ShadowOS/Facility-Location-Algorithm

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Created about 5 years ago
Updated about 5 years ago

ShadowOS/Facility-Location-Algorithm

ShadowOS

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Vechicle-Routing-Problem-VRP-with-Pickup-and-Delivery

Pickup-and-Delivery Problems (PDPs) constitute an important family of routing problems in which goods or passengers have to be transported from different origins to different destinations. These problems are usually defined on a graph in which vertices represent origins or destinations for the different entities (or commodities) to be transported. PDPs can be classified into three main categories according to the type of demand and route structure being considered. In many-to-many (M-M) problems, each commodity may have multiple origins and destinations and any location may be the origin or destination of multiple commodities. These problems arise, for example, in the repositioning of inventory between retail stores or in the management of bicycle or car sharing systems. One-tomany- to-one (1-M-1) problems are characterized by the presence of some commodities to be delivered from a depot to many customers and of other commodities to be collected at the customers and transported back to the depot. These have applications, for example, in the distribution of beverages and the collection of empty cans and bottles. They also arise in forward and reverse logistics systems where, in addition to delivering new products, one must plan the collection of used, defective, or obsolete products. Finally, in one-to-one (1-1) problems, each commodity has a single origin and a single destination between which it must be transported. Typical applications of these problems are less than- truckload transportation and urban courier operations.

Vehicle-Routing-Problem-VRP-with-Time-Window

The Vehicle Routing Problem with Time Windows (VRPTW) is the extension of the Capacitated Vehicle Routing Problem (CVRP) where the service at each customer must start within an associated time interval, called a time window. Time windows may be hard or soft. In case of hard time windows, a vehicle that arrives too early at a customer must wait until the customer is ready to begin service. In general, waiting before the start of a time window incurs no cost. In the case of soft time windows, every time window can be violated barring a penalty cost. The time windows may be one-sided, e.g., stated as the latest time for delivery. Time windows arise naturally in problems faced by business organizations which work on flexible time schedules. Specific problems with hard time windows include security patrol service, bank deliveries, postal deliveries, industrial refuse collection, grocery delivery, school bus routing, and urban newspaper distribution. Among the soft time window problems, dial-a-ride problems constitute an important example.

Inventory-Routing-Problem-IRP-

It can be described as the combination of vehiclerouting and inventory management problems, in which a supplier has to deliver products to a number of geographically dispersed customers, subject to side constraints. It provides integrated logistics solutions by simultaneously optimizing inventory management, vehicle routing, and delivery scheduling. Some exact algorithms and several powerful metaheuristic and matheuristic approaches have been developed for this class of problems, especially in recent years. The purpose of this article is to provide a comprehensive review of this literature, based on a new classification of the problem. We categorize IRPs with respect to their structural variants and the availability of information on customer demand.

-Multi-period-Managing-Inventory-and-Cash-Distribution-in-ATMs

This is generalize model for multi-product, multi-fleet, multi-period inventory routing problem. For ease of understanding i have taken a small example of managing inventory and cash distribution in ATMs. I try to address all the possible scenario or flexibility in this model

Vechicle-Routing-Problem

The vehicle routing problem (VRP) is a combinatorial optimization and integer programming problem which asks "What is the optimal set of routes for a fleet of vehicles to traverse in order to deliver to a given set of customers?". It generalises the well-known travelling salesman problem (TSP). It first appeared in a paper by George Dantzig and John Ramser in 1959,in which first algorithmic approach was written and was applied to petrol deliveries. Often, the context is that of delivering goods located at a central depot to customers who have placed orders for such goods. The objective of the VRP is to minimize the total route cost. In 1964, Clarke and Wright improved on Dantzig and Ramser's approach using an effective greedy approach called the savings algorithm.

Uncapacitated-Fixed-Charge-Facility-Location-Problems

In most of the models that we have considered so far, the number of facilities to be located was an input into the model. For example, in the P-median and P-center problems, we try to locate P facilities to minimize the total demand-weighted distance or the maximum demand-weighted distance. Similarly, in the maximum covering location problem,we try to locate a given number of facilities to maximize the number of demands that are within some specified distance of the nearest facility. One notable exception was the set covering location model in which we try to minimize the number of facilities needed to cover all demands within a specified distance. In that case, the number of facilities was determined endogenously. By optimizing an objective function subject to a constraint that we locate a fixed number of facilities, we are implicitly separating operating costs or benefits as captured by the objective function from the construction costs for which the number of facilities is taken as a proxy. Such a separation is often important and necessary in public sector problems in which different agencies or actors bear the costs and receive the benefits or in which the benefits and costs are incommensurable. In the private sector, these problems are less acute. Costs are generally borne and benefits are realized by the same organization. Furthermore, costs and benefits can typically be measured in monetary units.