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1997 | Buch

The Logic of Logistics

Theory, Algorithms, and Applications for Logistics Management

verfasst von: Julien Bramel, David Simchi-Levi

Verlag: Springer New York

Buchreihe : Springer Series in Operations Research and Financial Engineering

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Über dieses Buch

This book grew out a number of distribution and logistics graduate courses we have taught over the last ten years. In the first few years, the emphasis was on very basic models such as the traveling salesman problem, and on the seminal papers of Haimovich and Rinnooy Kan (1985), which analyzed a simple vehicle routing problem, and Roundy (1985), which introduced power-of-two policies and proved that they are effective for the one warehouse multi-retailer distribution system. At that time, few results existed for more complex, realistic distribution problems, stochastic inventory problems or the integration of these issues. In the last few years however, there has been renewed interest in the area of logistics among both industry and academia. A number of forces have contributed to this shift. First, industry has realized the magnitude of savings that can be achieved by better planning and management of complex logistics systems. In­ deed, a striking example is Wal-Mart's success story which is partly attributed to implementing a new logistics strategy, called cross-docking. Second, advances in information and communication technologies together with sophisticated decision support systems now make it possible to design, implement and control logistics strategies that reduce system-wide costs and improve service level. These decision support systems, with their increasingly user-friendly interfaces, are fundamentally changing the management of logistics systems.

Inhaltsverzeichnis

Frontmatter

Introduction

1. Introduction
Abstract
Fierce competition in today’s global markets, the introduction of products with short life cycles and the heightened expectation of customers have forced manufacturing enterprises to invest in and focus attention on their logistics systems. This, together with changes in communications and transportation technologies, for example, mobile communication and overnight delivery, has motivated continuous evolution of the management of logistics systems.
Julien Bramel, David Simchi-Levi

Performance Analysis Techniques

Frontmatter
2. Worst-Case Analysis
Abstract
Since most complicated logistics problems, for example, the Bin-Packing Problem and Traveling Salesman Problems, are NP-Hard it is unlikely that polynomial time algorithms will be developed for their optimal solutions. Consequently, a great deal of work has been devoted to the development and analyses of heuristics. In this chapter we demonstrate one important tool, referred to as worst-case performance analysis, which establishes the maximum deviation from optimality that can occur for a given heuristic algorithm. We will characterize the worst-case performance of a variety of algorithms for the Bin-Packing Problem and the Traveling Salesman Problem. The results obtained here serve as important building blocks in the analysis of algorithms for vehicle routing problems.
Julien Bramel, David Simchi-Levi
3. Average-Case Analysis
Abstract
Worst-case performance analysis is one method of characterizing the effectiveness of a heuristic. It provides a guarantee on the maximum relative difference between the solution generated by the heuristic and the optimal solution for any possible problem instance, even those that are not likely to appear in practice. Thus, a heuristic that works well in practice may have a weak worst-case performance, if, for example, it provides very bad solutions for one (or more) pathological instance(s).
Julien Bramel, David Simchi-Levi
4. Mathematical Programming Based Bounds
Abstract
An important method of assessing the effectiveness of any heuristic is to compare it to the value of a lower bound on the cost of an optimal solution. In many cases this is not an easy task; constructing strong lower bounds on the optimal solution may be as difficult as solving the problem. An attractive approach for generating a lower bound on the optimal solution to an NP-Complete problem is the following mathematical programming approach. First, formulate the problem as an integer program; then relax the integrality constraint and solve the resulting linear program.
Julien Bramel, David Simchi-Levi

Vehicle Routing Models

Frontmatter
5. The Capacitated VRP with Equal Demands
Abstract
A large part of many logistics systems involves the management of a fleet of vehicles used to serve warehouses, retailers and/or customers. In order to control the costs of operating the fleet, a dispatcher must continuously make decisions on how much to load on each vehicle and where to send it. These types of problems fall under the general class of Vehicle Routing Problems mentioned in Chapter 1.
Julien Bramel, David Simchi-Levi
6. The Capacitated VRP with Unequal Demands
Abstract
In this chapter we consider the Capacitated Vehicle Routing Problem with unequal demands (UCVRP). In this version of the problem, each customer i has a demand W i and the capacity constraint stipulates that the total amount delivered by a single vehicle cannot exceed Q. We let Z u * denote the optimal solution value of UCVRP, that is, the minimal total distance traveled by all vehicles.
Julien Bramel, David Simchi-Levi
7. The VRP with Time Window Constraints
Abstract
In many distribution systems each customer specifies, in addition to the load that has to be delivered to it, a period of time, called a time window, in which this delivery must occur. The objective is to find a set of routes for the vehicles, where each route begins and ends at the depot, serves a subset of the customers without violating the vehicle capacity and time window constraints, while minimizing the total length of the routes. We call this model the Vehicle Routing Problem with Time Windows (VRPTW).
Julien Bramel, David Simchi-Levi
8. Solving the VRP using a Column Generation Approach
Abstract
A classical method, first suggested by Balinski and Quandt (1964), for solving the VRP with capacity and time window constraints is based on formulating the problem as a set-partitioning problem. (See Chapter 4 for a general discussion of set partitioning.)
Julien Bramel, David Simchi-Levi

Inventory Models

Frontmatter
9. Economic Lot Size Models with Constant Demands
Abstract
Production planning is also an area where difficult combinatorial problems appear in day to day logistics operations. In this chapter, we analyze problems related to lot sizing when demands are constant and known in advance. Lot sizing in this deterministic setting is essentially the problem of balancing the fixed costs of ordering with the costs of holding inventory. In this chapter, we look at several different models of deterministic lot sizing. First we consider the most basic single-item model, the Economic Lot Size Model. Then we look at coordinating the ordering of several items with a warehouse of limited capacity. Finally, we look at a one-warehouse multiretailer system.
Julien Bramel, David Simchi-Levi
10. Economic Lot Size Models with Varying Demands
Abstract
Our analysis of inventory models so far has focused on situations where demand was both known in advance and constant over time. We now relax this latter assumption and turn our attention to systems where demand is known in advance, yet varies with time. This is possible, for example, if orders have been placed in advance, or contracts have been signed specifying deliveries for the next few months. In this case, a planning horizon is defined as those periods where demand is known. Our objective is to identify optimal inventory policies for single item models as well as heuristics for the multi-item case.
Julien Bramel, David Simchi-Levi
11. Stochastic Inventory Models
Abstract
The inventory models considered so far are all deterministic in nature; demand is assumed to be known and either constant over the infinite horizon or varying over a finite horizon. In many logistics systems, however, such assumptions are not appropriate. Typically, demand is a random variable whose distribution may be known.
Julien Bramel, David Simchi-Levi

Hierarchical Models

Frontmatter
12. Facility Location Models
Abstract
One of the most important aspects of logistics is deciding where to locate new facilities such as retailers, warehouses or factories. These strategic decisions are a crucial determinant of whether materials will flow efficiently through the distribution system.
Julien Bramel, David Simchi-Levi
13. Integrated Logistics Models
Abstract
The vehicle routing models discussed in Part II assume that the frequency, timing and sizes of customer deliveries are predetermined. There are however many distribution problems in which the vehicle schedules and the timing and size of the customer deliveries are (or should be) simultaneously determined. This is clearly the case in internal distribution systems in which the depot and the customers represent (part of) consecutive layers in the distribution network of a single company (see, e.g., Chapter 12).
Julien Bramel, David Simchi-Levi

Logistics Algorithms in Practice

Frontmatter
14. A Case Study: School Bus Routing
Abstract
We now turn our attention to a case study in transportation logistics. We highlight particular issues that arise when implementing an optimization algorithm in a real-life routing situation. The case concerns the routing and scheduling of school buses in the five boroughs of New York City.
Julien Bramel, David Simchi-Levi
15. A Decision Support System for Network Configuration
Abstract
In this chapter we present some of the issues involved in the development of a decision support system for logistics network configuration. These are issues that are often not dealt with in traditional operations research analyses. However, they are essential in transforming raw data and problem characteristics to modeling assumptions and input data for the models.
Julien Bramel, David Simchi-Levi
Backmatter
Metadaten
Titel
The Logic of Logistics
verfasst von
Julien Bramel
David Simchi-Levi
Copyright-Jahr
1997
Verlag
Springer New York
Electronic ISBN
978-1-4684-9309-2
Print ISBN
978-1-4684-9311-5
DOI
https://doi.org/10.1007/978-1-4684-9309-2