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.

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.

Hybrid Genetic Algorithm Based on Chaotic Migration Strategy for Solving Flow Shop Scheduling Problem with Fuzzy Delivery Time

2015 ◽  
Vol 19 (3) ◽  
pp. 359-364
Author(s):  
Wen-Zhan Dai ◽  
◽  
Kai Xia
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Flow Shop ◽  
Hybrid Genetic Algorithm ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Migration Strategy ◽  
Shop Scheduling ◽  
Delivery Times ◽  
Search Capability

In this paper, a hybrid genetic algorithm based on a chaotic migration strategy (HGABCM) for solving the flow shop scheduling problem with fuzzy delivery times is proposed. First, the initial population is divided into several sub-populations, and each sub-population is isolated and evolved. Next, these offspring are further optimized by a strategy that combines NEH heuristic algorithm proposed by M. Navaz, J.-E. Enscore, I. Ham in 1983 with a newly designed algorithm that has excellent local search capability, thereby enhancing the strategy’s local search capability. Then, the concept of a chaotic migration sequence is introduced to guide the ergodic process of the migration of individuals effectively so that information is exchanged sufficiently among sub-populations and the process of falling into a local optimal solution is thereby avoided. Finally, several digital simulations are provided to demonstrate the effectiveness of the algorithm proposed in this paper.

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 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 An Improved Hybrid Genetic Algorithm with a New Local Search Procedure

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Wen Wan ◽  
Jeffrey B. Birch
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Global Optimum ◽  
Good Choice ◽  
Fine Tuning ◽  
Learning Procedure ◽  
Local Search Procedure ◽  
Important Challenge

One important challenge of a hybrid genetic algorithm (HGA) (also called memetic algorithm) is the tradeoff between global and local searching (LS) as it is the case that the cost of an LS can be rather high. This paper proposes a novel, simplified, and efficient HGA with a new individual learning procedure that performs a LS only when the best offspring (solution) in the offspring population is also the best in the current parent population. Additionally, a new LS method is developed based on a three-directional search (TD), which is derivative-free and self-adaptive. The new HGA with two different LS methods (the TD and Neld-Mead simplex) is compared with a traditional HGA. Four benchmark functions are employed to illustrate the improvement of the proposed method with the new learning procedure. The results show that the new HGA greatly reduces the number of function evaluations and converges much faster to the global optimum than a traditional HGA. The TD local search method is a good choice in helping to locate a global “mountain” (or “valley”) but may not perform the Nelder-Mead method in the final fine tuning toward the optimal solution.

Solving MRCPSP by a Hybrid Genetic Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 2369-2372
Author(s):  
Yan Li ◽  
Lloyd Gibson
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Project Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Scheduling Problem ◽  
Computational Results ◽  
Nonrenewable Resource ◽  
Project Scheduling Problem ◽  
Checking Procedure ◽  
Multi Mode

In this paper we present a genetic algorithm for the multi-mode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. To solve the problem, we apply a hybrid genetic algorithm, which makes use of nonrenewable resource feasibility checking procedure, local search based mutation and topological sort procedure. We present detailed computational results for the MRCPSP, which reveal that our procedure is effective in solving the problem.

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