Minimasi Jalur Distribusi di PT. XYZ dengan Metode Improved Cluster First Route Second

Reducing distance in delivery service from distribution center to subsidiaries can be reached through well-managed routing which known as VRP - Vehicle Routing Problem. This study was conducted in a food industry PT. XYZ. Two methods, Cluster First Route Second algorithm and linier programming were used to obtain the minimum distance between distribution center to outlets in Jabodetabek area. The cluster first route second method was improved using linier programming – solver. The improved method shows 774.18 kilometer is better than Cluster Firs Route Second, 832.19 kolometer which is 6.97% shortened

In-Town Tour Optimization of Conventional Mode for Municipal Solid Waste Collection

This study illustrates the application of a simple and efficient Solver add-in tool in Microsoft Office Excel© 2010 software for in-town tour optimization of solid waste collection. Data required for tour optimization was obtained from the municipal authority, field studies, and a digitized map of Ilorin, Nigeria while collection operation was formulated as a Traveling Salesman Problem on Excel spreadsheet. The results obtained from the optimization of ten prominent routes revealed that two empirical routes had the same tour distance as the optimized tour. However, in the remaining eight routes, the optimization process reduced tour distance by 2.04-19.27 %, tour time by 0.33-22.80 %, and fuel consumption by 1.78-20.54 %. The cost incurred in purchasing diesel is also expected to decrease by US$0.11-US$1.65/vehicle/day. Therefore, the proposed method can serve as a valuable tool for reducing some socio-economic and environmental impacts associated with solid waste collection

Minimasi Jalur Distribusi di PT. XYZ dengan Metode Improved Cluster First Route Second

Reducing distance in delivery service from distribution center to subsidiaries can be reached through well-managed routing which known as VRP - Vehicle Routing Problem. This study was conducted in a food industry PT. XYZ. Two methods, Cluster First Route Second algorithm and linier programming were used to obtain the minimum distance between distribution center to outlets in Jabodetabek area. The cluster first route second method was improved using linier programming – solver. The improved method shows 774.18 kilometer is better than Cluster Firs Route Second, 832.19 kolometer which is 6.97% shortened

PENENTUAN RUTE PENGIRIMAN ES BATU MENGGUNAKAN NEAREST NEIGHBOR DAN EXCEL SOLVER

Permasalahan yang sering dijumpai dalam optimasi pengiriman sebuah produk adalah Travelling Salesman Problem (TSP). TSP merupakan masalah pencarian rute terpendek dari sejumlah node, di dalam satu kali perjalanan. Perusahaan Es Batu dalam melakukan pengiriman menginginkan rute pengiriman yang terpendek, untuk mengurangi keterlambatan pengiriman. Dampak keterlambatan dalam pengiriman Es Batu terhadap kualitas es batu dapat menurunkan kepuasan pelanggan. Rute pengiriman yang tidak diatur akan berpengaruh terhadap tingginya biaya transportasi yang dikeluarkan oleh perusahaan. Perusahaan saat ini belum menggunakan metode tertentu dalam menentukan rute pengiriman. Penelitian ini melakukan penentuan rute terpendek dengan membandingkan penentuan rute menggunakan algoritma nearest neighbor dan menggunakan bantuan Excel Solver. Penentuan rute dengan algoritma nearest neighbor, dipatkan hasil yaitu 40.72 km sedangan dengan menggunakan bantuan software Excel Solver mendapatkan hasil 36.52 km. Penelitian ini mendapatkan hasil bahwa penentuan rute menggunakan Excel Solver dapat mempersingkat rute perjalanan sebesar 22% dari rute sebelumny

METODOLOGIJA I ALGORITAM ZA OPTIMIZACIJU LANCA PROCESA TRANSPORTA ASFALTA

In this paper authors suggest and test evolutionary algorithm of multiple criteria solver (MCS) for asphalt supply chain optimization. On the basis of the defined imperfection of the basic mathematical model authors made an algorithm and conducted simulations of transportation problems cases generated from the given realistic input intervals in order to determine the constraints and possibilities for improvement of the original model of the transportation problem of large amounts of asphalt mixture. Results have shown that the suggested model verifies and eliminates the lack of the original model. As well, it is shown in which scenarios of transportation problem and to which extent the suggested methodology contributes to the total transportation costs savings and insurance of the program’s optimality.U radu je predložen i testiran evolucijski algoritam za optimizaciju lanca procesa transporta asfaltne mješavine na gradilišta. Izrađen je algoritam (rješavatelj MCS) na temelju definiranog nedostatka postojećeg matematičkog modela te provedena simulacija slučajeva transportnog problema kako bi se utvrdila ograničenja i mogućnosti za poboljšanje originalnog modela za rješavanje transportnog problema velikih količina asfaltne mješavine. Rezultati su pokazali da predložen model pridruživanja i isključivanja potencijalnih izvora potvrđuje i otklanja nedostatak originalnog modela. Pokazano je i u kojim slučajevima transportnog problema i u kojoj mjeri predložena metodologija doprinosi uštedi i osiguranju optimalnosti ponuđenog programa

Određivanje optimalne rute u mreži turističkih atrakcija grada Pule

U radu se razmatra određivanje optimalne rute po turističkim atrakcijama. Na primjeru odabranih deset atrakcija u gradu Puli, dokazuje se da je problem optimalne rute rješiv modeliranjem po uzoru na model putujućega trgovačkog predstavnika (TSP) te primjenom evolucijske metode. Postavljeni problem odnosi se na određivanje pješačke rute i modeliran je iz perspektiva minimizacije udaljenosti i minimizacije vremena potrebnog za obilazak. Prikaz rješavanja korištenjem MS Excela upućuje na mogućnost jednostavne i intuitivne pripreme podataka za analizu te relativno brzog utvrđivanja rješenja. Rezultati upućuju na mogućnosti upotrebe pri organizaciji obilaska u turističkoj destinaciji, prednosti i nedostatke primijenjene metode, ali i prednosti upotrebe kvantitativnih metoda u upravljanju turističkim atrakcijama. Uočena fleksibilnost primjene rezultata otvara mogućnosti daljnjeg obogaćivanja modela, ali i daljnji razvoj u obliku integracije u sustav potpore u upravljanju destinacijom kvantitativnim metodama, uz doprinos održivosti turizma destinacije

Određivanje optimalne rute u mreži turističkih atrakcija grada Pule

U radu se razmatra određivanje optimalne rute po turističkim atrakcijama. Na primjeru odabranih deset atrakcija u gradu Puli, dokazuje se da je problem optimalne rute rješiv modeliranjem po uzoru na model putujućega trgovačkog predstavnika (TSP) te primjenom evolucijske metode. Postavljeni problem odnosi se na određivanje pješačke rute i modeliran je iz perspektiva minimizacije udaljenosti i minimizacije vremena potrebnog za obilazak. Prikaz rješavanja korištenjem MS Excela upućuje na mogućnost jednostavne i intuitivne pripreme podataka za analizu te relativno brzog utvrđivanja rješenja. Rezultati upućuju na mogućnosti upotrebe pri organizaciji obilaska u turističkoj destinaciji, prednosti i nedostatke primijenjene metode, ali i prednosti upotrebe kvantitativnih metoda u upravljanju turističkim atrakcijama. Uočena fleksibilnost primjene rezultata otvara mogućnosti daljnjeg obogaćivanja modela, ali i daljnji razvoj u obliku integracije u sustav potpore u upravljanju destinacijom kvantitativnim metodama, uz doprinos održivosti turizma destinacije

A Patient Risk Minimization Model for Post-Disaster Medical Delivery Using Unmanned Aircraft Systems

The purpose of this research was to develop a novel routing model for delivery of medical supplies using unmanned aircraft systems, improving existing vehicle routing models by using patient risk as the primary minimization variable. The vehicle routing problem is a subset of operational research that utilizes mathematical models to identify the most efficient route between sets of points. Routing studies using unmanned aircraft systems frequently minimize time, distance, or cost as the primary objective and are powerful decision-making tools for routine delivery operations. However, the fields of emergency triage and disaster response are focused on identifying patient injury severity and providing the necessary care. This study addresses the misalignment of priorities between existing routing models and the emergency response industry by developing an optimization model with injury severity to measure patient risk. Model inputs for this study include vehicle performance variables, environmental variables, and patient injury variables. These inputs are used to construct a multi-objective mixed-integer nonlinear programming (MOMINLP) optimization model with the primary objective of minimizing total risk for a set of patients. The model includes a secondary aim of route time minimization to ensure optimal fleet deployment but is constrained by the risk minimization value identified in the first objective. This multi-objective design ensures risk minimization will not be sacrificed for route efficiency while still ensuring routes are completed as expeditiously as possible. The theoretical foundation for quantifying patient risk is based on mass casualty triage decision-making systems, specifically the emergency severity index, which focuses on sorting patients into categories based on the type of injury and risk of deterioration if additional assistance is not provided. Each level of the Emergency Severity Index is assigned a numerical value, allowing the model to search for a route that prioritizes injury criticality, subject to the appropriate vehicle and environmental constraints. An initial solution was obtained using stochastic patient data and historical environmental data validated by a Monte Carlo simulation, followed by a sensitivity analysis to evaluate the generalizability and reliability of the model. Multiple what-if scenarios were built to conduct the sensitivity analysis. Each scenario contained a different set of variables to demonstrate model generalizability for various vehicle limitations, environmental conditions, and different scales of disaster response. The primary contribution of this study is a flexible and generalizable optimization model that disaster planning organizations can use to simulate potential response capabilities with unmanned aircraft. The model also improves upon existing optimization tools by including environmental variables and patient risk inputs, ensuring the optimal solution is useful as a real-time disaster response tool

Surveillance Planning against Smart Insurgents in Complex Terrain

This study is concerned with finding a way to solve a surveillance system allocation problem based on the need to consider intelligent insurgency that takes place in a complex geographical environment. Although this effort can be generalized to other situations, it is particularly geared towards protecting military outposts in foreign lands. The technological assets that are assumed available include stare-devices, such as tower-cameras and aerostats, as well as manned and unmanned aerial systems. Since acquiring these assets depends on the ability to control and monitor them on the target terrain, their operations on the geo-location of interest ought to be evaluated. Such an assessment has to also consider the risks associated with the environmental advantages that are accessible to a smart adversary. Failure to consider these aspects might render the forces vulnerable to surprise attacks. The problem of this study is formulated as follows: given a complex terrain and a smart adversary, what types of surveillance systems, and how many entities of each kind, does a military outpost need to adequately monitor its surrounding environment? To answer this question, an analytical framework is developed and structured as a series of problems that are solved in a comprehensive and realistic fashion. This includes digitizing the terrain into a grid of cell objects, identifying high-risk spots, generating flight tours, and assigning the appropriate surveillance system to the right route or area. Optimization tools are employed to empower the framework in enforcing constraints--such as fuel/battery endurance, flying assets at adequate altitudes, and respecting the climbing/diving rate limits of the aerial vehicles--and optimizing certain mission objectives--e.g. revisiting critical regions in a timely manner, minimizing manning requirements, and maximizing sensor-captured image quality. The framework is embedded in a software application that supports a friendly user interface, which includes the visualization of maps, tours, and related statistics. The final product is expected to support designing surveillance plans for remote military outposts and making critical decisions in a more reliable manner

Optimal Partitioning of a Surveillance Space for Persistent Coverage Using Multiple Autonomous Unmanned Aerial Vehicles: An Integer Programming Approach

Unmanned aerial vehicles (UAVs) are an essential tool for the battle eld commander in part because they represent an attractive intelligence gathering platform that can quickly identify targets and track movements of individuals within areas of interest. In order to provide meaningful intelligence in near-real time during a mission, it makes sense to operate multiple UAVs with some measure of autonomy to survey the entire area persistently over the mission timeline. This research considers a space where intelligence has identi ed a number of locations and their surroundings that need to be monitored for a period of time. An integer program is formulated and solved to partition this surveillance space into the minimum number of subregions such that these locations fall outside of each partitioned subregion for e cient, persistent surveillance of the locations and their surroundings. Partitioning is followed by a UAV-to-partitioned subspace matching algorithm so that each subregion of the partitioned surveillance space is assigned exactly one UAV. Because the size of the partition is minimized, the number of UAVs used is also minimized