Location-allocation Problem Research Articles

The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization

Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000) to stations in a discrete space in a GIS, assuming that the stations’ capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions.

Resilient Network Design in a Location-Allocation Problem with Multi-Level Facility Hardening

There are many sources of risk affecting the network elements may lead to network failure, so planners need to consider them in the network design. One of the most important strategies for disruption risk management is the static resilience. In this strategy, the network functionality is maintained after the disruption event by the prevention and hardening actions. In this paper, a resilient capacitated fixed-charge location-allocation model is proposed. Both facility hardening and equipping of the network to backup facilities for disrupted elements are considered together to avoid supply network failure due to random disruption. Facilities are decided to be hardened in multiple levels before disruption events. The problem is formulated as a non-linear integer programming model, then its equivalent linear form is presented. A Lagrangian decomposition algorithm (LDA) is developed to solve large-scale instances. Computational results confirm the efficacy of the proposed solution approach comparing to classical solution approaches in large-scale problems. Moreover, the superiority of the proposed model is confirmed by comparing to the classical models.

A Novel Algorithm for Identifying and Analyzing Humanitarian Relief Logistics Problems: Studying Uncertainty on the Basis of Interaction with the Decision Maker

Integration of decision-making in relief logistics intends establishing a better condition in fast response. Also, regarding the unpredictability of relief demand, uncertain conditions should be studied in a more applicable way of the planning process. This paper investigates a comprehensive novel algorithm in order to analyze a multi-level location-allocation routing emergency problem in uncertain conditions considering a fast and stable response to the different situations. Due to this reason, system dynamics and its control are studied. In the presented model, the demand is simulated by Mont Carlo simulation. Sum of allocated resources is determined by the expected value of Poisson’s distribution based on the approximation of its confidence interval. This model is capable to cover all demands existing in the network. Moreover, allocation of ambulances is considered based on the fuzzy cost. A simulated annealing (SA) algorithm is developed to solve the problem. The obtained results show that the proposed algorithm has good performance and it has been found out that total cost of the emergency system decreases with the increase of covering of stochastic demands, and the responsiveness in each scenario increases. However, the proposed algorithm is highly able to recognize the problem space to create a more precise and adequate description of the problem regarding the conditions.

Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network

In the last few decades, production and procurement of food grain in India have steadily increased, however, storage capacity has not increased proportionally. The government of India (GOI) is establishing the various capacitated silos across the country to bridge this storage capacity gap. This paper presents a novel integrated multi-objective, multi-modal and multi-period mathematical model for grain silo location-allocation problem with Dwell time to support the decision-making process of GOI. Two conflicting objectives-minimization of total supply chain network cost and total lead time (transit and dwell time) are simultaneously optimized using two Pareto based multi-objective algorithms with calibrated parameters.

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.

Solving location allocation problem of waste recovery facilities through the use of network-based spatial analysis

Solving location allocation problem of waste recovery facilities through the use of network-based spatial analysis

A multi-objective optimization based method for evaluating earthquake shelter location–allocation

ABSTRACTConstructing shelters is an important part of earthquake disaster management which involves selecting the location of them and assigning the evacuees to them. For this work, selecting suitable objectives and solution is important. Thus, in this paper, a multi-objective mathematical model with four groups of the objectives, allied with a modified particle swarm optimization algorithm, has been developed to solve the location–allocation problem for earthquake shelter. The four objective groups are: total shelter number (TSN) and total evacuation distance (TED), TSN and total weighted evaluation time (TWET), total shelter area (TSA) and TED, and TSA and TWET. The solutions of the model include the determination of the shelters from the candidates and how to allocate population to them. Then the solutions of the model with four objective groups are given and compared using safety, capacity and investment evaluation index with the case of Chaoyang district of Beijing, China. Related to government's preferences and future city planning, the most suitable model solutions can be chosen to help decide where it is suitable to construct shelters and how to allocate the population.

Fuzzy Dynamic Location-Allocation Problem with Temporary Multi-Medical Centers in Disaster Management

Disaster follows a large number of injured people in a short period of time. Emergency Medical Service (EMS) cannot respond them with normal facilities, which needs to allocate temporary emergency stations for covering patients. Also, the normal facilities and hospital may encounter with damages. The aim of this study is to maximal-covering patients in a disaster situation by considering the priority to seriously injured people to treatment. For this purpose, a new fuzzy dynamic bi-objective location-allocation model with a multi-medical server and queuing M/M/c framework is introduced. Patients are responded and categorized into four types of different priority. Because of changing the patient type, the model will be dynamic. It can also accept new patients, and medical servers can displace among temporary emergency stations according to demands of areas in each period. The first objective is to minimize the construction cost of temporary emergency stations and the rate of losing patients, in which EMS cannot respond. The second objective is to minimize the travel time of patients and queue time simultaneously (i.e., minimize the response time). The augmented ε-constraint method and non-dominated sorting genetic algorithm (NSGA-II) are used to validate and solve the model for the given problems. Finally, this study considers preparedness and response phases in the EMS system of the Tehran city.

A location-allocation model for sustainable NdFeB magnet recovery under uncertainties

Neodymium-iron-boron (NdFeB) magnets play a critical role in clean power products, e.g., electric vehicles and wind turbines. Since China has near monopolistic control of the supply of these magnets, many parties are interested in recovering end-of-life magnets for additional use cycles. Such a strategy requires a cost-effective approach to collect and process used magnets while maximizing the economic and environmental benefits. This paper employs fuzzy logic and non-dominated sorting genetic algorithm (NSGA-II) to solve a location-allocation problem for NdFeB magnet recovery under supply and demand uncertainties. A Pareto front is constructed to evaluate the performance of the proposed design.

AN INVESTIGATION OF THE RELATIONSHIP BETWEEN FACILITY LOCATION DECISIONS, SERVICE LEVEL AND DISTRIBUTION COSTS: A PROPOSED MODEL FOR INDONESIAN LPG SUPPLY CHAIN

This paper proposes models of Indonesian multi-echelon LPG distribution system by opening new facilities (new echelon) taking into account the current facilities and investigates the implications of the proposed models to distribution components such as transportation, inventory and shortage decisions. The objectives are to determine the optimum location and allocation of LPG facilities and investigate the relation between distribution costs such as transportation, inventory cost and location decisions in Indonesian multi-echelon LPG supply chain. Capacitated fixed-charged facility location-allocation problem is used to determine the optimal solution of the proposed model. The output of the optimal solutions reported that the costs associated to the proposed distribution are lower than the associated costs in the existing distribution system. It is noted that there is US$ 106.57 million saving per year of total cost by applying the proposed distribution system. In the sensitivity analysis, it is indicated that the trade-offs between facility locations and distribution costs are exist. Results report that as the number of facility increases, total transportation and inventory cost also increase.Keywords: Facility Location; Allocation; Inventory; Transportation.

Optimal number and location of storage hubs and biogas production reactors in farmlands with allocation of multiple feedstocks

• Multi-residue, multi-hub, multi-reactor location-allocation problem for biogas generation. • Optimized allocation of feedstock to storage hubs in farmlands for biogas generation. • Developed a MINLP problem for optimal location of storage hubs and biogas reactors. • Developed a novel heuristic to improve efficiency as compared to optimal solution. • The methods have great significance in bio-methane gas generation and economy. This paper focuses on the problem for designing a logistics system for bio-methane gas (BMG) production. In practice, farm residues such as crop residue, wood residue, and livestock manure are used in reactors as reactants to generate BMG. A multi-residue, multi-hub, multi-reactor location-allocation model is developed to design the logistics of BMG production system. Both the hubs’ and reactors’ locations, and the residue's distribution plan are investigated to minimize the total construction and logistical cost. The costs of construction, transportation, feedstocks and labor are taken into consideration to reflect the lifecycle cost of the entire undertaking. In this paper, a mixed-integer nonlinear problem is proposed to simulate a BMG production supply chain. In addition to the optimal solution methods, a search-based heuristic was also proposed to determine the locations of hubs and reactors for large instances and along with a proper allocation of residues that are transported from the farms to the hubs to the reactors. Several numerical examples are tested to evaluate the performance of the heuristic as well.

Capacitated Maximal Covering Location Allocation Problem During Flood Disaster

Capacitated Maximal Covering Location Allocation Problem During Flood Disaster

A comparison of scenario-based hybrid bilevel and multi-objective location-allocation models for earthquake emergency shelters: a case study in the central area of Beijing, China

ABSTRACTThe accurate location and allocation of disaster emergency shelters are key components of effective urban planning and emergency management. Various models have been developed to solve the location-allocation problem, but gaps remain with regard to model realism and associated applicability. For the available location and allocation models of earthquake emergency shelters, uncertainty with respect to earthquake hazard, population exposure, rate of damage to buildings and the effects of evacuee behavior are often neglected or oversimplified. Moreover, modifying the models can be an alternative means of improving the solution quality when the optimization algorithm has difficulty coping with a complex, high-dimensional problem. This article develops a scenario-based hybrid bilevel model that addresses the concerns related to high-dimensional complexity and provides a higher degree of realism by incorporating the uncertainties of population dynamics and earthquake damage scenarios into location-allocation problems for earthquake emergency shelters. A modified particle swarm optimization algorithm combined with a simulated annealing algorithm was applied to derive solutions using the hybrid bilevel model and a conventional multi-objective model, and the solutions obtained using the two models were then compared. The novel features of the study include the hybrid bilevel model that considers the dynamic number of evacuees and its implementation for earthquake emergency shelter location and allocation. The results show that the solutions significantly differ between daytime and nighttime. When applied to the multi-objective model, the optimization algorithm is time consuming and may only find the local optima and provide suboptimal solutions in the considered scenarios with more evacuees. By contrast, the hybrid bilevel model shows more desirable performance because it significantly reduces the dimensionality of the location-allocation problem based on a two-step-to-reach approach. The proposed hybrid bilevel model is proven to be useful for optimal shelter allocation, and the presented results can be used as a reference for balancing the interests of the government and residents during the planning of shelters in Beijing.

A cost-effective decision-making algorithm for an RFID-enabled HMSC network design

PurposeThe purpose of this paper is to investigate the economic feasibility of a three-echelon Halal Meat Supply Chain (HMSC) network that is monitored by a proposed radio frequency identification (RFID)-based management system for enhancing the integrity traceability of Halal meat products and to maximize the average integrity number of Halal meat products, maximize the return of investment (ROI), maximize the capacity utilization of facilities and minimize the total investment cost of the proposed RFID-monitoring system. The location-allocation problem of facilities needs also to be resolved in conjunction with the quantity flow of Halal meat products from farms to abattoirs and from abattoirs to retailers.Design/methodology/approachFirst, a deterministic multi-objective mixed integer linear programming model was developed and used for optimizing the proposed RFID-based HMSC network toward a comprised solution based on four conflicting objectives as described above. Second, a stochastic programming model was developed and used for examining the impact on the number of Halal meat products by altering the value of integrity percentage. The ε-constraint approach and the modified weighted sum approach were proposed for acquisition of non-inferior solutions obtained from the developed models. Furthermore, the Max-Min approach was used for selecting the best solution among them.FindingsThe research outcome shows the applicability of the developed models using a real case study. Based on the computational results, a reasonable ROI can be achievable by implementing RFID into the HMSC network.Research limitations/implicationsThis work addresses interesting avenues for further research on exploring the HMSC network design under different types of uncertainties and transportation means. Also, environmentalism has been becoming increasingly a significant global problem in the present century. Thus, the presented model could be extended to include the environmental aspects as an objective function.Practical implicationsThe model can be utilized for food supply chain designers. Also, it could be applied to realistic problems in the field of supply chain management.Originality/valueAlthough there were a few studies focusing on the configuration of a number of HMSC networks, this area is overlooked by researchers. The study shows the developed methodology can be a useful tool for designers to determine a cost-effective design of food supply chain networks.

A capacitated bike sharing location-allocation problem under demand uncertainty using sample average approximation: A greedy Genetic-Particle Swarm Optimization algorithm

This paper considers a stochastic location-allocation problem for a capacitated bike sharing system (S-L&A-CBSS), in which a bike demand is uncertain. To tackle this uncertainty, a sample average approximation (SAA) method is used. Because this problem is an NP-hard problem, a hybrid greedy evolutionary algorithm based on genetic algorithm (GA) and particle swarm optimization (PSO), namely greedy GA-PSO is embedded in the SAA method in order to solve the given large-sized problems. The performance of the proposed hybrid algorithm is tested by a number of numerical examples and used for empirical test based on Tehran business zone. Furthermore, the associated results show its efficiency in comparison to an exact solution method in solving small-sized problems. Finally, the conclusion is provided.

A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem

Reverse logistics (RL) emerges as a hot topic in both research and business with the increasing attention on the collection and recycling of the waste products. Since Location and Routing Problem (LRP) in RL is NP-complete, heuristic algorithms, especially those built upon swarm intelligence, are very popular in this research. In this paper, both Vehicle Routing Problem (RP) and Location Allocation Problem (LAP) of RL are considered as a whole. First, the features of LRP in RL are analyzed. Second, a mathematical model of the problem is developed. Then, a novel discrete artificial bee colony (ABC) algorithm with greedy adjustment is proposed. The experimental results show that the new algorithm can approach the optimal solutions efficiently and effectively.

Geographic Information System Based Solution for Location Allocation Problem for Finding High Quality Service Locations

Location-Allocation Problem (LAP) is a combinatorial optimising network problem which has been widely studied by operational researchers due to its many practical applications. In real life, it is usually very hard to present the customer’s demands in a precise way and thus they are estimated from historical data. There are so many types of the methods like exact, heuristic and metaheuristic methods to solve this problem to get the optimal and near optimal solutions. In this study, a metaheuristic approach is applied in GIS environment which gives quick and near optimal solution. To achieve this, one case study based on supply and demand of milk from Vita Distributors to Vita Booths in Hisar City, Haryana have been performed. In this study, the effectiveness and robustness of the metaheuristic algorithm was tested over a GIS geodatabase based network dataset consisting of road network (line features) and facility and customers locations (point features). The performance of the algorithm was also checked for two types of impedance factors i.e. time and distance. The results of this location-allocation problem are very much satisfactory in term of minimization of total transportation cost in providing high quality service locations (Vita Booths) for milk distribution.

Minimising emergency response time of ambulances through pre-positioning in Dhaka city, Bangladesh

ABSTRACTThis study conducted a large-scale survey in Dhaka, Bangladesh; the survey involved 95 major hospitals, more than 3000 emergency room patients, and 2 of the largest ambulance operators. Currently, most ambulances are parked within the vicinity of hospitals and are either dispatched or fetched by the acquaintances of the patient on demand, resulting in lengthy round trips. Reducing the response time of ambulances would certainly improve the emergency service, and pre-positioning of the ambulances could be a solution to reducing the response time. This study used two approaches to address the problem. First, the location-allocation problem was solved to find the optimal number of ambulance locations by maximising the demand coverage. Second, separate location-allocation for the peak and off-peaks, using K-means clustering, was applied to systematically optimise the ambulance positioning in small clusters near demand points. These approaches could substantially improve the existing emergency response time. Distributing ambulances near demand points yielded greater improvements in response time than when the ambulances are stationed near hospitals.

A versatile adaptive aggregation framework for spatially large discrete location-allocation problems

We propose a versatile concept of the adaptive aggregation framework for the facility location problems that keeps the problem size in reasonable limits. Most location-allocation problems are known to be NP-hard. Thus, if a problem reaches the critical size, the computation exceeds reasonable time limits, or all computer memory is consumed. Aggregation is a tool that allows for transforming problems into smaller sizes. Usually, it is used only in the data preparation phase, and it leads to the loss of optimality due to aggregation errors. This is particularly remarkable when solving problems with a large number of demand points. The proposed framework embeds the aggregation into the solving process and it iteratively adjusts the aggregation level to the high quality solutions. To explore its versatility, we apply it to the p-median and to the lexicographic minimax problems that lead to structurally different patterns of located facilities. To evaluate the optimality errors, we use benchmarks which can be computed exactly, and to explore the limits of our approach, we study benchmarks reaching 670,000 demand points. Numerical experiments reveal that the adaptive aggregation framework performs well across a large range of problem sizes and is able to provide solutions of higher quality than the state-of-the-art exact methods when applied to the aggregated problem.

A modelling approach for city locating logistic platforms based on combined forward and reverse flows

Logistics platforms and reverse logistics are the most used solutions addressing daily problems in urban areas such as noise, pollution and cost. Their main goal is to ensure sustainable development in modern cities. The paper proposes an approach based on forward and reverse flows in urban logistic. To that end, we present a location-allocation of logistics platforms in a reverse logistics system. We, then, locate allocation and evaluate their effect on logistic platforms. In doing so, we present a mathematical model of the city logistics platform to specify location-allocation problem. Our model considers both flows: urban freight and reverse logistics. Subsequently, we improve city logistics platform’s financial situation by reducing operating costs. Finally, we use a numerical algorithm for optimization and illustration purposes.