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Designing a data-driven leagile sustainable closed-loop supply chain network
Published in Jiuping Xu, Syed Ejaz Ahmed, Zongmin Li, Big Data and Information Theory, 2022
Abdollah Babaeinesami, Hamid Tohidi, Seyed Mohsen Seyedaliakbar
In this paper, a novel data-driven league sustainable CLSC model to find a compromise solution between economic, environmental and social objective functions, was proposed. The main contributions of this study include: simultaneously addressing routing, inventory, lot sizing, reproduction and location-allocation decisions besides the capacity constraints; Considering economic, green, lean, agility and social factors concurrently; Regarding the strategic and operational decisions to decrease the impacts of disruption; proposing a robust possibilistic programming approach to deal with uncertainty; applying the FCCM to select the potential location for establishing production, distribution and reproduction centers based on proximity to the local retailers and applying the HPSO-SEO algorithm to solve large-sized instances with high performance.
First-Mile Ridesharing Using Autonomous Shuttle Service and IoT Cloud Platform
Published in Nishu Gupta, Srinivas Kiran Gottapu, Rakesh Nayak, Anil Kumar Gupta, Mohammad Derawi, Jayden Khakurel, Human-Machine Interaction and IoT Applications for a Smarter World, 2023
Shyam Sundar Rampalli, Pranjal Vyas, Anuj Abraham, Justin Dauwels
This section presents the K-means algorithm-based technique for pick-up location allocation. K-means is one of the popular unsupervised clustering algorithms used for clustering spatial and non-spatial datasets. The goal of the K-means clustering algorithm is to group the data points into non-overlapping subgroups. The inter-cluster data points are kept as similar as possible while we have tried to keep the clusters as different as possible. The similarity between the data points is usually determined using distance-based measurements such as Euclidean-based distance or correlation-based distance. The decision of similarity measure is application-specific. Following are the steps for the K-means algorithm: Step 1: Specify the number of clusters K.Step 2: Initialize the centroids by randomly selecting K points from the dataset.Step 3: Assign the data points to the closest centroid.Step 4: Recalculate the centroids for each cluster by computing the average of all the data points.Step 5: Keep iterating till there is no change in the centroids, i.e. assignment of the data points to the clusters is not changing.
Advanced Location and Routing Models
Published in Sunderesh S. Heragu, Facilities Design, 2022
Branch-and-bound algorithm for uncapacitated location–allocation model: Step 1: Set the best known upper bound UB = ∞, the node counter p = 1, S0 = S1 = {Φ}, and S2 = {1, 2, …, m}.Step 2: Construct a subproblem (node) p with the current values of the y variables.Step 3: Solve the subproblem corresponding to the node under consideration using the minimum coefficient rule and Equation (4.61).Step 4: If all the y variables in the solution take an integer (0 or 1) value, go to step 7. Otherwise, go to step 5.Step 5: Determine the lower bound of node p using Equation (4.62). Arbitrarily select one of the facilities—say, k—which has taken on a fractional value for yk (0 < yk < 1) and create two subproblems (nodes) p + 1 and p + 2 as follows.Subproblem p + 1: Include facility k and others with a yk value of 0 in S0, facilities with y value of 1 in S1, and all other facilities in S2.Subproblem p + 2: Include facility k and others with a yk value of 1 in S1, facilities with y value of 0 in S0, and all other facilities in S2. If, xkj = 1 for j = 1, 2, …, n, in the solution to subproblem p, remove each such customer j from consideration in subproblem p + 2, and reduce n by the number of j’s for which xkj = 1.Step 6: Solve subproblem p + 1 using the minimum coefficient rule and Equation (4.61). Set p = p + 2. Go to step 4.Step 7: Determine the lower bound of node p using Equation (4.62). If it is less than UB, set UB = lower bound of node p. Prune node p as well as any other node whose bound is greater than or equal to UB. If there are no more nodes to be pruned, stop. Otherwise, consider any unpruned node and go to step 3.
Why public health needs GIS: a methodological overview
Published in Annals of GIS, 2020
Location–allocation analysis seeks the optimal placement of facilities for a desirable objective under certain constraints. Among the classic location-allocation problems, the p-median problem minimizes the weighted sum of distances between users and facilities, the location set covering problem (LSCP) minimizes the number of facilities needed to cover all demand, and the maximum covering location problem (MCLP) maximizes the demand covered within a desired distance or time threshold by locating a given number of facilities (Church 1999). Most of these models emphasize efficiency, such as minimizing total travel, minimizing resources committed or maximizing population served. Only the minimax problem marginally addresses the issue of equity as it minimizes the travel for the most remote user. Social scientists have long argued the balance between the dual goals of efficiency and equality (e.g. Fried 1975). The literature of location-allocation analysis is rich on efficiency but scarce on equality. Therefore, this section focuses more on modelling equality and possible integration of the two.
Reverse supply network design for circular economy pathways of wind turbine blades in Europe
Published in International Journal of Production Research, 2022
Athanasios Rentizelas, Nikoletta Trivyza, Sarah Oswald, Stefan Siegl
The mathematical model of the reverse supply chain network design optimisation is formulated as a MILP problem. The model belongs to the wider family of location-allocation optimisation problems and specifically to the Supply Chain Network Design problems. The optimisation problem was implemented in GAMS and was solved with LINDO in an Intel(R) CoreTM8 i7-2600 CPU at 3.40 GHz, with computational times between 70,000 and 100,000 s. The global optimal solution was identified and the results are presented in Section 5.
Hierarchical supplement location-allocation optimization for disaster supply warehouses in the Beijing–Tianjin–Hebei region of China
Published in Geomatics, Natural Hazards and Risk, 2019
Yunjia Ma, Wei Xu, Lianjie Qin, Xiujuan Zhao, Juan Du
Emergency logistics models should drive coordination to the forefront by planning the facility location and supply allocation in tandem so that they correspond to the preparedness and response phases; in addition, location-allocation frameworks should be used in which the prepositioning of warehouses is accompanied by a resource allocation plan to satisfy the demand when disaster strikes (Caunhye et al. 2016). Duran et al. (2011) devised a typical location-allocation model where warehouse locations and inventory levels are determined and resources are allocated to regional demand locations. Mete and Zabinsky (2010) developed a two-stage stochastic programming model to first locate possible medical storage centres and the required inventory levels before the disaster and then deliver medical supplies to hospitals for each disaster scenario. Caunhye et al. (2016) proposed a two-stage location-routing model with recourse for integrated prepositioning and relief distribution planning under uncertainty. Davis et al. (2013) proposed a stochastic programming model to determine how supplies should be positioned and distributed among a network of cooperative warehouses. The model incorporated constraints that enforce equity in service while also considering traffic congestion resulting from possible evacuation behaviour and time constraints for providing effective response. Alem et al. (2016) developed a new two-stage stochastic network flow model under practical assumptions that have rarely been considered in previous papers, such as the dynamic multi-period nature of disaster relief operations, limited budgets, fleet sizing, and a variety of uncertain data. An unprecedented amount of work has been performed on the application of quantitative emergency location-allocation models, for example, Chang et al. (2007), Salmerón and Apte (2010), Noyan (2012), Bozorgi-Amiri et al. (2013), Rennemo et al. (2014), Ahmadi et al. (2015), Noyan et al. (2015), Zhao et al. (2017) and Xu et al. (2018). However, in the case of complex, high-dimensional problems related to large areas, the existing optimization algorithms are highly time consuming and difficult to find the global optimal solution when solving the above models (Xu et al. 2018).