Facility Location-allocation Model Research Articles

Drone-Based Environmental Emergency Response in the Brazilian Amazon

This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation model with mobile servers assumes a centralized network operated out of sight by first responders and government agents. The optimization problem seeks to find the minimal cost configuration that meets operational constraints and performance objectives. To test the practical applicability of the proposed model, a real-life case study was implemented for the municipality of Ji-Paraná, in the Brazilian Amazon, using demand data from a mobile whistle-blower application and from satellite imagery projects that monitor deforestation and fire incidents in the region. Experiments are performed to understand the model’s sensitivity to various demand scenarios and capacity restrictions.

Application of facility location-allocation model for tuberculosis sputum referral system

Application of facility location-allocation model for tuberculosis sputum referral system

A hierarchical capacitated facility location-allocation model for planning maternal healthcare facilities in India

The present work on maternal healthcare in India is motivated by its current status emerging out of limited availability and accessibility of related facilities. Planning for the required maternal healthcare facilities, from basic ones to neonatal ones, is a tedious task from the financial perspectives of both users and the government. This problem has been addressed by proposing a mixed-integer linear programming formulation for determining the optimal number and location of these capacitated and varied facilities and also for the allocation of mothers-to-be to these facilities while minimizing the overall cost. Suitable additional valid inequalities are proposed to be incorporated into the NP-hard model for reducing the computational burden. For further computational advantage, a sequential approach is also proposed. Comparative performance of the proposed approaches has been analyzed using several problems with wide variation in the problem parameters’ values. The sequential approach is found to be very effective and computationally efficient. The results of the sensitivity analyses with respect to coverage distance, capacity, referral proportion and fixed cost provide important and practical insights related to the mix of the facilities to be established. The application of the model for a district in India proves the suitability of the model in the Indian context.

Application of Facility Location-Allocation Model for Tuberculosis Sputum Referral System

Application of Facility Location-Allocation Model for Tuberculosis Sputum Referral System

Optimization of Facility Location-Allocation Model for Base Tranceiver Station Antenna Establishment Based on Genetic Al-gorithm Considering Network Effectiveness Criteria (Case Study North of Kermanshah)

It is clear that the location of the BTS antennas plays a very important role in the proper service and coverage of the mobile connection. Proper location of these antennas is a major challenge for operators in each country, as in addition to maximum network coverage, service costs must also be acceptable and competitive. This means that in busy areas, in order to provide better service, the number of antennas must be greater and closer to each other. In general, the location problem is a type of optimization problem that aims to select a subset of the set of candidate locations to create the facilities that provide the best service at the lowest cost. To solve such problems in a reasonable time, we can use meta-heuristic algorithms to find solutions that are very close to the optimal solution. Accordingly, this paper attempts to apply the genetic algorithm to find a suitable solution for finding BTS mobile antennas in north Kermanshah. A GA model is proposed that improves the location coordinates of the current BTS antennas extracted from the GIS system. Comparison of model results with the status of BTS active antennas in Kermanshah shows the performance of the model.

Design of facility location-allocation network with an emergency backup supply system

This paper considers an emergency backup supply (EBS) system from the secondary supplying facility (SSF) when the primal supplying facility (PSF) cannot satisfy the demand in case of disruptions. In this context, EBS requires each demand point to be covered by a PSF and an SSF. Using a multi-objective programming model, we propose a procedure of finding the option that would generate the most efficient EBS system and investigate the effect of backup supply from the SSFs on the facility location-allocation (FLA) design problem. We demonstrate the applicability of our proposed efficiency-driven approach and compare two cases, without and with the EBS system. From the numerical results, we observe that the FLA schemes with the EBS system perform well regarding increasing ENDS and yielding higher productivity. The proposed efficiency-driven FLA model with the EBS system would help decision-makers design and select efficient FLA schemes. [Received 29 June 2019; Revised 9 December 2019; Accepted 1 February 2020]

GIS-based assessment of sustainable crop residues for optimal siting of biogas plants

The assessment of biomass at high spatial resolution is critical to manage supply risks and to identify optimal plant sites for producing sustainable biofuels and co-products. The spatial variabilities in soil type, topography, climate, and crop management practices further require vast computational time and resources to estimate the availability of sustainable biomass for a large study area. In this study, we developed a GIS-based integrated predictive modeling platform to assess the availability of sustainable crop residues at high spatial (30 m) and temporal (2010–2022) scales. A GIS-based multi-criteria inclusion-exclusion analysis and facility location-allocation models were used to identify suitable sites, and optimal siting of biogas plants respectively with biomass delivered cost. The Artificial Neural Network (ANN) based predictive models were well suited to predict sustainability indicators (soil erosion SE- R2 = 0.96, soil conditioning index, SCI R2 = 0.98 and organic matter factor, OMF- R2 = 0.83) for assessing sustainable removal rates of crop residues (corn stover and wheat straw). The GIS-based integrated model was applied to the State of Ohio and found that about 4–13 dry Tg of crop residues can be sustainably available to build 1–25 regional biogas plants. A typical optimal biogas plant with a feedstock capacity of up to 500 dry Gg could be drawn from a transport radius of about 19–35 km with a delivered cost of 40–46 $ dry Mg−1. The temporal and spatial variations in assessing the availability of biomass largely affected the supply chain decisions and its delivered costs.

Random Energy-Efficient Models for Sustainable Facility Location Subject to Carbon Emission, Economical, Capacitated and Regional Constraints

An uncertainty facility location allocation (FLA) problem is a complex nonlinear optimization problem. Currently, researchers have discussed the stochastic cost/profit issues of FLA. However, rising energy conservation awareness and environmental concerns, energy-efficientcy and low-carbon emission should be regarded as key criteria in solving it. To do so, this paper presents a new FLA problem from a sustainable development point of view. By taking a typical facility service enterprise as a case, a new stochastic energy-efficient FLA model subject to carbon emission, economical, capacitated, and regional constraints is formulated. Then, stochastic simulation and scatter search are integrated as an intelligent algorithm to resolve it. Some examples are demonstrated to prove the availability of the proposed model and solution algorithm.

Designing distribution systems with reverse flows

Closed-loop supply chains involve forward flows of products from production facilities to customer zones as well as reverse flows from customer zones back to remanufacturing facilities. We present an integrated modeling framework for configuring a distribution system with reverse flows so as to minimize the total cost of satisfying customer demand and remanufacturing the returned items that are recoverable. Given a set of existing plants and customer zones, our basic model identifies the optimal number and location of distribution centers and return centers assuming that all plants have remanufacturing capability. We devise a Lagrangian heuristic for this problem. The proposed solution method proved to be computationally efficient for solving large-scale instances of the closed-loop supply chain design problem. The potential benefits of the integrated model are demonstrated by comparing its results with those obtained from an alternative approach that determines optimal forward and reverse network structures sequentially. We also extend the basic model to determine the optimal locations for establishing remanufacturing facilities. Using the extended model, we study the conditions under which the return centers can be co-located with remanufacturing facilities rather than being established at the downstream echelons of the supply chain. Different from the existing works on facility location-allocation models for closed-loop supply chain network design, the main focus in this paper is on the investigation of structural properties of the network such as co-locating return centers with remanufacturing facilities and quantifying the benefit of modeling forward and reverse flows simultaneously rather than sequentially.

Evaluating the Effectiveness of Integrated Benders Decomposition Algorithm and Epsilon Constraint Method for Multi-Objective Facility Location Problem under Demand Uncertainty

One of the most challenging issues in multi-objective problems is finding Pareto optimal points. This paper describes an algorithm based on Benders Decomposition Algorithm (BDA) which tries to find Pareto solutions. For this aim, a multi-objective facility location allocation model is proposed. In this case, an integrated BDA and epsilon constraint method are proposed and it is shown that how Pareto points in multi-objective facility location model can be found. Results are compared with the classic form of BDA and the weighted sum method for demand uncertainty and deterministic demands. To do this, Monte Carlo method with uniform function is used, then the stability of the proposed method towards demand uncertainty is shown. In order to evaluate the proposed algorithm, some performance metrics including the number of Pareto points, mean ideal points, and maximum spread are used, then the t-test analysis is done which points out that there is a significant difference between aforementioned algorithms.

Quantifying the effects of additive manufacturing on supply networks by means of a facility location-allocation model

Additive manufacturing (AM), or popular scientific 3D printing, disseminates in more and more production processes. This changes not only production processes themselves, e.g. by replacing subtractive production technologies, but AM will in all likelihood also impact the configuration of supply networks. Due to a more efficient use of raw materials, transportation relations may change and production sites may be relocated. How this change will look like is part of an ongoing discussion in industry and academia. However, quantitative studies on this question are scarce. In order to quantify the potential impact of AM on a two-stage supply network, we use a facility location model. The impact of AM on the production process is integrated into the model by varying resource efficiency ratios. We create a test data set of 700 instances. Features of this data set are, among others, different geographical clusters of source nodes, production nodes, and customer nodes. By means of a computational study, the impact of AM on the supply network structure is measured by four indicators. In the context of our experimental set-up, AM reduces the overall transportation costs of a supply network compared to subtractive production. However, the share of the transportation costs on the second stage of a supply network in the total costs increases significantly. Therefore, supply networks in which production sites and customer sites are closely spaced improve their cost-effectiveness stronger than other regional configurations of supply networks.

Stochastic Cost-Profit Tradeoff Model for Locating an Automotive Service Enterprise

Facility location allocation (FLA) is considered as the problem of finding optimally a facility's location with the maximum customer satisfaction, the maximum profit of investors of the facility, and the minimum transportation cost of its oriented-customers. In practice, some factors of the FLA problem, i.e., customer demands, allocations, even locations of customers and facilities, are usually changing, and thus the problem features with uncertainty. To account for this uncertainty, some researchers have addressed the stochastic profit and cost issues of FLA. However, a decision-maker hopes to obtain the specific profit of investors of building facility and meanwhile to minimize the cost of target customers. To handle this issue via a more practical manner, it is essential to address the cost-profit tradeoff issue of FLA. Moreover, some region constraints can greatly influence FLA. By taking the vehicle inspection station as a typical automotive service enterprise example, this work presents new stochastic cost-profit tradeoff FLA models with region constraints. A hybrid algorithm integrating stochastic simulation and Genetic Algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm.

Locating recycling facilities for IT-based electronic waste in Turkey

Turkey passed a directive on waste electric and electronic equipments in May 2012, which has been in effect since May 2013. This directive, like its counterparts in other countries such as the one in the European Union, holds producers and distributors responsible for the collection and recovery of a wide variety of end-of-use or end-of-life electronic products discarded by the consumers. The collection and recovery activities have to be planned and carried out with the help of municipalities. The success of this directive lies in the establishment of an efficient reverse logistics network design for the collection and recovery operations. An important component of this initiative is to determine the locations and capacities of recycling facilities that will handle the returned products with the objective of minimizing the total cost of opening facilities, operating them, and transporting the discarded products from their collection points to the facilities. We develop a multi-period capacitated facility location-allocation model that is formulated as a mixed-integer linear program. Since there is uncertainty in the number of used products that will be collected from the consumers, we generate a set of scenarios and try to obtain a solution with the objective of minimizing the maximum regret associated with the occurrence of different scenarios. The developed mathematical programming model is solved using estimates of discarded personal computers, inkjet and laserjet printers in 15 major cities of Turkey during the period 2013–2018. These estimates are based on real data that consist of scrapped items of these three types of products from 2007 to 2012. The results indicate that the best locations for opening recycling facilities are the largest three cities of Turkey, i.e., Ankara, İstanbul, and İzmir. We perform additional experiments to quantify the benefit of constructing a multi-period model rather than a static one.

Fuzzy cost-profit tradeoff model for locating a vehicle inspection station considering regional constraints

Facility location allocation (FLA) is one of the important issues in the logistics and transportation fields. In practice, since customer demands, allocations, and even locations of customers and facilities are usually changing, the FLA problem features uncertainty. To account for this uncertainty, some researchers have addressed the fuzzy profit and cost issues of FLA. However, a decision-maker needs to reach a specific profit, minimizing the cost to target customers. To handle this issue it is essential to propose an effective fuzzy cost-profit tradeoff approach of FLA. Moreover, some regional constraints can greatly influence FLA. By taking a vehicle inspection station as a typical automotive service enterprise example, and combined with the credibility measure of fuzzy set theory, this work presents new fuzzy cost-profit tradeoff FLA models with regional constraints. A hybrid algorithm integrating fuzzy simulation and genetic algorithms (GA) is proposed to solve the proposed models. Some numerical examples are given to illustrate the proposed models and the effectiveness of the proposed algorithm.

The $$\alpha $$ α -cost minimization model for capacitated facility location-allocation problem with uncertain demands

Facility location-allocation problem aims at determining the locations of some facilities to serve a set of spatially distributed customers and the allocation of each customer to the facilities such that the total transportation cost is minimized. In real life, the facility location-allocation problem often comes with uncertainty for lack of the information about the customers' demands. Within the framework of uncertainty theory, this paper proposes an uncertain facility location-allocation model by means of chance-constraints, in which the customers' demands are assumed to be uncertain variables. An equivalent crisp model is obtained via the $$\alpha $$ ? -optimistic criterion of the total transportation cost. Besides, a hybrid intelligent algorithm is designed to solve the uncertain facility location-allocation problem, and its viability and effectiveness are illustrated by a numerical example.

Minimum risk facility location-allocation problem with type-2 fuzzy variables.

Facility location decision is basically viewed as a long-term strategy, so the inherited uncertainty of main parameters ought to be taken into account in order to make models applicable. In this paper, we examine the impact of uncertain transportation costs and customers' demands on the choice of optimal location decisions and allocation plans. This leads to the formulation of the facility location-allocation (FLA) problem as a fuzzy minimum risk programming, in which the uncertain parameters are assumed to be characterized by type-2 fuzzy variables with known type-2 possibility distributions. Since the inherent complexity of type-2 fuzzy FLA may be troublesome, existing methods are no longer effective in handling the proposed problems directly. We first derive the critical value formula for possibility value-at-risk reduced fuzzy variable of type-2 triangular fuzzy variable. On the basis of formula obtained, we can convert original fuzzy FLA model into its equivalent parametric mixed integer programming form, which can be solved by conventional numerical algorithms or general-purpose software. Taking use of structural characteristics of the equivalent optimization, we design a parameter decomposition method. Finally, a numerical example is presented to highlight the significance of the fuzzy FLA model. The computational results show the credibility and superiority of the proposed parametric optimization method.

Fuzzy Possibilistic Modeling for Closed Loop Recycling Collection Networks

Nowadays, in the competitive market, commercial companies due to their economic problems and also restrictions imposed by international organizations to comply with environmental regulations are making noticeable efforts to reduce the level of their wastes in their manufacturing systems and consequently the level of waste in consumers’ products. In harmony with this issue, one of the most effective ways which has successfully been used and proven to be economically profitable is to take products lifecycles into consideration. Consideration of products lifecycle has made supply chain practitioners to investigate the reverse logistics activities in addition to forward logistics activities. Hence, corporations, in order to reduce their cost on the one hand and boosting their efficiency on the other hand, were obligated to employ closed loop supply chain models to concurrently benefit from its economical and environmental advantages. Therefore, in this paper, a mixed nonlinear facility location–allocation model is proposed for recycling collection centers. The considered closed loop logistics model consists of multiple echelons, multiple suppliers, multiple collection centers, multiple time period and also multiple facilities. In real-life problems parameters like demand, cost, capacity, distances, and quantities of returned products are always uncertain. Therefore, in order to solve a realistic problem, foregoing parameters are considered as fuzzy in our proposed model. Subsequently, to solve fuzzy mixed nonlinear programming model, one of the most effective technique in the literature is used. Additionally, in order to demonstrate the behavior of the parameters employed in the model, a comprehensive sensitivity analysis is conducted. Computational results demonstrate that the proposed model can show favorable efficiency in solving supply chain problems.

Pemodelan Lokasi-Alokasi Terminal Bahan Baku untuk Meminimasi Total Biaya Rantai Pasok pada Industri Produk Jadi Rotan

In Surakarta and its surroundings (Solo Raya), there exists more than 400 Small-Medium Enterprises (SMEs) that produce finished products of rattan. Due to the high procurement cost as total operational cost of raw rattan distribution network from the sources, e.g. Borneo and Celebes to the rattan industries, the competitiveness of their products is lower than products from other countries. Therefore, to overcome the problem, those costs should be reduced for example by aggregating the demand of rattan industries. This police needs to be supported by the warehouse for storing and distributing raw rattan. Hence, this research aims to develop a facility location-allocation model so that total supply chain costs, including raw rattan procurement costs as well as facilities opening and closing costs can be minimized. The location-allocation model will be presented in a mixed integer non-linear programming model and solved using MS-Excel Solver. The results are number and location of the warehouse along with the raw rattan allocation in the warehouse to ensure raw rattan supply for rattan industries.

Locating collection centers for incentive-dependent returns under a pick-up policy with capacitated vehicles

We address the problem of locating collection centers of a company that aims to collect used products from product holders. The remaining value in the used products that can be captured by recovery operations is the company’s motivation for the collection operation. We assume that a pick-up strategy is in place according to which vehicles with limited capacity are dispatched from the collection centers to the locations of product holders to transport the returns. Each product holder has an inherent willingness to return, and makes the decision on the basis of the financial incentive offered by the company. The incentive depends on the condition of the returned item referred to as return type. We formulate a mixed-integer nonlinear facility location-allocation model to find both the optimal locations of a predetermined number of collection centers and the optimal incentive values for different return types. Since the problem is NP-hard, we propose a heuristic method to solve medium and large-size instances. The main loop of the method is based on a tabu search method performed in the space of collection center locations. For each location set prescribed by tabu search, Nelder–Mead simplex search is called to obtain the best incentives and the corresponding net profit. We experiment with different quality profiles when there are two and three return types, and observe the effect of the uniform incentive policy (UIP) in which the same incentive is offered to product holders regardless of the quality of their returns. We conclude that the UIP is inferior to the quality-dependent incentive policy resulting in a higher profit loss when the proportion of lowest quality returns is relatively high.

Locating collection centers for distance- and incentive-dependent returns

We address the problem of locating collection centers of a company that aims to collect used products from consumers. The remaining value in the used products that can be captured by recovery operations is the company's main motivation for the collection operation. We assume that a drop-off strategy is in place according to which product holders travel to the collection centers established by the company for returning their used goods. Each product holder has an inherent willingness to return, and decides whether or not to return based on both the financial incentive offered by the company for the returned item and the proximity to the nearest collection center. Returned items are categorized with respect to their quality level into classes called return types, and a different incentive is offered for each return type. We formulate a mixed-integer nonlinear facility location-allocation model to determine both the optimal locations of the collection centers and the optimal incentive values for each return type so as to maximize the profit from the returns. Since the problem is NP -hard, we propose a nested heuristic method to solve medium- and large-sized instances. The outer loop of the heuristic is based on a tabu search implementation in the space of collection center locations. The inner loop performs Fibonacci search to obtain the best incentives in accordance with the collection center locations found in the outer loop. The performance of the proposed solution method has been demonstrated with computational experimentation.