Facility Location-allocation Problem Research Articles

An integrated fuzzy QFD and fuzzy goal programming approach for global facility location-allocation problem

Companies pursuing extension of their activities and new companies in establishment phase are using various concepts and techniques to consider location decision, because location greatly affects both fixed and variable costs and on the overall profit of the company. This paper suggests a new use of quality function deployment (QFD) for facility location selection problem instead of applying it to traditional product quality promotion. Fuzzy sets concept is also incorporated to deal with imprecise nature of the linguistic judgments of decision makers. First, fuzzy QFD as a stand-alone approach is presented to address international facility location selection decision. To consider resource limitations and operational constraints, fuzzy goal programming is combined with fuzzy quality function deployment to present a developed approach to deal with global facility location-allocation decision. A demonstration of the applicability of proposed methodologies in a real-world problem is presented.

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.

Combining Geographic Information Systems for Transportation and Mixed Integer Linear Programming in Facility Location-Allocation Problems

In this study, we aimed to assess the solution quality for location-allocation problems from facilities generated by the software TransCAD® , a Geographic Information System for Transportation (GIS-T). Such facilities were obtained after using two routines together: Facility Location and Transportation Problem, when compared with optimal solutions from exact mathematical models, based on Mixed Integer Linear Programming (MILP), developed externally for the GIS. The models were applied to three simulations: the first one proposes opening factories and customer allocation in the state of Sao Paulo, Brazil; the second involves a wholesaler and a study of location and allocation of distribution centres for retail customers; and the third one involves the location of day-care centers and allocation of demand (0 - 3 years old children). The results showed that when considering facility capacity, the MILP optimising model presents results up to 37% better than the GIS and proposes different locations to open new facilities.

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.

Offshore oil network design with transportation alternatives

Petroleum industry plays a vital role in the modern global economy, owing to the fact that it is the largest source of energy in the world. Design and planning of the oilfield developments have become crucial concerns of the oil industry managers. In addition, crude oil transportation plays a critical role in the global supply chain of oil industry. In this work, we develop a model to design the oilfield development and plan crude oil transportation problems. A mixed integer model is proposed to extend the classical facility location-allocation problems by several features. Additionally, the model supports the selection of the transportation system and the planning of pipeline networks installations. This work ends with two sensitivity analyses. Two one-factor-at-a-time sensitivity analyses have been done with respect to the length of the planning horizon and the number of potential wells.

Solving a Capacitated <i>p</i><sub><i></i></sub>-Median Location Allocation Problem Using Genetic Algorithm: A Case Study

Facility location-allocation problems have various applications in private and public sectors. A capacitated p-median problem is considered in this work which is computationally NP-Hard. The primary goal of this paper was to determine a set of p-facilities location in which all demand points are allocated and its average distance traveled from the customers’ location to the selected p-facilities is minimized. In addition, the model also considered supplier’s allocation for p facilities. A real world case study has been addressed, and genetic algorithm which consists of crossover and mutation operators was proposed in order to solve the problem. Computational results for different values of p were generated, and finally the optimum solution based on minimum cost was reported.

A Capacitated Location-Allocation Model for Flood Disaster Service Operations with Border Crossing Passages and Probabilistic Demand Locations

Potential consequences of flood disasters, including severe loss of life and property, induce emergency managers to find the appropriate locations of relief rooms to evacuate people from the origin points to a safe place in order to lessen the possible impact of flood disasters. In this research, a p-center location problem is considered in order to determine the locations of some relief rooms in a city and their corresponding allocation clusters. This study presents a mixed integer nonlinear programming model of a capacitated facility location-allocation problem which simultaneously considers the probabilistic distribution of demand locations and a fixed line barrier in a region. The proposed model aims at minimizing the maximum expected weighted distance from the relief rooms to all the demand regions in order to decrease the evacuation time of people from the affected areas before flood occurrence. A real-world case study has been carried out to examine the effectiveness and applicability of the proposed model.

A network design model considering inventory cost from a third party logistics perspective

The importance of third party logistics (3PLs) service providers is increasing in supply chain management, particularly in warehousing and transportation services as clients expect 3PLs to improve lead times, fill rates, and inventory levels. Therefore, in a dynamic and uncertain business environment, 3PLs are under pressure to meet client service requirements. Few studies have addressed the problem of logistic network design from viewpoint of 3PLs. The main objective of this paper is to propose a new network design problem from 3PLs perspectives by considering inventory to determine economic order quantity. This paper proposes a new nonlinear mixed integer programming model for integrated logistics network design that in facility location-allocation and inventory location problems. To optimize the model, new auxiliary variables and constraints are added. The model was solved by using DICOPT solver.

Stochastic location-routing model and algorithm in emergency distribution by airlift

To optimize Location-Routing Problem(LRP) in post-earthquake emergency logistics systems,a stochastic optimization model with uncertain demand of relief commodities and breakage of road network was developed to determine the locations of distributing centers of relief commodities and relief distribution centers,as well as the relief airplane routes during relief process.According to the characteristics of the model,an improved genetic algorithm was proposed.And a special real-valued coding scheme,punishment function method and demand split strategy were adopted to deal with the restriction in the model.The results of a numerical example show that the proposed model and algorithm can resolve the facility location-allocation and airplane routing problem in post-earthquake emergency logistics systems efficiently.

Some optimal models for facility location–allocation problem with random fuzzy demands

Facility location–allocation (FLA) problem is widely used in practical life. Many papers have introduced the problem with the random or fuzzy demands of customers, but few give the case that the customers’ demands are random fuzzy. In this paper, the expected cost minimization model, ( α , β ) -cost minimization model and chance maximization model are presented with random fuzzy demands. In order to solve these random fuzzy models, the simplex algorithm, random fuzzy simulations and genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, some numerical examples are provided for the sake of illustration.

A study on facility location–allocation problem in mixed environment of randomness and fuzziness

In logistics system, facility location---allocation problem, which can be used to determine the mode, the structure and the form of the whole logistics system, is a very important decision problem in the logistics network. It involves locating plants and distribution centers, and determining the best strategy for allocation the product from the plants to the distribution centers and from the distribution centers to the customers. Often uncertainty may be associated with demand, supply or various relevant costs. In many cases, randomness and fuzziness simultaneously appear in a system, in order to describe this phenomenon; we introduce the concept of hybrid variable and propose a mixed-integer programming model for random fuzzy facility location---allocation problem. By expected value and chance constraint programming technique, this model is reduced to a deterministic model. Furthermore, a priority-based genetic algorithm is designed for solving the proposed programming model and the efficacy and the efficiency of this method and algorithm are demonstrated by a numerical example. Till now, few has formulated or attacked the FLA problems in the above manner. Furthermore, the techniques illustrated in this paper can easily be applied to other SCN problems. Therefore, these techniques are the appropriate tools to tackle other supply chain network problems in realistic environments.

Convergence of optimal solutions about approximation scheme for fuzzy programming with minimum-risk criteria

This paper presents a class of two-stage fuzzy programming with minimum-risk criteria in the sense of Value-at-Risk (VaR). Since the proposed two-stage fuzzy minimum-risk problem (FMRP) often includes fuzzy variable coefficients defined through possibility distributions with infinite supports, it is inherently an infinite-dimensional optimization problem that can rarely be solved directly. Thus, algorithm procedures for solving such an optimization problem must rely on soft computing and approximation schemes, which result in a finite-dimensional optimization problem. In this paper, we develop an approximation method to compute the objective function of the two-stage FMRP, and discuss the convergent results about the use of the approximation method in FMRP, including the convergence of the objective value, optimal value, and the optimal solutions. To apply the convergent results about the approximation method, we consider a two-stage fuzzy facility location-allocation (FLA) problem with VaR objective, and solve the problem indirectly by solving its approximating problem. Since the approximating fuzzy FLA problem is neither linear nor convex, conventional optimization algorithms cannot be used to solve it. In this paper, we design a hybrid particle swarm optimization (PSO) algorithm to solve the approximating fuzzy FLA problem. One numerical example with five facilities and ten customers is also presented to demonstrate the effectiveness of the designed algorithm.

Optimization of the facility location-allocation problem in a customer-driven supply chain

This paper develops and applies an integrated multiple criteria decision making approach to optimize the facility location-allocation problem in the contemporary customer-driven supply chain. Unlike the traditional optimization techniques, the proposed approach, combining the analytic hierarchy process (AHP) and the goal programming (GP) model, considers both quantitative and qualitative factors, and also aims at maximizing the benefits of deliverer and customers. In the integrated approach, the AHP is used first to determine the relative importance weightings or priorities of alternative locations with respect to both deliverer oriented and customer oriented criteria. Then, the GP model, incorporating the constraints of system, resource, and AHP priority is formulated to select the best locations for setting up the warehouses without exceeding the limited available resources. In this paper, a real case study is used to demonstrate how the integrated approach can be applied to deal with the facility location-allocation problem, and it is proved that the integrated approach outperforms the traditional costbased approach.

On the convergence of the Weiszfeld algorithm for continuous single facility location–allocation problems

A general family of single facility continuous location–allocation problems is introduced, which includes the decreasingly weighted ordered median problem, the single facility Weber problem with supply surplus, and Weber problems with alternative fast transportation network. We show in this paper that the extension of the well known Weiszfeld iterative decrease method for solving the corresponding location problems with fixed allocation yields an always convergent scheme for the location allocation problems. In a generic way, from each starting point, the limit point will be a locally minimal solution, whereas for each possible exceptional situation, a possible solution is indicated. Some computational results are presented, comparing this method with an alternating location–allocation approach.

Toward Finding an Optimal P2P Network Structure: A Stochastic Facility Location Allocation Approach

A stochastic model based on the Capacitated Facility Location Allocation problem is developed to find the optimal Peer-to-Peer (P2P) network structure. Given the locations of the peers and their demand distributions in a grid space, the model finds the optimal points in the space to place the main servers (brokers) that facilitate the communication between the peers by offering them indexing and searching services for all the files in the space. Although the place of each peer is fixed, they may not be online all the time. Therefore, the demand distribution for each agent is specified probabilistically and the corresponding model is solved stochastically.

Fuzzy facility location-allocation problem under the Hurwicz criterion

Facility location-allocation (FLA) problem has been widely studied by operational researchers due to its many practical applications. Many researchers have studied the FLA problem in a deterministic environment. However, the models they proposed cannot accommodate satisfactorily various customer demands in the real world. Thus, we consider the FLA problem with uncertainties. In this paper, a new model named α-cost model under the Hurwicz criterion is presented with fuzzy demands. In order to solve this model, the simplex algorithm, fuzzy simulations and a genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed algorithm.

Facility location–allocation problem in random fuzzy environment: Using (α,β) -cost minimization model under the Hurewicz criterion

Facility location–allocation (FLA), which has been proved to be a valuable method in siting service facility, is widely used in real life, such as emergency service systems, telecommunication net works, public services, etc. Many researchers have studied the FLA problem in a deterministic, stochastic or fuzzy environment. However, those models cannot satisfy various customers’ demands in some cases. Thus, this paper considers the FLA problem under random fuzzy environment using ( α , β ) -cost minimization model under the Hurwicz criterion. It will be proved that this model can deal with various FLA problems in random, fuzzy and random fuzzy environments. By varying the value λ , it can balance the optimistic level of the decision makers. For solving the random fuzzy model efficiently, the simplex algorithm, random fuzzy simulation and genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, a numerical example is presented for illustration.

Genetic subgradient method for solving location–allocation problems

Facility location–allocation (FLA) problem is a very important subject in today's business. It is an important part of a company's global logistic system . Various FLA problems have been considered in operations research (OR) under somehow stringent conditions. Restrictive conditions are placed to reduce the size of the search space, however, they also make the model inappropriate for the real-business world. In this paper, we consider a class of FLA problems that can assume more realistic conditions in real-life applications. A hybrid method of genetic algorithm and subgradient technique is used to solve the problem efficiently.

Combining the analytic hierarchy process and goal programming for global facility location-allocation problem

Strategic global facility location-allocation decisions involve many factors that may be conflicting in nature, and can pose a difficult selection problem. Recognizing the multiple and conflicting objective nature of the location-allocation problem in an international setting, this paper proposes the use of the Analytic Hierarchy Process and multi-objective goal-programming methodology as aids in making location-allocation decisions. The methodology presented can help facility planning authorities to formulate viable location strategies in the volatile and complex global decision environment. A demonstration of the application of these methodologies in a real life problem is presented.

A service logistics model for simultaneous siting of facilities and multiple levels of equipment

Service Logistics aims at the most efficient utilization of facilities, minimizing the cost of excess capacity, while making the service more responsive to customer demands. Location of facilities and allocation of equipment to facilities is an important decision making area in service logistics. Earlier models [1–3] developed to address this issue either consider all demands to be covered or all demands cannot be covered because of equipment unavailability. Most of the models developed in the literature only consider single type of equipment usage. We develop a model for the multiple facility, multiple levels of equipment problem called the Multiple Equipment Multiple Cover facility Location-Allocation problem (MEMCOLA) which considers maximal coverage of demand, including multiple coverage of demand by equipment of a specific type, given that each equipment has a certain probability of being unavailable. MEMCOLA simultaneously locates a given number of facilities and allocates different types of equipment to the facilities.