Coordinated replenishment systems with discount opportunities

Inventory Control;Inventory Models;inkoop

Rail Infrastructure Manager Problem: Analyzing Capacity Pricing and Allocation in Shared Railway System

This paper proposes a train timetabling model for shared railway systems. The model is formulated as a mixed integer linear programming problem and solved both using commercial software and a novel algorithm based on approximate dynamic programming. The results of the train timetabling model can be used to simulate and evaluate the behavior of the infrastructure manager in shared railway systems under different capacity pricing and allocation mechanisms. This would allow regulators and decision makers to identify the implications of these mechanisms for different stakeholders considering the specific characteristics of the system

Truckload Shipment Planning and Procurement

This dissertation presents three issues encountered by a shipper in the context of truckload transportation. In all of the studies, we utilize optimization techniques to model and solve the problems. Each study is inspired from the real world and much of the data used in the experiments is real data or representative of real data. The first topic is about the freight consolidation in truckload transportation. We integrate it with a purchase incentive program to increase truckload utilization and maximize profit. The second topic is about supporting decision making collaboration among departments of a manufacturer. It is a bi-objective optimization model. The third topic is about procurement in an adverse market. We study a modification of the existing procurement process to consider the market stochastic into marking decisions. In all three studies, our target is to develop effectively methodologies to seek optimal answers within a reasonable amount of time

Train Timetable Design for Shared Railway Systems using a Linear Programming Approach to Approximate Dynamic Programming

In the last 15 years, the use of rail infrastructure by different train operating companies (shared railway system) has been proposed as a way to improve infrastructure utilization and to increase efficiency in the railway industry. Shared use requires coordination between the infrastructure manager and multiple train operators in a competitive framework, so that regulators must design appropriate capacity pricing and allocation mechanisms. However, the resulting capacity utilization from a given mechanism in the railway industry cannot be known in the absence of operations. Therefore assessment of capacity requires the determination of the train timetable, which eliminates any potential conflicts in bids from the operators. Although there is a broad literature that proposes train timetabling methods for railway systems with single operators, there are few models for shared competitive railway systems. This paper proposes a train timetabling model for shared railway systems that explicitly considers network effects and the existence of multiple operators requesting to operate several types of trains traveling along different routes in the network. The model is formulated and solved both as a mixed integer linear programming (MILP) problem (using a commercial solver) and as a dynamic programming (DP) problem. We solve the DP formulation with a novel algorithm based on a linear programming (LP) approach to approximate dynamic programming (ADP) that can solve much larger problems than are computationally intractable with commercial MILP solvers. The model simulates the optimal decisions by an infrastructure manager for a shared railway system with respect to a given objective function and safety constraints. This model can be used to evaluate alternative capacity pricing and allocation mechanism. We demonstrate the method for one possible capacity pricing and allocation mechanism, and show how the competing demands and the decisions of the infrastructure manager under this mechanism impact the operations on a shared railway system for all stakeholders

Single Item Supplier Selection and Order Allocation Problem with a Quantity Discount and Transportation Costs

In this paper, we address a single item supplier selection, economic lot-sizing, and order assignment problem under quantity discount environment and transportation costs. A mixed-integer nonlinear program (MINP) model is developed with minimization of cost as its objective, while lead-time, the capacity of the supplier and demand of the product are incorporated as constraints. The total cost considered includes annual inventory holding cost, ordering cost, transportation cost and purchase cost. An efficient and effective genetic algorithm (GA) with problem-specific operators is developed and used to solve the proposed MINP model.  The  model is illustrated through a numerical example and the results show that the GA can solve the model in less than a minute. Moreover, the results of the numerical illustration show that the item cost and transportation cost are the deciding factors in selecting suppliers and allocating orders. Keywords: Supplier selection, Economic Order Quantity, Order allocation, Mixed-integer nonlinear programming

Modelling Freight Allocation and Transportation Lead-Time

The authors have investigated sustainable environment delivery systems and identified transportation lead-time investigation cases. This research study aimed to increase freight delivery lead-time and minimize distance in transportation. To reach the goal, the paper\u27s authors, after analysis of the hierarchy of quantitative methods and models, proposed the framework for modeling freight allocation and transportation lead-time and delivered a study that includes discrete event simulation. During the simulation, various scenarios have been revised. Following the simulation mentioned above analysis, around 3.8 % of distance could be saved during freight delivery if lead-time for transportation were revised by choosing five days criteria for modeling freight allocation. The savings depend on the number of received orders from different geographic locations

Network hub locations problems: the state of the art

Cataloged from PDF version of article.Hubs are special facilities that serve as switching, transshipment and sorting points in many-to-many distribution systems. The hub location problem is concerned with locating hub facilities and allocating demand nodes to hubs in order to route the traffic between origin-destination pairs. In this paper we classify and survey network hub location models. We also include some recent trends on hub location and provide a synthesis of the literature. (C) 2007 Elsevier B.V. All rights reserved

The Siting Of Multi-User Inland Intermodal Container Terminals In Transport Networks

Almost without exception, cargo movements by sea have their origins and destinations in the hinterlands and efficient land transport systems are required to support the transport of these cargo to and from the port. Furthermore, not all goods produced are exported or all goods consumed are imported. Those produced and consumed domestically also require efficient transport to move them from their production areas to areas of consumption. The use of trucks for these transport tasks and their disproportionate contribution to urban congestion and harmful emissions has led governments, transport and port authorities and other policy-makers to seek for more efficient and sustainable means of transport. A promising solution to these problems lies in the implementation of intermodal container terminals (IMTs) that interface with both road and rail or possibly inland waterway networks to promote the use of intermodal transport. This raises two important linked questions; where should IMTs be located and what will be their likely usage by individual shippers, each having a choice of whether or not to use the intermodal option. The multi-shipper feature of the problem and the existence of competing alternative modes means the demand for IMTs are outcome of many individual mode choice decisions and the prevailing cargo production and distribution patterns in the study area. This thesis introduces a novel framework underpinned by the principle of entropy maximisation to link mode choice decisions and variable cargo production and distribution problems with facility location problems. The overall model allows both decisions on facility location and usage to be driven by shipper preferences, following from the random utility interpretation of the discrete choice model. Several important properties of the proposed model are presented as propositions including the demonstration of the link between entropy maximisation and welfare maximisation. Exact and heuristic algorithms have been also developed to solve the overall problem. The computational efficiency, solution quality and properties of the heuristic algorithm are presented along with extensive numerical examples. Finally, the implementation of the model, illustration of key model features and use in practice are demonstrated through a case study