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  1. Article ; Online: A bi-objective hybrid vibration damping optimization model for synchronous flow shop scheduling problems

    Madjid Tavana / Vahid Hajipour / Mohammad Alaghebandha / Debora Di Caprio

    Machine Learning with Applications, Vol 11, Iss , Pp 100445- (2023)

    2023  

    Abstract: Flow shop scheduling deals with the determination of the optimal sequence of jobs processing on machines in a fixed order with the main objective consisting of minimizing the completion time of all jobs (makespan). This type of scheduling problem appears ...

    Abstract Flow shop scheduling deals with the determination of the optimal sequence of jobs processing on machines in a fixed order with the main objective consisting of minimizing the completion time of all jobs (makespan). This type of scheduling problem appears in many industrial and production planning applications. This study proposes a new bi-objective mixed-integer programming model for solving the synchronous flow shop scheduling problems with completion time. The objective functions are the total makespan and the sum of tardiness and earliness cost of blocks. At the same time, jobs are moved among machines through a synchronous transportation system with synchronized processing cycles. In each cycle, the existing jobs begin simultaneously, each on one of the machines, and after completion, wait until the last job is completed. Subsequently, all the jobs are moved concurrently to the next machine. Four algorithms, including non-dominated sorting genetic algorithm (NSGA II), multi-objective simulated annealing (MOSA), multi-objective particle swarm optimization (MOPSO), and multi-objective hybrid vibration-damping optimization (MOHVDO), are used to find a near-optimal solution for this NP-hard problem. In particular, the proposed hybrid VDO algorithm is based on the imperialist competitive algorithm (ICA) and the integration of a neighborhood creation technique. MOHVDO and MOSA show the best performance among the other algorithms regarding objective functions and CPU Time, respectively. Thus, the results from running small-scale and medium-scale problems in MOHVDO and MOSA are compared with the solutions obtained from the epsilon-constraint method. In particular, the error percentage of MOHVDO’s objective functions is less than 2% compared to the epsilon-constraint method for all solved problems. Besides the specific results obtained in terms of performance and, hence, practical applicability, the proposed approach fills a considerable gap in the literature. Indeed, even though variants of the aforementioned ...
    Keywords Multi-objective optimization ; Synchronous flow shop scheduling ; Meta-heuristics ; Epsilon-constraints ; Cybernetics ; Q300-390 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 000
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: A multi-objective parameter-tuned soft computing-based algorithm to optimize competitive congested location-pricing problem within multi-type service

    Parviz Fattahi / Vahid Hajipour / Sara Hajiloo

    Array, Vol 10, Iss , Pp 100062- (2021)

    2021  

    Abstract: One of the issues that has attracted many researchers in the last decade is the problem of locating facilities i.e. hospitals, shops, banks and ATMs. One of the basic needs of the people of the community is easy access to the facilities, so that by ... ...

    Abstract One of the issues that has attracted many researchers in the last decade is the problem of locating facilities i.e. hospitals, shops, banks and ATMs. One of the basic needs of the people of the community is easy access to the facilities, so that by spending little time can reach to the facility and with spending low cost to receive their facilities. In this paper, the location-pricing problem of the congested facilities by considering competition between available facilities and new facilities were investigated and bi-objective non-linear mathematical model that follow from M/M/m/k queuing system, was presented. In the first goal, maximize the profit of system by minimizing the total cost of establishing the facilities, shipping costumers and expectation time of the costumers in the queue and in the second goal the share of facility market minimized. The proposed model is in the category of non-linear integer programming problems, that, due to the complexity of the problem in the large scales and in order to solve the model, different approaches such as multi-objective meta-heuristic algorithms including non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) has been presented. At the end, by applying the Taguchi method, the efficiency performance of NSGA-II algorithm perform better than MOPSO.
    Keywords Soft computing-based algorithms ; Competitive facility location ; Pricing ; Queuing theor∖ ; Computer engineering. Computer hardware ; TK7885-7895 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 000
    Language English
    Publishing date 2021-07-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article ; Online: A business retrieval model using scenario planning and analytics for life during and after the pandemic crisis

    Vahid Hajipour / Mohammad Aminian / Ali Gharaei / Sajjad Jalali

    Healthcare Analytics, Vol 1, Iss , Pp 100004- (2021)

    2021  

    Abstract: The COVID-19 pandemic crisis has fundamentally changed the way we live and work forever. The business sector is forecasting and formulating different scenarios associated with the impact of the pandemic on its employees, customers, and suppliers. Various ...

    Abstract The COVID-19 pandemic crisis has fundamentally changed the way we live and work forever. The business sector is forecasting and formulating different scenarios associated with the impact of the pandemic on its employees, customers, and suppliers. Various business retrieval models are under construction to cope with life after the COVID-19 Pandemic Crisis. However, the proposed plans and scenarios are static and cannot address the dynamic pandemic changes worldwide. They also have not considered the peripheral in-between scenarios to propel the shifting paradigm of businesses from the existing condition to the new one. Furthermore, the scenario drivers in the current studies are generally centered on the economic aspects of the pandemic with little attention to the social facets. This study aims to fill this gap by proposing scenario planning and analytics to study the impact of the Coronavirus pandemic on large-scale information technology-led Companies. The primary and peripheral scenarios are constructed based on a balanced set of business continuity and employee health drivers. Practical action plans are formulated for each scenario to devise plausible responses. Finally, a damage management framework is developed to cope with the mental disorders of the employees amid the disease.
    Keywords Scenario planning ; Predictive modeling ; Pandemic crisis ; Analytics ; Business continuity ; Resiliency ; Computer applications to medicine. Medical informatics ; R858-859.7
    Subject code 650
    Language English
    Publishing date 2021-11-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  4. Article ; Online: A comparative performance analysis of intelligence-based algorithms for optimizing competitive facility location problems

    Vahid Hajipour / Seyed Taghi Akhavan Niaki / Madjid Tavana / Francisco J. Santos-Arteaga / Sanaz Hosseinzadeh

    Machine Learning with Applications, Vol 11, Iss , Pp 100443- (2023)

    2023  

    Abstract: Most companies operate to maximize profits and increase their market shares in competitive environments. Since the proper location of the facilities conditions their market shares and profits, the competitive facility location problem (CFLP) has been ... ...

    Abstract Most companies operate to maximize profits and increase their market shares in competitive environments. Since the proper location of the facilities conditions their market shares and profits, the competitive facility location problem (CFLP) has been extensively applied in the literature. This problem generally falls within the class of NP-hard problems, which are difficult to solve. Therefore, choosing a proper solution method to optimize the problem is a key factor. Even though CFLPs have been consistently solved and investigated, an important question that keeps being neglected is how to choose an appropriate solution technique. Since there are no specific criteria for choosing a solution method, the reasons behind the selection approach are mostly unclear. These models are generally solved using several optimization techniques. As harder-to-solve problems are usually solved using meta-heuristics, we apply different meta-heuristic techniques to optimize a new version of the CFLP that incorporates reliability and congestion. We divide the algorithms into four categories based on the nature of the meta-heuristics: evolution-based, swarm intelligence-based, physics-based, and human-based. GAMS software is also applied to solve smaller-size CFLPs. The genetic algorithm and differential evolution of the first category, particle swarm optimization and artificial bee colony optimization of the second, Tabu search and harmony search of the third, and simulated annealing and vibration damping optimization of the fourth are applied to solve our CFLP model. Statistical analyses are implemented to evaluate and compare their relative performances. The results show the algorithms of the first and third categories perform better than the others.
    Keywords Competitive facility location ; Optimization ; Computational intelligence ; Meta-heuristics ; Cybernetics ; Q300-390 ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 000
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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