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Natural or man-made disasters impose destructive effects like human injuries and urban infrastructure damages, which lead to disruptions that affect the entire distribution system. This research addresses the routing-allocation problem in the response phase of disaster management. The related literature shows that the researchers had less attention...
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Microservice-based architectures enable different aspects of web applications to be created and updated independently, even after deployment. Associated technologies such as service mesh provide application-level fault resilience through attribute configurations that govern the behavior of request-response service -- and the interactions among them...
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... They used the εconstraint method to achieve the Pareto front. Mamashli et al. (2021) suggested a MOM to investigate the relief logistics problem considering the resiliency concept. The authors studied the RSC under mixed uncertainty and employed the FRS approach to deal with this type of uncertainty. ...
The crucial role of the Relief Supply Chains (RSCs) in the response phase of disaster management is undeniable. However, the literature shows that the simultaneous consideration of the resilience and responsiveness dimensions in designing the RSCs under mixed uncertainty has been ignored by researchers. In this regard, to cover the mentioned gap, the current study aims to configure an RSC by considering two critically important features namely resilience and responsiveness under mixed uncertainty. For this purpose, this work proposed a multi-stage Decision-Making Framework (DMF). In the first stage, a Multi-Objective Model (MOM) is proposed that minimizes the total cost, maximizes the responsiveness level, and maximizes the resilience of the RSC. In the second stage, to deal with mixed uncertainty, a data-driven robust approach based on the Fuzzy Robust Stochastic (FRS), Seasonal Auto-Regressive Integrated Moving Average Exogenous (SARIMAX), and Artificial Neural Networks (ANN) methods is developed. In the third stage, to solve the proposed model, a novel variant of the goal programming method is developed. In general, the main contribution of this study is to develop a novel data-driven DMF to design a resilient-responsive RSC. To show the applicability and efficiency of the developed decision-making method, a real-world case study, the flood that happened in 2019 in Golestan province, Iran, is considered. Eventually, sensitivity analysis, managerial insights, and theoretical implications are presented. According to the achieved results, primary suppliers 1, 3, 5, and 7 and also backup supplier 1 are selected. Also, the results demonstrate that distribution centers 1, 2, 3, and 5 are established. Moreover, the optimal utilization of different transportation modes is specified in the achieved results. The outputs demonstrate that the developed data-driven FRS approach has better performance in comparison with the deterministic and traditional FRS models. Besides, the outputs indicate that the developed solution method has better performance in comparison with the traditional approaches.
... Ketidakpastian ini dapat diantispasi dengan penggunaan Goal Programming, yang menggabungkan batasan dan fungsi tujuan (Güzel et al., 2022). Goal Programming sendiri dapat diterapkan pada permasalahan rute (Hu et al., 2018) (Yousefi et al., 2017)(Aghdaghi & Jolai, 2008 (Mamashli et al., 2021). Dari permasalahan diatas, maka tujuan penelitian ini adalah menyelesaikan masalah rute wisata di Yogyakarta dengan Goal Programming. ...
Ketika wisatawan ke suatu daerah wisata, maka besar kemungkinan wisatawan tersebut mengunjungain beberapa obyek wisata. Kunjungan pada beberapa lokasi tersebut menyebabkan memerlukan waktu dan biaya karena beberapa lokasi tersebut terkadang mempunyai jarak yang dapat berjauhan. Untuk itu diperlukan pemilihan rute yang optimal afar dapat menghemat waktu dan jarak tempuh. Selama ini terdapat beberapa metode optimasi yang dapat digunakan untuk masalah pemilihan rute, salah satu yang yaitu goal programming. Goal programing digunakan karena dapat mengakomodasi beberapa tujuan yang dalam kasus pemilihan rute ini adalah meminimalkan jarak dan waktu. Hasil solusi pemilihan rute dengan goal programming didapatkan perjalanan diawali dari Hotel di Kawasan Malioboro kemudian ke Kraton. Kemudian perjalanan dilanjutkan ke Candi Prambanan, setelah itu perjalanan dilanjutkan ke Wisata Obelix. Setelah itu kemudian tujuan lokasi wisata terakhir ke Merapi dan kemudian kembali ke Hotel di Kawasan malioboro. Rute yang terpilih telah melakukan penghematan sebesar 4 menit untuk waktu tempuh dan 7 Km untuk jarak tempuh
... GP is known as efficient method to solve the MOP models (Nayeri et al. 2018). In formulating goal programming approaches, the aspiration levels of objective functions should be precisely determined (Mamashli et al. 2021). However, in real-life situations finding these aspiration levels would be almost impossible for decision-makers because of the existing uncertainty in data (Khan and Mahmood 2019). ...
The crucial role of sustainable development and resiliency strategies is undeniable in today’s competitive market space, especially after the Coronavirus outbreak. Hence, this research develops a multistage decision-making framework to investigate the supply chain network design problem considering the sustainability and resiliency dimensions. In this way, the scores of the potential suppliers based on the sustainability and resilience dimensions were calculated using the MADM methods, and then, these scores were applied as inputs in the proposed mathematical model (the second stage), which determined which supplier should be selected. The proposed model aims to minimize the total costs, maximize the suppliers' sustainability and resiliency, and maximize the distribution centers' resiliency. Then, the proposed model is solved by the preemptive fuzzy goal programming method. Overall, the main objectives and aims of the current work are to present a comprehensive decision-making model that can incorporate the sustainability and resilience dimensions into the supplier selection and supply chain configuration processes. In general, the main contributions and advantages of this work can be summarized as follows: (i) this research simultaneously investigates the sustainability and resiliency concepts in the dairy supply chain, (ii) the current work develops an efficient multistage decision-making model that can evaluate the suppliers based on the resilience and sustainability dimensions and configure the supply chain network, simultaneously. Based on the obtained results, the responsiveness and facilities reinforcement indicators are the most important indicators for the resilient aspect. On the other hand, reliability and quality are the most important indicators of sustainability aspect. Also, the results show that a large percentage of supply chain costs are related to purchasing and production costs. Besides, according to the outputs, the total cost of supply chain increases by enhancing the demand.
Graphical abstract
... Meanwhile, the proposed model is formulated using a multiperiod approach. Mamashli et al. [14] studied the allocation routing problem in the crisis reaction phase. Tey proposed a scenario-based multiobjective planning model for examining the sustainable allocation of routing problem to cover factors such as sustainability and resilience (rarely considered in previous studies). ...
... Equation (13) determines the unmet demand in each period. Equation (14) determines the fair conditions in each period. Equation (15) demonstrates that the total time of relief products and service distribution by private distribution centers is shorter than public distribution centers in each period. ...
Relief operations and planning to implement them are essential due to the unpredictability of natural disaster occurrences and their concomitant damages. The Crisis and Disaster Management Organization’s responsibility is to execute plans, coordinate, control relief, and rescue operations to reduce the impacts and consequences of these disasters. Thus, this organization should attend to the struck regions’ needs with their maximum power as soon as possible. This study has initially reviewed the latest available literature regarding the humanitarian supply chain to determine the research gap. Accordingly, in this research, a mathematical model with a leader-follower approach for relief delivery in the crisis response phase was designed, which took into account the policies of the country of Iran for the distribution of relief items. The amount of inventory of suppliers was also considered as uncertain. Then, to validate it, a numerical example was solved by the metaheuristic algorithm MOEA/D. The results indicated that the designed model is valid and the selected algorithm to solve it has an acceptable performance. Finally, some effective parameters were selected in the model and the sensitivity of the model was evaluated based on their changes.
... The research includes multiple decisions such as location, routing, allocation and fair distribution of relief items. Mamashli et al. (2021) investigated a routing-allocation problem in the response phase of disaster management to minimize total traveling time, total environmental impacts and total demand loss. A fuzzy robust stochastic optimization approach is utilized to cope with uncertain data arisen in disaster conditions. ...
In this paper, we investigate an integrated procurement and capacitated vehicle routing problem for the distribution of multiple relief goods after the disaster, to determine the best tour for vehicles as well as the best selection of multiple relief goods and their quantity to be loaded on vehicles. Due to the uncertain nature of the parameters, the demand distribution and cost parameters are considered as fuzzy parameters. Furthermore, this paper examines the impact of information and communication technology in the affected areas so that instant information, communicate between the affected areas and the disaster coordination center due to new events caused by the disaster. We have examined the impact of information and communication technology on reducing demand uncertainty such that with consideration of the cost of equipping GPS in affected areas, as well as its impact on reducing demand uncertainty and the cost of dissatisfaction as a result; the best affected areas are selected to be equipped with GPS. To have robust solutions, a robust possibilistic programming model is proposed. The results of the model are shown in a real case study in district 7 of Tehran which acclaim that the proposed model achieves a better result than the traditional models without considering ICT.
... The importance of the SC management problem led to conducting several studies in this field in recent years (see Hajiaghaei-Keshteli and Fard 2019;Jamali et al. 2021;Mamashli et al. 2021aMamashli et al. , 2021bNayeri et al. 2021Nayeri et al. , 2020Razavi et al. 2021;Samadi et al. 2018;Sazvar et al. 2021aSazvar et al. , 2021b. In this section, we report the related literature in the three sections as follows: (i) sustainable SSP, (ii) lean and agile SSP, and (iii) SSP based on I4.0. ...
Given the crucial role of the supplier selection problem (SSP) in today’s competitive business environment, the present study investigates the SSP by considering the leagile, sustainability, and Industry 4.0 (I4.0) indicators for the medical devices industry (MDI). In this regard, at the outset, the list of criteria and sub-criteria is provided based on the literature and experts’ opinions. Then, the importance of the indicators is measured utilizing the rough best–worst method (RBWM). In the next step, the potential suppliers are ranked employing the multi-attributive border approximation area comparison (IR-MABAC) method. Due to the crucial role of medical devices during the COVID-19 outbreak, the present work selects a project-based organization in this industry as a case study. The obtained results show that agility and sustainability are the most important criteria, and manufacturing flexibility, cost, reliability, smart factory, and quality are the most important sub-criteria. The main theoretical contributions of this study are considering the leagile, sustainability, and I4.0 criteria in the SSP and employing the hybrid RBWM-IR-MABAC method in this area for the first time. On the other side, The results of this research can help supply chain managers to become more familiar with the sustainability, agility, leanness, and I4.0 criteria in the business environment.
... On the other side, in general, since considering more other environmental factors might drastically increase the uncertainty of the problem, we considered the most important environmental factors based on our problem definition and case study. Considering reverse logistics and minimizing carbon emissions are two widely considered factors in previous similar studies (see [2,23,25,27,34,43,51,52]). ...
... Here, to better understand the nature of the proposed model, we define the main features of the problem using the verbal definition in the following. It should be noted that such a complex model to investigate the correlation and dependencies among green-resilient, green-responsive, and resilient responsive factors previously developed by researchers (see [34,38,51,52,54]). However, this study aims at investigating green-resilient-responsive factors. ...
In recent years, the hyper-competitive marketplace has led to a drastic enhancement in the importance of the supply chain problem. Hence, the attention of managers and researchers has been attracted to one of the most crucial problems in the supply chain management area called the supply chain network design problem. In this regard, this research attempts to design an integrated forward and backward logistics network by proposing a multi-objective mathematical model. The suggested model aims at minimizing the environmental impacts and the costs while maximizing the resilience and responsiveness of the supply chain. Since uncertainty is a major issue in the supply chain problem, the present paper studies the research problem under the mixed uncertainty and utilizes the robust possibilistic stochastic method to cope with the uncertainty. On the other side, since configuring a supply chain is known as an NP-Hard problem, this research develops an enhanced particle swarm optimization algorithm to obtain optimal/near-optimal solutions in a reasonable time. Based on the achieved results, the developed algorithm can obtain high-quality solutions (i.e. solutions with zero or a very small gap from the optimal solution) in a reasonable amount of time. The achieved results demonstrate the negative impact of the enhancement of the demand on environmental damages and the total cost. Also, according to the outputs, by increasing the service level, the total cost and environmental impacts have increased by 41% and 10%, respectively. On the other hand, the results show that increasing the disrupted capacity parameters has led to a 17% increase in the total costs and a 7% increase in carbon emissions.
Supplementary information:
The online version contains supplementary material available at 10.1007/s00521-022-07739-8.
... In the field of the SCND problem, different variants of the goal programming method is applied and they achieved good results (see Hocine et al. 2020;Mamashli and Javadian 2020;Nayeri et al. 2020;Pourmehdi et al. 2020;Homayouni et al. 2021; Jamali et al. Mamashli et al. 2021a). In the current study, the meta-goal programming (MGP) method offered by Urıá et al. (2002) is applied to solve the proposed MOMIP. ...
... Comparing to the other versions of the goal programming, metagoal programming, which has been applied in this study, has several advantages such as its ability to obtain more balanced solutions and its flexibility to model the preferences of the DMs (Nayeri et al. 2021b). Also, the heuristic method that utilized in this research is an efficient one to solve the complex problems with binary variables (Kaur and Singh 2018;Homayouni et al. 2021;Mamashli et al. 2021a;Shafiee et al. 2021). Hence, this hybrid approach can solve the multi-objective complex models, efficiently. ...
This study aims to investigate the closed-loop supply chain network design problem considering the environmental and responsiveness features. For this purpose, a multi-objective mathematical model is suggested that minimizes the carbon emissions and the total costs and maximizes the responsiveness of the system. Due to the dynamic space of the business environment, uncertainty is an integral part of the supply chain problem. Therefore, this research applies the robust possibilistic programming method to cope with uncertainty. Afterwards, since the research problem has a high level of the complexity, a hybrid solution approach based on a heuristic method and the meta-goal programming method is developed to solve the research problem in a reasonable time. Then, due to the importance of the ventilator device during the recent pandemic (COVID-19), this study considers this product as a case study. The main contribution of the current study is to design a green-responsive closed-loop supply chain network under uncertainty using a multi-objective robust possibilistic programming model, for the first time in the literature, especially in the medical devices industry. On the other side, the other contribution of this study is to develop an efficient hybrid solution method. The achieved results demonstrate the efficiency of the offered model and the developed hybrid method. Eventually, by carrying out sensitivity analysis, the impact of some of the critical parameters on the model is investigated. Based on the obtained results, an increase in the demand sizes leads to increasing the environmental damages and the total costs while reducing the responsiveness level. On the other side, an increase in the rate of return leads to an increase in all of the objective functions. Also, the achieved results show that when the capacity parameter is increased, the total costs are decreased, but the responsiveness and environmental impacts are increased.
... In the last two decades, researchers carried out many studies in terms of relief logistics (interest readers can see Zheng and Ling 2013;Billhardt et al. 2014;Camacho-Vallejo et al. 2015;Huang et al. 2015;Djahangiri and Ghaffari-Hadigheh 2018;Liu et al. 2018;Cotes and Cantillo 2019;Maharjan and Hanaoka 2019;Rezaei-Malek et al. 2019;Sarma et al. 2020;Zhang et al. 2020). Also, some of them were investigated the various issues such as the location problem, vehicle routing problem (VRP), and allocation of the relief supplies (Rawls and Turnquist 2010;Campbell and Jones 2011;Noyan 2012;Najafi et al. 2013;Chen et al. 2016;Rezaei-Malek et al. 2016b;Sung and Lee 2016;Pradhananga et al. 2016;Gu et al. 2018;Memari et al. 2020;Seraji et al. 2021;Ebrahimnejad et al. 2021;Mamashli et al. 2021). ...
Natural disasters cause heavy casualties and financial losses annually. To reduce these damages, the rescue teams need to be planned effectively. In this regard, in this research, a mixed-integer programming model is offered to allocate and schedule rescue teams in a response phase of disaster management under uncertainty. The objective function minimizes the incident’s total weighted completion times. The literature review shows that the uncertain condition and time windows have been less addressed in the previous studies. To cover these gaps, this paper investigates the problem under uncertainty and considers time windows for incidents. Besides, the fatigue effect is considered in this paper. Accordingly, within a planning horizon, incident processing times are not fixed. Since the considered problem is an NP-hard one and exact methods cannot solve it within a reasonable amount of time, this research develops a heuristic-based simulated annealing algorithm. The presented model is solved using the developed algorithm and three known meta-heuristic algorithms. Then, the results obtained by algorithms are compared and analyzed. Finally, the sensitivity analysis is carried out on some crucial parameters of the presented model, and the related results are reported.
... Macias et al. [53] solve a problem of Unmanned Aerial Vehicles to be used in the HSC, a novel multi-stage model is designed and a routing algorithm that is solved through Large Neighborhood Search. Other distribution works are Mamashli et al. [54], Talebi and Salari [55] and Molladavoodi et al. [56]. ...
Everyday there are more disasters that require Humanitarian Supply Chain (HSC) attention; generally these problems are difficult to solve in reasonable computational time and metaheuristics (MHs) are the indicated solution algorithms. To our knowledge, there has not been a review article on MHs applied to HSC. In this work, 78 articles were extracted from 2016 publications using systematic literature review methodology and were analyzed to answer two research questions: (1) How are the HSC problems that have been solved from Metaheuristics classified? (2) What is the gap found to accomplish future research in Metaheuristics in HSC? After classifying them into deterministic (52.56%) and non-deterministic (47.44%) problems; post-disaster (51.28%), pre-disaster (14.10%) and integrated (34.62%); facility location (41.03%), distribution (71.79%), inventory (11.54%) and mass evacuation (10.26%); single (46.15%) and multiple objective functions (53.85%), single (76.92%) and multiple (23.07%) period; and the type of Metaheuristic: Metaphor (71.79%) with genetic algorithms and particle swarm optimization as the most used; and non-metaphor based (28.20%), in which search algorithms are mostly used; it is concluded that, to consider the uncertainty of the real context, future research should be done in non-deterministic and multi-period problems that integrate pre- and post-disaster stages, that increasingly include problems such as inventory and mass evacuation and in which new multi-objective MHs are tested.
