Uncovered Demand Research Articles

Incorporating Socio-Economic Factors in Maximizing Two-Dimensional Demand Coverage and Minimizing Distance to Uncovered Demand: A Dual-Objective MCLP Approach for Fire Station Location Selection

In this study, we employ a dual-objective optimization model, utilizing the Implicit Modified Coverage Location Problem (MCLP-Implicit) approach, to determine an appropriate fire station allocation. Our objectives encompass maximizing coverage in areas with a heightened projected demand, based on socioeconomic predictors of building fires, and concurrently minimizing the distance of uncovered demand zones to the closest fire station. The challenges of this model reflect the criticality of strategic placement, aiming for not just swift response times but also the complete coverage of a region with priority given to subregions with a pronounced potential for incidents. The applicability of the proposed approach is demonstrated through a case study in south-east Queensland. Our findings indicate that there is a tangible justification for adopting this model. The broader coverage and wider spread of locations it produces can greatly aid in the dynamic deployment of personnel during surges caused by seasonal fluctuations or unforeseen calamities. By weaving in socioeconomic aspects into demand predictions, our model has also maintained appropriate coverage to socioeconomically disadvantaged communities in the region. Nevertheless, it is paramount to underline the recommendation that further research should account for road connectivity—a pivotal factor when pinpointing these locations in the real world. This research paves the way for enriched insights in long-term urban planning and fire response protocol crafting, especially in regions mirroring similar socioeconomic profiles or risks of natural disasters, not to mention its commercial implications to the relevant industry servicing the fire and rescue authorities.

Locating helicopter ambulance bases in Iceland: efficient and fair solutions.

Fixed-wing air ambulances play an important role in healthcare in rural Iceland. More frequent use of helicopter ambulances has been suggested to shorten response times and increase equity in access to advanced emergency care. In finding optimal base locations, the objective is often efficiency-maximizing the number of individuals who can be reached within a given time. This approach benefits people in densely populated areas more than people living in remote areas and the solution is not necessarily fair. This study aimed to find efficient and fair helicopter ambulance base locations in Iceland. We used high-resolution population and incident location data to estimate the service demand for helicopter ambulances, with possible base locations limited to twenty-one airports and landing strips around the country. Base locations were estimated using both the maximal covering location problem (MCLP) optimization model, which aimed for maximal coverage of demand, and the fringe sensitive location problem (FSLP) model, which also considered uncovered demand (i.e., beyond the response time threshold). We explored the percentage of the population and incidents covered by one to three helicopter bases within 45-, 60-, and 75-min response time thresholds, conditioned or not, on the single existing base located at Reykjavík Airport. This resulted in a total of eighteen combinations of conditions for each model. The models were implemented in R and solved using Gurobi. Model solutions for base locations differed between the demand datasets for two out of eighteen combinations, both with the lowest service standard. Base locations differed between the MCLP and FSLP models for one combination involving a single base, and for two combinations involving two bases. Three bases covered all or almost all demand with longer response time thresholds, and the models differed in four of six combinations. The two helicopter ambulance bases can possibly obtain 97% coverage within 60 min, with bases in Húsafell and Grímsstaðir. Bases at Reykjavík Airport and Akureyri would cover 94.2%, whereas bases at Reykjavík Airport and Egilsstaðir would cover 88.5% of demand. An efficient and fair solution would be to locate bases at Reykjavík Airport and in Akureyri or Egilsstaðir.

A meta heuristic approach for reliable capacitated facility joint inventory-location problem with round-trip transportation under imperfect information of disruption in a Fuzzy environment

In today’s systems and networks, disruption is inevitable. Designing a reliable system to overcome probable facility disruptions plays a crucial role in planning and management. This article proposes a reliable capacitated facility joint inventory-location problem where location-independent disruption may occur in facilities. The system tries to satisfy customer’s demands and considers penalty costs for unmet customer demand. The article aims to minimize total costs such as establishing inventory, uncovered demand’s penalty, and transportation costs. While many articles in this area only use exact methods to solve the problem, this article uses a metaheuristic algorithm, the red deer algorithm, and the exact methods. Various numerical examples have shown the outstanding performance of the red deer algorithm compared to exact methods. Sensitivity analyses show the impacts of various parameters on the objective function and the optimal facility layouts. Lastly, managerial insights will be proposed based on sensitivity analysis.

A column generation-based algorithm for midterm nurse scheduling with specialized constraints, preference considerations, and overtime

This paper presents an augmented mixed-integer linear programming (MILP) formulation for the nurse scheduling problem designed to accommodate overtime and several practical considerations that are part of the rostering process but have been absent in previous work. The goal is to minimize the sum of weighted uncovered demand and nurse preference violations over a one-month period. Model features include maximum number of consecutive working days and days off, minimum number of rest hours between shifts, vacations and birthdays. Preference violations are measured by the number of times certain days-off and days-on patterns appear in a nurse’s schedule, and the number of times an assignment contains a shift transition on two consecutive days. In addition, the formulation accounts for each nurse’s working status in the last few days of the previous month to ensure that constraints are not violated when new schedules are developed, and that preference violations are properly counted. The problem is solved in two stages due to the complexity of dealing with regular time and overtime in an integrated model. In Stage 1, a mixed-integer linear program is used to assign shifts to nurses without exceeding their contracted regular hours. Column generation is applied to derive solutions. In Stage 2, a heuristic is developed to add overtime to the rosters found in Stage 1. This is done with the goal of minimizing total overtime hours while trying to balance each nurse’s workload. Experimental results are provided for instances with up to 60 nurses based on data obtained from a 1000-bed hospital. We conclude with a sensitivity analysis of the uncovered demand weight to determine its impact on solution quality and computational effort.

A two-step planning method to increase accessibility to medium complexity procedures for public secondary healthcare.

The specialized care level of the public Brazilian national health system is critical and chronically underfunded. Few studies have evaluated public secondary care planning on a strategic level, so there are open issues yet to examine. This study aims at locating medical centers and sizing equipment based on a two-step optimization process to meet the population's needs. The models consider physicians' propensity for working on a metropolis and the patients' choice on moving the least from their municipalities, therefore, conflicting decisions. The models provide the location of medical centers, the assignment of equipment to such locations, and the additional hours of specialists required to meet official standards of demand. Available equipment with idle capacity should partly satisfy the requirement for exams within the current infrastructure. For the remaining uncovered demand, the second step of the optimization model suggests the acquisition of additional equipment for the elected medical centers to meet established needs. The proposed location of secondary care facilities covers 834 municipalities, corresponding to 97.77% of the estate, with an average patient displacement of 58.73 km (CI95%: 56.18 km - 61.28 km). In general, 39 out of 77 health regions should hire additional hours of medical specialties. Pediatrics and gynecology represent the major gap.

Responsive governance in PPP projects to manage uncertainty

Despite the awareness that uncertainty impacts projects with long time horizons, public-private partnership (PPP) research has not extensively examined the operations phase of such projects where interactions between public and private counterparties will be frequent and long-term while subject to a changing environment. Accordingly, two distinct but related investigations examining how governance strategies can address uncertainty in PPP projects are presented. The first identified contractual mechanisms for risk sharing formed during the shaping phase of a broad set of PPP projects; a majority of these mechanisms were strategically designed to address risks as they unfold so counterparty project managers can rely on current information and circumstances during risk resolution. The second explored the emergence of relational mechanisms over time, which revealed that project managers had to consistently adjust established relational approaches to respond to unforeseen events in PPPs. The findings of both studies illustrate the necessity for responsiveness and complementarity of contractual and relational mechanisms in PPPs. Further, the mechanisms uncovered demand project management agility and flexibility as well as improved relational strategies, which is aligned with the emphasis on learning and adaptation in the broader, contemporary project management literature. Consequently, principles from project management 2.0 such as empowering competent project managers and shared global awareness are likely to enhance responsive PPP governance and warrant further exploration.

An iterated greedy algorithm with variable neighborhood descent for the planning of specialized diagnostic services in a segmented healthcare system

In this paper, a problem arising in the planning of specialized diagnostic services in a segmented public healthcare system is addressed. The problem consists of deciding which hospitals will provide the service and their capacity levels, the allocation of demand in each institution, and the reallocation of uncovered demand to other institutions or private providers, while minimizing the total equivalent annual cost of investment and operating cost required to satisfy all the demand. An associated mixed-integer linear programming model can be solved by conventional branch and bound for relatively small instances; however, for larger instances the problem becomes intractable. To effectively address larger instances, a hybrid metaheuristic framework combining iterated greedy (IGA) and variable neighborhood descent (VND) components for this problem is proposed. Two greedy construction heuristics are developed, one starting with an infeasible solution and iteratively adding capacity and the other starting with a feasible, but expensive, solution and iteratively decrease capacity. The iterated greedy algorithm includes destruction and reconstruction procedures. Four different neighborhood structures are designed and tested within a VND procedure. In addition, the computation of local search components benefit from an intelligent exploitation of problem structure since, when the first-level location variables (hospital location and capacity) are fixed, the remaining subproblem can be solved efficiently as it is very close to a transshipment problem. All components and different strategies were empirically assessed both individually and within the IGA-VND framework. The resulting metaheuristic is able to obtain near optimal solutions, within 3% of optimality, when tested over a data base of 60- to 300-hospital instances.

A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

This study considers a multi-objective combined budget constrained facility location/network design problem (FL/NDP) in which the system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical service centers. In order to assure the network reliability versus uncertainty, an efficient robust optimization approach is applied to model the proposed problem. The formulation is minimizing the total expected costs, including, transshipment costs, facility location (FL) costs, fixed cost of road/link utilization as well as minimizing the total penalties of uncovered demand nodes. Then, in order to consider of several system uncertainty, the proposed model is changed to a fuzzy robust model by suitable approaches. An efficient Sub-gradient based Lagrangian relaxation algorithm is applied. In addition, a practical example is studied. At the following, a series of experiments, including several test problems, is designed and solved to evaluate of the performance of the algorithm. The obtained results emphasize that considering of practical factors (e.g., several uncertainties, system disruptions, and customer satisfaction) in modelling of the problem can lead to significant improvement of the system yield and subsequently more efficient utilization of the established network.

A hybrid multi-objective decision model for emergency shelter location-relocation projects using fuzzy analytic hierarchy process and goal programming approach

This paper presents a hybrid algorithm for efficiently managing location and relocation projects by proposing a hybrid multi-objective decision model based on analytic hierarchy process (AHP), fuzzy set theory and goal programming approach. The objectives of proposition are to minimise distance, risk, number of sites and uncovered demand and simultaneously maximise suitability based on qualitative factors while taking into consideration demand, capacity, utilisation and budgetary constraints. Since the problem is of multi-objective decision making, we solve it by converting all objectives into a single objective function using goal programming approach. Project managers can benefit from collective expertise of multiple decision makers as proposed model leverages their knowledge into automating shelter site selection and relocation process. The model attempts to achieve a compromise solution to multiple objectives in disaster recovery projects involving shelter location decisions. The results are validated by considering two real case studies of Nepal earthquake.

Locating a minisum annulus: a new partial coverage distance model

The problem is to find the best location in the plane of a minisum annulus with fixed width using a partial coverage distance model. Using the concept of partial coverage distance, those demand points within the area of the annulus are served at no cost, while for ‘uncovered’ demand points there will be additional costs proportional to their distances to the annulus. The objective of the problem is to locate the annulus such that the sum of distances from the uncovered demand points to the annulus (covering area) is minimized. The distance is measured by the Euclidean norm. We discuss the case where the radius of the inner circle of the annulus is variable, and prove that at least two demand points must be on the boundary of any optimal annulus. An algorithm to solve the problem is derived based on this result.

Bi-objective bilevel optimization of distribution center locations considering user equilibria

We propose a bi-objective, bilevel optimization model for the location of relief distribution centers (DCs) in humanitarian logistics. The upper-level decision-maker (an aid-providing organization) selects locations for capacitated DCs. On the lower level, beneficiaries choose a DC according to distance and amount of supply to be expected. This effects a user equilibrium on the lower decision level. Upper level objectives are to minimize total opening cost for the DCs and total uncovered demand. We develop an exact algorithm for determining the Pareto frontier of the problem, integrating the adaptive epsilon-constraint method, a branch-and-bound procedure, and the Frank–Wolfe procedure.

A multi-objective genetic algorithm for a bi-objective facility location problem with partial coverage

In this study, we present a bi-objective facility location model that considers both partial coverage and service to uncovered demands. Due to limited number of facilities to be opened, some of the demand nodes may not be within full or partial coverage distance of a facility. However, a demand node that is not within the coverage distance of a facility should get service from the nearest facility within the shortest possible time. In this model, it is assumed that demand nodes within the predefined distance of opened facilities are fully covered, and after that distance the coverage level decreases linearly. The objectives are defined as the maximization of full and partial coverage, and the minimization of the maximum distance between uncovered demand nodes and their nearest facilities. We develop a new multi-objective genetic algorithm (MOGA) called modified SPEA-II (mSPEA-II). In this method, the fitness function of SPEA-II is modified and the crowding distance of NSGA-II is used. The performance of mSPEA-II is tested on randomly generated problems of different sizes. The results are compared with the solutions of the most well-known MOGAs, NSGA-II and SPEA-II. Computational experiments show that mSPEA-II outperforms both NSGA-II and SPEA-II.

A modelling and simulation approach to assessment of a negative binomial approximation in a multi-echelon inventory system

Some studies in the multi-echelon inventory systems literature have used a negative binomial distribution to approximate a critical random variable arising in the model. Graves (1996) developed a multi-echelon inventory model with fixed replenishment intervals, where each site follows a base stock policy. He proposed in the one-warehouse, N-retailer case a negative binomial distribution to approximate a random variable he referred to as ‘uncovered demand’. Computational evidence was provided to demonstrate the effectiveness of the approximation. Graves then suggested search procedures for approximately optimal base stock levels at the warehouse and N identical retailers under two customer service criteria: 1) probability of no stockout; 2) fill rate. A separate analytical evaluation of the negative binomial approximation has been preliminarily reported elsewhere. In the current study, we apply a modelling and simulation approach to assess whether the approximation-based search procedures, in fact, lead to optimal or near-optimal stock levels.

The bi-objective stochastic covering tour problem

We formulate a bi-objective covering tour model with stochastic demand where the two objectives are given by (i) cost (opening cost for distribution centers plus routing cost for a fleet of vehicles) and (ii) expected uncovered demand. In the model, it is assumed that depending on the distance, a certain percentage of clients go from their homes to the nearest distribution center. An application in humanitarian logistics is envisaged. For the computational solution of the resulting bi-objective two-stage stochastic program with recourse, a branch-and-cut technique, applied to a sample-average version of the problem obtained from a fixed random sample of demand vectors, is used within an epsilon-constraint algorithm. Computational results on real-world data for rural communities in Senegal show the viability of the approach.

Fuzzy goal programming models for analyzing demand coverage

The main objective of demand coverage models is to locate servers so that a given demand space is appropriately covered. Most existing models assume that demand is located at specific points within an area and that coverage is evaluated by certain quantifiable criteria. However, in realistic applications, the concept of coverage may also include qualitative aspects. Moreover, the location of the servers may be determined on the basis of more than one objective. In this paper, we present a number of fuzzy goal programming models for demand coverage. We consider three objectives: (a) maximization of total coverage, (b) maximization of minimum coverage, and (c) minimization of distance to servers of uncovered demand points. Through a series of realistic problem instances, we demonstrate that the proposed models provide satisfactory solutions with respect to all three objectives.

Demand Side Analysis of Microlending Markets in Germany

Summary In developing and transition economies, microlending has become an effective instrument for providing micro businesses with the necessary financial resources to launch operations. In industrialized countries, with their highly developed banking systems, however, there has been ongoing debate on the question of whether an uncovered demand for microlending services exists. The present pilot study explores customer preferences formicrolending products in Germany. Among the interviewed business owners, 15 % reported revolving funding needs and an interest in microloans. We find that potential recipients of microloan products are retail business owners, foreign business owners, and persons who had previously received private loans. Furthermore, financial products should feature rapid access to short-term loans.

Demand Side Analysis of Microlending Markets in Germany

In developing and transition economies, microlending has become an effective instrument for providing micro businesses with the necessary financial resources to launch operations. In the industrialized countries, with their highly developed banking systems, however, there has been ongoing debate on the question of whether an uncovered demand for microlending services exists. The present pilot study explores customer preferences for microlending products in Germany. Among the interviewed business owners, 15% reported revolving funding needs and an interest in microloans. We find that potential recipients of microloan products are retail business owners, foreign business owners, and persons who had previously received private loans. Furthermore, financial products should feature rapid access to short-term loans.

Marginal analysis for the fuzzy p-median problem

The solutions to the fuzzy p-median problem make it possible to leave part of the demand uncovered in order to obtain significant reductions in costs. Moreover, the fuzzy formulation provides the decision-maker with many flexible solutions that he or she may prefer to the classical crisp solution. We introduce some marginal analysis techniques to study how solutions depend on membership functions. Taking into account the internal structure of the problem, we propose a practical criterion to fix the tolerances for the uncovered demand, which happens to be the most sensitive aspect of the fuzzy p-median.

Fuzzy logic based algorithms for maximum covering location problems

This paper concerns a class of maximum covering location problems in networks in uncertain environments. It is assumed that (a) relative weights of demand nodes are either deterministic or imprecise, described by linguistic expressions and (b) potential facility site locations are limited to network nodes. The concept of coverage is extended to include a degree of node coverage which means that the borders between the subset of covered demand nodes and the subset of uncovered demand nodes are inexact. The acceptable service distance/travelling times from a facility site to demand nodes are modelled by fuzzy sets. Three new algorithms for choosing the best facility locations are developed which assume that (1) demands at all nodes are equally important, (2) relative weights of demand at nodes are deterministic and (3) weights of demand at nodes are imprecise and described by linguistic terms, respectively. The algorithms are based on searching among potential facility nodes by applying comparison operations on discrete fuzzy sets. It is shown how to extend the proposed algorithms from one-site to multi-site covering problems. Illustrative examples of selecting locations for logistics centres in a distribution company are given.

Labor Shortages and Industrial Growth in Penang, Malaysia

Geografisk Tidsskrift, Danish Journal of Geography 97: 111–119, 1997. During the last two decades, industrial growth in Penang has been rapid. In particular, production and employment in the labor-intensive segments of the electronics industry have increased tremendously due to investments by transnational companies from the USA and Japan. In the first decade, the demand for adequately skilled and disciplined labor from Malaysia's rural areas was covered without major bottlenecks. However, recent reorganization of production in the electronics industry and the general expansion of manufacturing production in the area imply a more complex and uncovered demand for skilled and flexible labor in combination with unskilled and low paid labor. On the basis of scattered statistical data and a series of interviews with representatives for private capital, labor, and state authorities in the Penang industrial complex, this paper illuminates the nature of the labor shortages and the forms of response by individual capital. The objective is to assess the ability of existing labor market institutions to deal with the labor demand and to promote localized learning capacity. We find that a number of informal labor institutions have developed while the federal and local state have failed to establish adequate regulatory mechanisms. We conclude that the official preoccupation with the maintenance of low wages hampers the creation of new, productivity enhancing labor market institutions.