scholarly journals A Multifactory Integrated Production and Distribution Scheduling Problem with Parallel Machines and Immediate Shipments Solved by Improved Whale Optimization Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Vahid Abdollahzadeh ◽  
Isa Nakhaikamalabadi ◽  
Seyyed Mohammad Hajimolana ◽  
Seyyed Hesamoddin Zegordi
Keyword(s):  
Supply Chain ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Parallel Machines ◽  
Programming Model ◽  
Whale Optimization Algorithm ◽  
Mixed Integer ◽  
Test Problems ◽  
Whale Optimization ◽  
Production And Distribution

This paper discusses the integrated scheduling of production and distribution operations in a multifactory supply chain with make-to-order production system. For the production side of the supply chain, we considered distributed parallel-established factories with identical parallel machines available at each factory. We assumed that the factories could produce all customers’ orders with different production rates and costs. For the distribution side of the supply chain, we considered a limited number of homogeneous vehicles that immediately distribute the finalized orders to the customers. Then, a mixed-integer nonlinear programming model is developed to determine the detailed scheduling of production and distribution that minimizes the total costs of the supply chain including production, distribution, and late delivery costs. To solve the real-world scale problems, we developed a new whale optimization algorithm (WOA). Moreover, we conducted computational experiments by generating several test problems to evaluate the proposed algorithm. Statistical analysis showed that the proposed algorithm has better performance than traditional WOA for different scales of the problem. Moreover, it confirms the capability of the improved whale optimization algorithm (IWOA) to solve the medium-scale instances; however, the results indicate the better performance of genetic algorithm (GA) for the large-scale instances.

scholarly journals Supply chain optimization with variable demand by considering financial criteria and scenarios

2017 ◽  
Vol 26 (44) ◽  
pp. 21 ◽  
Author(s):  
John Willmer Escobar
Keyword(s):  
Supply Chain ◽  
Large Scale ◽  
Net Present Value ◽  
Programming Model ◽  
Supply Chain Design ◽  
Mixed Integer ◽  
Supply Network ◽  
Distribution Centers ◽  
Supply Chain Optimization ◽  
Starting Point

This paper contemplates the supply chain design problem of a large-scale company by considering the maximization of the Net Present Value. In particular, the variability of the demand for each type of product at each customer zone has been estimated. As starting point, this paper considers an established supply chain for which the main problem is to determine the decisions regarding expansion of distribution centers. The problem is solved by using a mixed-integer linear programming model, which optimizes the different demand scenarios. The proposed methodology uses a scheme of optimization based on the generation of multiple demand scenarios of the supply network. The model is based on a real case taken from a multinational food company, which supplies to the Colombian and some international markets. The obtained results were compared with the equivalent present costs minimization scheme of the supply network, and showed the importance and efficiency of the proposed approach as an alternative for the supply chain design with stochastic parameters.

scholarly journals An Optimization Model for Construction Stage and Zone Plans of Rockfill Dams Based on the Enhanced Whale Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 466 ◽  
Author(s):  
Jun Zhang ◽  
Denghua Zhong ◽  
Mengqi Zhao ◽  
Jia Yu ◽  
Fei Lv
Keyword(s):  
Optimization Algorithm ◽  
Optimization Model ◽  
Large Scale ◽  
Flow Shop ◽  
Whale Optimization Algorithm ◽  
Data Driven ◽  
Construction Stage ◽  
Construction Scheme ◽  
Rockfill Dam ◽  
Whale Optimization

Rockfill dams are among the most complex, significant, and costly infrastructure projects of great national importance. A key issue in their design is the construction stage and zone optimization. However, a detailed flow shop construction scheme that considers the opinions of decision makers cannot be obtained using the current rock-fill dam construction stage and zone optimization methods, and the robustness and efficiency of existing construction stage and zone optimization approaches are not sufficient. This research presents a construction stage and zone optimization model based on a data-driven analytical hierarchy process extended by D numbers (D-AHP) and an enhanced whale optimization algorithm (EWOA). The flow shop construction scheme is optimized by presenting an automatic flow shop construction scheme multi-criteria decision making (MCDM) method, which integrates the data-driven D-AHP with an improved construction simulation of a high rockfill dam (CSHRD). The EWOA, which uses Levy flight to improve the robustness and efficiency of the whale optimization algorithm (WOA), is adopted for optimization. This proposed model is implemented to optimize the construction stages and zones while obtaining a preferable flow shop construction scheme. The effectiveness and advantages of the model are proven by an example of a large-scale rockfill dam.

Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

2019 ◽  
Vol 72 (2) ◽  
pp. 243-259 ◽  
Author(s):  
Mohammed M. Ahmed ◽  
Essam H. Houssein ◽  
Aboul Ella Hassanien ◽  
Ayman Taha ◽  
Ehab Hassanien
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Whale Optimization Algorithm ◽  
Wireless Sensor ◽  
Multi Objective ◽  
Whale Optimization

scholarly journals Supply chain integration within mass customization: Tactical procurement, production and distribution modeling

2021 ◽  
Vol 14 (2) ◽  
pp. 250
Author(s):  
Mouad Benbouja ◽  
Achraf Touil ◽  
Abdelwahed Echchatbi ◽  
Abdelkabir Charkaoui
Keyword(s):  
Supply Chain ◽  
Inventory Management ◽  
Mass Customization ◽  
Programming Model ◽  
Exact Method ◽  
Raw Material ◽  
Mixed Integer ◽  
Dyadic Relationships ◽  
Distribution Modeling ◽  
Production And Distribution

Purpose: The actual market characteristic oriented toward customers’ requirements compels decision-makers to foresee customization abilities. Mass customization represents a valuable approach to combine customizable offers with mass production processes. From a supply chain standpoint, this paper attempts to develop an integrated procurement, production and distribution modeling to describe the generated framework structure formulation within tactical decision planning level.Design/methodology/approach: The paper provides a mixed integer linear programming model of a three echelon supply chain illustrated from the automotive industry with (a) customers: Original Equipment Manufacturers (OEMs) identified as leaders and (b) first-tier supplier: wiring harnesses manufacturer (c) second-tier supplier: raw material supplier, identified as followers. The model formulation is depicted through dyadic relationships between stakeholders considering the specific operation enablers of the environment such as make to order, modular approach in addition to the corresponding inventory management policy.Findings: The integrated model is solved by an exact method which illustrates the feasibility of the formulation in addition to the observance of the applied constraints. A sensitivity analysis is performed to highlight the interdependency across some key parameters to provide managerial insights within the studied framework while keeping the optimal solvability of the model.Research limitations/implications: The limitation of this study is the computational experiment study. An extensive experiment with a real-word case will outline the optimal solvability status of the exact method and the necessity for a performance benchmark through the approximate solving approaches.Originality/value: The present research aims to contribute as first studies toward mathematical modeling for supply chain decision planning endeavor operating within mass customization business model.

scholarly journals Closed-Loop Supply Chain Design and Pricing in Competitive Conditions by Considering the Variable Value of Return Products Using the Whale Optimization Algorithm

2021 ◽  
Vol 13 (12) ◽  
pp. 6663
Author(s):  
Muhammad Salman Shabbir ◽  
Ahmed Faisal Siddiqi ◽  
Lis M. Yapanto ◽  
Evgeny E. Tonkov ◽  
Andrey Leonidovich Poltarykhin ◽  
...  
Keyword(s):  
Supply Chain ◽  
Optimization Algorithm ◽  
Closed Loop ◽  
Optimal Solution ◽  
Supply Chain Design ◽  
Whale Optimization Algorithm ◽  
Special Focus ◽  
Closed Loop Supply Chain ◽  
Proposed Model ◽  
Whale Optimization

In today’s competitive environment, organizations, in addition to trying to improve their production conditions, have a special focus on their supply chain components. Cooperation between supply chain members always reduces unforeseen costs and speeds up the response to customer demand. In the new situation, according to the category of return products and their reprocessing, supply chains have found a closed-loop structure. In this research, the aim was to design a closed-loop supply chain in competitive conditions. For this purpose, the key decisions of this chain included locating retail centers, adjusting the inventory of chain members, and selling prices of final products, optimally determined. For this purpose, a nonlinear integer mathematical model is presented. One of the most important innovations of this research was considering the variable value for return products. Then, in order to solve the proposed model, a whale optimization algorithm was developed. Numerical results from the sample examples showed that the whale algorithm had a very good performance in terms of response quality and speed-of-action in finding the optimal solution to this problem.

scholarly journals Small signal stability enhancement of a large scale power system using a bio-inspired whale optimization algorithm

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Butti Dasu ◽  
Sivakumar Mangipudi ◽  
Srinivasarao Rayapudi
Keyword(s):  
Power System ◽  
New England ◽  
Optimization Algorithm ◽  
Design Methodology ◽  
Large Scale ◽  
Whale Optimization Algorithm ◽  
Small Signal ◽  
Small Signal Stability ◽  
Signal Stability ◽  
Whale Optimization

AbstractA whale optimization algorithm (WOA)-based power system stabilizer (PSS) design methodology on modified single machine infinite bus (MSMIB) and multi-machine systems to enhance the small-signal stability (SSS) of the power system is presented. The PSS design methodology is implemented using an eigenvalue (EV)-based objective function. The performance of the WOA is tested with several CEC14 and CEC17 test functions to investigate its potential in optimizing the complex mathematical equations. The New England 10-generator 39-bus system and the MSMIB system operating at various loading conditions are considered as the test systems to examine the proposed method. Extensive simulation results are obtained which validate the effectiveness of the proposed WOA method when compared with other algorithms.

scholarly journals Whale Optimization Algorithm for Ship Path Optimization in Large-Scale Complex Marine Environment

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 57168-57179
Author(s):  
Qilong Han ◽  
Xiao Yang ◽  
Hongtao Song ◽  
Shanshan Sui ◽  
Hui Zhang ◽  
...  
Keyword(s):  
Marine Environment ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Path Optimization ◽  
Whale Optimization Algorithm ◽  
Whale Optimization

scholarly journals Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Qiangqiang Jiang ◽  
Yuanjun Guo ◽  
Zhile Yang ◽  
Zheng Wang ◽  
Dongsheng Yang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Whale Optimization Algorithm ◽  
Swarm Optimization ◽  
Complex Problems ◽  
Computational Performance ◽  
Whale Optimization ◽  
Time Required

Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.

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