Location-allocation Decisions Research Articles

A Systematic Literature Review of the Design of Intermodal Freight Transportation Networks Addressing Location-Allocation Decisions

This systematic literature review focuses on planning models jointly addressing location and allocation decisions related to the design of intermodal freight transportation networks. Since this body of literature is evolving quickly, a methodology based on a linked two-stage analysis is proposed. The first stage analyses recent surveys to establish the guidelines and criteria that enable the subsequent systematic review. Then, the review concentrates on analysing contributions to the current state of the art on intermodal freight transportation from two close, yet different research streams: transportation networks and supply chain networks. Key features identified in the first stage such as: 1) the research problem's characteristics; 2) the intermodal networks design's particularities; 3) proposed solution techniques, among others, are used to classify and analyse the different contributions. The review identifies current trends, emerging topics and some issues that merit being researched. [Received: 4 May 2019; Revised: 12 December 2019; Accepted: 1 February 2020]

Redesigning a transportation network: the case of a pharmaceutical supply chain

Designing transportation networks - as one of the most important decision problems in supply chain management - has become the focus of research attention in recent years. This paper introduces a framework which deals with the design of a transportation network for pharmaceutical supply chains. It consists of three steps: 1) identifying the configuration of the current distribution network; 2) designing an optimal distribution network while determining the appropriate location-allocation decisions; 3) choosing the most appropriate transportation network through the application of the appropriate multi-criteria decision analysis method (MCDA). The proposed framework is illustrated in redesigning a transportation network for a pharmaceutical supply chain.

Embedding the production policy in location-allocation decisions

This paper investigates how the production policy, as well as other factors, affect the facility location-allocation decisions. We focus on a p-median location problem in which one single perishable product is to be produced and shipped to a set of users. The time-correlated demands of the clients are generated by autoregressive processes, and they are forecasted from historical data. Empirically, we show that: (i) embedding the production policy in the location-allocation decision problem may lead to a facilities-clients assignment which does not necessarily correspond to the minimum cost allocation, but produces better profits, (ii) taking into account the autocorrelation of the demand can significantly improve the performance of the supply chain, and (iii) the variability of the demand strongly affects the performance of the supply chain, so a careful choice of production strategy is especially recommended in this case.

Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO

Due to the prominent role of blood in human life, designing an efficient blood supply chain in case of an emergency situation is essential especially considering blood compatibility. This research proposes a multi-objective model for emergency blood supply chain management considering blood compatibility, routing, and location–allocation decisions. The blood supply chain network consists of donors, collection facilities, laboratories, blood centers, and hospitals. The mathematical model aims to minimize total supply chain cost and time while maximizing minimum reliability of established routes by making decisions regarding location–allocation, blood flow, inventory levels, and optimal routes. In order to solve the problem, a novel algorithm called Multi-Objective Grey Wolf Optimizer is used and compared to two classical algorithms Multi-Objective Particle Swarm Optimization and Non-dominated Sorting Genetic Algorithm-II. Performance of the algorithms is evaluated in various test problems using powerful measures. Also, the application of the proposed model is investigated in a case study in Iran’s capital, Tehran. Based on the results, important managerial insights are derived and optimal locations for facilities, inventory levels, routes and blood flow between facilities are determined.

Reliable location allocation for hazardous materials

Decision-making on location allocation has gained considerable attention since it involves strategic and operational policies with mid-term and long-term effects. In this paper, a reliable location allocation mechanism in the context of hazardous materials is proposed, considering that the depots are subject to the risk of disruption that may be caused by many factors, including hazardous materials depot accident, depot maintenance upgrade, equipment fault, and power outage. According to the characteristics of hazardous materials, this study aims to minimize the systemic risk of storage and transportation under budget constraints. The problem is formulated as an integer linear programming model to simultaneously determine: (i) the optimal depot locations; (ii) the amount of hazardous materials stored in each located depot; (iii) the optimal allocation (transportation) plans; and (iv) the contingency plans for depot disruption. Numerical examples demonstrate that the proposed modeling method leads to 8.33% risk reduction and 1.92% cost savings compared with traditional location allocation without disruption consideration. This reveals the necessity and importance of taking reliability into account and making contingency plans in disruption scenarios regarding hazardous materials location allocation decisions.

The Location-allocation Decision under The Dynamic Increment of Demand for Selecting The Local Distribution Centers to Face Sumatra Megathrust: Study Case of Padang City

The efforts of providing effective relief assistance in the aftermath of the disaster is prominent to prevent losses and suffering. During the emergency response, the level of preparedness becomes a decisive factor to obtain an immediate countermeasure in order to enhance the capability and to reduce the impact caused by the disaster. Due to its vital role as a transit point to manage large inflow and outflow of the relief commodities, the location-allocation decision to select the appropriate Local Distribution Centers (LDCs) is essential. This research proposes the location-allocation planning for encountering Sumatra Megathrust during 72 hours of the critical period. The dynamic increment of demand over the planning period is considered as a constraint to obtain the set of appropriate LDCs to be established per day. The consideration regarding the expected impassable paths based on the worst case tsunami inundation scenario is acknowledged to ensure the proper access for distributing the relief aids. This work utilizes Geographical Information Systems (GIS) with the maximum coverage analysis to generate the set of alternative LDCs per day. The effectiveness of the alternative LDCs is measured by calculating the opening cost and transportation cost. The finding recommends two LDCs to be opened in the first two days, and three LDCs are considered to be established on the third day. The total costs of the LDCs establishment per day are 1369.05 USD, 669.85 USD, and 1126.53 USD. The depiction of the selected depots-recipient counterparts is provided to acquire the spatial information of the study area.

Data driven hybrid evolutionary analytical approach for multi objective location allocation decisions: Automotive green supply chain empirical evidence

The strategic location of manufacturing plants and warehouses and the allocation of resources to the various stages of a supply chain using big data is of paramount importance in the era of internet of things. A multi-objective mathematical model is formulated in this paper to solve a location-allocation problem in a multi-echelon supply chain network to optimize three objectives simultaneously such as minimization of total supply chain cost (TSCC), maximization of fill rate and minimization of CO2 emissions. Data driven hybrid evolutionary analytical approach is proposed by integrating Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) to handle multiple objectives into Differential Evolution (DE) algorithm. Five variants of the hybrid algorithm are evaluated in addition to comparing the performance with the existing Multi-Objective Hybrid Particle Swarm Optimization (MOHPSO) algorithm. Extensive computational experiments confirm the superiority of the proposed Data driven hybrid evolutionary analytical approach over the existing MOHPSO algorithm. This study identifies a specific variant that is capable of producing the best solution in a higher order simulated instances and complex realistic scenario such as an automotive electronic parts supply chain in Malaysia.

Hybrid Genetic Algorithm and Invasive Weed Optimization via Priority Based Encoding for Location-Allocation Decisions in a Three-Stage Supply Chain

In this paper, location–allocation problem of a three-stage supply chain network, including suppliers, plants, distribution centers (DCs) and customers is investigated. With respect to the total cost, the aim is determining opened plants and DCs and designing transportation trees between the facilities. Considering the capacity of suppliers, plants and DCs are limited and there is a limitation on the maximum number of opened plants and DCs, a mixed-integer linear programming (MILP) model of the problem is presented. Since multi-stage supply chain networks have been recognized as NP-hard problems, applying priority-based encoding and a four-step backward decoding procedure, a meta-heuristic algorithm, namely GAIWO, based on the best features of genetic algorithm (GA) and invasive weed optimization (IWO) is designed to solve the problem. In small size problems, the efficiency of the GAIWO is checked by solutions of GAMS software. For larger size problems, the performance of the proposed approach is compared with four evolutionary algorithms in both aspects of the structure of the GAIWO and the efficiency of the proposed encoding–decoding procedure. Besides usual evaluation criteria, Wilcoxon test and a chess rating system are used for evaluating and ranking the algorithms. The results show higher efficiency of the proposed approach.

Integrated Production, Inventory, and Location-Allocation Decisions in Designing Supply Chain Networks

This paper studies the design and development of an inventory model for manufacturers with constant production rates considering location and allocation decisions in a three-level supply chain. In this supply chain, the demands of customers and the lead times are assumed to be uncertain. Therefore, each distribution center retains some amount of safety stock to provide suitable service level for customers. The proposed non-linear model aims to minimize location and inventory costs of distribution centers, manufacturers and transportation costs subject to relevant constraints. To solve the model, an efficient imperialist competitive algorithm and a Tabu search algorithm, each using variable neighborhood search, are proposed. The model outputs are decisions such as which distribution centers and manufacturers are opened, the allocation of customers to distribution centers, and distribution centers to manufacturers. Results are also the ordering quantity of each opened distribution center and the production rate of each opened manufacturer. The computational results for several instances of the problem are represented to show the efficiency of proposed algorithm.

Cold chain configuration design: location-allocation decision-making using coordination, value deterioration, and big data approximation

The study proposes a cold chain location-allocation configuration decision model for shippers and customers by considering value deterioration and coordination by using big data approximation. Value deterioration is assessed in terms of limited shelf life, opportunity cost, and units of product transportation. In this study, a customer can be defined as a member of any cold chain, such as cold warehouse stores, retailers, and last mile service providers. Each customer only manages products that are in a certain stage of the product life cycle, which is referred to as the expected shelf life. Because of the geographical dispersion of customers and their unpredictable demands as well as the varying shelf life of products, complexity is another challenge in a cold chain. Improved coordination between shippers and customers is expected to reduce this complexity, and this is introduced in the model as a longitudinal factor for service distance requirement. We use big data information that reflects geospatial attributes of location to derive the real feasible distance between shippers and customers. We formulate the cold chain location-allocation decision problem as a mixed integer linear programming problem, which is solved using the CPLEX solver. The proposed decision model increases efficiency, adequately equates supply and demand, and reduces wastage. Our study encourages managers to ship full truck load consignments, to be aware of uneven allocation based on proximity, and to supervise heterogeneous product allocation according to storage requirements.

Multi-objective optimisation of facility location decisions within integrated forward/reverse logistics under uncertainty

Increasing interest to the environmental, social and economic aspects of the supply chains has motivated supply chain managers to optimise location-allocation decisions within closed-loop logistics networks. This paper presents a multi-objective model to optimise facility location decisions in integrated forward/reverse streams under uncertainty. The objectives of the model are to minimise total costs and simultaneously maximise customer satisfaction considering uncertainties in demand and return rate. The proposed model is solved by integrating genetic algorithm with sampling average method. The application of the model is examined in a real case study of car after sales network. The result of the model is compared to a deterministic model to identify how uncertainties affect the optimal configurations. The other experiment is carried out to study the effect of integrating forward and reverse logistics operations on the stakeholder's objectives. Finally, a post-analysis is applied to help in choosing one solution among many different solutions.

Balancing efficiency and equity in location-allocation models with an application to strategic EMS design

This paper presents an integrated location-allocation model balancing efficiency and equity criteria. The new formulation combines two domains: facility location and data envelopment analysis. To support the decision maker with more realistic solutions based on the optimal location-allocation decisions, we endogenize the outputs of the model as a function dependent on the allocation variables. To illustrate the viability of the proposed approach, we investigated the potential application of the model to the design of an emergency medical service system.

Multi-criteria decision analysis for efficient location-allocation problem combining DEA and goal programming

Facility location-allocation (FLA) decisions play a significant role in the performance of supply network in many practical applications, such as emergency service system, supply chain system, public service system, etc . In this paper, a multi-criteria model (including multi-attribute and multi-objective) for optimal and efficient facility location-allocation patterns was proposed. We first utilize multi attribute decision making (MADM) method–DEA to evaluate the relative efficiency of each potential location, and then combine the efficiency identified from DEA as a goal in a multi objective decision making (MODM) framework by using goal programming. A hypothetical example is presented to illustrate the effectiveness and the efficiency of the proposed model. Results demonstrate that the proposed multi-criteria model is an effective tool for generating a set of more realistic and flexible optimal solution in solving facility location- allocation problems by adjusting the goal priorities with respect to the importance of each objective and the aspiration level with respect to desired target values. The proposed model is also flexible and general enough to consider other specific location decisions such as emergency facilities, undesirable facilities and supply chain design by combing specific location modeling goal with the DEA model.

Optimization of warehouse location through fuzzy multi-criteria decision making methods

Strategic warehouse location-allocation problem is a multi-staged decision-making problem having both numerical and qualitative criteria. In order to survive in the global business scenario by improving supply chain performance, companies must examine the crossfunctional drivers in the optimization of logistic systems. A meticulous observation makes evident that strategy warehouse location selection has become challenging as the number of alternatives and conflicting criteria increases. The issue becomes particularly problematic when the conventional concept has been applied in dealing with the imprecise nature of the linguistic assessment. The qualitative decisions for selection process are often complicated by the fact that often it is imprecise for the decision makers. Such problem must be overcome with defined efforts. Fuzzy multi-criteria decision making methods have been used in this research as aids in making location-allocation decisions. The anticipated methods in this research consist of two steps at its core. In the first step, the criteria of the existing problem are inspected and identified and then the weights of the sector and subsector are determined that have come to light by using Fuzzy AHP. In the second step, eligible alternatives are ranked by using TOPSIS and Fuzzy TOPSIS comparatively. A demonstration of the application of these methodologies in a real life problem is presented.

Location–allocation approaches for hospital network planning under uncertainty

This study proposes two location–allocation models for handling uncertainty in the strategic planning of hospital networks. The models aim to inform how the hospital networking system may be (re)organized when the decision maker seeks to improve geographical access while minimizing costs. Key features relevant in the design of hospital networks, such as hospitals being multiservice providers operating within a hierarchical structure, are modelled throughout a planning horizon in which network changes may occur. The models hold different assumptions regarding decisions that have to be taken without full information on uncertain parameters and on the recourse decisions which will be made once uncertainty is disclosed. While the first model is in line with previous literature and considers location as first-stage decisions, the second model considers location and allocation as first-stage decisions. Uncertainty associated with demand is modelled through a set of discrete scenarios that illustrate future possible realizations. Both models are applied to a case study based on the Portuguese National Health Service. The results illustrate the information that can be obtained with each model, how models can assist health care planners, and what are the consequences of different choices on the decisions to be taken without complete information. The second model has shown to be advantageous on grounds that location–allocation decisions are not scenario dependent, and it appears to be more flexible to handle the planning problem at hand.

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.

Optimization of pre-treatment selection for the use of woody waste in co-combustion plants

This work is focused on the use of biomass waste to feed already existing coal combustion plants as a part of paving the way toward the reduction of the environmental impact. The biomass waste supply chain optimization is critical to conceive long-term viable projects and deal with the biomass heterogeneous nature and drawbacks to be used with coal, i.e. principally high moisture content and low bulk density. This paper studies biomass transportation, storage and change of properties (moisture content and hence dry matter, energy density and bulk density) through the use of different pre-treatments: (i) torrefaction, (ii) torrefaction combined with pelletization, (iii) pelletization, (iv) fast pyrolysis and (v) fast pyrolysis combined with char grinding, which produce a range of very different pre-treated biomass. The optimization problem is formulated as a mixed integer linear program (MILP) that evaluates the net present value and the environmental impact through a life cycle assessment (LCA). The results propose location–allocation decision together with the selection/capacity of pre-treatment technologies for each scenario. The scenarios contemplate different biomass characteristics, availability and distribution for a supply chain case study located in Spain: forest and agricultural woody residues used to replace at least 10% of the total thermal inlet power provided by coal in the existing network of thermal plants.

Centralized versus distributed sterilization service: A location–allocation decision model

The concept of “networking” has become central to the reform of healthcare systems. The objective is to reduce costs while improving the quality of service. This paper deals with the problem of sterilization service configuration within a hospital network. Two alternatives are considered: (1) each hospital in the network maintains its sterilization service in-house; (2) a central sterilization service ensures this function for all hospitals in the network. This decision is based on a location–allocation model of the sterilization service. A Mixed Integer Linear Program (MILP) is proposed to find the optimal configuration of the sterilization service (centralized vs. distributed), the optimal location and the optimal capacity of the centralized sterilization service over a multi-period planning horizon. The objective is to minimize costs related to transportation, production and resource acquisition and transfer. A solution method based on the addition of appropriate customized cuts to the original MILP is then proposed. The proposed models are applied to 30 scenarios extracted from a real-life case study. The obtained results show that the considered problem can be solved to optimality for moderate size scenarios with the use of commercial MILP solvers and the addition of the proposed customized cuts to the original model. Further analysis was conducted and pointed out how network configuration is sensitive to the number of human and material resources available in each hospital of the network.

A Two Objective Model For Location-allocation In A Supply Chain

In today's competitive world, location-allocation (LA) decisions are one of the most important aspects of supply chain (SC) optimization. This LA decisions are including selection of known sites for construction of facilities and allocation of the distribution network between the levels of SC. In this paper, a nonlinear programming model to location facilities and allocate the supply chain distribution network in order to minimize both the cost and time of three-echelon are presented. The proposed model due to computational complexity in high dimensions cannot be solved with conventional and accurate methods, Therefore to achieve a solution of a method metaheuristic called genetic algorithm is used. Finally, to examine and the effectiveness of the proposed algorithm, computational results obtained are compared with output of lingo 12 software.

LTL logistics networks with differentiated services

This research addresses the problem of designing a LTL logistics network over individual lanes by offering different delivery services (1-day, 2-day, 3-day). A network design is proposed which links lane-specific delivery service choices and service profitability to the hub location-allocation decisions. A hybrid-heuristic is developed which uses a search interval reduction technique with a meta-heuristic. The performance of the hybrid-heuristic is evaluated in a computational study. A factorial experiment design with real world data is used to analyze the network structure, service choices and shipment flows, which results in managerial insights about the design and operation of a LTL logistics network with differentiated services.