Location-allocation Decisions Research Articles

University campuses’ location-allocation: a case study of Egypt with insights from the Japanese context

PurposeThis paper aims to identify the most suitable location for a university campus in Egypt based on governorates’ social needs by employing the analytic hierarchy process (AHP). The paper, then, reflects the findings retrieved from the Egyptian context on the Japanese context to reveal how different countries deal with the location-allocation decision problem for university campuses.Design/methodology/approachThe AHP is employed to evaluate and rank Egyptian governorates based on 13 distinct criteria obtained from governmental open-source databases. These criteria measure the social needs of each governorate, guiding the decision on the location of new university campuses.FindingsThe results expose a disparity between Egypt's current campus development plan and recommendations derived from AHP analysis. The location-allocation decision for new university campuses appears to be influenced by subjective assessments, indicating a gap between planned developments and identified social needs. Additionally, contextual social and cultural differences between developing and developed countries impact the identification and fulfilment of the demand for a new university campus.Originality/valueThis paper contributes by offering decision-makers a robust location-allocation framework. It serves as a valuable tool for policy formulation in establishing new public universities in both developing and developed countries. Comparative analysis with the Japanese context enriches the understanding of how countries address the location-allocation decision problem for university campuses, emphasising the significance of context-specific considerations in such decisions.

Simulation optimization for stochastic casualty collection point location and resource allocation problem in a mass casualty incident

After a severe disaster strikes, a large number of casualties in need of urgent treatment, known as a mass casualty incident (MCI) event, arise from multiple disaster areas in a very short period of time and overwhelm available resources of the local healthcare system. This paper focuses on the effective design of casualty collection point (CCP) locations and the efficient allocation of limited emergency medical service (EMS) resources to transport casualties quickly to appropriate hospitals and increase the survival rate of casualties. A hybrid simulation–optimization approach to optimize the CCP location and EMS resource allocation problem over mixed binary and integer feasible domains for the minimization of expected complete delivery time of all casualties from disaster region to hospitals is presented in this work due to the stochastic and dynamic nature of the MCI logistics environment. A high-resolution stochastic discrete event simulation model considering time-varying stochastic casualty arrivals, random triage service times, and stochastic travel times caused by road network vulnerability is first constructed to describe a more detailed modeling of comprehensive MCI humanitarian logistics from the disaster regions to the hospitals. Then, a novel two-stage sequential algorithm, namely a combination of optimal computing budget allocation-based rapid-screening algorithm and adaptive particle global and hyperbox local search (ORSA-APGHLS), is developed to speed up convergence to near-optimal solutions compared to three common existing algorithms (Genetic Algorithm, Tabu Search and Stochastic Nelder-Mead Algorithm) under a limited simulation budget. We collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in Taiwan to conduct computational experiments to demonstrate the efficiency and efficacy of the proposed two-stage ORSA-APGHLS algorithm according to a potential earthquake scenario occurring in Tainan City, Taiwan. Through sensitivity analysis, the influence of different levels of scarce emergency medical resources and degrees of road damage on the expected complete delivery time of all casualties and the location-allocation decisions are investigated.

Optimal locations and flow allocations for aggregation hubs in supply chain networks of perishable products

Establishment of aggregation hubs in a supply chain network (SCN) is typically a facility location-allocation (FLA) decision, which is known to be a NP-hard optimization problem. Considering the flow of heterogeneous perishable products, like fresh produce, with different spoilage rates, further increases the complexity of such a problem. This is due to the effect of transportation time and conditions, services provided in the hub, and hub proximity to supply sources, on the quality and quantity of products eventually reaching the demand destinations, and hence on the location-allocation decision. In this paper, this problem is formulated as a mixed integer linear programming (MILP) model that considers a number of problem characteristics simultaneously for the first time, to minimize the transportation, spoilage, processing, and capacity-based hub establishment costs. Due to its complexity, two hybrid algorithms that combine a meta-heuristic with a perishability-modified transportation algorithm, are proposed to solve the problem. The algorithms are based on binary particle swarm optimization (BPSO) and simulated annealing (SA). Taguchi analysis is used to tune the significant parameters of both algorithms considering different problem sizes. Computational analysis is further conducted to evaluate and compare the performances of the algorithms using randomly generated test instances and exact solutions obtained using CPLEX. Results show that while both algorithms are capable of obtaining optimum solutions for most instances, the hybrid BPSO slightly outperforms the hybrid SA in terms of consistency and solution time.

Models and network insights for edge-based districting with simultaneous location-allocation decisions

We introduce two edge-based districting optimization models with no pre-fixed centers to partition a road network into a given number of compact, contiguous, and balanced districts. The models are applicable to logistics applications. The first model is a mixed-integer programming model with network flow-based contiguity constraints. Since this model performs poorly on medium-to-large instances, a second model with cut set-based contiguity constraints is introduced. The full specification of the contiguity constraints requires substantial computational resources and is impractical except for very small instances. However, paired with an iterative branch-and-bound algorithm with a cut generation scheme (B&B&Cut), the second model tends to outperform the first computationally. We show that the underlying problem is -hard. Moreover, we derive network insights, from which cutting planes that enable a reduction in the solution space can be generated. The cuts are tested on road networks with up to 500 nodes and 687 edges, leading to speed up in computational time up to almost 27x relative to the computational time of solving the second optimization model exactly with only B&B&Cut.

A goal programming model for two-stage COVID19 test sampling centers location-allocation problem.

The COVID19 virus, which first appeared in Wuhan, China, and has become a pandemic in a short time, has threatened the health system in many countries and put it into a bottleneck. Simultaneously, the second wave's expectation spread it necessary to plan the health services correctly. In this study, a location-allocation problem in the two-echelon system, which considers different test sampling alternatives, is examined to obtain test sampling centers' location-allocation. The problem is modeled as a goal programming model to create a network that tests samples at a minimum total distance, establishes a minimum number of test sampling centers, and reaches the distance of PCR test laboratories at minimum total distances. The proposed model is applied as a case study for the two cities located in Turkey, and the obtained locations and inventory levels of each location are presented. Besides, different scenarios are examined to understand the structure of the model. As a result, only testing in hospitals will increase the risk of contamination. Since testing at all points will not be possible administratively, it will be ensured that the most appropriate location-allocation decisions are taken by considering all the proposed model's objectives.

Spatial Decision Support System for Efficient School Location-Allocation

In Greece, a lack of a planning strategy was identified in the context of allocating students to schools. Particularly, the Secondary Educational Management of Lesvos Prefecture along with school Principals decide upon student allocation based on empirical knowledge and approximation techniques. As a consequence, during the school season of 2018-2019 capacity and proximity limitations were violated. This study introduces a Spatial Decision Support System (SDSS) to assist school location-allocation decisions in future seasons. The objective of the proposed SDSS is to minimize commute-to-school distance concerning capacity and proximity limitations. For this purpose, a capacitated P-median approach is adopted and formulated as a mixed-integer linear problem. The SDSS is further evaluated using actual data for students' transition from primary to secondary education in the city of Mytilene, Greece. Evaluation of current allocation practices carried out and further compared to those obtained by the SDSS. The results indicate a decrease of 8% in total distance whereas proximity and capacity constraints were respected accordingly. The results may be potentially useful for school planners to assist the allocation decisions in the city of Mytilene.

Evaluating on-demand warehousing via dynamic facility location models

On-demand warehousing platforms match companies with underutilized warehouse and distribution capabilities with customers who need extra space or distribution services. These new business models have unique advantages, in terms of reduced capacity and commitment granularity, but also have different cost structures compared with traditional ways of obtaining distribution capabilities. This research is the first quantitative analysis to consider distribution network strategies given the advent of on-demand warehousing. Our multi-period facility location model – a mixed-integer linear program – simultaneously determines location-allocation decisions of three distribution center types (self-distribution, 3PL/lease, on-demand). A simulation model operationally evaluates the impact of the planned distribution strategy when various uncertainties can occur. Computational experiments for a company receiving products produced internationally to fulfil a set of regional customer demands illustrate that the power of on-demand warehousing is in creating hybrid network designs that more efficiently use self-distribution facilities through improved capacity utilization. However, the business case for on-demand warehousing is shown to be influenced by several factors, namely on-demand capacity availability, responsiveness requirements, and demand patterns. This work supports a firm’s use of on-demand warehousing if it has tight response requirements, for example for same-day delivery; however, if a firm has relaxed response requirements, then on-demand warehousing is only recommended if capacity availability of planned on-demand services is high. We also analyze capacity flexibility options leased by third-party logistics companies for a premium price and draw attention to the importance of them offering more granular solutions to stay competitive in the market.

Solving a Real-World Urban Postal Service System Redesign Problem

Due to recent technological advancements, more diversified customer demand, and increasingly harder competition, traditional postal service systems have experienced significant changes all over the world. In Norway, through a strategic reform called post-in-shop, undertaken in 2013, most postal services are now provided at postal service counters located in retailer stores in order to improve accessibility, operational efficiency, and cost-effectiveness. This has led to a complex decision-making problem for the redesign of urban postal service networks across the country. In this paper, a two-stage method is proposed to solve a real-world urban postal service network redesign problem. First, two location models are employed to determine the optimal locations of postal service counters. In the second stage, a simulation model is built to evaluate the urban postal service system with different location and demand allocation plans under a realistic and stochastic environment. Among other insights, our results show that the proposed two-stage method can be used to effectively improve the accessibility of postal service networks by making optimal location-allocation decisions.

Multi-stage, multi-product and multi-period milk supply chain network design: a case of coastal region of Odisha

Due to the complexity of supply chain networks in today’s business environment, it is important to consider several strategic planning decisions along with the classical location-allocation decisions to achieve an integrated system. In this paper, a milk supply chain network design (SCND) problem of coastal region Odisha is considered. The proposed model is focussed on multi-stage, multi-product and multi-period supply chain network design of milk of a dairy industry. The network design includes the strategic decision-making approach of influence area, order quantity, replenishment cycle time, preservation factor and number of proposed distribution centre of milk products. This supply chain network design generic model of the milk products is developed as a mixed integer non-linear programming (MINLP) model. It is solved by using piecewise nonlinear optimization approach for location-allocation decisions of the clusters. The sensitivity analysis is also conducted to check the importance and influence of the model parameters on total supply chain network profit per unit time of the multi-milk product. Through a real-world dairy industrial case of multi products, this proposed SCND generic model is demonstrated and then customized model is developed for the case analysis. It is observed that the total supply chain network profit per unit time for the proposed model is more than that of existing model. Hence the strategic conditions like the service region must be greater than the influence area and imposing preservation effort for the multi-milk product can be a mechanism for revenue growth of the supply chain network design.

Multi-stage, multi-product and multi-period milk supply chain network design: a case of coastal region of Odisha

Due to the complexity of supply chain networks in today’s business environment, it is important to consider several strategic planning decisions along with the classical location-allocation decisions to achieve an integrated system. In this paper, a milk supply chain network design (SCND) problem of coastal region Odisha is considered. The proposed model is focussed on multi-stage, multi-product and multi-period supply chain network design of milk of a dairy industry. The network design includes the strategic decision-making approach of influence area, order quantity, replenishment cycle time, preservation factor and number of proposed distribution centre of milk products. This supply chain network design generic model of the milk products is developed as a mixed integer non-linear programming (MINLP) model. It is solved by using piecewise nonlinear optimization approach for location-allocation decisions of the clusters. The sensitivity analysis is also conducted to check the importance and influence of the model parameters on total supply chain network profit per unit time of the multi-milk product. Through a real-world dairy industrial case of multi products, this proposed SCND generic model is demonstrated and then customized model is developed for the case analysis. It is observed that the total supply chain network profit per unit time for the proposed model is more than that of existing model. Hence the strategic conditions like the service region must be greater than the influence area and imposing preservation effort for the multi-milk product can be a mechanism for revenue growth of the supply chain network design.

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]

A GIS – BASED OPTIMIZATION FOR THE FUEL LOCATION-ALLOCATION TO FACE THE PROBABLE TSUNAMI: CASE OF PADANG CITY

The Mentawai Megathrust is predicted to cause a tsunami with the tremendous effects to societies and environments. As the coastal area located across the epicenter, Padang City is expected to have a high vulnerability to be surged by this future hazard. This issue has gradually grown in importance to provide an effective disaster countermeasure for minimizing losses and suffering. As the fuel is one of the most critical items required in the aftermath of disaster, this study seeks to propose the disaster relief planning in Padang City, particularly in the fuel location-allocation decisions to cope with the effects caused by the probable tsunami. In this research the Geographic Information System (GIS)-based optimization is utilized to generate the appropriate number of facilities as well as its allocated capacity to serve several demand points (e.g. hospitals and refugee camps) during the emergency periods. This paper initially considers the potential inundation area based on the worst-case scenario developed by the disaster countermeasure agency to identify the likelihood of the impassable paths and the candidate facilities in the safe zone. By using P-Median analysis, this work recommends 9 strategic facilities to handle 18 demand points during the critical periods. This study provides an exciting opportunity to comprehensively obtain the suitable facilities in order to fulfill the fuel needs in the disaster aftermath. Due to practical constraints, this paper may be further improved to be Decisions Support Systems (DSS) with the consideration to the real time conditions during emergency periods.

Fast-moving consumer goods network design with pricing policy in an uncertain environment with correlated demands

Facility location is one of the most important strategic decisions in designing a supply chain network. However, given the interaction of strategic decisions on tactical and operational decisions, it seems necessary to provide models that integrate these decisions. In this study, a mixed-integer non-linear programming model is developed to determine the optimal pricing of products in different sales and distribution channels while integrating location-allocation decisions and inventory control of distribution centers in an uncertain environment. In order to provide a more realistic model, it is assumed that there is a correlation between demands for products, which poses a critical challenge for modeling and pricing. A Memetic algorithm is used to solve the mixed-integer nonlinear programming model. The implementation results of the designed sample problems show the good performance of the proposed algorithm.

Application of Integrated Multiple Criteria Data Envelopment Analysis to Humanitarian Logistics Network Design

This paper considers a humanitarian logistics network (HTLN) design problem, including the emergency relief facilities (ERFs) location-allocation decision for the efficient distribution of emergency supplies from the ERFs to the affected areas. A goal programming (GP) approach is applied to consider the multiple objectives simultaneously. Solving the GP model with a given weight assigned to each goal yields a single HTLN scheme, so there will be various schemes available by solving the GP with multiple values of the weights. For evaluating these schemes and identifying the most efficient one, we apply the data envelopment analysis (DEA) methods considering each scheme as a decision-making unit (DMU). Since the classical DEA (C-DEA) intrinsically aims to identify efficient DMUs and the efficient frontier, the use of C-DEA may not lead to a full ranking in many situations. There are several independent evaluation approaches to increasing discriminating power. Among them, this study integrates the multiple criteria DEA (MC-DEA) with the following three DEA methods, (i) stratification DEA (S-DEA), (ii) cross-efficiency DEA (CE-DEA), and (iii) super-efficiency DEA (SE-DEA), to make the most use of each method’s strengths. Through a case study of designing the HTLN system for South Carolina, the procedure of implementing the integrated multiple criteria DEA (IMC-DEA) method is demonstrated. It is observed that the IMC-DEA method performs well in terms of designing the HTLN system and would help the decision-makers consider more efficient options and make a final decision.

A mathematical model to design dynamic cellular manufacturing systems in multiple plants with production planning and location–allocation decisions

In this research, a mixed-integer nonlinear programming model is formulated to design cellular manufacturing systems (CMSs) in multiple plants in a dynamic condition. The proposed mathematical model integrates significant manufacturing characteristics in designing CMSs along with the main strategies of production planning (PP) and location–allocation (LA) problems. In addition, there are some novel characteristics that make the designed model remarkable regarding the literature including: (1) multi-plant location; (2) allocation of plants capacity to fulfill multiple markets demand; and (3) the integration of dynamic cell formation, PP and LA decisions. The objective function terms are to optimize the sale revenue and total costs of machine operating, machine overhead, inter-cell material handling, inventory holding, outsourcing, machine installation/uninstallation, products transportation, dispersing machines, establishing plants, and forming cells. A sample test problem is solved by CPLEX solver to show the achievement obtained by the characteristics incorporated into the model. Since the proposed model is NP-hard, two meta-heuristics algorithms as grey wolf optimization and genetic algorithm are developed. To verify the computational effectiveness of the employed meta-heuristics in comparison with that of CPLEX solver, its performance is tested using a number of test problems.

Uncertain location–allocation decisions for a bi‐objective two‐stage supply chain network design problem with environmental impacts

AbstractIn the cases that the historical data of an uncertain event is not available, belief degree‐based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi‐objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree‐based uncertainty theory. This article is almost the first study on belief degree‐based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance‐constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi‐objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.

Dimensionality-reduction Procedure for the Capacitated p-Median Transportation Inventory Problem

The capacitated p-median transportation inventory problem with heterogeneous fleet (CLITraP-HTF) aims to determine an optimal solution to a transportation problem subject to location-allocation, inventory management and transportation decisions. The novelty of CLITraP-HTF is to design a supply chain that solves all these decisions at the same time. Optimizing the CLITraP-HTF is a challenge because of the high dimension of the decision variables that lead to a large and complex search space. The contribution of this paper is to develop a dimensionality-reduction procedure (DRP) to reduce the CLITraP-HTF complexity and help to solve it. The proposed DRP is a mathematical proof to demonstrate that the inventory management and transportation decisions can be solved before the optimization procedure, thus reducing the complexity of the CLITraP-HTF by greatly narrowing its number of decision variables such that the remaining problem to solve is the well-known capacitated p-median problem (CPMP). The conclusion is that the proposed DRP helps to solve the CLITraP-HTF because the CPMP can be and has been solved by applying different algorithms and heuristic methods.

Evaluation of a facility location for a food assistance supply chain. The case of Bienestarina in Colombia

The evaluation of the strategic supply chain configuration is considered one of the strategic logistics decisions, especially in food assistance supply chains focused on generating better nutritional conditions in vulnerable populations. In Colombia, there is a social program called Bienestarina, which aims to promote food and nutritional security in a vulnerable population. Although the government supports the program for improving nutritional support, there are currently inconsistencies in freight flows, lack of coverage in some areas, and delivery delays. Therefore, this work aims to evaluate the current configuration of the supply chain and propose improvements related to the facility location. Such advances would enable the increase in the efficacy of the network and the reduction of malnutrition in the country. For this purpose, a mixed-integer mathematical programming model is presented, which considers the weighted distance criterion for different demand scenarios and supports the location-allocation decision in a social assistance supply chain. The current network configuration was compared with the optimal proposed structure. The comparisons show highly potential improvements in freight flow allocation, suggests several variations in the existing warehouses emplacement, and generates public policy implications to reduce the logistic cost in the system, prioritizing in turn the demand covering.

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.

A meta-heuristic for a bi-objective multi-commodity green distribution network

This paper presents a bi-objective optimisation model for solving the location-allocation problem (LAP) of a distribution network that consists of four echelons including suppliers, manufacturing plants, distribution centres and customers. Multiple product types and raw materials are considered. A bi-objective mathematical model is formulated to minimise the total costs and the greenhouse gas emissions arising from the location-allocation decisions. A meta-heuristic algorithm is developed to solve the proposed mathematical model. The proposed algorithm was implemented using multi-objective particle swarm optimisation (MOPSO) and multi-objective differential evolution (MODE) solution methods and results were analysed.