NSGA-II algorithm for hub location-allocation problem considering hub disruption and backup hub allocation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehnoosh Soleimani ◽  
Mohammad Khalilzadeh ◽  
Arman Bahari ◽  
Ali Heidary
Keyword(s):  
Genetic Algorithm ◽  
Fuel Consumption ◽  
Programming Model ◽  
Mathematical Programming Model ◽  
Hub Location ◽  
Nsga Ii ◽  
Content Type ◽  
Location Allocation ◽  
Proposed Model ◽  
Location Allocation Problem

Purpose One of the practical issues in the area of location and allocation is the location of the hub. In recent years, exchange rates have fluctuated sharply for a number of reasons such as sanctions against the country. Natural disasters that have occurred in recent years caused delays in hub servicing. The purpose of this study is to develop a mathematical programming model to minimize costs, maximize social responsibility and minimize fuel consumption so that in the event of a disruption in the main hub, the flow of materials can be directed to its backup hub to prevent delays in flow between nodes and disruptions in hubs. Design/methodology/approach A multi-objective mathematical programming model is developed considering uncertainty in some parameters, especially cost as fuzzy numbers. In addition, backup hubs are selected for each primary hub to deal with disruption and natural disasters and prevent delays. Then, a robust possibilistic method is proposed to deal with uncertainty. As the hub location-allocation problem is considered as NP-Hard problems so that exact methods cannot solve them in large sizes, two metaheuristic algorithms including a non-dominated sorting genetic algorithm non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied to tackle the problem. Findings Numerical results show the proposed model is valid. Also, they demonstrate that the NSGA-II algorithm outperforms the MOPSO algorithm. Practical implications The proposed model was implemented in one of the largest food companies in Iran, which has numerous products manufactured in different cities, to seek the hub locations. Also, due to several reasons such as road traffic and route type the difference in the rate of fuel consumption between nodes, this model helps managers and decision-makers to choose the best locations to have the least fuel consumption. Moreover, as the hub set up increases the employment rate in that city and has social benefits as it requires hiring some staff. Originality/value This paper investigates the hub location problem considering backup hubs with multiple objective functions to deal with disruption and uncertainty. Also, this study examines how non-hub nodes are assigned to hub nodes.

Modeling of the time-dependent multi-skilled RCPSP considering learning effect

2019 ◽  
Vol 14 (2) ◽  
pp. 521-558 ◽  
Author(s):  
Amir Hossein Hosseinian ◽  
Vahid Baradaran ◽  
Mahdi Bashiri
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Learning Effect ◽  
The Other ◽  
Time Dependent ◽  
Computational Time ◽  
Nsga Ii ◽  
Content Type ◽  
Proposed Model ◽  
Objective Function Values

Purpose The purpose of this paper is to propose a new mixed-integer formulation for the time-dependent multi-skilled resource-constrained project scheduling problem (MSRCPSP/t) considering learning effect. The proposed model extends the basic form of the MSRCPSP by three concepts: workforces have different efficiencies, it is possible for workforces to improve their efficiencies by learning from more efficient workers and the availability of workforces and resource requests of activities are time-dependent. To spread dexterity from more efficient workforces to others, this study has integrated the concept of diffusion maximization in social networks into the proposed model. In this respect, the diffusion of dexterity is formulated based on the linear threshold model for a network of workforces who share common skills. The proposed model is bi-objective, aiming to minimize make-span and total costs of project, simultaneously. Design/methodology/approach The MSRCPSP is an non-deterministic polynomial-time hard (NP-hard) problem in the strong sense. Therefore, an improved version of the non-dominated sorting genetic algorithm II (IM-NSGA-II) is developed to optimize the make-span and total costs of project, concurrently. For the proposed algorithm, this paper has designed new genetic operators that help to spread dexterity among workforces. To validate the solutions obtained by the IM-NSGA-II, four other evolutionary algorithms – the classical NSGA-II, non-dominated ranked genetic algorithm, Pareto envelope-based selection algorithm II and strength Pareto evolutionary algorithm II – are used. All algorithms are calibrated via the Taguchi method. Findings Comprehensive numerical tests are conducted to evaluate the performance of the IM-NSGA-II in comparison with the other four methods in terms of convergence, diversity and computational time. The computational results reveal that the IM-NSGA-II outperforms the other methods in terms of most of the metrics. Besides, a sensitivity analysis is implemented to investigate the impact of learning on objective function values. The outputs show the significant impact of learning on objective function values. Practical implications The proposed model and algorithm can be used for scheduling activities of small- and large-size real-world projects. Originality/value Based on the previous studies reviewed in this paper, one of the research gaps is the MSRCPSP with time-dependent resource capacities and requests. Therefore, this paper proposes a multi-objective model for the MSRCPSP with time-dependent resource profiles. Besides, the evaluation of learning effect on efficiency of workforces has not been studied sufficiently in the literature. In this study, the effect of learning on efficiency of workforces has been considered. In the scarce number of proposed models with learning effect, the researchers have assumed that the efficiency of workforces increases as they spend more time on performing a skill. To the best of the authors’ knowledge, the effect of learning from more efficient co-workers has not been studied in the literature of the RCPSP. Therefore, in this research, the effect of learning from more efficient co-workers has been investigated. In addition, a modified version of the NSGA-II algorithm is developed to solve the model.

Resource levelling in projects considering different activity execution modes and splitting

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Khalilzadeh
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Programming Model ◽  
Exact Method ◽  
Computational Time ◽  
Resource Usage ◽  
Optimal Solutions ◽  
Content Type ◽  
Proposed Model ◽  
Multi Mode

Purpose This study aims to develop a mathematical programming model for preemptive multi-mode resource-constrained project scheduling problems in construction with the objective of levelling resources considering renewable and non-renewable resources. Design/methodology/approach The proposed model was solved by the exact method and the genetic algorithm integrated with the solution modification procedure coded with MATLAB software. The Taguchi method was applied for setting the parameters of the genetic algorithm. Different numerical examples were used to show the validation of the proposed model and the capability of the genetic algorithm in solving large-sized problems. In addition, the sensitivity analysis of two parameters, including resource factor and order strength, was conducted to investigate their impact on computational time. Findings The results showed that preemptive activities obtained better results than non-preemptive activities. In addition, the validity of the genetic algorithm was evaluated by comparing its solutions to the ones of the exact methods. Although the exact method could not find the optimal solution for large-scale problems, the genetic algorithm obtained close to optimal solutions within a short computational time. Moreover, the findings demonstrated that the genetic algorithm was capable of achieving optimal solutions for small-sized problems. The proposed model assists construction project practitioners with developing a realistic project schedule to better estimate the project completion time and minimize fluctuations in resource usage during the entire project horizon. Originality/value There has been no study considering the interruption of multi-mode activities with fluctuations in resource usage over an entire project horizon. In this regard, fluctuations in resource consumption are an important issue that needs the attention of project planners.

A flexible mathematical model for crew pairing optimization to generate n-day pairings considering the risk of COVID-19: a real case study

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bahareh Shafipour-Omrani ◽  
Alireza Rashidi Komijan ◽  
Seyed Jafar Sadjadi ◽  
Kaveh Khalili-Damghani ◽  
Vahidreza Ghezavati
Keyword(s):  
Genetic Algorithm ◽  
Programming Model ◽  
Cost Minimization ◽  
Crew Scheduling ◽  
Mixed Integer ◽  
Medium Size ◽  
Content Type ◽  
Solution Approach ◽  
Proposed Model ◽  
Crew Pairing

PurposeOne of the main advantages of the proposed model is that it is flexible to generate n-day pairings simultaneously. It means that, despite previous researches, one-day to n-day pairings can be generated in a single model. The flexibility in generating parings causes that the proposed model leads to better solutions compared to existing models. Another advantage of the model is minimizing the risk of COVID-19 by limitation of daily flights as well as elapsed time minimization. As airports are among high risk places in COVID-19 pandemic, minimization of infection risk is considered in this model for the first time. Genetic algorithm is used as the solution approach, and its efficiency is compared to GAMS in small and medium-size problems.Design/methodology/approachOne of the most complex issues in airlines is crew scheduling problem which is divided into two subproblems: crew pairing problem (CPP) and crew rostering problem (CRP). Generating crew pairings is a tremendous and exhausting task as millions of pairings may be generated for an airline. Moreover, crew cost has the largest share in total cost of airlines after fuel cost. As a result, crew scheduling with the aim of cost minimization is one of the most important issues in airlines. In this paper, a new bi-objective mixed integer programming model is proposed to generate pairings in such a way that deadhead cost, crew cost and the risk of COVID-19 are minimized.FindingsThe proposed model is applied for domestic flights of Iran Air airline. The results of the study indicate that genetic algorithm solutions have only 0.414 and 0.380 gap on average to optimum values of the first and the second objective functions, respectively. Due to the flexibility of the proposed model, it improves solutions resulted from existing models with fixed-duty pairings. Crew cost is decreased by 12.82, 24.72, 4.05 and 14.86% compared to one-duty to four-duty models. In detail, crew salary is improved by 12.85, 24.64, 4.07 and 14.91% and deadhead cost is decreased by 11.87, 26.98, 3.27, and 13.35% compared to one-duty to four-duty models, respectively.Originality/valueThe authors confirm that it is an original paper, has not been published elsewhere and is not currently under consideration of any other journal.

Sustainability risk management in a cooperative environment under uncertainty

Kybernetes ◽  
2019 ◽  
Vol 48 (3) ◽  
pp. 385-406 ◽  
Author(s):  
Mojtaba Moradi ◽  
Ashkan Hafezalkotob ◽  
Vahidreza Ghezavati
Keyword(s):  
Risk Assessment ◽  
Net Present Value ◽  
Programming Model ◽  
Mathematical Programming Model ◽  
Additive Property ◽  
Content Type ◽  
Proposed Model ◽  
Project Risks ◽  
Sustainability Concept ◽  
Project Sustainability

Purpose This study considers a project scheduling model to assess the project risks and the impacts on project sustainability when subcontractors collaborate under uncertainty. Moreover, some allocation methods are applied for fair allocating utility of the project and supper-additivity, stability and satisfaction level of each coalition. Finally, sustainability concept is considered in risk assessment in all coalitions. Design/methodology/approach The proposed mathematical programming model evaluates project risks when the subcontractors cooperate with each other by sharing their limited resources. Then, some cooperative game theory methods are applied for fair allocation of net present value, of the cooperation and finally sustainability aspects (economic, social and environmental) are investigated in risk assessment for each possible coalition. Finding The results of the proposed model indicate that the subcontractors can increase their profit by 10 per cent ($14,028,450 thousand) and save the equilibrium between sustainability aspects especially in grand coalition. It means that subcontractors do not have incentive to leave the coalition and the supper-additive property is feasible. Furthermore, risk assessment shows that project risks have less impact on subcontractor profits when they cooperate with each other. Originality/value Sustainability aspects may be investigated in project management in previous studies, but the authors study sustainability indicators when subcontractors form a coalition and share their resources in response to the risks of availability to resources and delay in completing the project under uncertainty.

Is data science a science? The essence of phenomenon and the role of theory in the emerging field

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pedro Jácome de Moura Jr
Keyword(s):  
Data Science ◽  
Allocation Problem ◽  
Theoretical Development ◽  
Content Type ◽  
Location Allocation ◽  
Theoretical Approaches ◽  
Science Theory ◽  
Proposed Model ◽  
Definition Of ◽  
Location Allocation Problem

PurposeData science lacks a distinctive identity and a theory-informed approach, both for its own sake and to properly be applied conjointly to the social sciences. This paper’s purposes are twofold: to provide (1) data science an illustration of theory adoption, able to address explanation and support prediction/prescription capacities and (2) a rationale for identification of the key phenomena and properties of data science so that the data speak through a contextual understanding of reality, broader than has been usual.Design/methodology/approachA literature review and a derived conceptual research model for a push–pull approach (adapted for a data science study in the management field) are presented. A real location–allocation problem is solved through a specific algorithm and explained in the light of the adapted push–pull theory, serving as an instance for a data science theory-informed application in the management field.FindingsThis study advances knowledge on the definition of data science key phenomena as not just pure “data”, but interrelated data and datasets properties, as well as on the specific adaptation of the push-pull theory through its definition, dimensionality and interaction model, also illustrating how to apply the theory in a data science theory-informed research. The proposed model contributes to the theoretical strengthening of data science, still an incipient area, and the solution of the location-allocation problem suggests the applicability of the proposed approach to broad data science problems, alleviating the criticism on the lack of explanation and the focus on pattern recognition in data science practice and research.Research limitations/implicationsThe proposed algorithm requires the previous definition of a perimeter of interest. This aspect should be characterised as an antecedent to the model, which is a strong assumption. As for prescription, in this specific case, one has to take complementary actions, since theory, model and algorithm are not detached from in loco visits, market research or interviews with potential stakeholders.Practical implicationsThis study offers a conceptual model for practical location–allocation problem analyses, based on the push–pull theoretical components. So, it suggests a proper definition for each component (the object, the perspective, the forces, its degrees and the nature of the movement). The proposed model has also an algorithm for computational implementation, which visually describes and explains components interaction, allowing further simulation (estimated forces degrees) for prediction.Originality/valueFirst, this study identifies an overlap of push–pull theoretical approaches, which suggests theory adoption eventually as mere common sense, weakening further theoretical development. Second, this study elaborates a definition for the push–pull theory, a dimensionality and a relationship between its components. Third, a typical location–allocation problem is analysed in the light of the refactored theory, showing its adequacy for that class of problems. And fourth, this study suggests that the essence of a data science should be the study of contextual relationships among data, and that the context should be provided by the spatial, temporal, political, economic and social analytical interests.

scholarly journals Location of regional and international hub ports in liner shipping

2017 ◽  
Vol 2 (2) ◽  
pp. 114-125 ◽  
Author(s):  
Jianfeng Zheng ◽  
Cong Fu ◽  
Haibo Kuang
Keyword(s):  
Design Methodology ◽  
Location Problem ◽  
Programming Model ◽  
Liner Shipping ◽  
Mixed Integer ◽  
Eastern Asia ◽  
Hub Location ◽  
Hub Location Problem ◽  
Content Type ◽  
Western Asia

Purpose This paper aims to investigate the location of regional and international hub ports in liner shipping by proposing a hierarchical hub location problem. Design/methodology/approach This paper develops a mixed-integer linear programming model for the authors’ proposed problem. Numerical experiments based on a realistic Asia-Europe-Oceania liner shipping network are carried out to account for the effectiveness of this model. Findings The results show that one international hub port (i.e. Rotterdam) and one regional hub port (i.e. Zeebrugge) are opened in Europe. Two international hub ports (i.e. Sokhna and Salalah) are located in Western Asia, where no regional hub port is established. One international hub port (i.e. Colombo) and one regional hub port (i.e. Cochin) are opened in Southern Asia. One international hub port (i.e. Singapore) and one regional hub port (i.e. Jakarta) are opened in Southeastern Asia and Australia. Three international hub ports (i.e. Hong Kong, Shanghai and Yokohama) and two regional hub ports (i.e. Qingdao and Kwangyang) are opened in Eastern Asia. Originality/value This paper proposes a hierarchical hub location problem, in which the authors distinguish between regional and international hub ports in liner shipping. Moreover, scale economies in ship size are considered. Furthermore, the proposed problem introduces the main ports.

scholarly journals Integrated Harvest and Farm-to-Door Distribution Scheduling with Postharvest Quality Deterioration for Vegetable Online Retailing

Agronomy ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 724
Author(s):  
Yiping Jiang ◽  
Bei Bian ◽  
Lingling Li
Keyword(s):  
Genetic Algorithm ◽  
Numerical Experiments ◽  
Programming Model ◽  
Time Windows ◽  
Sensitivity Analyses ◽  
Postharvest Quality ◽  
Online Retailing ◽  
Quality Deterioration ◽  
Distribution Scheduling ◽  
Proposed Model

With the rise of vegetable online retailing in recent years, the fulfillment of vegetable online orders has been receiving more and more attention. This paper addresses an integrated optimization model for harvest and farm-to-door distribution scheduling for vegetable online retailing. Firstly, we capture the perishable property of vegetables, and model it as a quadratic postharvest quality deterioration function. Then, we incorporate the postharvest quality deterioration function into the integrated harvest and farm-to-door distribution scheduling and formulate it as a quadratic vehicle routing programming model with time windows. Next, we propose a genetic algorithm with adaptive operators (GAAO) to solve the model. Finally, we carry out numerical experiments to verify the performance of the proposed model and algorithm, and report the results of numerical experiments and sensitivity analyses.

Air quality management using genetic algorithm based heuristic fuzzy time series model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lalit Bhagat ◽  
Gunjan Goyal ◽  
Dinesh C.S. Bisht ◽  
Mangey Ram ◽  
Yigit Kazancoglu
Keyword(s):  
Genetic Algorithm ◽  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Quality Management ◽  
Fuzzy Time Series ◽  
Selection Algorithm ◽  
Content Type ◽  
Proposed Model ◽  
Adaptive Time

PurposeThe purpose of this paper is to provide a better method for quality management to maintain an essential level of quality in different fields like product quality, service quality, air quality, etc.Design/methodology/approachIn this paper, a hybrid adaptive time-variant fuzzy time series (FTS) model with genetic algorithm (GA) has been applied to predict the air pollution index. Fuzzification of data is optimized by GAs. Heuristic value selection algorithm is used for selecting the window size. Two algorithms are proposed for forecasting. First algorithm is used in training phase to compute forecasted values according to the heuristic value selection algorithm. Thus, obtained sequence of heuristics is used for second algorithm in which forecasted values are selected with the help of defined rules.FindingsThe proposed model is able to predict AQI more accurately when an appropriate heuristic value is chosen for the FTS model. It is tested and evaluated on real time air pollution data of two popular tourism cities of India. In the experimental results, it is observed that the proposed model performs better than the existing models.Practical implicationsThe management and prediction of air quality have become essential in our day-to-day life because air quality affects not only the health of human beings but also the health of monuments. This research predicts the air quality index (AQI) of a place.Originality/valueThe proposed method is an improved version of the adaptive time-variant FTS model. Further, a nature-inspired algorithm has been integrated for the selection and optimization of fuzzy intervals.

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