THE CHARACTERISTICS STUDY OF SOLVING VARIANTS OF VEHICLE ROUTING PROBLEM AND ITS APPLICATION ON DISTRIBUTION PROBLEM

Vehicle Routing Problem (VRP) is one of the most challenging problems in combinatorial optimization. Objective of VRP is to find minimum length route starts and ends in a depot. There are some additional constraints such as more than one depot, service time, time window, capacity of vehicle, and many more. These are cause of VRP variants. Vehicle Routing Problem with Time Windows (VRPTW) is a variant of VRP with some additional constrains, that are number of requests may not exceed the vehicle capacity, as well as travel time and service time may not exceed the time window. Multi Depot Vehicle Routing Problem (MDVRP) has number of depots serving all customers, a number of vehicles distributing goods to customers with a minimum distance of distribution route without exceeding the capacity of the vehicle. Many researches have presented algorithms to solve VRPTW and MDVRP. This article discusses solution characteristics of VRPTW and MDVRP algorithms, and their performance. VRPTW algorithms reviewed are Tabu Search, Clarke and Wright, Nearest Insertion Heuristics, Harmony Search, Simulated Annealing, and Improved Ant Colony System algorithm. Performance of MDVRP algorithms studied are Self-developed Algorithm, Upper Bound, Clarke and Wright, Ant Colony Optimization, and Genetic Algorithm. Each algorithm is studied on its performance, process, advantages, and disadvantages. This article presents example of distribution problem in VRPTW and MDVRP, based on characteristic of the real problem. A computer program created using Delphi is implemented for VRPTW and MDVRP, to solve distribution problem for any number of vehicles and customer locations. Keywords: VRPTW, MDVRP, Distribution proble

A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing

The overwhelmingly increasing amount of stored data has spurred researchers seeking different methods in order to optimally take advantage of it which mostly have faced a response time problem as a result of this enormous size of data. Most of solutions have suggested materialization as a favourite solution. However, such a solution cannot attain Real- Time answers anyhow. In this paper we propose a framework illustrating the barriers and suggested solutions in the way of achieving Real-Time OLAP answers that are significantly used in decision support systems and data warehouses

Approximation of System Components for Pump Scheduling Optimisation

© 2015 The Authors. Published by Elsevier Ltd.The operation of pump systems in water distribution systems (WDS) is commonly the most expensive task for utilities with up to 70% of the operating cost of a pump system attributed to electricity consumption. Optimisation of pump scheduling could save 10-20% by improving efficiency or shifting consumption to periods with low tariffs. Due to the complexity of the optimal control problem, heuristic methods which cannot guarantee optimality are often applied. To facilitate the use of mathematical optimisation this paper investigates formulations of WDS components. We show that linear approximations outperform non-linear approximations, while maintaining comparable levels of accuracy

SLS-PLAN-IT: A knowledge-based blackboard scheduling system for Spacelab life sciences missions

The primary scheduling tool in use during the Spacelab Life Science (SLS-1) planning phase was the operations research (OR) based, tabular form Experiment Scheduling System (ESS) developed by NASA Marshall. PLAN-IT is an artificial intelligence based interactive graphic timeline editor for ESS developed by JPL. The PLAN-IT software was enhanced for use in the scheduling of Spacelab experiments to support the SLS missions. The enhanced software SLS-PLAN-IT System was used to support the real-time reactive scheduling task during the SLS-1 mission. SLS-PLAN-IT is a frame-based blackboard scheduling shell which, from scheduling input, creates resource-requiring event duration objects and resource-usage duration objects. The blackboard structure is to keep track of the effects of event duration objects on the resource usage objects. Various scheduling heuristics are coded in procedural form and can be invoked any time at the user's request. The system architecture is described along with what has been learned with the SLS-PLAN-IT project

An Evolutionary Algorithm for Solving Academic Courses Timetable Scheduling Problem

جدولة أوقات الدروس في الأقسام الكبيرة في الجامعات تعتبر مشكلة صعبة للغاية وغالبًا ما يتم حلها من قبل الموظفين على الرغم من أن النتائج مثالية بشكل جزئي. لقد حدت للغاية مشكلة جدولة الوقت من مشكلة التحسين التوافقي، يهدف هذا العمل تطبيق مبدأ الخوارزمية التطورية باستخدام النظريات الوراثية لحل مشكلة الجدولة الزمنية في محاولة الحصول على جدول زمني عشوائي ومثالي مع القدرة على إنشاء جدول زمني متعدد الاحتمالات ومثالي بشكل كامل لكل مرحلة دراسية في القسم المعني وبما يتلاءم مع القيود التي يفرضها الطلبة والكادر التدريسي وضمن ساعات عمل محددة مسبقا. تتمثل الفكرة الرئيسية في امكانية إنشاء جداول زمنية للدروس بطريقة تلقائية بعد تحديد الشروط المجدية للحصول على جدول زمني مرن ومثالي بدون تكرار من خلال تقديم جدول زمني قابل للتبديل والتدوير. تكمن المساهمة الرئيسية في هذا العمل من خلال زيادة مرونة توليد جداول زمنية مثالية بنسخ مختلفة من خلال زيادة احتمال إعطاء أفضل جدول زمني لكل مرحلة في الحرم الجامعي مع القدرة على استبدال الجدول الزمني عند الحاجة. الخوارزمية التطورية (EA) المستخدمة في هذه الورقة هي الخوارزمية الجينية (GA) التي هي عبارة عن بحث متعدد الحلول يعتمد على عدد المجتمع التطوري الذي يمكن تطبيقه لحل مشاكل معقدة مثل مشاكل الجدول الزمني. في هذا العمل، جميع المدخلات: الدروس والكادر التدريسي والوقت قد تمثلت بمجموعة واحدة لتحقيق البحث المحلي ودمج هذا التمثيل للجدول الزمني باستخدام التبادل الموجه لضمان عدم خرق الشروط الأساسية التي تم تحديدها مسبقا كدالة تطابق. قدمت النتائج نظام جدولة مرن حيث اظهرت نتائج الاختبار تنوع جميع الجداول الزمنية الممكنة التي يمكن إنشاؤها بما يتلاءم مع شروط المستخدم وحاجاته.Scheduling Timetables for courses in the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal. This work implements the principle of an evolutionary algorithm by using genetic theories to solve the timetabling problem to get a random and full optimal timetable with the ability to generate a multi-solution timetable for each stage in the collage. The major idea is to generate course timetables automatically while discovering the area of constraints to get an optimal and flexible schedule with no redundancy through the change of a viable course timetable. The main contribution in this work is indicated by increasing the flexibility of generating optimal timetable schedules with different copies by increasing the probability of giving the best schedule for each stage in the campus with the ability to replace the timetable when needed. The Evolutionary Algorithm (EA) utilized in this paper is the Genetic Algorithm (GA) which is a common multi-solution metaheuristic search based on the evolutionary population that can be applied to solve complex combinatorial problems like timetabling problems. In this work, all inputs: courses, teachers, and time acted by one array to achieve local search and combined this acting of the timetable by using the heuristic crossover to ensure that the essential conditions are not broken. The result of this work is a flexible scheduling system, which shows the diversity of all possible timetables that can be created depending on user conditions and needs

Time-cost Trade-off Analysis for Highway Construction projects

The Construction industry, which can be in the form of residential building, commercial, public and utility buildings, or civil engineering building, has a huge influence on any nation\u27s economy. Its influence can be either manifested in its contribution to the economy or the service it provides to the community. In order to build any infrastructure project with a balanced cost, time, and quality, project managers search for alternatives that can satisfy these contradicting attributes. The traditional time-cost trade-off was enhanced with the three-dimensional time-cost- quality optimization in the last two decades. The optimization is aimed to minimize the time and cost as much as possible while increasing the quality of the infrastructure to be built. The issue of financing in developing countries has been a bottle neck of success in constructing infrastructure like highway. Many researchers have concluded in their studies the causes of time and cost overrun in high-way construction were, contractors\u27 financial problems, Inflation, progress payments delay by owner, political issues, variations, lack of managemental skills, cost fluctuation of materials during construction, environmental issues, Shortage in equipment, Inadequate contractor experience etc. The number of studies in the literature that deals with financial optimization and cash-flow analysis to address the problem of financing and inflation are getting more attention. The cash-flow analysis and maximum overdraft to be paid give a good indication to the main participants about the trends toward cost and time overrun. They can also help in making a proper decision right at the beginning. The purpose of this study is to deal with the optimization of time and profit of highway constructions taking in to consideration the amount of available credit and future value of the cost of each activity and cash-flow analysis in a comprehensive model. This type of analysis gives the contractor how its profit will be influenced with his allowable credits and the time associated with it. Besides, the model also generates a line of balance scheduling for the project as highways are among the repetitive projects. The cash-flow analysis gives extra information on the overdraft so that it can be optimized to find good combination of execution of the activities which will minimize the overdraft, interest paid to banks and most importantly maximize the profit to be gained by the project using GA approach. This type of analysis also gives alternatives for contractors how much profit would they like to gain by providing different amount of credits. At first the profit and time are optimized individually to get the maximum profit and minimum time for completing the project. Then the multi-objective optimization using goal programing takes place which tries to minimize the deviation from the optimum individual values by assigning importance weight to the individual objectives to find the near optimal solution. The model is tested for different allowable credits and its sensitivity analysis outcomes are plotted to see the relationship between the allowable credits and the profit. To validate the efficiency of the developed model, it is applied to a project from the literature that addresses scheduling and cost optimization of repetitive projects. It is found that the outcome of the model that maximizes the profit and minimizing the time outlooks the results of the literature with 4.65% and 0.38% improvement in duration and cost of the project respectively