Multicriteria Flow-Shop Scheduling Problem

2010 ◽  
pp. 211-233 ◽  
Author(s):  
Ethel Mokotoff
Keyword(s):  
Combinatorial Optimization ◽  
Flow Shop ◽  
Real Life ◽  
Combinatorial Problems ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Recent Developments ◽  
Metaheuristic Techniques

Quality is, in real-life, a multidimensional notion. A schedule is described and valued on the basis of a number of criteria, for example: makespan, work-in-process inventories, idle times, observance of due dates, etc. An appropriate schedule cannot be obtained unless one observes the whole set of important criteria. The multidimensional nature of the scheduling problems leads us to the area of Multicriteria Optmization. Thus considering combinatorial problems with more than one criterion is more relevant in the context of real-life scheduling problems. Research in this important field has been scarce when compared to research in single-criterion scheduling. The proliferation of metaheuristic techniques has encouraged researchers to apply them to combinatorial optimization problems. The chapter presents a review regarding multicriteria flow-shop scheduling problem, focusing on Multi-Objective Combinatorial Optimization theory, including recent developments considering more than one optimization criterion, followed by a summary discussion on research directions.

scholarly journals Analysis of flow shop problems and their algorithms

2009 ◽  
Vol 50 ◽  
Author(s):  
Lina Rajeckaitė ◽  
Narimantas Listopadskis
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Flow Shop ◽  
Exact Algorithm ◽  
Combinatorial Optimization Problem ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

The combinatorial optimization problem considered in this paper is flow shop scheduling problem arising in logistics, management, business, manufacture and etc. A set of machines and a set of jobs are given. Each job consists of a set of operations. Machines are working with unavailability intervals. The task is to minimize makespan, i.e. the overall length of the schedule. There is overview of combinatorial optimization, scheduling problems and methods used to solve them. There is also presented and realized one exact algorithm – Branch and Bound, and two meta-heuristics: Simulated Annealing and Tabu Search. Analysis of these three algorithms is made.

scholarly journals A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 222 ◽  
Author(s):  
Han ◽  
Guo ◽  
Su
Keyword(s):  
Reinforcement Learning ◽  
Flow Shop ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
First Choice ◽  
Hybrid Flow Shop Scheduling

The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitation during choosing action in RL. Compared with the first-come-first-serve strategy that is frequently adopted when coding in most of the traditional intelligent algorithms, the rule in the RL method is first-come-first-choice, which is more conducive to achieving the global optimal solution. For validation, the RL method is utilized for scheduling in a metal processing workshop of an automobile engine factory. Then, the method is applied to the sortie scheduling of carrier aircraft in continuous dispatch. The results demonstrate that the machining and support scheduling obtained by this RL method are reasonable in result quality, real-time performance and complexity, indicating that this RL method is practical for HFSP.

scholarly journals Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2250
Author(s):  
Mei Li ◽  
Gai-Ge Wang ◽  
Helong Yu
Keyword(s):  
Artificial Bee Colony ◽  
Flow Shop ◽  
Real Life ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Bee Colony ◽  
Mutation Strategy

In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

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.

Alternative Heuristic Algorithm for Flow Shop Scheduling Problem

2013 ◽  
pp. 277-297
Author(s):  
Vladimír Modrák ◽  
R. Sudhakra Pandian ◽  
Pavol Semanco
Keyword(s):  
Heuristic Algorithm ◽  
Flow Shop ◽  
Idle Time ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Realistic Situation ◽  
Permutation Flow Shop

In this chapter an alternative heuristic algorithm is proposed that is assumed for a deterministic flow shop scheduling problem. The algorithm is addressed to an m-machine and n-job permutation flow shop scheduling problem for the objective of minimizing the make-span when idle time is allowed on machines. This chapter is composed in a way that the different scheduling approaches to solve flow shop scheduling problems are benchmarked. In order to compare the proposed algorithm against the benchmarked, selected heuristic techniques and genetic algorithm have been used. In realistic situation, the proposed algorithm can be used as it is without any modification and come out with acceptable results.

An Overview of Hybrid Flow Shop Scheduling

2012 ◽  
Vol 3 (2) ◽  
pp. 78-91 ◽  
Author(s):  
M. Saravanan ◽  
S. Sridhar
Keyword(s):  
Parallel Machines ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Future Research ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Research Attention ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

This paper is survey of hybrid flow shop scheduling problems. An HFS scheduling problem is a classical flow-shop in which parallel machines are available to perform the same operation. Most real world scheduling problems are NP-hard in nature. The hybrid flow shop scheduling problems have received considerable research attention. The several optimization and heuristic solution procedures are available to solve a variety of hybrid flow shop scheduling problems. It discusses and reviews sustainability of several variants of the hybrid flow shop scheduling problem for economical analysis, each in turn considering different assumptions, constraints and objective functions. Sustainability is the long-term maintenance of responsibility, which analysis the economics and encompasses the concept of stewardship. The Hybrid flow shop problem has sustained for several decades with multi – objective constraints. The paper shows some fruitful directions for future research and opportunities in the area of hybrid flow shop.

An Effective Immune Based Approach for the No-Wait Flow Shop Scheduling Problems with Multiple Machines

2010 ◽  
Vol 97-101 ◽  
pp. 2432-2435 ◽  
Author(s):  
Yi Chih Hsieh ◽  
Y.C. Lee ◽  
Peng Sheng You ◽  
Ta Cheng Chen
Keyword(s):  
Flow Shop ◽  
Immune Algorithm ◽  
Flow Shop Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Practical Applications ◽  
Shop Scheduling ◽  
No Wait ◽  
Better Than

For scheduling problems, no-wait constraint is an important requirement for many industries. As known, the no-wait scheduling problem is NP-hard and has several practical applications. This paper applies an immune algorithm to solve the multiple-machine no-wait flow shop scheduling problem with minimizing the makespan. Twenty-three benchmark problems on the OR-Library are solved by the immune algorithm. Limited numerical results show that the immune algorithm performs better than the other typical approaches in the literature for most of instances.

scholarly journals Solving flow shop scheduling problems with the objective of minimizing TCT when tie occurs

2021 ◽  
Vol 5 (2) ◽  
pp. 1-8
Author(s):  
SATHIYA SHANTHI R ◽  
MEGANATHAN R ◽  
JAYAKUMAR S ◽  
VIJAYARAGAVAN R
Keyword(s):  
Completion Time ◽  
Systematic Approach ◽  
Flow Shop ◽  
Experimental Results ◽  
The Other ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Makespan Minimization ◽  
Shop Scheduling

Scheduling process arises naturally upon availability of resources through a systematic approach in which prior planning and decisions should be made. Two machine flow shop scheduling problem (FSSP) was solved by Johnson in the mid of 1954 with makespan minimization as objective. Earlier we proposed two algorithms for the makespan objective; in this paper we intend to investigate the same algorithms for the objective of Total Completion Time of all the jobs (TCT). Experimental results had shown that one of our algorithms gives better results than the other two when the machine order is reversed.

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.

A COMPARISON OF HEURISTICS FOR SCHEDULING PROBLEMS IN TEXTILE INDUSTRY

2016 ◽  
Vol 78 (6-6) ◽  
Author(s):  
Cecilia E. Nugraheni ◽  
Luciana Abednego
Keyword(s):  
Textile Industry ◽  
Flow Shop ◽  
Experimental Results ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling

Scheduling is an important problem in textile industry. The scheduling problem in textile industry generally belongs to the flow shop scheduling problem (FSSP). There are many heuristics for solving this problem. Eight heuristics, namely FCFS, Gupta, Palmer, NEH, CDS, Dannenbring, Pour, and MOD are considered and compared. Experimental results show the best heuristic is NEH and the worst heuristic is FCFS.

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