GIS and Network Analysis

Both geographic information systems (GIS) and network analysis are burgeoning fields, characterised by rapid methodological and scientific advances in recent years. A geographic information system (GIS) is a digital computer application designed for the capture, storage, manipulation, analysis and display of geographic information. Geographic location is the element that distinguishes geographic information from all other types of information. Without location, data are termed to be non-spatial and would have little value within a GIS. Location is, thus, the basis for many benefits of GIS: the ability to map, the ability to measure distances and the ability to tie different kinds of information together because they refer to the same place (Longley et al., 2001). GIS-T, the application of geographic information science and systems to transportation problems, represents one of the most important application areas of GIS-technology today. While traditional GIS formulation's strengths are in mapping display and geodata processing, GIS-T requires new data structures to represent the complexities of transportation networks and to perform different network algorithms in order to fulfil its potential in the field of logistics and distribution logistics. This paper addresses these issues as follows. The section that follows discusses data models and design issues which are specifically oriented to GIS-T, and identifies several improvements of the traditional network data model that are needed to support advanced network analysis in a ground transportation context. These improvements include turn-tables, dynamic segmentation, linear referencing, traffic lines and non-planar networks. Most commercial GIS software vendors have extended their basic GIS data model during the past two decades to incorporate these innovations (Goodchild, 1998). The third section shifts attention to network routing problems that have become prominent in GIS-T: the travelling salesman problem, the vehicle routing problem and the shortest path problem with time windows, a problem that occurs as a subproblem in many time constrained routing and scheduling issues of practical importance. Such problems are conceptually simple, but mathematically complex and challenging. The focus is on theory and algorithms for solving these problems. The paper concludes with some final remarks.

Topology Network Optimization of Facility Planning and Design Problems

The research attempts to provide a graphical theory-based approach to solve the facility layout problem. Which has generally been approached using Quadratic Assignment Problem (QAP) in the past, an algebraic method. It is a very complex problem and is part of the NP-Hard optimization class, because of the nonlinear quadratic objective function and (0,1) binary variables. The research is divided into three phases which together provide an optimal facility layout, block plan solution consisting of MHS (material handling solution) projected onto the block plan. In phase one, we solve for the position of departments in a facility based on flow and utility factor (weight for location). The position of all the departments is identified on the vertices of MPG (maximal planar graph), which maximizes the possibility of flow. We use named MPG produced in literature, throughout the research. The grouping of the department is achieved through GMAFLAD, a QSP (quadratic set packing) based optimizer. In Phase 2, the dual for the MPG’s is solved consisting of department location as per phase 1, to generate Voronoi graphs. These graphs are then, expanded by an ingenious parameter optimization formulation to achieve area fitting for individual cases. Optimization modeling software, Lingo17.0 is used for solving the parameter optimization for generating coordinates of the block plan. The plotting of coordinates for the block plan graphics is done via Autodesk inventor 2019. In phase 3, the solution for MHS is achieved using an RSMT (Rectilinear Steiner minimal tree) graph approach. The Voronoi seed coordinates produced through phase 2 results are computed by GeoSteiner package to generated the RSMT graph for projection onto the block plan (Also, done by Inventor 2019). The graphical method employed in this research, itself has complex and NP-hard problem segments in it, which have been relaxed to polynomial time complexity by fragmenting into groups and solving them in sections. Solving for MPG & RSMT are a class of NP-Hard problem, which have been restricted to N=32 here. Finally, to validate the research and its methodology a real-life case study of a shipyard building for the data set of PDVSA, Venezuela is performed and verified

Kinematically optimal hyper-redundant manipulator configurations

“Hyper-redundant” robots have a very large or infinite degree of kinematic redundancy. This paper develops new methods for determining “optimal” hyper-redundant manipulator configurations based on a continuum formulation of kinematics. This formulation uses a backbone curve model to capture the robot's essential macroscopic geometric features. The calculus of variations is used to develop differential equations, whose solution is the optimal backbone curve shape. We show that this approach is computationally efficient on a single processor, and generates solutions in O(1) time for an N degree-of-freedom manipulator when implemented in parallel on O(N) processors. For this reason, it is better suited to hyper-redundant robots than other redundancy resolution methods. Furthermore, this approach is useful for many hyper-redundant mechanical morphologies which are not handled by known methods

Combinatorial Optimization

[no abstract available

Parametric freeform-based construction site layout optimization

Traditional approaches to the construction site layout problem have been focused mainly on rectilinear facilities where the importance proximity measures are mainly based on Cartesian distances between the centroids of the facilities. This is a fair abstraction of the problem; however it ignores the fact that many facilities on construction sites assume non-rectilinear shapes that allow for better compaction within tight sites. The main focus of this research is to develop a new approach of modeling site facilities to surpass limitations and inefficiencies of previous models and to ensure a more realistic approach to construction site layout problems. A construction site layout optimization model was developed that can suit both static and dynamic site layouts. The developed model is capable of modeling any rectilinear and non-rectilinear site shapes, especially splines, since it utilizes a parametric modeling software. The model also has the ability to mimic the “dynamic” behavior of the objects’ shapes through the introduction and development of three different algorithms for dynamic shapes; where the geometrical shapes representing site facilities automatically modify their geometrical forms to fit in strict areas on site. Moreover, the model provides different proximity measures and distance measurement techniques rather than the normal centroidal Cartesian distances used in most models. The new proximity measures take into consideration actual movement between the facilities including any passageways or access roads on site. Furthermore, the concept of selective zoning was introduced and a corresponding algorithm was provided; where the concept significantly enhances optimization efficiency by minimizing the number of solutions through selection of pre-determined movement zones on site. Soft constraints for buffer zones around the site facilities were developed as well. The site layout modeling was formulated on commercial parametric modeling tools (Rhino® and Grasshopper®) and the optimization was performed through genetic algorithms. After each of the algorithms was verified and validated, a case study of a real dynamic site layout planning problem was made to validate the comprehensive model combining all of the modules together. Different proximity measures and distance measurement techniques were considered, along with different static and dynamic geometrical shapes for the temporary facilities. The model produced valid near-optimum solutions, a comparison was then made between the layout that is produced with the model and the layout that would have been produced by other models to demonstrate the capabilities and advantages of the produced model

Exact algorithms for the order picking problem

Order picking is the problem of collecting a set of products in a warehouse in a minimum amount of time. It is currently a major bottleneck in supply-chain because of its cost in time and labor force. This article presents two exact and effective algorithms for this problem. Firstly, a sparse formulation in mixed-integer programming is strengthened by preprocessing and valid inequalities. Secondly, a dynamic programming approach generalizing known algorithms for two or three cross-aisles is proposed and evaluated experimentally. Performances of these algorithms are reported and compared with the Traveling Salesman Problem (TSP) solver Concorde

Facility Planning and Associated Problems: A Survey

In this study, we have classified and reviewed different types of problems which are related to facility planning and layout design for different types of manufacturing processes. The main problems which are related to location of  facilities which also affects the system performance  such as distribution of man, material and machine in a plant or a factory and their optimization technique while using of mathematical models, their solutions and application related to whole problems is presented. For solving this type of problems, intelligent techniques such as expert systems, fuzzy logic and neutral networks have been used. In this paper the recent analysis on facility layout is incorporated and facility layout problem is surveyed. Many intelligent techniques and conventional algorithms for solving FLP are presented. In our discussion different research direction, general remarks and tendencies have been mentioned Keywords—Facility Planning, Material handling Optimization metho

Modeling and analysis of hospital facility layout problem

The optimal solution to any facility layout problem is an important aspect and a major concem as it involves significant material handling and transportation cost. The objective is to arrange the departments within the predefined facility boundaries in the way that the interaction between the functions is efficient and the overall movement cost is minimized. While facility layout problems have traditionally focused on manufacturing facilities, there has been little work on analyzing layouts for hospitals. The thesis focuses on hospital facility layout problems (HLP) to (i) minimize the movements of patients and (ii) minimize the movements of accompanying resources such as doctors, nurses, equipment and paramedical staff. The thesis consists of two sections. In the first section, a model for the multi-floor layout problem is presented based on the minimization of movement cost. The model has travel frequency or number of trips, trip difficulty rating, baseline travel cost and distance as parameters for determining the movement cost. In the second section, some additional parameters and constraints are imposed on the model and it is simulated using Microsoft Excel. Simulations are also run to study the effect of different proposed strategies on movement cost. These proposed strategies show a reduction in movement cost from the sample layout strategy in section one. A representative example is used to illustrate the applicability of the proposed formulation

Application of general semi-infinite Programming to Lapidary Cutting Problems

We consider a volume maximization problem arising in gemstone cutting industry. The problem is formulated as a general semi-infinite program (GSIP) and solved using an interiorpoint method developed by Stein. It is shown, that the convexity assumption needed for the convergence of the algorithm can be satisfied by appropriate modelling. Clustering techniques are used to reduce the number of container constraints, which is necessary to make the subproblems practically tractable. An iterative process consisting of GSIP optimization and adaptive refinement steps is then employed to obtain an optimal solution which is also feasible for the original problem. Some numerical results based on realworld data are also presented