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Growing Science » International Journal of Industrial Engineering Computations

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Sort articles by: Volume | Date | Most Rates | Most Views | Reviews | Alphabet
1.

An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes Pages 365-380 Right click to download the paper Download PDF

Authors: Sergio Nesmachnow, Diego Gabriel Rossit, Jamal Toutouh, Francisco Luna

DOI: 10.5267/j.ijiec.2021.5.005

Keywords: Smart cities, Energy consumption planning problem, User preferences, Multiobjective optimization, Evolutionary algorithm, Greedy algorithms

Abstract:
Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.
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Journal: IJIEC | Year: 2021 | Volume: 12 | Issue: 4 | Views: 1065 | Reviews: 0

 
2.

New models and algorithms to solve integrated problems of production planning and control taking into account worker skills in flexible manufacturing systems Pages 381-400 Right click to download the paper Download PDF

Authors: Norbert Tóth, Gyula Kulcsár

DOI: 10.5267/j.ijiec.2021.5.004

Keywords: Production planning and control, Scheduling, Search algorithm, Worker skills, Flexible manufacturing system

Abstract:
The paradigm of the cyber-physical manufacturing system is playing an increasingly important role in the development of production systems and management of manufacturing processes. This paper presents an optimization model for solving an integrated problem of production planning and manufacturing control. The goal is to create detailed production plans for a complex manufacturing system and to control the skilled manual workers. The detailed optimization model of the problem and the developed approach and algorithms are described in detail. To consider the impact of human workers performing the manufacturing primary operations, we elaborated an extended simulation-based procedure and new multi-criteria control algorithms that can manage varying availability constraints of parallel workstations, worker-dependent processing times, different product types and process plans. The effectiveness of the proposed algorithms is demonstrated by numerical results based on a case study.
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Journal: IJIEC | Year: 2021 | Volume: 12 | Issue: 4 | Views: 1290 | Reviews: 0

 
3.

A homogenously weighted moving average scheme for observations under the effect of serial dependence and measurement inaccuracy Pages 401-414 Right click to download the paper Download PDF

Authors: Maonatlala Thanwane, Sandile C. Shongwe, Muhammad Aslam, Jean-Claude Malela-Majika, Mohammed Albassam

DOI: 10.5267/j.ijiec.2021.5.003

Keywords: Autocorrelation, Control chart, Homogeneously weighted moving average (HWMA), Measurement errors, Mixed samples strategy, Multiple measurements, Skipping sampling strategy

Abstract:
The combined effect of serial dependency and measurement errors is known to negatively affect the statistical efficiency of any monitoring scheme. However, for the recently proposed homogenously weighted moving average (HWMA) scheme, the research that exists concerns independent and identically distributed observations and measurement errors only. Thus, in this paper, the HWMA scheme for monitoring the process mean under the effect of within-sample serial dependence with measurement errors is proposed for both constant and linearly increasing measurement system variance. Monte Carlo simulation is used to evaluate the run-length distribution of the proposed HWMA scheme. A mixed-s&m sampling strategy is incorporated to the HWMA scheme to reduce the negative effect of serial dependence and measurement errors and its performance is compared to the existing Shewhart scheme. An example is given to illustrate how to implement the proposed HWMA scheme for use in real-life applications.
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Journal: IJIEC | Year: 2021 | Volume: 12 | Issue: 4 | Views: 1459 | Reviews: 0

 
4.

Minimization of total tardiness in no-wait flowshop production systems with preventive maintenance Pages 415-426 Right click to download the paper Download PDF

Authors: Tuane Tonani Yamada, Marcelo Seido Nagano, Hugo Hissashi Miyata

DOI: 10.5267/j.ijiec.2021.5.002

Keywords: No-wait flowshop, Preventive maintenance, Total tardiness, Heuristic methods

Abstract:
Efficient business organizations must balance quality, cost, and time constraints in competitive environments. Reflecting the complexity of this task, we consider manufacturing systems including several stages of production chains requiring time measurement. When production scheduling is not prioritized in such enterprises, several negative effects may occur. A corporation may suffer financial penalties as well as negative brand exposure, and thus may find its credibility challenged. Therefore, in this study, we propose constructive methods to minimize a total tardiness criterion, considering preventative maintenance constraints to reflect the reality of industrial practice, focusing on a no-wait flowshop environment in which jobs are successively processed without operational interruptions. In addition to proposing constructive methods to solve the no-wait flowshop production scheduling problem, a metaheuristic is presented as an approach to improve results obtained by constructive methods. Computational experiments were designed and performed to compare several production scheduling algorithms. Among various constructive heuristics considered, an algorithm called HENLL using an insertion logic showed the best performance. The proposed metaheuristic is based on the iterated greedy (IG) search method, and the results obtained demonstrated significant improvement compared to the heuristics alone. It is expected that this study may be used by production planning and control (PPC) professionals to apply the proposed method to schedule production more efficiently. We show that the proposed method successfully presented a better solution in relation to total tardiness, considering the above mentioned environment.
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Journal: IJIEC | Year: 2021 | Volume: 12 | Issue: 4 | Views: 1153 | Reviews: 0

 
5.

A producer-retailer incorporated multi-item EPQ problem with delayed differentiation, the expedited rate for common parts, multi-delivery and scrap Pages 427-440 Right click to download the paper Download PDF

Authors: Yuan-Shyi Peter Chiu, Tiffany Chiu, Fan-Yun Pai, Hua Yao Wu

DOI: 10.5267/j.ijiec.2021.5.001

Keywords: Producer-retailer incorporated problem, Multi-item EPQ model, Expedited rate, Delayed differentiation, Random scrap, Multi-delivery

Abstract:
Transnational producers facing the present-day competitive global supply-chain environments need to pursue the most appropriate manufacturing scheme, quality screening task, and stock shipping plan to satisfy customer’s timely multi-item requirements under minimum overall product fabrication-delivery expenses. This study develops a producer-retailer incorporated multi-item two-stage economic production quantity- (EPQ-) based system with delayed differentiation, expedited-rate for common parts, multiple deliveries plan, and random scrap. It aims to assist current manufacturing firms in achieving the aforementioned operating goals. Mathematical methods help us build an analytical model to explicitly portray the studied problem’s features and derive its overall system expenses. Hessian matrix equations and optimization approaches help us prove convexity and derive the cost-minimized fabrication- delivery decision. This study gives a simulated example to illustrate the research outcome’s applicability and the proposed model’s capabilities numerically. Consequently, diverse crucial information becomes obtainable to the manufacturers to facilitate various operating decision makings as follows: (i) the cost-minimized fabrication-delivery policy; (ii) the behavior of system’s overall expenses and operating policy regarding mean scrap rate, and different relationships between common part’s values and completion-rate; (iii) the system’s detailed cost components; (iv) the system’s overall expenses, utilization, and common part’s uptime concerning different common part’s expedited rates; and (v) the collective effects of critical system features on the overall expenses, uptime, and optimal cycle length, etc.
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Journal: IJIEC | Year: 2021 | Volume: 12 | Issue: 4 | Views: 1236 | Reviews: 0

 
6.

Solution of capacitated vehicle routing problem with invasive weed and hybrid algorithms Pages 441-456 Right click to download the paper Download PDF

Authors: Ümit Yıldırım, Yusuf Kuvvetli

DOI: 10.5267/j.ijiec.2021.4.002

Keywords: Vehicle routing problem with capacity constraints, Invasive weed optimization algorithm, Genetic algorithm, Savings algorithm, Hybrid metaheuristics

Abstract:
The vehicle routing problem is widespread in terms of optimization, which is known as being NP-Hard. In this study, the vehicle routing problem with capacity constraints is solved using cost- and time-efficient metaheuristic methods: an invasive weed optimization algorithm, genetic algorithm, savings algorithm, and hybridized variants. These algorithms are tested using known problem sets in the literature. Twenty-four instances evaluate the performance of algorithms from P and five instances from the CMT data set group. The invasive weed algorithm and its hybrid variant with savings and genetic algorithms are used to determine the best methodology regarding time and cost values. The proposed hybrid approach has found optimal P group problem instances with a 2% difference from the best-known solution on average. Similarly, the CMT group problem is solved with about a 10% difference from the best-known solution on average. That the proposed hybrid solutions have a standard deviation of less than 2% on average from BKS indicates that these approaches are consistent.
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Journal: IJIEC | Year: 2021 | Volume: 12 | Issue: 4 | Views: 1177 | Reviews: 0

 

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