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... solutions from both DE and GA algorithms are compared with the optimal solution of GAMs software, while comparison is applied with up to 10 nodes. As a result, the comparison procedure is done only between DE and GA for large-sized problems tabulated in Table 2. The gap between both DE and GA with GAMs is calculated with percentage of a relative gap value for small-sized problems. ...Context 2
... the performance of DE is better than GA in the entire test problem. Computational times for small and large-sized problems are shown in Tables 2 and 3, respectively. Fig. 4 illustrates the average of percentage relative gaps of both DE and GA with regarding to GAMS software; however on the other hand, Fig. 5 shows the average of percentage relative Gap of the GA algorithm in comparison to the DE algorithm. ...Similar publications
As long as “steady flow” describes a flow, a “stationary process” describes a process. It is then a tautology to say that in a process there is change. Even a stationary process describes a system changing in time, rather than a static one which keeps a constant state all the time. However, this is often missed, which has led to misusing the term “...
Citations
... Results demonstrate the formulation's efficacy, with the branch-and-cut algorithm solving instances containing up to 50 nodes. Sadeghi et al. (2015) focused on minimizing total transportation cost while maximizing network flow capacity, addressing scenarios where transportation routes may face disruption due to various factors like daily traffic, earthquakes, floods, etc. In the study by Sadjadi et al. (2015), a three-tier supply chain network was explored. ...
Sustainable development in supply chain management is of great importance, as it emphasizes that the procurement process in sustainable supply chains addresses not only current needs but also future requirements. This subject, by improving processes, reducing waste and energy consumption , increasing efficiency, and establishing sustainable relationships with suppliers and customers, contributes to the sustainability and success of companies in international competition. Moreover, hub location is crucial for optimizing distribution processes and reducing costs in the supply chain, especially for perishable products with a defined and limited shelf life, which imposes various constraints on the supply chain. Additionally, to bring mathematical models closer to reality, uncertainty approaches can be employed, and strategies to deal with uncertainty can be considered. This research delves into these issues in the realm of supply chain management, focusing on sustainability and uncertainty, and provides a review of research conducted in the literature of this field.
... As another application of GA in USAHCP, queue estimation was taken into account while optimising hub locations and numbers (Hasanzadeh et al., 2018). Sadeghi et al. (2015) presented a spatial model for the HCP that aimed to minimise transportation costs while maximising flow. They assessed the quality of their results using the GA. ...
E-commerce's rapid expansion has transformed consumer shopping habits and business operations. While it offers the potential to reduce customer trips, it has also contributed to increased vehicle kilometres travelled (VKT) by delivery vehicles, resulting in elevated greenhouse gas (GHG) emissions, especially in last-mile deliveries. This paper proposes a spatial approach to address the uncapacitated single allocation hub covering problem (USAHCP) to reduce the VKT by optimising the number and location of logistics hub. In this approach, the spatial features and strategic legislation of metropolitan areas are analysed. Integrating existing models, this location-based approach aims to design a collaborative last mile distribution network by locating micro-consolidation centres (MCCs) in metropolitan areas to service parcel lockers where end customers pick up their parcels. The method is applied to Sydney, Australia, using data provided by the largest courier companies in Australia. The results show that incorporating MCCs in the network reduces VKT due to their proximity to final destinations. Sensitivity analyses are conducted by differentiating maximum driving time constraints and specific times of day reflecting traffic patterns. The results provide significant advantages for logistics stakeholders, notably Transport for NSW, by reducing VKT and GHG emissions. Moreover, the developed method can be utilised by carriers and shippers in their strategic and operational decisions to enhance overall delivery speed and reliability.
... They used the SAA and the Benders decomposition algorithm to solve the model. Sadeghi, Jolai, Tavakkoli-Moghaddam and Rahimi (2015) proposed a reliable stochastic hub location problem considering road disruption and uncertainty about road capacity. A differential evolution algorithm (DEA) and genetic algorithm (GA) were employed to solve the model and to obtain near-optimal solutions in a reasonable computation time. ...
... Adibi and Razmi (2015) modeled a stochastic p-hub median problem with uncertain transportation costs and demand. Sadeghi et al. (2015) proposed a stochastic hub covering location problem taking into account road disruptions, that is, the road capacity was uncertain. Furthermore, the differential evolution algorithm (DEA) and genetic algorithm (GA) were employed to solve the model more efficiently and obtain nearoptimal solutions in a reasonable computation time. ...
This article proposes a two-stage stochastic profit-maximizing hub location problem (HLP) with uncertain demand. Additionally, the model incorporates several carbon regulations, such as carbon tax policy (CTP), carbon cap-and-trade policy (CCTP), carbon cap policy (CCP), and carbon offset policy (COP). In the proposed models, an enhanced sample average approximation (ESAA) method was used to obtain a suitable number of scenarios. To cluster similar samples, k-means clustering and self-organizing map (SOM) clustering algorithms were embedded in the ESAA. The L-shaped algorithm was employed to solve the model inside the ESAA method more efficiently. The proposed models were analyzed using the well-known Australian Post (AP) data set. Computational experiments showed that all of the carbon regulations could reduce overall carbon emissions. Among carbon policies, CCTP could achieve better economic results for the transportation sector. The results also demonstrated that the SOM clustering algorithm within the ESAA method was superior to both k-means inside ESAA and classical SAA algorithms according to the %gap and standard deviation measures. In addition, the results showed that the L-shaped algorithm performed better than the commercial solver in large-scale instances.
... There are several successful approaches addressing uncertainty in the literature including stochastic and robust optimization techniques. In stochastic optimization, the behavior of uncertain parameters is described by known probability distributions and the expected value of the objective function is evaluated based on these distributions (e.g., Kleywegt et al. 2002;Sim et al. 2009;Contreras et al. 2011;Mohammadi et al. 2014;Adulyasak et al. 2015;Sadeghi et al. 2015;Correia et al. 2018;Correia and Saldanha-da Gama 2019;Peiró et al. 2019a;Taherkhani et al. 2020). Robust optimization, on the other hand, works with a deterministic, set-based description of the uncertainty where there is no probabilistic information for the uncertain parameters. ...
In this paper, optimization and simulation techniques are integrated to address single and multiple allocation hub network design problems under uncertainty. Using a scenario-based iterative optimization–simulation approach, four sources of uncertainty are considered: the demand to be transported within the network, the associated transportation costs as well as the fixed costs for both opening hub facilities and establishing the connections between them. Additionally, flow-dependent economies of scale on all network connections are incorporated in the simulation phase. A value of simulation measure is introduced to evaluate the performance of the methodology. The computational tests conducted on the well-known CAB dataset with varying levels of uncertainty show that the approach can result in better solutions with up to 6.6% lower cost compared to its deterministic counterpart.
... Assuming that travel times are normally distributed, these authors used exact methods to solve instances based on the standard Australian Post (AP) and CAB data sets. Furthermore, Sadeghi et al. (2015) proposed a stochastic approach based on the chance-constraint definition for a reliable p-hub covering location problem. The authors modeled the link capacities of the network by employing a truncated Erlang probability distribution. ...
Modern telecommunication networks are comprised of a countless number of nodes exchanging data among them. In particular, multimedia traffic – composed of audio, video, and images – represents a challenging scenario requiring link optimisation techniques. The hub-and-spoke topology is frequently used to design more effective telecommunication networks. This work considers a hub-and-spoke network in which a large number of nodes are exchanging real-time multimedia data, and where the quantity of data sent from one node to another is a random variable. Given a fixed number of hubs, p , the goal is to select the best location for these p hubs in order to minimise the total expected cost of transmission. This scenario is modelled as an uncapacitated single-allocation p -hub median problem under uncertainty conditions. Additionally, with the purpose of considering the effect of transmission delays on the video signal, service quality thresholds are assigned to every potential hub in the network. Since real-life networks tend to be large in size, we propose a simheuristic algorithm to cope with this stochastic and large-scale optimisation problem. A series of computational experiments illustrate these concepts and allow for testing the performance of our simheuristic approach. Finally, a statistical analysis of the obtained results is provided.
... They reformulate a stochastic hub center problem and solve it with an algorithm based on variable reduction and separation. Sadeghi et al. (2015) investigate a stochastic model for p-HCP. They aim to minimize the total transportation cost under the condition of the link capacities being subject to stochastic degradation. ...
The hub covering problem can be viewed as a combination of the set covering and the hub location problem. In a traditional hub covering problem, all parameters are assumed to be deterministic. However, neglecting uncertainties associated with demand and transportation costs may lead to inferior solutions. Thus, we introduce three novel stochastic optimization models to formulate a multiple allocation hub covering problem in which deterministic transportation cost and demand assumptions are relaxed. Further, we also reflect scale economy to the models via inter-hub transportation cost discount factor. The L-Shaped Algorithm is applied to solve the sculpted problems. To test the efficiency of the proposed models, several test instances are generated and solved based on simulated data. The effects of the change of coverage radius, inter-hub discount factor, and hub opening cost are also examined. Results of this study validate that modeling the hub covering problem as a stochastic optimization model yields up 13.05%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document} efficiency when compared to the expectation of the expected value method.
... For more surveys on the HLP, the interested reader may refer to review papers in the literature given by Campbell et al. (2002), Alumur and Kara (2008), Zabihi and Gharakhani (2018), Contreras and O'Kelly (2019) and Basu et al. (2015). Sadeghi et al. (2015) presented two-stage stochastic programming for formulating stochastic incapacitated multiple allocation HLPs. Zhalechian et al. (2017a, b) modeled a multiobjective mathematical model for a multimodal HLP under a possibilistic-stochastic uncertainty that minimizes the total traffic noise pollution costs. ...
Purpose:
This research presents a multiproject scheduling and resource management (MPSRM) model that includes an M/M/c/n queue system, a p-hub median model, a parallel machine scheduling and a hub location problem solution method. This research aims to design a project network and then sequence raw materials delivery to hub factories.
Design/methodology/approach:
This research is implemented as a case study in construction and industrial company. It considers several mines to supply raw materials for production in hub factories. In the sequencing phase, a parallel mine scheduling problem specifies the delivery of raw materials to hub factories. Furthermore, a multisource project scheduling model is studied and designed for building project-oriented companies.
Findings:
The results suggest that the proposed MPSRM model in this study significantly reduces project transportation costs. Therefore, creating accurate planning in projects, especially in construction projects where transportation plays an important role, can help reduce time and costs. Researchers, project managers and those dealing with projects can use this model to exploit their projects.
Research limitations/implications:
When the number of construction projects in a construction company increases in a region, choosing an appropriate strategy to supply resources and raw materials becomes very important in terms of profit and loss and project completion as scheduled. An increase in transportation demands alongside the economic development on the one hand and providing competitive transportation services, on the other hand, have increasingly spotlighted the significance of hub networks in transportation systems. In addition, in this research, there is no suitable access road from the mines to the project sites, and considering the workload, if any of the projects become a hub, a proper road should be built.
Originality/value:
To the best of the knowledge, there is no outstanding research in which a p-hub median location problem by considering queuing model and different transportation modes and a parallel machine scheduling problem are studied simultaneously.
... Simulated annealing and imperialistic competitive metaheuristic algorithms are presented in [39] to address a static sustainable fuzzy multiobjective HLP under uncertainty, based on chance constrained optimization. Another HLP and a genetic metaheuristic are presented in [40], where the overall transportation cost is minimized with a reliable constraint based on the flow maximization through the network. One of its new features is that the hub edge capacity is subject to stochastic degradation, as in a form of daily traffic, earthquake, flood, etc. ...
... The main limitations of model (1)-(30) are as follows: (a) no hub closing along the time horizon, see [3] for hub leasing and contract cancellation in an uncapacitated deterministic environment; (b) no degradation on the hub and hub edge capacities, due to disruptive events, see [33,34] for multiperiod deterministic, [35] for static stochastic, and [40] for two-stage stochastic with capacity degradation; and (c) no more than the classic two hub paradigm, at most, for each origin-destination flow, see [25] for uncapacitated static deterministic, where more than two hubs are allowed. See in Section 7.1 the outline of the future research agenda. ...
The hub location problem (HLP) basically consists of selecting nodes from a network to act as hubs to be used for flow traffic directioning, i.e., flow collection from some origin nodes, probably transfer it to other hubs, and distributing it to destination nodes. A potential expansion on the hub building and capacitated modules increasing along a time horizon is also considered. So, uncertainty is inherent to the problem. Two types of time scaling are dealt with; specifically, a long one (viz., semesters, years), where the strategic decisions are made, and another whose timing is much shorter for the operational decisions. Thus, two types of uncertain parameters are also considered; namely, strategic and operational ones. This work focuses on the development of a stochastic mixed integer linear optimization modeling framework and a matheuristic approach for solving the multistage multiscale allocation hub location network expansion planning problem under uncertainty. Given the intrinsic difficulty of the problem and the huge dimensions of the instances (due to the network size of realistic instances as well as the cardinality of the strategic scenario tree and operational ones), it is unrealistic to seek an optimal solution. A matheuristic algorithm, so-called SFR3, is introduced, which stands for scenario variables fixing and iteratively randomizing the relaxation reduction of the constraints and variables’ integrality. It obtains a (hopefully, good) feasible solution in reasonable time and a lower bound of the optimal solution value to assess the solution quality. The performance of the overall approach is computationally assessed by using stochastic-based perturbed well-known CAB data.
... Consequently, uncertainty should be taken into account in these kinds of problems. Contreras et al. (2011), Alumur et al. (2012a, Zhou and Liu (2007), Taghipourian et al. (2012), Mohammadi et al. (2014), Sadeghi et al. (2015) and Rahimi et al. (2016) considered a number of features of hub location problems under uncertainty. ...
This paper presents a new bi-objective multi-modal hub location problem with
multiple assignment and capacity considerations for the design of an urban public
transportation network under uncertainty. Because of high construction costs of hub
links in an urban public transportation network, it is not economic to create a complete
hub network, so the presented hub network is supposed to be incomplete. Moreover, the
demand is assumed to be dependent on the utility proposed by each hub. Thus, the
elasticity of the demand is considered in this paper. The presented model also has the
ability to compute the number of each type of transportation vehicles between every
two hubs. The objectives of this model are to maximize the benefits of transportation by
establishing the hub facilities and minimize the total network transportation time. As
exact values of some parameters are not specified in advance, a fuzzy multi-objective
programming based approach is proposed to optimally solve small-sized problems. For
medium and large-sized problems, a meta-heuristic algorithm, namely multi-objective
particle swarm optimization (MOPSO) is applied and its performance is compared with
results from the non-dominated sorting genetic algorithm (NSGA-II). It is demonstrated
that the developed model is applicable and a number of sensitivity analyses are carried
out on a real-case study inspired by a monorail project of Qom city.
