Display omitted
•A multi-criteria framework is proposed for bioethanol facility location.•The best worst method is used to calculate the weights of the criteria.•The provinces of Iran are evaluated ...as alternatives in this study.•A questionnaire answered by 41 experts from Iran is used for the BWM.•Province of Khuzestan (followed by Tehran) is selected for bioethanol production.
One of the major factors in the success of renewable energy is finding a proper location for production facilities. At a national level, different parts of a country (e.g. provinces) can be seen as alternatives that can be assessed based on a set of criteria, and ranking them to identify the best location. The focus in this paper is on identifying the best location for the production of bioethanol. After a comprehensive literature review, an evaluation framework is proposed based on the three dimensions of sustainability (economic, environmental and social). Using data provided by a sample of experts in a developing country – Iran – and applying the best-worst method (BWM), a number of decision-making criteria are evaluated. Performance data involving the various provinces of Iran are collected from different sources. The performance data and the weights identified through BWM are used to calculate an overall score for each province, which is then used to rank the provinces, with the province of Khuzestan (closely followed by Tehran) being identified as the most suitable province for bioethanol production in Iran.
The deployment of edge computing infrastructure requires a careful placement of the edge servers, with an aim to improve application latencies and reduce data transfer load in opportunistic Internet ...of Things systems. In the edge server placement, it is important to consider computing capacity, available deployment budget, and hardware requirements for the edge servers and the underlying backbone network topology. In this paper, we thoroughly survey the existing literature in edge server placement, identify gaps and present an extensive set of parameters to be considered. We then develop a novel algorithm, called PACK, for server placement as a capacitated location–allocation problem. PACK minimizes the distances between servers and their associated access points, while taking into account capacity constraints for load balancing and enabling workload sharing between servers. Moreover, PACK considers practical issues such as prioritized locations and reliability. We evaluate the algorithm in two distinct scenarios: one with high capacity servers for edge computing in general, and one with low capacity servers for Fog computing. Evaluations are performed with a data set collected in a real-world network, consisting of both dense and sparse deployments of access points across a city area. The resulting algorithm and related tools are publicly available as open source software.
•Detailed survey of existing edge server placement solutions.•Novel placement algorithm that minimizes latencies with extensive set of parameters.•Different placement scenarios, i.e. MEC and Fog, studied with QoS and QoE evaluation.•Algorithm performance evaluated with a large-scale real-world data set.•Algorithm published as an open-source software package, implemented in R.
•Multi-objective CFLP with consideration of flexibility and robustness over time.•Addresses opening depots and assigning customers in a two stage process.•SEAMO2 is hybridized with LR with ...allocations for cost (LR1) and CO2 (LR2).•Large data instances utilize characteristics of a ‘real-world’ retail network.•Presents a LR solution formulation for a multiple products.
We propose an efficient evolutionary multi-objective optimization approach to the capacitated facility location–allocation problem (CFLP) for solving large instances that considers flexibility at the allocation level, where financial costs and CO2 emissions are considered simultaneously. Our approach utilizes suitably adapted Lagrangian Relaxation models for dealing with costs and CO2 emissions at the allocation level, within a multi-objective evolutionary framework at the location level. Thus our method assesses the robustness of each location solution with respect to our two objectives for customer allocation. We extend our exploration of selected solutions by considering a range of trade-offs for customer allocation.
The delineation of the transportation network is a strategic issue for all over the place. The problem of locating new facilities among several existing facilities and minimizing the total ...transportation cost are the main topics of the location network system. This paper addresses the
transportation-
p
-
facility location problem
(T-
p
-FLP) which makes a connection between the facility location problem and the transportation problem, where
p
corresponds to the number of facilities. The T-
p
-FLP is a generalization of the classical transportation problem in which we have to seek where and how we impose the
p
-number of facilities such that the total transportation cost from existing facility sites to the potential facility sites will be minimized. The exact approach, based on the iterative procedure, and a heuristic approach as applied to the T-
p
-FLP are discussed and corresponding results are compared. An experimental example is incorporated to explore the efficiency and effectiveness of our proposed study in reality. Finally, a summary is given together with suggestions for future studies.
•We propose a framework for the optimal siting of electric vehicle charging stations.•In it, we solve a multi-period optimization problem with demand dynamics.•We combine node-based and flow-based ...approaches to model the charging demand.•We propose a rolling horizon heuristic that efficiently produces good solutions.•Our approach solves instances based on much larger territories than previous ones.
We present a new strategic multi-period optimization problem for the siting of electric vehicle (EV) charging stations. One main novelty in this problem is that EV adoption over time is influenced by the availability of charging opportunities, as well as by local EV diffusion. Furthermore, to the best of our knowledge, this is the first contribution where the distribution of charging demand is modeled with a combination of node-based - more appropriate for urban or suburban settings - and flow-based approaches - with which we can model the needs of EVs to recharge on intermediary stops on long-haul travels. We propose a mixed-integer linear programming (MILP) formulation for this problem. Our computational experiments show that by simply implementing it in state-of-art MILP solvers, we are unable to obtain feasible solutions for realistically-sized instances. As such, we propose a rolling horizon-based heuristic that efficiently provides provably good solutions to instances based on much larger territories (namely the province of Quebec and the state of California) than those tackled by the methods proposed in the literature for the location of EV charging stations.
•A new two-echelon pre-disaster relief network design model for rare disasters.•Ensures demand satisfaction and assignment to closest relief center for all scenarios.•Service adequacy and fairness ...are the main drivers and cost is secondary.•A practical demand covering scheme captures expert intuition for service adequacy.•An exact logic-based Benders decomposition algorithm to solve large problem instances.
Humanitarian network design decisions belonging to the preparedness stage of disaster management life-cycle are of critical importance since they set the frame for all further post-disaster operations. Having an adequate number of strategically located storage and distribution centers for critical supplies is the key that enables effectiveness, efficiency and fairness when responding to a disaster situation. The preparedness model proposed in this study selects locations and inventory levels of these facilities such that the right mix of relief items can be supplied at the right time. Our mixed integer linear model aims to find a robust relief network design that satisfies the demand for all given disaster scenarios, and to help achieve a better response during the response stage when the relief items are distributed. The assumptions and the parameters used in the model are justified by authorities of humanitarian organizations. We propose a logic-based Benders decomposition approach to solve this problem to optimality. Although the problem is NP-hard, our numerical studies demonstrate that it is possible to obtain optimal or very good solutions to problem instances with realistic sizes.
Supply chain network design (SCND) is a key strategic decision in supply chain management (SCM). One particular area of SCND is concerned with disruption risk modelling. This paper presents a ...systematic literature review of quantitative models of SCND under disruption risks in industrial SCM and logistics. More specifically, our analysis is focused on different costs induced by the planning of proactive investments in robustness and through parametrical/structural adaptation at the recovery stage. This review can be of value for researchers and decision-makers alike for several reasons. First, we categorise the existing knowledge based on decision-making problems, which can be instructive for a convenient association of a particular SCND problem to a modelling domain according to network-wise, supply-side and demand-side perspectives. Second, our analysis focuses on the costs specifically induced by disruption risks and resilience investments. Third, we offer a dedicated section related to disruption probability formulation methods and their impact on resilience costs. Fourth, the integration of different SCM dimensions (i.e., social impact, environmental impact, responsiveness, and risk-aversion) and the associated multi-objective modelling settings are discussed along with disruption risks in SCND models. Finally, we summarize our findings as insights from a managerial perspective. Drawbacks and missing aspects in the related literature are highlighted, and we lay out several research directions and open questions for future research.
•Integrated supplier selection into the decision making of pre-positioning relief supplies.•Considered the use of supplier-owned inventory for disaster response with complementation.•Formulated this ...problem as a two-stage stochastic programming model with a consideration of failure risks under disasters.•Presented a case study of hurricane threats in the Gulf Coast area of the US.•Provided managerial insights about establishing close relationships with commercial suppliers from sensitivity analysis.
Pre-positioning of relief supplies is one important preparedness action for natural disasters. This paper proposes the importance of supplier selection in humanitarian relief, and integrates it into the pre-positioning strategy. These suppliers have own physical inventories for regular business, and relief agencies are assumed to be able to use such inventories for disaster response by providing compensation. The supplier selection criteria include price discounts offered by suppliers based on order quantity and required lead time as well as physical inventory. By considering disruption risks, this paper presents a two-stage stochastic programming model to produce plans including facility location and inventory, supplier selection, and distribution of relief supplies. A case study focused on hurricane threats in the Gulf Coast area of the US illustrates the application of the proposed model. Sensitivity analysis of comparison experiments offers managerial insights for relief agencies.
The lack of access to Second-level Health Care Services (SHCS) in developing countries is primarily due to the scarcity of facilities and the limited investment of resources in the public sector. ...Access to these services directly relates to the distance the population travels to these facilities. In that sense, a maximal covering location problem can be helpful to maximize the impact of decisions related to the location of new SHCS. In this paper, we propose a model to guide the location of additional sites where second-level services can be installed in a network of public hospitals. The partial coverage and variable radius are considered in the problem to assess a large territory with different characteristics and population densities. The system is composed of multiple institutions that supply differentiated varying levels of coverage concerning their own demand and external demand. The objective of the problem is to improve the demand coverage in the system by locating new sites, since there are already sites offering different services. A case study in the Mexican public health system is conducted to assess four specialized SHCS. The obtained results evidence for the benefit of using optimization tools in the resource planning of SHCS.
•A problem based on the maximal covering location problem is applied to second-level specialized health care services.•An integer programing model integrating partial coverage and novel features such as multiple institutions is proposed.•The usefulness of the proposed model is illustrated in a real-world case study applied to the Mexican Health Care System.•Variable coverage radius based on population density and existing facilities interaction are proposed.
Abstract The present paper introduces a modified flower pollination algorithm (FPA) enhanced by evolutionary operators to solve the uncapacitated facility location problem (UFLP), which is one of the ...well-known location science problems. The aim in UFLP is to select some locations to open facilities among a certain number of candidate locations so as to minimize the total cost, which is the sum of facility opening costs and transportation costs. Since UFLP is a binary optimization problem, FPA, which is introduced to solve real-valued optimization problems, is redesigned to be able to conduct search in binary domains. This constitutes one of the contributions of the present study. In this context, some evolutionary operators such as crossover and mutation are adopted by the proposed FPA. Next, the mutation operator is further enhanced by making use of an adaptive procedure that introduces greater level of diversity at earlier iterations and encourages intensification toward the end of search. Thus, while premature convergence and local optima problems at earlier iterations are avoided, a more intensified search around the found promising regions is performed. Secondarily, as demonstrated in this study, by making use of the reported evolutionary procedures, FPA is able to run in binary spaces without employing any additional auxiliary procedures such as transfer functions. All available benchmarking instances are solved by the proposed approach. As demonstrated by the comprehensive experimental study that includes statistically verified results, the developed approach is found as a promising algorithm that can be extended to numerous binary optimization problems.
PDF
