Minimization Of Travel Distance Research Articles

A GIS-based approach for improving urban sanitation planning and services delivery: A case study from Ouagadougou, Burkina Faso

Spatial planning and resource allocation are decisive factors in the effective delivery of sanitation services, especially in urban contexts. In low-income countries, the use of Geographic Information Systems (GIS) in the design and decision-making process in the delivery of such services is still very limited. This study, conducted in Ouagadougou, Burkina Faso, designs a GIS system with a data-oriented modelling infrastructure to assist and improve faecal sludge management (FSM) in the urban context. The estimation of faecal sludge (FS) daily production is achieved through field surveys and direct observations. The spatial planning system is further used to map and analyse the spatial distribution of existing FSM infrastructure, district-wise, address the inadequate distribution of emptying trucks parking lots within the city and therefore propose suitable sites for the establishment of new faecal sludge treatment plants (FSTPs). Moreover, an optimal path planning circuit for faecal sludge collection, transport and evacuation is designed based on the minimization of travel distance to the nearest FSTP. The proposed framework leverages GIS in city-wide FSM, virtually appealing to all urban contexts across sub-Saharan Africa, and sheds light on urban planners and managers on the need to incorporate GIS infrastructure within urban services delivery.

The shape and size of urban blocks

Two measures of shape compactness and fragmentation are coupled together into a plot that is defined as a two-dimensionalmatrix for classifying boundary shapes. Block shapes in a large sample of cities result in a swallowtail distribution in the matrix, which exposes two fundamental ways of transforming the basic compact block: by dissection, corresponding to large blocks with internal dendritic streets, and by stretching and bending, corresponding to serpentine blocks in hilly terrains and edge blocks along highways, railroads, and canals. The density of cases in each matrix zone reveals the realization of actual blocks out of the probable shape combinations as a manifestation of the social logic of urban form. The observed affinity between the shape and size of non-basic blocks in cities is used to formulate a model that explains them according to the constraints of arranging plots along the streets combined with the requirements for the intelligibility of navigation and the minimization of travel distance. Considering blocks as intra-street cells, the proposed block classification reveals important links between topological and geometric aspects of the street networks thus contributing to urban modeling, morphological classification, and comparative studies.

Route selection but not trail clearing are influenced by detour length in the Australian meat ants

Animals travelling through the environment often face trade-offs between environmental parameters such as risk, travel speed and ease of movement when selecting their routes. Route selection is of particular importance for central place foragers like ants, which collectively and repeatedly use trails to exploit stable sources of food. We investigated how colonies of meat ants (Iridomyrmex purpureus) select and clear trail routes when faced with semi-permeable obstructions (strips of grass turf) that substantially slow their travel speed. Meat ant colonies usually re-routed their trails to avoid obstructions when short strips of turf were laid across existing trails, but always travelled directly across the turf when avoiding the turf would have significantly increased travel time. No significant difference in trail clearing activity was found between the short and long obstruction treatments. On binary mazes, meat ants were equally likely to choose paths obstructed with turf and equal length smooth paths, despite much higher time costs associated with the obstructed route. Colonies always chose the shorter, turf-covered path on mazes where the length of the smooth path was increased by 50%, suggesting that meat ants prioritise the minimisation of travel distance when selecting new trail routes. Meat ant route selection and clearing behaviour may reflect a long-term foraging optimisation strategy whereby colonies pay high short-term costs to minimise long-term travelling costs by selecting relatively direct, short distance trail routes which can be cleared of obstructions over time.

Material handling improvement in warehouses by parts clustering

A major part of warehouse operations is related to the collection of parts from the warehouse which is called the Order Picking Problem. To improve order picking operations, the total travel distance and generally picking time must be reduced. In this paper, a two-level approach is proposed that determines the locations of parts in the warehouse. The first step clusters parts into part families. Four different clustering methods based on principal component analysis, singular value decomposition and Two-Step Cluster Component are applied. In the second step, four different heuristics are proposed to determine the locations of parts. In addition to the minimisation of travel distance, we also consider the minimisation of the total congestion in aisles due to multiple workers. The proposed algorithms also consider the interactions between part families to minimise intergroup movements. As a result of the implementation, we achieved more than 40% reduction in material handling compared to the current set-up of the warehouse. The applied algorithms can easily be modified to be used for warehouses with different configurations. The algorithms utilised in this case study can be helpful to researchers to become familiar with new heuristics, as well as practitioners to design improved warehouses.

Multi-objective Construction Site Layout Planning Using Genetic Algorithms

Efficient layout planning of a construction site is fundamental for successful project undertaking as it enhances both productivity and safety in construction sites. This task usually consists of identifying the temporary facilities needed to support construction operations, determining their size and shape, and optimally positioning them in the unoccupied areas within the site boundaries. The site layout planning problem is a complex combinatorial optimization problem involving multiple objectives and it grows significantly in size as the number of facilities and constraints increases. The existing literature includes a variety of analytical, heuristic, and meta-heuristic techniques for solving the problem but existing studies usually examine a small number of facilities and focus on travel distance minimization, ignoring generally cost related or other decision parameters. The objective of this study is to develop feasible and efficient site layout solutions in a realistic representation scheme taking into consideration not only the total distance traveled but also cost and safety parameters as well. A multi-objective optimization model is developed aiming at minimizing a generalized cost function which results from the construction cost of a facility placed at alternative locations, the transportation cost among locations, and any safety concern in the form of preferred proximity or remoteness of particular facilities to other facilities or work areas. The development integrates the required robust search objective with the optimization capabilities of the genetic algorithms (GAs). The model has been tested on several test cases and the results of a comparative study with existing methods from the literature are presented. The evaluation indicates that the proposed model provides effective and rational solutions, in response to decision parameters and problem constraints, and that it results in more robust layout planning than previous methods both qualitatively and quantitatively.

CNC Machining Path Planning Optimization for Circular Hole Patterns via a Hybrid Ant Colony Optimization Approach

This paper presents a path-planning optimization study for a Computer Numerical Control (CNC) machining center tasked with machining jobs involving a large number of holes to drill that are mostly arranged in concentric circular patterns. Benefits of this research may contribute to shortening the machining time in certain components used in heat exchangers, boilers, condensers, trammel screens and food separators. Optimization of tool travel distance and machining cost are typically overlooked aspects when generating tool paths and CNC codes from commercially available CAD software packages. Tool path travel distance minimization can be modelled Travelling Salesman Problem (TSP). Optimization algorithms have been heavily applied in the literature to the TSP with varying levels of success. Ant Colony Optimization (ACO) is one of the most prominent approaches that mimics the natural behavior of ant colonies. The research in this paper proposes a hybrid ACO that has a biasing mechanic designed to take advantage of the geometric hole-pattern arrangement, as well as a local search. Simulation examples show the proposed approach exhibiting superior performance compared to the classic ACO approach, a genetic algorithm (GA) approach, as well as the simple spiral path generated via commercial CAD software. The proposed approach is then applied to the drilling path planning of a two-thousand-hole food-industry separator plate.

A method for designing centralized emergency supply network to respond to large-scale natural disasters

This paper proposes a method for designing a seamless centralized emergency supply network by integrating three sub-networks (shelter network, medical network, and distribution network) to support emergency logistics operations in response to large-scale natural disasters. The proposed method primarily involves three stage multi-objective (travel distance minimization, operational cost minimization, and psychological cost minimization), mixed-integer linear programming models. The three sub-networks are designed using the proposed programming models. The distinctive features of the proposed method are as follows. (1) The proposed method is demand-driven. The order of the designed sub-networks is shelter, medical, and distribution, with the connections of the latter networks based on the arrangements for the former. (2) The objective functions of three stage programming models include not only traditional objectives such as minimizing total travel distance and operational cost, which supply-side members focus on, but also minimizing the psychological cost experienced by demand-side members. Model tests are conducted to demonstrate that the superiority of a centralized emergency supply network designed by the proposed method over a decentralized one, especially with regard to distribution network design.

Extensions of the unidirectional flow path design problem solved by efficient metaheuristics

The flow path design problem is one of the most important issues in AGV (automated guided vehicles) systems design. It consists in the determination of each segment direction and the paths to used for automated vehicles in production units. Performances of a system is influenced by organization of these roads because the transport time and the number of necessary vehicles depends on. This work deals with flow path design problem for a conventional unidirectional network. Some new extensions of the total travel distance minimization considering both loaded and empty travels is presented and solved by efficient metaheuristics. Extensions consist in adding different type of vehicle, not allowed some segments and taking into account construction costs. This study is made in collaboration with ANDRA (National Agency for Radioactive Waste Management).

A block transportation scheduling system considering a minimisation of travel distance without loading of and interference between multiple transporters

Material transportation scheduling problems concerning scheduling optimisation have been extensively investigated by researchers in such fields as industrial engineering and management science. Various algorithms have been proposed to solve such problems. However, the majority of these algorithms cannot be applied to a block transportation problem when a shipyard that uses a transporter, a large vehicle employed for moving weight, is considered. In this study, a hybrid optimisation algorithm is proposed for solving a block transportation problem when multiple transporters are used. With regards to the transporters, a minimisation of the travel distance without loading of and interference between the transporters is considered. A block transportation scheduling system is then developed based on the proposed algorithm. The developed system is applied to an actual block transportation scheduling problem of a shipyard. From the attained results, we demonstrate that the proposed algorithm has the ability to effectively solve the block transportation scheduling problems of a shipyard.

A dynamic programming heuristic for vehicle routing with time-dependent travel times and required breaks

For the intensively studied vehicle routing problem (VRP), two real-life restrictions have received only minor attention in the VRP-literature: traffic congestion and driving hours regulations. Traffic congestion causes late arrivals at customers and long travel times resulting in large transport costs. To account for traffic congestion, time-dependent travel times should be considered when constructing vehicle routes. Next, driving hours regulations, which restrict the available driving and working times for truck drivers, must be respected. Since violations are severely fined, also driving hours regulations should be considered when constructing vehicle routes, even more in combination with congestion problems. The objective of this paper is to develop a solution method for the VRP with time windows (VRPTW), time-dependent travel times, and driving hours regulations. The major difficulty of this VRPTW extension is to optimize each vehicle’s departure times to minimize the duty time of each driver. Having compact duty times leads to cost savings. However, obtaining compact duty times is much harder when time-dependent travel times and driving hours regulations are considered. We propose a restricted dynamic programming (DP) heuristic for constructing the vehicle routes, and an efficient heuristic for optimizing the vehicle’s departure times for each (partial) vehicle route, such that the complete solution algorithm runs in polynomial time. Computational experiments demonstrate the trade-off between travel distance minimization and duty time minimization, and illustrate the cost savings of extending the depot opening hours such that traveling before the morning peak and after the evening peak becomes possible.

Localization: Approximation and Performance Bounds to Minimize Travel Distance

Localization, which is the determination of one's location in a known terrain, is a fundamental task for autonomous robots. This paper presents several new basic theoretical results about localization. We show that, even under the idealized assumptions of accurate sensing and perfect actuation, it is intrinsically difficult to localize a robot with a travel distance that is close to minimal. Our result helps to theoretically justify the common use of fast localization heuristics, such as greedy localization, which always moves the robot to a closest informative location (where the robot makes an observation that decreases the number of its possible locations). We show that the travel distance of greedy localization is much larger than minimal in some terrains because the closest informative location can distract greedy localization from a slightly farther, but much more informative, location. However, we also show that the travel distance of greedy localization can be larger, but not much larger, than the terrain size <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> . Thus, the travel distance of greedy localization scales well with the terrain size and is much larger than minimal in some terrains, not because it is large with respect to the terrain size, but because the minimal travel distance is exceptionally small in these terrains. As a corollary to our analysis, we show that the travel distance of greedy mapping (which always moves the robot to a closest location, where it makes an observation that increases its knowledge of the terrain) cannot be much larger than the terrain size. In theoretical terms, we prove the NP-hardness of minimization of travel distance for localization to within a logarithmic factor of the terrain size. We prove that the travel distance of greedy localization is at least order <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> / log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> larger than minimal in some terrains and that it is at least order <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> log <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> / log log <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> in the worst case. Finally, we prove that the travel distance of both greedy localization and greedy mapping is at most order <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> log <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> . Previously, it was only known that it is NP-hard to localize with minimal travel distance and that the travel distances of greedy localization and greedy mapping are at most order <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3/2</sup> .

Two Evolutionary Metaheuristics For The Vehicle Routing Problem With Time Windows

The vehicle routing problem with time windows (VRPTW) is an extension of the well-known vehicle routing problem with a central depot. The objective is to design an optimal set of routes that services all customers and satisfies the given constraints, especially the time window constraints. The objective function considered here combines the minimization of the number of vehicles (primary criterion) and the total travel distance minimization (secondary criterion). In this paper, two evolution strategies for solving the VRPTW are proposed. The evolution strategies were tested on 58 problems from the literature with sizes varying from 100 to 417 customers and 2 to 54 vehicles. The generated new best known solutions indicate that evolution strategies are effective in reducing both the number of vehicles and the total travel distance

Land use planning: An optimizing model

An optimizing model for comprehensive land use planning (OPTPLAN) is described. Multiple objectives and constraints on desired growth patterns are considered simultaneously in arriving at optimal acreages of ten land use types in several regions within a county or city. Objectives are (i) minimization of conflict between different land uses, (ii) minimization of travel distance of new trips to the existing transportation network, (iii) generation of a fiscally sound plan, and (iv) minimization of environmental impact. Constraints are on each type of land use based on population and employment projections, projected mix of residential dwelling units, local commercial and institutional land needed per thousand persons, location and extent of new industries, open space requirements per thousand population, and natural resource considerations. Application of the model to Du Page County, Illinois is shown.