Charles R. Sox

Hub location–allocation in intermodal logistic networks

Within the context of intermodal logistics, the design of transportation networks becomes more complex than it is for single mode logistics. In an intermodal network, the respective modes are characterized by the transportation cost structure, modal connectivity, availability of transfer points and service time performance. These characteristics suggest the level of complexity involved in designing intermodal logistics networks. This research develops a mathematical model using the multiple-allocation p-hub median approach. The model encompasses the dynamics of individual modes of transportation through transportation costs, modal connectivity costs, and fixed location costs under service time requirements. A tabu search meta-heuristic is used to solve large size (100 node) problems. The solutions obtained using this meta-heuristic are compared with tight lower bounds developed using a Lagrangian relaxation approach. An experimental study evaluates the performance of the intermodal logistics networks and explores the effects and interactions of several factors on the design of intermodal hub networks subject to service time requirements.

Design of intermodal logistics networks with hub delays

In an intermodal hub network, cost benefits can be achieved through the use of intermodal shipments and the economies of scale due to consolidation of flows at the hubs. However, due to limited resources at the logistics hubs, shipment delays may affect the service performance. In this research hub operations are modeled as a GI/G/1 queuing network and the shipments as multiple job classes with deterministic routings. By integrating the hub operation queuing model and the hub location-allocation model, the effect of limited hub resources on the design of intermodal logistics networks under service time requirements is investigated. The managerial insights gained from a study of 25-city road-rail intermodal logistics network show that the level of available hub resources significantly affects the logistics network structure in terms of number and location of hubs, total network costs, choice of single-hub and inter-hub shipments and service performance.

The storage constrained, inbound inventory routing problem

Purpose – This paper aims to describe the storage constrained, inbound inventory routeing problem and presents bounds and heuristics for solutions to this problem. It also seeks to analyze various characteristics of this problem by comparing the solutions generated by the two proposed heuristics with each other and with the lower bound solutions.Design/methodology/approach – The proposed heuristics use a sequential decomposition strategy for generating solutions for this problem. These heuristics are evaluated on a set of problem instances which are based on an actual application in the automotive manufacturing industry.Findings – The storage space clearly has a significant effect on both the routeing and inventory decisions, and there are complex and interesting interactions between the problem factors and performance measures.Practical implications – Facility design decisions for the storage of inbound materials should carefully consider the impact of storage space on transportation and logistics costs....

Near‐optimal heuristics and managerial insights for the storage constrained, inbound inventory routing problem

Purpose – The purpose of this paper is to develop efficient heuristics for determining the route design and inventory management of inbound parts which are delivered for manufacturing, assembly, or distribution operations and for which there is limited storage space. The shipment frequencies and quantities are coordinated with the available storage space and the vehicle capacities.Design/methodology/approach – Two heuristics that generate near optimal solutions are proposed. The first heuristic has an iterative routing phase that maximizes the savings realized by grouping suppliers together into routes without considering the storage constraint and then calculates the pickup frequencies in the second phase to accommodate the storage constraint. The second heuristic iteratively executes a routing and a pickup frequency phase that both account for the storage constraint. A lower bound is also developed as a benchmark for the heuristic solutions.Findings – Near optimal solutions can be obtained in a reasonab...

The impact of market state information on inventory performance

This research studies the impact of market state information on the long run average cost of a single item inventory system facing dynamic, random demand. The probability distribution of the demand in any time period is determined by the current market state in that period which is modelled as a Markov chain. The inventory manager must infer the market state from the demand observations and a market state signal that is not necessarily accurate. The proposed model accommodates a range of different levels of accuracy for this market state information. The computational results indicate that the value of the market information increases at an increasing rate as the accuracy increases suggesting a strong, persistent value from increasing the information accuracy. The computational results also characterise the effects of lead time, the difference between the market state distributions, autocorrelation, and service level on the value of the market state information.

Optimal product pricing and lot sizing decisions for multiple products with nonlinear demands

Most manufacturing firms consider product pricing as a key strategic decision. Production planning and scheduling, on the other hand, are mostly treated as a non-strategic decisions. Most businesses, therefore, follow some sequential decision-making process; where product prices are determined first and then operational plans are made to fulfill the resulting demand at the lowest possible cost. In this paper, we present a coordinated decision model approach for multi-product pricing and lot sizing decisions for a manufacturer who has limited production capacity. Although the presented model is specific to demands that follow constant elasticity of the price, it can easily be extended to other convex demand functions. We show that a coordinated decision-making process where price and production plans are determined simultaneously may lead to substantially higher profits. We propose an efficient solution methodology for finding optimal prices and product quantities and use real-world data to demonstrate the...

An Integrated Load-Planning Algorithm for Outbound Logistics at Webb Wheel

We present an integrated model for simultaneous optimization of the loading and routing decisions associated with an automotive suppliers outbound supply chain. The supplier, Webb Wheel WW, is a manufacturer of brake drums, rotors, hubs, and spoke wheels. WW accepts new orders from customers each day. Given sufficient inventory, it combines these orders into loads, releases them based on various dispatch criteria e.g., truck-utilization, route-utilization, or penalty-based dispatch policies and due-date considerations, and ships them in truckloads, less-than-truckloads, and containers. Dynamically changing demand information, inventory rationing, inventory interactions among orders, and lead-time considerations are some of the challenging aspects of the problem. Our optimization model is based on the decomposition of the problem into assignment and routing subproblems. The assignment subproblem determines the transportation mode and carrier choices, while considering total transportation costs. These costs depend on a variety of factors, including destination, number of drop locations on the route, and needs of customers on the route. Given the customer clusters and transportation modes from the assignment subproblem, the routing subproblem determines the sequence of drops and the true cost of the shipment using a modified traveling salesman problem. A scalable database with a graphical user interface supports the optimization model. We test our algorithm using four months of WW data and compare these data to the companys practice. Our results demonstrate the impact of transportation mode-specific capacities, customer locations, inventory availabilities, and due-date restrictions on outbound logistics costs. Since implementing our load-planning algorithm, WW has achieved cost savings of 4.4 percent over its previous load-planning process.