated Annealing Algorithms (SAAs) to solve the task scheduling problem. In conclusion, we verified the proposed model and the algorithm efficiency, using case studies.

scholarly journals Realtime production scheduling and control systems in smart industry

Impact ◽  
2020 ◽  
Vol 2020 (8) ◽  
pp. 60-61
Author(s):  
Wei Weng
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Production Scheduling ◽  
Production Control ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Scheduling Problems ◽  
Smart Industry ◽  
And Control

For a production system, 'scheduling' aims to find out which machine/worker processes which job at what time to produce the best result for user-set objectives, such as minimising the total cost. Finding the optimal solution to a large scheduling problem, however, is extremely time consuming due to the high complexity. To reduce this time to one instance, Dr Wei Weng, from the Institute of Liberal Arts and Science, Kanazawa University in Japan, is leading research projects on developing online scheduling and control systems that provide near-optimal solutions in real time, even for large production systems. In her system, a large scheduling problem will be solved as distributed small problems and information of jobs and machines is collected online to provide results instantly. This will bring two big changes: 1. Large scheduling problems, for which it tends to take days to reach the optimal solution, will be solved instantly by reaching near-optimal solutions; 2. Rescheduling, which is still difficult to be made in real time by optimization algorithms, will be completed instantly in case some urgent jobs arrive or some scheduled jobs need to be changed or cancelled during production. The projects have great potential in raising efficiency of scheduling and production control in future smart industry and enabling achieving lower costs, higher productivity and better customer service.

Modified Migrating Birds Optimization for Solving the Low-carbon Scheduling Problem

2020 ◽  
Vol 12 (4) ◽  
pp. 63-75
Author(s):  
Zhifeng Zhang ◽  
Yusheng Sun ◽  
Yadong Cui ◽  
Haodong Zhu
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Optimal Solution ◽  
Scheduling Problem ◽  
Low Carbon ◽  
Scheduling Problems ◽  
Solution Quality ◽  
Migrating Birds Optimization ◽  
Initialization Scheme ◽  
Migrating Birds

Production scheduling problems have historically emphasized cycle time without involving the environmental factors. In the study, a low-carbon scheduling problem in a flexible job shop is considered to minimize the energy consumption, which mainly consists of two parts: the useful part and the wasted part. First, a mathematical model is built based on the features of the workshop. Second, a modified migrating bird's optimization (MMBO) is developed to obtain the optimal solution. In the MMBO, a population initialization scheme is designed to enhance the solution quality and convergence speed. Five types of neighborhood structures are introduced to create neighborhood solutions. Furthermore, a local search method and a reset mechanism are developed to improve the computational results. Finally, experimental results validate that the MMBO is effective and feasible.

scholarly journals Task Scheduling Problem Using Fuzzy Graph

2016 ◽  
Vol 1 (1-2) ◽  
pp. 16-20
Author(s):  
Vivek Raich ◽  
Shweta Rai
Keyword(s):  
Task Scheduling ◽  
Single Machine ◽  
Scheduling Problem ◽  
Scheduling Theory ◽  
Scheduling Problems ◽  
Fuzzy Graph ◽  
Minimum Value ◽  
Chromatic Sum ◽  
Completion Times

The concept of obtaining fuzzy sum of fuzzy colorings problem has a natural application in scheduling theory. The problem of scheduling N jobs on a single machine and obtain the minimum value of the job completion times is equivalent to finding the fuzzy chromatic sum of the fuzzy graph modeled for this problem. The aim of this paper is to solve task scheduling problems using fuzzy graph.

scholarly journals Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Ruey-Maw Chen ◽  
Chuin-Mu Wang
Keyword(s):  
Grid Computing ◽  
Task Scheduling ◽  
Project Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Resource Assignment ◽  
Grid Environment ◽  
Standard Particle Swarm Optimization ◽  
Communication Topology ◽  
Np Complete

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

Re-Entrant Production Scheduling Problem under Uncertainty Based on QPSO Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 1061-1066
Author(s):  
Feng Shan Pan ◽  
Chun Ming Ye ◽  
Ji Hua Zhou
Keyword(s):  
Production Scheduling ◽  
Product Life Cycle ◽  
Market Competition ◽  
Product Diversification ◽  
Theoretical Research ◽  
Scheduling Problem ◽  
Scheduling Theory ◽  
Scheduling Problems ◽  
The One ◽  
Construction Strategies

Production scheduling problem is the one of the most basic, important and difficult theoretical research in a manufacturing system. In the past decades of research, the classical scheduling theory has made signficant progress, but the actual scheduling problems are much more complicated than the classical theory. In order to study, the actual scheduling problems are often made much simpler. Therefore, it is difficult for the results of the classical scheduling theory to been put into practice. The considerable gap also exists between the classical scheduling theory and the actual scheduling problem. But with the increasingly fierce market competition, customers have become increasingly demanding product diversification, product life cycle is shorter and more sophisticated structure of the product makes the actual scheduling a large number of uncertainties, the traditional model has become difficult to obtain satisfactory results. This paper makes a analysis on the uncertainties of production scheduling,and tries to solve re-entrant production scheduling problem based on QPSO. It introduces the mathematical model and the solving process based on QPSO algorithm. Then it makes construction strategies to solve it. The paper simulates with Visual C. The results show that this algorithm is feasibility.

scholarly journals A Hybrid Discrete Bacterial Memetic Algorithm with Simulated Annealing for Optimization of the Flow Shop Scheduling Problem

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1131
Author(s):  
Anita Agárdi ◽  
Károly Nehéz ◽  
Olivér Hornyák ◽  
László T. Kóczy
Keyword(s):  
Simulated Annealing ◽  
Production Scheduling ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Research Work ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

This paper deals with the flow shop scheduling problem. To find the optimal solution is an NP-hard problem. The paper reviews some algorithms from the literature and applies a benchmark dataset to evaluate their efficiency. In this research work, the discrete bacterial memetic evolutionary algorithm (DBMEA) as a global searcher was investigated. The proposed algorithm improves the local search by applying the simulated annealing algorithm (SA). This paper presents the experimental results of solving the no-idle flow shop scheduling problem. To compare the proposed algorithm with other researchers’ work, a benchmark problem set was used. The calculated makespan times were compared against the best-known solutions in the literature. The proposed hybrid algorithm has provided better results than methods using genetic algorithm variants, thus it is a major improvement for the memetic algorithm family solving production scheduling problems.

scholarly journals The Task Scheduling Problem: A NeuroGenetic Approach

2014 ◽  
Vol 12 (4) ◽  
pp. 327
Author(s):  
Anurag Agarwal ◽  
Selcuk Colak ◽  
Jason Deane ◽  
Terry Rakes
Keyword(s):  
Neural Network ◽  
Task Scheduling ◽  
Computational Time ◽  
Test Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Network Approach ◽  
Neural Network Approach ◽  
One Machine ◽  
Better Than

This paper addresses the task scheduling problem which involves minimizing the makespan in scheduling n tasks on m machines (resources) where the tasks follow a precedence relation and preemption is not allowed. The machines (resources) are all identical and a task needs only one machine for processing. Like most scheduling problems, this one is NP-hard in nature, making it difficult to find exact solutions for larger problems in reasonable computational time. Heuristic and metaheuristic approaches are therefore needed to solve this type of problem. This paper proposes a metaheuristic approach - called NeuroGenetic - which is a combination of an augmented neural network and a genetic algorithm. The augmented neural network approach is itself a hybrid of a heuristic approach and a neural network approach. The NeuroGenetic approach is tested against some popular test problems from the literature, and the results indicate that the NeuroGenetic approach performs significantly better than either the augmented neural network or the genetic algorithms alone.

Research on Production Scheduling Problems in Process Industry Based on Ant Colony System

2010 ◽  
Vol 108-111 ◽  
pp. 519-524
Author(s):  
Lie Ping Zhang ◽  
Yun Sheng Zhang
Keyword(s):  
Production Scheduling ◽  
Stochastic Scheduling ◽  
Ant Colony ◽  
Process Industry ◽  
Process Time ◽  
Ant Colony System ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Process Step ◽  
Scheduling Model

In order to improve the production of process industry, the ant colony system(ACS) was applied to the production scheduling problem. Based on the analysis of the production scheduling problem for process industry, a production scheduling model was established, whose goal was to obtain the shortest total process time. The search strategy, heuristic information rules, pheromone updating mechanism, process step starting time and detailed algorithm implementation of ACS were discussed. Using a practical production scheduling problem as an example, the established model and designed algorithm were applied to implement the scheduling simulation. The simulation results show that the scheduling model and algorithm are feasible, and have a better scheduling performance than the stochastic scheduling method, and can be applied to solve practical production scheduling problem for process industry.

Learning the Distribution Characteristics of Critical Machines in Production Scheduling Problems

2011 ◽  
Vol 337 ◽  
pp. 142-145
Author(s):  
Rui Zhang
Keyword(s):  
Production Scheduling ◽  
Simulated Annealing Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Identification Algorithm ◽  
Scheduling Problem ◽  
Distribution Characteristics ◽  
Scheduling Problems ◽  
Critical Machine ◽  
Critical Machines

A critical machine identification algorithm is proposed for the job shop scheduling problem in which the total tardiness must be minimized. An optimization-based procedure is devised to learn the distribution characteristics of critical machines in a specific scheduling instance. The proposed simulated annealing algorithm optimizes the scheduling problem after the capacity constraints for each machine are modified. A genetic algorithm based on combined dispatching rules is designed to verify the effectiveness of the proposed methodology.

scholarly journals A Combination of Palmer Algorithm and Gupta Algorithm for Scheduling Problem in Apparel Industry

2021 ◽  
Vol 11 (1) ◽  
pp. 1-12
Author(s):  
Cecilia E. Nugraheni ◽  
Luciana Abednego ◽  
Maria Widyarini
Keyword(s):  
Genetic Programming ◽  
Computer Program ◽  
Production Scheduling ◽  
Textile Industry ◽  
Flow Shop ◽  
Apparel Industry ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.

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