scholarly journals Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3382 ◽  
Author(s):  
Chen Peng ◽  
Tao Peng ◽  
Yi Zhang ◽  
Renzhong Tang ◽  
Luoke Hu
Keyword(s):  
Energy Consumption ◽  
Flow Shop ◽  
Mathematic Model ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Special Focus ◽  
Mixed Flow ◽  
Flow Shops ◽  
Trade Offs ◽  
Processing Energy

To meet the increasingly diversified demand of customers, more mixed-flow shops are employed. The flexibility of mixed-flow shops increases the difficulty of scheduling. In this paper, a mixed-flow shop scheduling approach (MFSS) is proposed to minimise the energy consumption and tardiness fine (TF) of production with a special focus on non-processing energy (NPE) reduction. The proposed approach consists of two parts: firstly, a mathematic model is developed to describe how NPE and TF can be determined with a specific schedule; then, a multi-objective evolutionary algorithm with multi-chromosomes (MCEAs) is developed to obtain the optimal solutions considering the NPE-TF trade-offs. A deterministic search method with boundary (DSB) and a non-dominated sorting genetic algorithm (NSGA) are employed to validate the developed MCEA. Finally, a case study on an extrusion die mixed-flow shop is performed to demonstrate the proposed approach in industrial practice. Compared with three traditional scheduling approaches, the better performance of the MFSS in terms of computational time and solution quality could be demonstrated.

Layout design of a mixed-flow production line based on processing energy consumption and buffer configuration

2021 ◽  
Author(s):  
Cai-Xia Zhang ◽  
Shu-Lin Dong ◽  
Hong-Yan Chu ◽  
Guo-Zhi Ding ◽  
Zhi-Feng Liu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Production Line ◽  
Layout Design ◽  
Mixed Flow ◽  
Processing Energy

scholarly journals EVOLUTIONARY ALGORITHMS FOR RESOURCE CONSTRAINED NON-SERIAL MIXED FLOW SHOPS

2003 ◽  
Vol 03 (04) ◽  
pp. 411-435 ◽  
Author(s):  
ROBERT L. BURDETT ◽  
ERHAN KOZAN
Keyword(s):  
Evolutionary Algorithms ◽  
Numerical Investigation ◽  
Flow Shop ◽  
Resource Constrained ◽  
Mixed Flow ◽  
Flow Shops ◽  
Constrained Flow

In this paper the resource-constrained flow shop (RCF) problem is addressed. A number of realistic extensions are incorporated, including non-serial precedence requirements, mixed flow shop situations, and the distribution of the human workforce among a number of pre-determined groups. The RCF is then solved by meta-heuristics, primarily of the evolutionary type. An extensive numerical investigation, including a case study of a particular industrial situation, details the implementation and execution of the heuristics, and the efficiency of the proposed algorithms.

Energy-efficient multi-objective scheduling algorithm for hybrid flow shop with fuzzy processing time

2019 ◽  
Vol 233 (10) ◽  
pp. 1282-1297 ◽  
Author(s):  
Binghai Zhou ◽  
Wenlong Liu
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Processing Time ◽  
Flow Shop ◽  
Scheduling Algorithm ◽  
Mixed Integer ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Multi Objective ◽  
Fuzzy Processing Time

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

scholarly journals Bi-Objective Re-Entrant Hybrid Flow Shop Scheduling considering Energy Consumption Cost under Time-of-Use Electricity Tariffs

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Kaifeng Geng ◽  
Chunming Ye ◽  
Zhen hua Dai ◽  
Li Liu
Keyword(s):  
Energy Consumption ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
High Price ◽  
Hybrid Flow Shop ◽  
Shop Scheduling ◽  
Ant Lion Optimization ◽  
Hybrid Flow Shop Scheduling ◽  
Consumption Cost ◽  
Ant Lion

Re-entrant hybrid flow shop scheduling problem (RHFSP) is widely used in industries. However, little attention is paid to energy consumption cost with the raise of green manufacturing concept. This paper proposes an improved multiobjective ant lion optimization (IMOALO) algorithm to solve the RHFSP with the objectives of minimizing the makespan and energy consumption cost under Time-of-Use (TOU) electricity tariffs. A right-shift operation is then used to adjust the starting time of operations by avoiding the period of high electricity price to reduce the energy consumption cost as far as possible. The experimental results show that IMOALO algorithm is superior to multiobjective ant lion optimization (MOALO) algorithm, NSGA-II, and MOPSO in terms of the convergence, dominance, and diversity of nondominated solutions. The proposed model can make enterprises avoid high price period reasonably, transfer power load, and reduce the energy consumption cost effectively. Meanwhile, parameter analysis indicates that the period of TOU electricity tariffs and energy efficiency of machines have great impact on the scheduling results.

Optimization of Two-Stage Hybrid Flow Shop Scheduling Problems Using Genetic Algorithm

2015 ◽  
Vol 766-767 ◽  
pp. 962-967
Author(s):  
M. Saravanan ◽  
S. Sridhar ◽  
N. Harikannan
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

The two-stage Hybrid flow shop (HFS) scheduling is characterized n jobs m machines with two-stages in series. The essential complexities of the problem need to solve the hybrid flow shop scheduling using meta-heuristics. The paper addresses two-stage hybrid flow shop scheduling problems to minimize the makespan time with the batch size of 100 using Genetic Algorithm (GA) and Simulated Annealing algorithm (SA). The computational results observed that the GA algorithm is finding out good quality solutions than SA with lesser computational time.

scholarly journals The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

2019 ◽  
Vol 20 (2) ◽  
pp. 105
Author(s):  
Ikhlasul Amallynda
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms 

scholarly journals Sustainable production through balancing trade-offs among three metrics in flow shop scheduling

Procedia CIRP ◽  
2019 ◽  
Vol 80 ◽  
pp. 209-214 ◽  
Author(s):  
Amin Abedini ◽  
Wei Li ◽  
Fazleena Badurdeen ◽  
I.S. Jawahir
Keyword(s):  
Flow Shop ◽  
Sustainable Production ◽  
Flow Shop Scheduling ◽  
Shop Scheduling ◽  
Trade Offs

An Improved Palmer-Based Heuristic for Two-Stage Flexible Flow Shop with Group Constraint

2011 ◽  
Vol 314-316 ◽  
pp. 2076-2081
Author(s):  
Zhan Tao Li ◽  
Qing Xin Chen ◽  
Ning Mao
Keyword(s):  
Job Shop ◽  
Flow Shop ◽  
Mathematic Model ◽  
Flow Shop Scheduling ◽  
Release Dates ◽  
Two Stage ◽  
Flexible Flow Shop ◽  
Unrelated Machines ◽  
Shop Scheduling ◽  
Flexible Flow Shop Scheduling

Based on a background to the mould job shop, this paper considers a two-stage flexible flow shop scheduling problem subject to release dates, where the first stage is made up of unrelated machines and tasks have group constraint. The objective is to find a schedule that minimizes makespan in that flexible flow shop environment. For this problem, a mathematic model is formulated. Because this problem is NP-hard, an improved Palmer-based heuristic (denoted by MPL) is proposed. Based on MPL, a new heuristic (denoted by IMPL) is developed. In order to test the efficiency of the two heuristics, sets of examples are designed. Compared to the MPL, the performance of IMPL is more superior.

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