scholarly journals A Hybrid Genetic Algorithm for Integrated Truck Scheduling and Product Routing on the Cross-Docking System with Multiple Receiving and Shipping Docks

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Wooyeon Yu ◽  
Chunghun Ha ◽  
SeJoon Park
Keyword(s):  
Genetic Algorithm ◽  
Completion Time ◽  
Hybrid Genetic Algorithm ◽  
New Product ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Cross Docking ◽  
Truck Scheduling ◽  
Chromosome Representation ◽  
Nested Structure

In this research, a truck scheduling problem for a cross-docking system with multiple receiving and shipping docks is studied. Until recently, single-dock cross-docking problems are studied mostly. This research is focused on the multiple-dock problems. The objective of the problem is to determine the best docking sequences of inbound and outbound trucks to the receiving and shipping docks, respectively, which minimize the maximal completion time. We propose a new hybrid genetic algorithm to solve this problem. This genetic algorithm improves the solution quality through the population scheme of the nested structure and the new product routing heuristic. To avoid unnecessary infeasible solutions, a linked-chromosome representation is used to link the inbound and outbound truck sequences, and locus-pairing crossovers and mutations for this representation are proposed. As a result of the evaluation of the benchmark problems, it shows that the proposed hybrid GA provides a superior solution compared to the existing heuristics.

scholarly journals Extended Hybrid Genetic Algorithm for Solving Job Shop Scheduling Problem

2021 ◽  
Vol 10 (02) ◽  
pp. 017-020
Author(s):  
Sumaia E. Eshim ◽  
Mohammed M. Hamed
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Solution Quality ◽  
Shop Scheduling

In this paper, a hybrid genetic algorithm (HGA) to solve the job shop scheduling problem (JSSP) to minimize the makespan is presented. In the HGA, heuristic rules are integrated with genetic algorithm (GA) to improve the solution quality. The purpose of this research is to investigate from the convergence of a hybrid algorithm in achieving a good solution for new benchmark problems with different sizes. The results are compared with other approaches. Computational results show that a hybrid algorithm is capable to achieve good solution for different size problems.

A HYBRID GENETIC ALGORITHM FOR THE EARLY/TARDY SCHEDULING PROBLEM

2006 ◽  
Vol 23 (03) ◽  
pp. 393-405 ◽  
Author(s):  
JORGE M. S. VALENTE ◽  
JOSÉ FERNANDO GONÇALVES ◽  
RUI A. F. S. ALVES
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm ◽  
Scheduling Problem ◽  
Solution Quality ◽  
New Approach ◽  
Random Keys ◽  
Chromosome Representation ◽  
Local Search Procedure ◽  
Better Than

In this paper, we present a hybrid genetic algorithm for a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys. The genetic algorithm is used to establish the order in which the jobs are initially scheduled, and a local search procedure is subsequently applied to detect possible improvements. The approach is tested on a set of randomly generated problems and compared with existing efficient heuristic procedures based on dispatch rules and local search. The computational results show that this new approach, although requiring slightly longer computational times, is better than the previous algorithms in terms of solution quality.

Optimisation of Assembly Line Balancing Type-E with Resource Constraints Using NSGA-II

2016 ◽  
Vol 701 ◽  
pp. 195-199 ◽  
Author(s):  
Masitah Jusop ◽  
Mohd Fadzil Faisae Ab Rashid
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Resource Constraints ◽  
Hybrid Genetic Algorithm ◽  
Assembly Line Balancing ◽  
Search Space ◽  
Line Balancing ◽  
Benchmark Problems ◽  
Nsga Ii ◽  
Multi Objective Genetic Algorithm

Assembly line balancing of Type-E problem (ALB-E) is an attempt to assign the tasks to the various workstations along the line so that the precedence relations are satisfied and some performance measures are optimised. A majority of the recent studies in ALB-E assume that any assembly task can be assigned to any workstation. This assumption lead to higher usage of resource required in assembly line. This research studies assembly line balancing of Type-E problem with resource constraint (ALBE-RC) for a single-model. In this work, three objective functions are considered, i.e. minimise number of workstation, cycle time and number of resources. In this paper, an Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) has been proposed to optimise the problem. Six benchmark problems have been used to test the optimisation algorithm and the results are compared to multi-objective genetic algorithm (MOGA) and hybrid genetic algorithm (HGA). From the computational test, it was found NSGA-II has the ability to explore search space, has better accuracy of solution and also has a uniformly spaced solution. In future, a research to improve the solution accuracy is proposed to enhance the performance of the algorithm.

Hybrid Genetic Algorithm for Solving Assembly Line Balancing Problem in Footwear Industry

2014 ◽  
Vol 939 ◽  
pp. 623-629 ◽  
Author(s):  
James C. Chen ◽  
Chien Wei Wu ◽  
Tran Dinh Duy Thao ◽  
Ling Huey Su ◽  
Wen Haiung Hsieh ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Assembly Line ◽  
Hybrid Genetic Algorithm ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Production Cycle ◽  
Product Model ◽  
Solution Quality ◽  
Footwear Industry ◽  
Assembly Line Balancing Problem

This research develops a heuristic algorithm for assembly line balancing problem (ALBP) of stitching lines in footwear industry. The proposed algorithm can help to design the stitching line with workstations, machines and operators for the production of every new product model. Rank-positional-weighted heuristics and hybrid genetic algorithms are proposed to solve ALBP. First, the heuristics assign tasks and machines to workstations. This solution is then used as an initiative population for hybrid genetic algorithm for further improvement. Real data from footwear manufacturers and experimental designs are used to verify the performance of the proposed algorithm, comparing with one existing bidirectional heuristic. Results indicate that when the size and shape of shoes increase, the proposed genetic algorithm achieves better solution quality than existing heuristics.Production managers can use the research results to quickly design stitching lines for short production cycle time and high labor utilization.

Development of Hybrid Genetic Algorithms for the Resource Constrained Multi-Project Scheduling Problem

2005 ◽  
Author(s):  
Wenjian Liu ◽  
Jinghua Li
Keyword(s):  
Genetic Algorithm ◽  
Project Scheduling ◽  
Resource Constraints ◽  
Hybrid Genetic Algorithm ◽  
Second Phase ◽  
Scheduling Problem ◽  
Priority Rules ◽  
Resource Constrained ◽  
Chromosome Representation ◽  
Project Scheduling Problem

In multi-project environment, multiple projects share and compete for the limited resources to achieve their own goals. Besides resource constraints, there exist precedence constraints among activities within each project. This paper presents a hybrid genetic algorithm to solve the resource-constrained multi-project scheduling problem (RCMPSP), which is well known NP-hard problem. Objectives described in this paper are to minimize total project time of multiple projects. The chromosome representation of the problem is based on activity lists. The proposed algorithm was operated in two phases. In the first phase, the feasible schedules are constructed as the initialization of the algorithm by permutation based simulation and priority rules. In the second phase, this feasible schedule was optimized by genetic algorithm, thus a better approximate solution was obtained. Finally, after comparing several different algorithms, the validity of proposed algorithm is shown by a practical example.

scholarly journals Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation

2019 ◽  
Vol 11 (2) ◽  
pp. 502 ◽  
Author(s):  
Hyun Lee ◽  
Chunghun Ha
Keyword(s):  
Genetic Algorithm ◽  
Process Planning ◽  
Flexible Manufacturing ◽  
Benchmark Problems ◽  
Integrated Process ◽  
Genetic Operators ◽  
Complete Class ◽  
Planning And Scheduling ◽  
Chromosome Representation ◽  
Single String

This paper proposes a genetic algorithm (GA) to find the pseudo-optimum of integrated process planning and scheduling (IPPS) problems. IPPS is a combinatorial optimization problem of the NP-complete class that aims to solve both process planning and scheduling simultaneously. The complexity of IPPS is very high because it reflects various flexibilities and constraints under flexible manufacturing environments. To cope with it, existing metaheuristics for IPPS have excluded some flexibilities and constraints from consideration or have built a complex structured algorithm. Particularly, GAs have been forced to construct multiple chromosomes to account for various flexibilities, which complicates algorithm procedures and degrades performance. The proposed new integrated chromosome representation makes it possible to incorporate various flexibilities into a single string. This enables the adaptation of a simple and typical GA procedure and previously developed genetic operators. Experiments on a set of benchmark problems showed that the proposed GA improved makespan by an average of 17% against the recently developed metaheuristics for IPPS in much shorter computation times.

scholarly journals MANUFACTURING INDUSTRY JOB SHOP FLOOR DECISIONS THROUGH HYBRID GENETIC ALGORITHM

2021 ◽  
Vol 9 (2) ◽  
pp. 62-76
Author(s):  
Dr. Nageswara Rao. M, Et. al.
Keyword(s):  
Genetic Algorithm ◽  
Completion Time ◽  
Processing Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Hybrid Genetic Algorithm ◽  
Shop Floor ◽  
Scheduling Problems ◽  
Priority Rules ◽  
Simultaneous Scheduling

This article addresses flexible manufacturing system (FMS) Performance is likely to improve with employment of various resources efficiently. Initially simultaneous scheduling problems are solved by means of priority rules like first come first serve (FCFS), shortest processing time (SPT) and longest processing time (LPT) to find out the operational completion time for 120 problems. Later gene rearrangement genetic algorithm (HGA) is implemented for same set of problems with makespan as objective and the results are compared with the results of priority rules. The results are performed well by using HGA.  The same HGA is used to find the finest optimal sequence that minimize the operational completion time.  

A Hybrid Biomimetic Genetic Algorithm Using a Local Fuzzy Simplex Search

2010 ◽  
Author(s):  
George S. Ladkany ◽  
Mohamed B. Trabia
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Rate Of Convergence ◽  
Genetic Information ◽  
Hybrid Genetic Algorithm ◽  
Global Optimum ◽  
Benchmark Problems ◽  
Simplex Search ◽  
Successful Approach ◽  
Comparison Of The Results

This paper presents a hybrid genetic algorithm that expands upon the previously successful approach of twinkling genetic algorithm (TGA) by incorporating a highly efficient local fuzzy-simplex search within the algorithm. The TGA was in principle a bio-mimetic algorithm that introduced a controlled deviation from a typical GA method, by not requiring that every genevariable of an offspring be the result of a crossover. Instead, twinkling allowed the genetic information of the randomly chosen gene locations to be directly passed on from one parent, which was shown to increase the likelihood of survival of a successful gene value within the offspring, rather than requiring it to be blended. The twinkling genetic algorithms proved highly effective at locating exact global optimum with a competitive rate of convergence for a wide variety of benchmark problems. In this work, it is proposed to couple the TGA with a fuzzy simplex local search to increase the rate of convergence of the algorithm. The proposed algorithm is tested using common mathematical and engineering design benchmark problems. Comparison of the results of this algorithm with earlier algorithms is presented.

scholarly journals The Ordered Clustered Travelling Salesman Problem: A Hybrid Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Zakir Hussain Ahmed
Keyword(s):  
Genetic Algorithm ◽  
Travelling Salesman Problem ◽  
Hybrid Genetic Algorithm ◽  
Computational Time ◽  
Computational Results ◽  
Travelling Salesman ◽  
Solution Quality ◽  
Heuristic Solution ◽  
Present Solution ◽  
Sequencing Problems

The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The problem is NP-hard, and it arises in practical transportation and sequencing problems. This paper develops a hybrid genetic algorithm using sequential constructive crossover, 2-opt search, and a local search for obtaining heuristic solution to the problem. The efficiency of the algorithm has been examined against two existing algorithms for some asymmetric and symmetric TSPLIB instances of various sizes. The computational results show that the proposed algorithm is very effective in terms of solution quality and computational time. Finally, we present solution to some more symmetric TSPLIB instances.

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