This paper discusses the notion of workflow congestion in the context of material handling equipment interruptions in a manufacturing or warehousing facility. Development of a combination of ...probabilistic and physics-based models for workflow interruptions permits evaluation of the expected link travel time. The problem is then of routing in a way that minimizes total expected travel time. The rerouting problem is modeled as a multicommodity flow problem with link capacity design. A greedy upper bounding and Lagrangean relaxation algorithm are developed to solve this efficiently. To calibrate our modeling process we develop an object-oriented simulation model that explicitly considers various workflow interruptions. Our major finding is that rerouting traffic in a congested facility can significantly alleviate congestion delays and improve the efficiency of material movement. A managerial insight derived from this work is that rerouting is most effective in medium traffic-intensity situations.
The type of humanitarian logistics problem of interest is an earthquake with significant damage, prioritized items for delivery, and an extensive time period over which supplies need to be delivered. ...The problem of interest is an outgrowth of a recent paper by 10, where they focused on supplying relief items from a central depot for a prolonged period of time. The drawback of their approach is that long travel distances of vehicles are required between demand points and the central depot. In this paper, we propose the location of temporary depots around the disaster-affected area, along with the required vehicles and resources, to improve logistical efficiency. A two-phase heuristic approach is proposed; it locates temporary depots and allocates covered demand points to an open depot in Phase I, and explores the best logistics performance under the given solution from Phase I in Phase II. Results from computational experiments and an earthquake case study are used to illustrate the benefits of this approach.
► Location of temporary depots around a disaster-affected area. ► Improvement of logistical efficiency in disaster relief. ► Case study based on earthquake scenario in Southern California.
This paper seeks to evaluate the performance of genetic algorithms (GA) as an alternative procedure for generating optimal or near-optimal solutions for location problems. The specific problems ...considered are the uncapacitated and capacitated fixed charge problems, the maximum covering problem, and competitive location models. We compare the performance of the GA-based heuristics developed against well-known heuristics from the literature, using a test base of publicly available data sets.
Genetic algorithms are a potentially powerful tool for solving large-scale combinatorial optimization problems. This paper explores the use of this category of algorithms for solving a wide class of location problems. The purpose is not to “prove” that these algorithms are superior to procedures currently utilized to solve location problems, but rather to identify circumstances where such methods can be useful and viable as an alternative/superior heuristic solution method.
The United Nations (UN) urged acceleration efforts to tackle inequity through its Sustainable Development Goals set in 2015. The need for focusing on equity has been highlighted in the field of ...public health for improved health outcomes. Drug stock-outs at the last mile is a complex challenge that hinders the efforts of equitable pharmaceutical access and availability. Severity experienced due to drug stock-outs are not equal among a group of mutually exclusive and collectively exhaustive populations. This paper proposes the stock-out severity index (SSI) to measure severity due to availability of drugs or lack thereof by incorporating risk factors and determinants of health from a combination of data sources. The spread of inequity among these groups of populations can be measured using the mean absolute deviation or the GINI coefficient on the SSI assigned to each population group. The paper demonstrates how the SSI can help define priority levels of varying population groups, rather than making decisions solely from an equality perspective that assumes uniformity across populations. It also demonstrates the impact on rearranging the priorities when the SSI is used. The paper presents guidelines for incorporating and implementing the SSI.
•This paper addresses hazmat transport of medical waste. This is a significant research question and requires a different type of modeling and analysis than other hazmat transportation problems.
...•Medical waste pickup.
•Hazardous materials risk modeling.
•Vehicle routing via column generation.
We consider a Periodic Load-dependent Capacitated Vehicle Routing Problem (PLCVRP) encountered by healthcare centers and medical waste collection companies for the design of a weekly inventory routing schedule to transport medical wastes to treatment sites. In addition to minimization of transportation risk, occupational risk related to temporary storage of hazardous wastes at the healthcare centers is considered. The transport risk on each arc is dependent on the weight of hazardous medical waste on the vehicle when it traverses that arc. We devise a decomposition based heuristic algorithm to solve this problem. We analyze the characteristics of the PLCVRP’s solutions with respect to four different criteria: (i) transport and occupational risk, (ii) transport risk, (iii) occupational risk, and (iv) transportation cost. Solving different versions of PLCVRP reveals that minimizing both transport and occupational risk on the network can aid decision makers to develop a better routing schedule in terms of the imposed risk of hazardous medical waste. Experimental results confirm the efficiency of our heuristic. We present a case study to illustrate solution attributes obtained by our solution methodology. The case study is based on medical waste management in Dolj, Romania.
Road segments, telecommunication wiring, water and sewer pipelines, canals and the like are important features of the urban environment. They are often conceived of and represented as network-based ...arcs. As a result of the usefulness and significance of arc-based features, there is a need to site facilities along arcs to serve demand. Examples of such facilities include surveillance equipment, cellular towers, refueling centers and emergency response stations, with the intent of being economically efficient as well as providing good service along the arcs. While this amounts to a continuous location problem by nature, various discretizations are generally relied upon to solve such problems. The result is potential for representation errors that negatively impact analysis and decision making. This paper develops a solution approach for the continuous arc covering problem that theoretically eliminates representation errors. The developed approach is applied to optimally place acoustic sensors and cellular base stations along a road network. The results demonstrate the effectiveness of this approach for ameliorating any error and uncertainty in the modeling process.
This paper considers a bi-level hazmat transportation network design problem in which hazmat shipments have to be transported over a road network between specified origin-destination points. The ...bi-level framework involves a regulatory authority and hazmat carriers. The control variables for the regulatory authority are locations of hazmat response teams and which additional links to include for hazmat travel. The regulatory authority (upper level) aims to minimize the maximum transport risk incurred by a transportation zone, which is related to risk equity. Our measure of risk incorporates the average response time to the hazmat incidents. Hazmat carriers (lower level) seek to minimize their travel cost. Using optimality conditions, we reformulate the non-linear bi-level model as a single-level mixed integer linear program, which is computationally tractable for medium size problems using a commercial solver. For large size problems, we propose a greedy heuristic approach, which we empirically demonstrate to find good solutions with reasonable computational effort. We also seek a robust solution to capture stochastic characteristics of the model. Experimental results are based on popular test networks from the Sioux Falls and Albany areas.
•We consider joint decision on network design and hazmat response team locations.•We define a new risk measure with the average response time to hazmat incidents.•We propose a robust solution method for the uncertain nature of hazmat incidents.
This paper considers scheduling spatially distributed jobs with degradation. A mixed integer programming (MIP) model is developed for the linear degradation case in which no new jobs arrive. ...Properties of the model are analyzed, following which three heuristics are developed, enhanced greedy, chronological decomposition and simulated annealing. Numerical tests are conducted to: (i) establish limits of the exact MIP solution, (ii) identify the best heuristic based on an analysis of performance on small problem instances for which exact solutions are known, (iii) solve large problem instances and obtain lower bounds to establish solution quality, and (iv) study the effect of three key model parameters. Findings from our computational experiments indicate that: (i) exact solutions are limited to instances with less than 14 jobs; (ii) the enhanced greedy heuristic followed by the application of the simulated annealing heuristic yields high quality solutions for large problem instances in reasonable computation time; and (iii) the factors “degradation rate” and “work hours” have a significant effect on the objective function. To demonstrate applicability of the model, a case study is presented based on a pothole repair scenario from Buffalo, New York, USA. Findings from the case study indicate that scheduling spatially dispersed jobs with degradation such as potholes requires: (i) careful consideration of the number of servers assigned, degradation rate and depot location; (ii) appropriate modeling of continuously arriving jobs; and (iii) appropriate incorporation of equity consideration.
•Scheduling spatially distributed jobs with degradation•MIP model developed for the linear degradation case•Properties of the model analyzed•Numerical tests conducted•A case study is presented based on a pothole repair scenario
Interurban roads are constantly used by transient vehicles. In some places, however, network users are subject to attacks, resulting in assaults to drivers and cargo theft. In an attempt to solve ...this problem, a binary integer programming model is developed, whose objective is to maximize the expected vehicle coverage across the network. The model dynamically locates patrol units through a fixed time horizon, subject to movement and location constraints, considering a probability of not being able to attend to an attack, due to a distance factor. A chronological decomposition heuristic is developed, and achieves an optimality gap of less than 1 %, in less than 5 min. The problem is also solved by developing a geographical decomposition heuristic. To introduce a measure of equity, two sets of constraints are proposed. Three measures are considered: total vehicle coverage, inequity and network coverage. A trade-off between these three measures is observed and discussed. Finally, scalability of the model is explored, using decomposition in terms of patrolling units, until we obtain subproblems of equal size as the original instance. All of these features are applied to a case study in Northern Israel. In the last section, some adaptations and additions to the model that can be made in further research are discussed.
The last mile delivery in humanitarian relief supply often happens on a tree or an almost-tree network allowing split deliveries. We present a relief delivery model incorporating a tree network for ...last mile delivery. We developed a mixed integer programming (MIP) formulation with the goal of minimizing the unsatisfied demand of the population. For better computational performance, we reformulated the MIP exploiting the tree network structure and found that this gave an order of magnitude reduction in computational time. To further improve computational efficiency, we developed a heuristic solution method based on a decomposition scheme applied to the tree network formulation. This led to the Capacitated Vehicle Routing Problem on trees with split deliveries, for which we derived a closed-form solution. This decomposition scheme resulted in a further order of magnitude reduction in computation time. To demonstrate the application of our approach we applied our model to the humanitarian logistics relief operation encountered in the 2015 Nepal earthquake.
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