A two-stage method for the capacitated multi-facility location-allocation problem

This is the author accepted manuscript. The final version is available from Inderscience via the DOI in this recordThis paper examines the capacitated planar multi-facility location-allocation problem, where the number of facilities to be located is specified and each of which has a capacity constraint. A two-stage method is put forward to deal with the problem where in the first stage a technique that discretises continuous space into discrete cells is used to generate a relatively good initial facility configurations. In stage 2, a variable neighbourhood search (VNS) is implemented to improve the quality of solution obtained by the previous stage. The performance of the proposed method is evaluated using benchmark datasets from the literature. The numerical experiments show that the proposed method yields competitive results when compared to the best known results from the literature. In addition, some future research avenues are also suggested

A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling

We propose an efficient evolutionary multi-objective optimization approach to the capacitated facility location–allocation problem (CFLP) for solving large instances that considers flexibility at the allocation level, where financial costs and CO2 emissions are considered simultaneously. Our approach utilizes suitably adapted Lagrangian Relaxation models for dealing with costs and CO2 emissions at the allocation level, within a multi-objective evolutionary framework at the location level. Thus our method assesses the robustness of each location solution with respect to our two objectives for customer allocation. We extend our exploration of selected solutions by considering a range of trade-offs for customer allocation

Mathematical Modelling and a Meta-heuristic for Cross Border Supply Chain Network of Re-configurable Facilities

In supply chain management (SCM), Facility location-allocation problem (FLAP) comes under strategic planning and has been a well-established research area within Operations Research (OR). Owing to the billion dollar trade between USA-Canada the supply chain costs and difficulties are growing. Binary Integer Linear Programming (BILP) mathematical model is formulated to incorporate several parameters which would optimize the overall supply chain cost. Capacitated, single commodity, multiple time period (dynamic) and multi-facility location allocation problem is considered. Canada being a part of “The Kyoto protocol”, a part of the United Nations Framework Convention on Climate Change, has declared to abide by global effort to reduce GHG emissions. Developed math model will include an important constraint to optimize production keeping the Carbon di-oxide gas [��2] emission levels within specified limits. Simulated annealing based Meta-heuristic is developed to solve the problem to near optimality

A Hybrid Multi-Criteria Analysis Model for Solving the Facility Location–Allocation Problem: a Case Study of Infectious Waste Disposal

Choosing locations for infectious waste disposal (IWD) is one of the most significant issues in hazardous waste management due to the risk imposed on the environment and human life. This risk can be the result of an undesirable location of IWD facilities. In this study a hybrid multi-criteria analysis (Hybrid MCA) model for solving the facility location–allocation (FLA) problem for IWD was developed by combining two objectives: total cost minimization and weight maximization. Based on an actual case of forty-seven hospitals and three candidate municipalities in the northeastern region of Thailand, first, the Fuzzy AHP and Fuzzy TOPSIS techniques were integrated to determine the closeness of the coefficient weights of each candidate municipality. After that, these weights were converted to weighting factors and then these factors were taken into the objective function of the FLA model. The results showed that the Hybrid MCA model can help decision makers to locate disposal centers, hospitals and incinerator size simultaneously. Besides that the model can be extended by incorporating additional selection criteria/objectives. Therefore, it is believed that it can also be useful for addressing other complex problems

A New Hybrid Algorithm to Optimize Stochastic-fuzzy Capacitated Multi-Facility Location-allocation Problem

Facility location-allocation models are used in a widespread variety of applications to determine the number of required facility along with the relevant allocation process. In this paper, a new mathematical model for the capacitated multi-facility location-allocation problem with probabilistic customer's locations and fuzzy customer’s demands under the Hurwicz criterion is proposed. This model is formulated as α-cost minimization model according to different criteria. Since our problem is strictly Np-hard, a new hybrid intelligent algorithm is presented to solve the stochastic-fuzzy model. The proposed algorithm is based on a vibration damping optimization (VDO) algorithm which is combined with the simplex algorithm and fuzzy simulation (SFVDO). Finally, a numerical example is presented to illustrate the capability of the proposed solving methodologies

A Facility Location-Allocation Model for Determining Number of Depot to Distribute Material in the Rattan Furniture Industry by Considering Dynamic Demand

This paper is a study of a facility location-allocation problem in the rattan furniture industry. There are six production centers (PCs) of rattan furniture in Surakarta and its surroundings. However, their export sales are decline due to some possible problems in raw rattan distribution network from the sources centers (SCs), e.g. Borneo and Celebes Island to production centers. In the previous research, the model was expanded to support local government decide to determine optimal number of depot by consider static demand. This policy is aimed to cut the distribution channel and reduce total supply chain costs. Due to changing of global market, the demand is fluctuate. The previous model cannot anticipate this situation; consequently the local government needs a facility location-allocation model by considering dynamic demand. The objective of this research is to develop a model for supporting the local government to decide optimal number of depot by considers dynamic demand. A mixed integer non-linear programming (MINLP) was proposed to minimize total supply chain costs. The proposed model assumed that the demand for multiple products is known in advance. The potential raw rattan depot and source locations as well as their maximum capacities are also known. Finally, the proposed model can be used as instrument decision making to determine facility location-allocation. Keywords: dynamic demand, a facility location-allocation model, rattan industry competitiveness, total supply chain costs

Multi-objective optimization of a transportation network of a HMSC

In this paper, we investigated a transportation network of a tri-layer Halal meat supply chain (HMSC) in which Halal meat transportation process was monitored by a Radio Frequency Identification (RFID) communication system to ensure safety and integrity of Halal meats. This monitoring system is subject to an extra cost in investment that needs to be taken into account. Thus, a multi-objective linear programming model (MOLPM) was developed aiming to minimize the total cost in transportation and number of transportation vehicles and maximize the service level in product quantity as requested by abattoirs and retailers. The facility location-allocation problem in farms, abattoirs and retailers needs also to be addressed in relevance to the quantity flow of products from farms to abattoirs and from abattoirs to retailers. The utility function method was employed to obtain Pareto-optimal solutions and the global criterion method was used for searching the most suitable Pareto solution by minimizing the distance to its ideal objective value. The research work shows that the developed model can be useful for supply chains design through a case study based on numerical results

A Simulated Annealing Algorithm within the Variable Neighbourhood Search Framework to Solve the Capacitated Facility Location-Allocation Problem

In this study, we discuss the capacitated facility location-allocation problem with uncertain parameters in which the uncertainty is characterized by given finite numbers of scenarios. In this model, the objective function minimizes the total expected costs of transportation and opening facilities subject to the robustness constraint. To tackle the problem efficiently and effectively, an efficient hybrid solution algorithm based on several meta-heuristics and an exact algorithm is put forward. This algorithm generates neighborhoodsby combining the main concepts of variable neighborhood search, simulated annealing, and tabu search and finds the local optima by using an algorithm that uses an exact method in its framework. Finally, to test the algorithms’ performance, we apply numerical experiments on both randomly generated and standard test problems. Computational experiments show that our algorithm is more effective and efficient in term of CPU time and solutions quality in comparison with CPLEX solver

Redesign of Three-Echelon Multi-Commodity Distribution Network

This research studies the distribution network redesign of an actual electronics company. The problems are formulated based on multi-echelon capacitated Location Routing Problem (LRP) with two commodities: home products and service items. The objective function consists of three components: facility cost, closing cost of facility and transportation cost. We propose solution method based on clustering technique. The problem is decomposed into the Facility Location Allocation Problem (FLAP) and the Multi-Depot Vehicle Routing Problem (MDVRP). MDVRP is solved by clustering method and feed the results to the modified FLAP to allocate the demand nodes to facilities and configure all distribution networks, for the 2nd and 3rd echelon. The distribution is divided into five region zones. Previously, each region was operated independently but this research compares the solutions from solving each region independently and solving all five zones simultaneously. The results indicate that the proposed solution method can achieve computation time and total cost that are comparable to ones obtained from solving the problem to optimality. Exact approach can only solve small and medium problems, whereas the proposed solution method provides the acceptable solution of real-life largest problem in limit of computation time. Finally, we perform sensitivity analysis on the results