Up and Down and Back Again: Troubled Childhood Childhood Notwithstanding, Washington\u27s Stand Alone Estate Tax Deserves to be Defended

This Comment evaluates the history of Washington\u27s estate tax from the pre-2005 frozen scheme, through the Supreme Court\u27s analysis and mandate in Estate of Hemphill v. State, and up to the legislation enacted in May 2005. Part II provides a background on EGTRRA and evaluates the extent of its changes nationwide. Part III critically reviews Washington\u27s estate tax history, and examines both the seminal Initiative 402 and the legislative history supporting the shift away from federal conformation. Part IV analyzes how the court\u27s 2005 ruling provided the catalyst for legislative change, and provides a summary of Hemphill and the arguments presented therein. Part V argues that Senate Bill 6096 is a sound step towards dealing with the inevitable fiscal issues resulting both from Washington\u27s pre-2005 scheme and from the choice Washington had to make in light of Hemphill. Part VI evaluates the ramifications and problems that the change to EGTRRA would have inflicted and that Senate Bill 6096 specifically avoids, and encourages the legislature to treat the bill as but one step of an ongoing process of proactive taxation. Part VII concludes the Comment with the point that the newly enacted stand-alone tax is the best way to square the interests of all parties involved and notes that, although it may not be an appropriate permanent fix, Senate Bill 6096 is a positive step for our state and the legislature should be encouraged to continue improving upon it

A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling

We propose an efficient evolutionary multi-objective optimization approach to the capacitated facility location–allocation problem (CFLP) for solving large instances that considers flexibility at the allocation level, where financial costs and CO2 emissions are considered simultaneously. Our approach utilizes suitably adapted Lagrangian Relaxation models for dealing with costs and CO2 emissions at the allocation level, within a multi-objective evolutionary framework at the location level. Thus our method assesses the robustness of each location solution with respect to our two objectives for customer allocation. We extend our exploration of selected solutions by considering a range of trade-offs for customer allocation

New heuristic and evolutionary operators for the multi-objective urban transit routing problem

The urban transit routing problem (UTRP) involves finding efficient routes in a public transport system. However, developing effective heuristics and metaheuristics for the UTRP is hugely challenging because of the vast search space and multiple constraints that make even the attainment of feasible results exceedingly difficult, as the problem size increases. Moreover, progress with academic research on the UTRP appears to be seriously hampered by: 1) a lack of benchmark data, and 2) the complex and diverse range of methods used in the literature to evaluate solution quality. It is not currently possible for researchers to effectively compare the performance of their algorithms with anyone else's. This paper presents new problem-specific genetic operators within a multi-objective evolutionary framework, and furthermore proposes an effective and efficient heuristic method for seeding the population with feasible route sets. In addition new data sets are provided and made available for download, to aid future researchers. Excellent results are presented for Mandl's problem, which is currently the only benchmark available, while the results obtained for the new data sets provide a challenge for future researchers to beat

Solving urban transit route design problem using selection hyper-heuristics

The urban transit routing problem (UTRP) focuses on finding efficient travelling routes for vehicles in a public transportation system. It is one of the most significant problems faced by transit planners and city authorities throughout the world. This problem belongs to the class of difficult combinatorial problems, whose optimal solution is hard to find with the complexity that arises from the large search space, and the number of constraints imposed in constructing the solution. Hyper-heuristics have emerged as general-purpose search techniques that explore the space of low level heuristics to improve a given solution under an iterative framework. In this work, we evaluate the performance of a set of selection hyper-heuristics on the route design problem of bus networks, with the goal of minimising the passengers’ travel time, and the operator’s costs. Each selection hyper-heuristic is empirically tested on a set of benchmark instances and statistically compared to the other selection hyper-heuristics to determine the best approach. A sequence-based selection method combined with the great deluge acceptance method achieved the best performance, succeeding in finding improved results in much faster run times over the current best known solutions

Solving the One-Commodity Pickup and Delivery Problem Using an Adaptive Hybrid VNS/SA Approach

Abstract. In the One-Commodity Pickup and Delivery Problem (1-PDP), a single commodity type is collected from a set of pickup customers to be delivered to a set of delivery customers, and the origins and destinations of the goods are not paired. We introduce a new adaptive hybrid VNS/SA (Variable Neighborhood Search/Simulated Annealing) approach for solving the 1-PDP. We perform sequences of VNS runs, where neighborhood sizes, within which the search is performed at each run, are adaptable. Experimental results on a large number of benchmark instances indicate that the algorithm outperforms previous heuristics in 90% of the large size test cases. Nevertheless, this comes at the expense of an increased processing time

Solving the one-commodity pickup and delivery problem using an adaptive hybrid VNS/SA approach

In the One-Commodity Pickup and Delivery Problem (1- PDP), a single commodity type is collected from a set of pickup customers to be delivered to a set of delivery customers, and the origins and destinations of the goods are not paired. We introduce a new adaptive hybrid VNS/SA (Variable Neighborhood Search/Simulated Annealing) approach for solving the 1-PDP. We perform sequences of VNS runs, where neighborhood sizes, within which the search is performed at each run, are adaptable. Experimental results on a large number of benchmark instances indicate that the algorithm outperforms previous heuristics in 90% of the large size test cases. Nevertheless, this comes at the expense of an increased processing time

Footfall Signatures and Volumes: Towards a Classification of UK Centres

The changing nature of retail coupled with rapid technological and social developments, are posing great challenges to the attractiveness of traditional retail areas in the UK. In this paper we argue that the definitions and classifications of town centres currently adopted by UK planners and policy makers are outdated, because of their focus on retail occupancy. Instead, we propose a more dynamic definition and classification of centres, based on their activity volumes and patterns, which we obtain from footfall data. Our expectation is that adopting this activity-based approach to defining and classifying centres will radically alter the way in which they are developed and managed

Optimising bus routes with fixed terminal nodes

The urban transit routing problem (UTRP) is concerned with finding efficient travelling routes for public transportation systems. This problem is highly complex, and the development of effective algorithms to solve it is very challenging. Furthermore, realistic benchmark data sets are lacking, making it difficult for researchers to compare their problem-solving techniques with those of other researchers. In this paper we contribute a new set of benchmark instances that have been generated by a procedure that scales down a real world transportation network, yet preserves the vital characteristics of the network layout including "terminal nodes" from which buses are restricted to start and end their journeys. In addition, we present a hyper-heuristic solution approach, specially tailored to solving instances with defined terminal nodes. We use our hyper-heuristic technique to optimise the generalised costs for passengers and operators, and compare the results with those produced by an NSGAII implementation on the same data set. We provide a set of competitive results that improve on the current bus routes used by bus operators in Nottingham

Public Transport Network Optimisation in PTV Visum using Selection Hyper-Heuristics

Despite the progress in the field of automatic public transport route opti-misation in recent years, there exists a clear gap between the development of opti-misation algorithms and their applications in real-world planning processes. In this study, we bridge this gap by developing an interface between the Urban Transit Routing Problem (UTRP) and the professional transport modelling software PTV Visum. The interface manages the differences in data requirements between the two models and allows the optimisation of public transport lines in Visum network models. This is demonstrated with the application of Selection Hyper-heuristics on two network models representing real world urban areas. The optimisation objectives include the passengers' average travel time and operators' costs. Furthermore, we show how our approach can be combined with a mode choice model to optimise the use of public transport in relation to other modes. This feature is applied in a special optimisation experiment to reduce the number of private vehicles on a selected set of links in the network. The results demonstrate the successful implementation of our interface and the applied optimisation methods for a multi-modal public transport network

How safe is it to shop? Estimating the amount of space needed to safely social distance in various retail environments

COVID-19 has had a devastating effect on towns and cities throughout the world. However, with the gradual easing of lockdown policies in most countries, the majority of non-essential retail businesses are trying their best to bounce back both economically and socially. Nevertheless, the efforts of retail traders are hampered by uncertainty regarding what capacity measures need to be taken, and there is an urgent need to understand how social distancing can be safely followed and implemented in these spaces. This paper draws from retail space allocation, crowd science, operational research and ergonomics/biomechanics to develop a method for identifying the minimum amount of space an individual needs to socially distance in shops, markets, shopping centres and open commercial spaces, when there are other people present. The area required per person is calculated for both static space (where people are seated, standing or queuing, for example) and dynamic space (where people need to walk freely). We propose our method as a step forward in understanding the very practical problem of capacity, which can hopefully allow retail spaces to operate safely, and minimise the risk of virus transmission