Model For Location-routing Problem Research Articles

Soft computing for hazardous waste routing in Malaysia: a review

<span lang="EN-US">Nowadays, a significant number of researchers are focusing on utilizing soft computing approaches to address the issue of scheduling in applications concerned with hazardous waste management. In Malaysia, there is thoughtless awareness of the management of hazardous waste, even though the production of wastes in hazardous domains at the industrial and domestic levels has been rising lately. According to previous research findings, the location routing problem (LRP) can be designated as one of the models closer to the actual situation, evaluating the most suitable and optimal location for establishing facilities and utilizing transportation for pick-up and distribution. Recent studies have <span>focused on enhancing the LRP model, and its methodologies approach to solve the waste management problem in hazardous domains. In this paper, a comprehensive review of the better promising and practicable mathematical model of LRP and its methodology approach is discussed, as well as an analysis of the publishing pattern and the trend of research over the preceding five years and more, as retrieved from the web of science (WoS) database. In conclusion, this research is significant in ensuring the effectiveness of reliable mathematical model development and suitable methodologies in the future for solving hazardous waste management</span> problems.</span>

A hybrid metaheuristic algorithm for the multi-objective location-routing problem in the early post-disaster stage

<p style='text-indent:20px;'>Disasters such as earthquakes, typhoons, floods and COVID-19 continue to threaten the lives of people in all countries. In order to cover the basic needs of the victims, emergency logistics should be implemented in time. Location-routing problem (LRP) tackles facility location problem and vehicle routing problem simultaneously to obtain the overall optimization. In response to the shortage of relief materials in the early post-disaster stage, a multi-objective model for the LRP considering fairness is constructed by evaluating the urgency coefficients of all demand points. The objectives are the lowest cost, delivery time and degree of dissatisfaction. Since LRP is a NP-hard problem, a hybrid metaheuristic algorithm of Discrete Particle Swarm Optimization (DPSO) and Harris Hawks Optimization (HHO) is designed to solve the model. In addition, three improvement strategies, namely elite-opposition learning, nonlinear escaping energy, multi-probability random walk, are introduced to enhance its execution efficiency. Finally, the effectiveness and performance of the LRP model and the hybrid metaheuristic algorithm are verified by a case study of COVID-19 in Wuhan. It demonstrates that the hybrid metaheuristic algorithm is more competitive with higher accuracy and the ability to jump out of the local optimum than other metaheuristic algorithms.</p>

Optimization Model of Location Routing Problem for Disaster Relief Distribution

Disaster relief distribution is a very important component in the overall disaster response process. Consideration of limited funds, time pressure and surge of demand that come together increase to the complexity of the distribution process that must be done in a short time. Meanwhile, delays in the delivery of relief can lead to a decrease in the level of safety and welfare of disaster-affected victims. This paper proposes a location routing problem optimization model for disaster relief distribution with a multi-objective approach that minimizes waiting time and total costs. This model can help decision makers to determine the number and location of distribution centers which are opened and optimal vehicle routes.

A multi-objective location-routing problem model for multi-device relief logistics under uncertainty using meta-heuristic algorithm

During natural and abnormal accidents, many people are injured, and a large number of wastes and rubbish are produced, so it is necessary to collect the injured and take them to treatment centers, which must be done in the reaction phase. Also, in the recovery and reconstruction phase, since a large amount of hazardous and non-hazardous waste is produced during accidents, effective measures should be taken to collect and recycle them if necessary. Both of these cases can be considered as a reverse logistics problem. This paper investigates reverse logistics planning in the response, improvement, and reconstruction phases in earthquake conditions. Due to the nature of the problem, it is expected that we will face a multi-objective problem, and the problem condition causes the issue of uncertainty. By increasing the dimensions of the problem, the NSGA-II meta-heuristic algorithm has been used to solve the two-objective model of the problem and the result indicates that the proposed solution algorithm works well and the quality of the answer and its solution time are appropriate. The results indicate that as capacity increases, the number of distribution centers built to meet demand decreases and the distribution center constructed may be far from some shelters, leading to increased transportation costs. According to the mentioned issues, this research uses reverse logistics in the response and recovery phases. Also, information about Tehran city will be used as data for the case study.

Depot Location Analysis for Capacitated Vehicle Routing Problem: A Case Study of Solid Waste Management

This paper presents an optimized solution to locate a depot on multiobjective instances of Capacitated Vehicle Pouting Problem (CVRP) using firefly algorithm (FA). The main objective of a depot location routing problem (LRP) is to obtain the optimal position to locate a depot in other to serve a set of customers ensuring the minimum possible total travelled distance across a search space. In this paper, the instances of solid waste management were created to simulate a real-life scenario of CVRP. This was formulated into a multiobjective optimization problem considering various depot positioning. Firefly Algorithm (FA) which is a metaheuristic technique was employed to navigate the travel path towards an optimal depot placement for solving the LRP model. Various depot positions which includes random, optimized, centered and eccentric were evaluated. Results showed that the optimized depot positioning approach obtained the best depot position as against the other possible positions. Results when compared with two metaheuristics approach Unified Hybrid Genetic Search (UHGS) and Iterated Local Search with Set Partitioning (ILS-SP) presented in literature also showed that, optimized depot positioning obtained the best results and FA can compete effectively with other metaheuristics approaches.

Scenarios for handling the impact of COVID-19 based on food supply network through regional food hubs under uncertainty

This paper discusses an optimization model for handling the impact of the COVID-19 pandemic based on food supply network through regional food hubs (RFHs) under uncertainty. To this end, uncertainty is assumed in the demand and production data. During the Pandemic COVID-19 period, uncertainty has increased and the food supply chain system has changed. Thus, a new configuration of the food supply network requires analysis. In this paper, the concept of RFH is introduced to connect producers in rural areas and customers in urban areas. This paper determines the location and capacity of RFHs, the food supply network, the sum of maximum food supplies, and minimum logistics cost. This is done via a Multi-Objective Many-to-Many Location-Routing Problem model. Furthermore, since the conditions of the COVID-19 pandemic is uncertain, robust optimization is employed to handle uncertainties. During the current pandemic, red zones are defined to indicate the severity of the pandemic in a region. In this paper, the numerical experiment is considered for three scenarios: when a region is in large-scale social distancing, partial social distancing, or normal conditions. This social distancing situation is based on the defined red zones. The optimal food supply network is obtained for the three scenarios and the best scenario among the three is identified.

The Model for Location Routing Problem with Roaming Delivery Locations

The Location Routing Problem with Roaming Delivery Locations (LRPRDL) is a model that represents company activities in delivering products to final customers. Direct delivery to final customers has increased significantly over the growth of e-commerce in the world. E-commerce or business-to-customer companies are urged to increase their last-mile distribution efficiency to survive in the global competition. For that purpose, the LRPRDL model was proposed to increase the efficiency of the company’s last-mile distribution. The model aims to minimize the sum of open depots and transportation costs by determining the number and location of depots and the shipping routes. The LRPRDL was implemented in an instance with four depot candidates, 15 customers, and six vehicles. The instance was solved to the optimality by using a public solver Gurobi. Furthermore, this research conducted a sensitivity analysis on the open depots and fuel costs, customer demand, and radius. The study indicated that customer’s demand and radius have a significant impact on the purchase decision.

Location Routing Problem with Consideration of CO2 Emissions Cost: A Case Study

Location and routing are the main critical problems investigated in a logistic. Location-Routing Problem (LRP) involves determining the location of facilities and vehicle routes to supply customer's demands. Determination of depots as distribution centers is one of the problems in LRP. In LRP, carbon emissions need to be considered because these problems cause global warming and climate change. In this paper, a new mathematical model for LRP considering CO2 emissions minimization is proposed. This study developed a new Mixed Integer Linear Programming (MILP) model for LRP with time windows and considered the environmental impacts. Finally, a case study was conducted in the province of Central Java, Indonesia. In this case study, there are three depot candidates. The study results indicated that using this method in existing conditions and constraints provides a more optimal solution than the company's actual route. A sensitivity analysis was also carried out in this case study.

The HSABA for Emergency Location-Routing Problem

This article presents a Location-Routing Problem (LRP) model to assist decision makers in emergency logistics. The model attempts to consider the relationship between the location of warehouses and the delivery routes in order to maximize the rescue efficiency. The objective function of the minimization of time and cost is established in the single-stage LRP model considering different scenarios. The hybrid self-adaptive bat algorithm (HSABA) is an improved nature-inspired algorithm for solving this LRP model, hard optimization problem. The HSABA with self-adaptation mechanism and hybridization mechanism effectively improves the defect of the original BA, that is, trapping into the local optima easily. An example is provided to prove the effectiveness of our model. The studied example shows that the single-stage LRP model can effectively select supply locations and plan rescue routes faced with different disasters and the HSABA outperforms the basic BA.

Location-Routing Problem for Relief Distribution in the Early Post-Earthquake Stage from the Perspective of Fairness

To address the shortage of relief in disaster areas during the early stages after an earthquake, a location-routing problem (LRP) was studied from the perspective of fairness. A multi-objective model for the fair LRP was developed by lexicographic order object optimal method in consideration of the urgent window constraints, partial road damage, multimodal relief delivery, disaster severity, and vulnerability of each demand node when its demand is not satisfied. The goals of this model are to minimize (1) the maximum loss of demand node, (2) the total loss of demand node, and (3) the maximum time required for the demand node to receive relief. A hybrid heuristic algorithm was proposed to solve the model. Finally, the utility and fairness of the model and algorithm were demonstrated by a case study during the first day after the great Wenchuan earthquake in China.

Location routing problem with delivery modes

To realise efficient delivery, the location routing problem (LRP), which determines the locations of the facility and the vehicle routes, needs to be solved. Previous studies have attempted to improve the delivery efficiency of a route delivery, which is an efficient delivery mode if customers are densely located. However, if customers are sparsely located, route delivery is not efficient because the fixed costs of the vehicle become too expensive. In this case, courier delivery, i.e., delivery by a third party, is more efficient than route delivery because the courier delivery costs are proportional to the weight of the delivery amount and delivery distance. In this paper, we extended the LRP model to add the courier delivery model. Our model can select a suitable delivery mode for each customer by evaluating the trade-off between the courier delivery costs and the route delivery costs. The results of numerical experiments are shown.

Finding of urban rainstorm and waterlogging disasters based on microblogging data and the location-routing problem model of urban emergency logistics

Due to the climate change and the rapid progress of urbanization, extreme weather disasters such as urban rainstorm and waterlogging are frequent. Therefore, how to find the waterlogging points in the presence of disasters and how to optimize the distribution of urban emergency logistics and reduce the negative impact of disasters have become a hot and difficult issue for government departments and scholars. First of all, the idea and method of using the big data of microblogging to obtain urban rainstorm and waterlogging disasters and public sentiment are put forward. In addition,this thesis constructed the location-routing problem model of urban emergency logistics in the situation of rainstorm and waterlogging disaster, and found out the dynamic emergency distribution path of Nanjing in the situation of waterlogging disaster by using NSGA-III algorithm. Research shows that the risk management of urban rainstorm and waterlogging disasters, together with social media data, is a feasible way to obtain on-site data of disasters and carry out risk assessment of disasters. At the same time, the emergency logistics location-positioning model and algorithm can provide a reference for similar disaster emergency logistics distribution network and the conclusion can provide empirical reference for cities to cope with rainstorm and waterlogging disasters.

Metodología para crear rutas alimentadoras en sistemas de transporte masivo

Se propone una metodología para identificar rutas alimentadoras en zonas no conectadas para un sistema de transporte masivo, con el fin de aumentar la cobertura del servicio y mejorar el nivel de ocupación del sistema. La metodología propuesta consta de dos etapas: 1) estructurar escenarios de áreas no conectadas al sistema de transporte y 2) combinar técnicas heurísticas y exactas para resolver el problema de rutas alimentadoras. La metodología considera dentro de sus restricciones la duración de la ruta y la capacidad del vehículo alimentador. Para su modelamiento se establece una analogía entre los problemas del transporte de pasajeros y el problema de localización y ruteo, Location Routing Problem (LRP), que usualmente es aplicado a problemas de transporte de mercancías. La metodología de solución propuesta es una matheurística que combina las heurísticas Lin-Kernighan-Helsgaun (LKH) y ahorros con el algoritmo de ramificación y corte, Branch-and-Cut, aplicado sobre un modelo lineal entero mixto de partición de conjuntos (Set Partitioning) para LRP. Esta propuesta metodológica es validada con casos de prueba reales del sistema de transporte masivo de la ciudad de Pereira (Megabús), donde se consideran algunas zonas no conectadas del Área Metropolitana Centro Occidente, localizada en el eje cafetero colombiano.

Solving a bi-objective mathematical model for location-routing problem with time windows in multi-echelon reverse logistics using metaheuristic procedure

During the last decade, the stringent pressures from environmental and social requirements have spurred an interest in designing a reverse logistics (RL) network. The success of a logistics system may depend on the decisions of the facilities locations and vehicle routings. The location-routing problem (LRP) simultaneously locates the facilities and designs the travel routes for vehicles among established facilities and existing demand points. In this paper, the location-routing problem with time window (LRPTW) and homogeneous fleet type and designing a multi-echelon, and capacitated reverse logistics network, are considered which may arise in many real-life situations in logistics management. Our proposed RL network consists of hybrid collection/inspection centers, recovery centers and disposal centers. Here, we present a new bi-objective mathematical programming (BOMP) for LRPTW in reverse logistic. Since this type of problem is NP-hard, the non-dominated sorting genetic algorithm II (NSGA-II) is proposed to obtain the Pareto frontier for the given problem. Several numerical examples are presented to illustrate the effectiveness of the proposed model and algorithm. Also, the present work is an effort to effectively implement the e-constraint method in GAMS software for producing the Pareto-optimal solutions in a BOMP. The results of the proposed algorithm have been compared with the e-constraint method. The computational results show that the e-constraint method is able to solve small-size instances to optimality within reasonable computing times, and for medium-to-large-sized problems, the proposed NSGA-II works better than the e-constraint.

A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study

This paper proposes a model for obnoxious waste location-routing problem (LRP) considering various types of wastes and several treatment technologies. The investigated distribution network includes three echelons of generation nodes, treatment and disposal facilities. A multi-objective LRP model is developed with three objective functions minimizing the treatment and disposal facility undesirability, different costs related to the problem, and eventually the risk associated with transportation of untreated materials. An effective memetic algorithm is developed in which a tabu search algorithm performs the local search. Comparison of exact and meta-heuristic methods run times confirms that the proposed method is effective. Eventually, the developed algorithm is tested on a real-life case study.

A new approach on auxiliary vehicle assignment in capacitated location routing problem

The location routing problem (LRP) considers locating depots and vehicle routing decisions simultaneously. In classic LRP the number of customers in each route depends on the capacity of the vehicle. In this paper a capacitated LRP model with auxiliary vehicle assignment is presented in which the length of each route is not restricted by main vehicle capacity. Two kinds of vehicles are considered: main vehicles with higher capacity and fixed cost and auxiliary vehicles with lower capacity and fixed cost. The auxiliary vehicles can be added to the transportation system as an alternative strategy to cover the capacity limitations and they are just used to transfer goods from depots to vehicles and cannot serve the customers by themselves. To show the applicability of the proposed model, some numerical examples derived from the well-known instances are used. Moreover the model has been solved by some meta-heuristics for large sized instances. The results show the efficiency of the proposed model and the solution approach, considering the classic model and the exact solution approach, respectively.

A location-routing problem model with multiple periods and fuzzy demands

This paper puts forward a dynamic capacitated location-routing problem with fuzzy demands (DCLRP-FD). It is given on input a set of identical vehicles (each having a capacity, a fixed cost and availability level), a set of depots with restricted capacities and opening costs, a set of customers with fuzzy demands, and a planning horizon with multiple periods. The problem consists of determining the depots to be opened only in the first period of the planning horizon, the customers and the vehicles to be assigned to each opened depot, and performing the routes that may be changed in each time period due to fuzzy demands. A fuzzy chance-constrained programming (FCCP) model has been designed using credibility theory and a hybrid heuristic algorithm with four phases is presented in order to solve the problem. To obtain the best value of the fuzzy parameters of the model and show the influence of the availability level of vehicles on final solution, some computational experiments are carried out. The validity of the model is then evaluated in contrast with CLRP-FD’s models in the literature. The results indicate that the model and the proposed algorithm are robust and could be used in real world problems.

A New Formulation Approach for Location-Routing Problems

A Location-Routing Problem (LRP) combines two difficult problems, facility location and vehicle routing, and as such it is inherently hard to solve. In this paper, we propose a different formulation approach than the common arc-based product-flow (Arc-BPF) approach in the literature. We associate product amounts to the nodes of the network resulting in a node-based product-flow (Node-BPF) formulation. Our main objective is to develop LRP models with fewer constraints and variables, which can be solved more efficiently. To introduce the proposed approach, we reformulate a complex four-index Arc-BPF LRP model from the literature as a three-index Node-BPF model, which computationally outperforms the former. We then introduce a heuristic method.

Using clustering analysis in a capacitated location-routing problem

The location routing problem (LRP) appears as a combination of two difficult problems: the facility location problem (FLP) and the vehicle routing problem (VRP). In this work, we consider a discrete LRP with two levels: a set of potential capacitated distribution centres (DC) and a set of ordered customers. In our problem we intend to determine the set of installed DCs as well as the distribution routes (starting and ending at the DC). The problem is also constrained with capacities on the vehicles. Moreover, there is a homogeneous fleet of vehicles, carrying a single product and each customer is visited just once. As an objective we intend to minimize the routing and location costs. Several authors have integrated cluster analysis procedures in heuristics for LRPs. As a contribution to this direction, in this work several hierarchical and non-hierarchical clustering techniques (with several proximity functions) are integrated in a sequential heuristic algorithm for the above mentioned LRP model. All the versions obtained using different grouping procedures were tested on a large number of instances (adapted from data in the literature) and the results were compared so as to obtain some guidelines concerning the choice of a suitable clustering technique.