On the single-assignment p-hub center problem

We study the computational aspects of the single-assignment p-hub center problem on the basis of a basic model and a new model. The new model's performance is substantially better in CPU time than different linearizations of the basic model. We also prove the NP-Hardness of the problem

Recent advances in location analysis

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.cor.2015.04.013 © 2015. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Introduction to a special issue of Computers & Operations Research dedicated to recent advances in location analysis

The green location-routing problem

This paper introduces the Green Location-Routing Problem (GLRP), a combination of the classical Location-Routing Problem (LRP) and the Pollution-Routing Problem (PRP). The GLRP consists of (i) locating depots on a subset of a discrete set of points, from where vehicles of limited capacity will be dispatched to serve a number of customers with service requirements, (ii) routing the vehicles by determining the order of customers served by each vehicle and (iii) setting the speed on each leg of the journey such that customers are served within their respective time windows. The objective of the GLRP is to minimize a cost function comprising the fixed cost of operating depots, as well as the costs of the fuel and CO2 emissions. The amount of fuel consumption and emissions is measured by a widely used comprehensive modal emission model. The paper presents a mixed integer programming formulation and a set of preprocessing rules and valid inequalities to strengthen the formulation. Two solution approaches; an integer programming based algorithm and an iterated local search algorithm are also presented. Computational analyses are carried out using adaptations of literature instances to the GLRP in order to analyze the effects of a number parameters on location and routing decisions in terms of cost, fuel consumption and emission. The performance of the heuristic algorithms are also evaluated

Spatial Analysis of Single Allocation Hub Location Problems

The final publication is available at Springer via http://dx.doi.org/10.1007/s11067-015-9311-9Hubs are special facilities that serve as switching, transshipment and sorting nodes in many-to-many distribution systems. Flow is consolidated at hubs to exploit economies of scale and to reduce transportation costs between hubs. In this article, we first identify general features of optimal hub locations for single allocation hub location problems based on only the fundamental problem data (demand for travel and spatial locations). We then exploit this knowledge to develop a straightforward heuristic methodology based on spatial proximity of nodes, dispersion and measures of node importance to delineate subsets of nodes likely to contain optimal hubs. We then develop constraints for these subsets for use in mathematical programming formulations to solve hub location problems. Our methodology can also help narrow an organization's focus to concentrate on more detailed and qualitative analyses of promising potential hub locations. Results document the value of including both demand magnitude and centrality in measuring node importance and the relevant tradeoffs in solution quality and time.Turkish Academy of Science

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Global logistics management/ Edit.: Bahar Y. Kara

xxi, 310 p.: ill, tab.; 23 cm

Global logistics management/ Edit.: Bahar Y. Kara

xxi, 310 p.: ill, tab.; 23 cm