Economic effects of mobile technologies on operations of sales agents

In the presented paper we introduce an approach to assess particular economic effects which may arise with bringing mobile technologies into the field of sales and distribution. The research problem posed here comprises quite a special case where sales operations of a company are carried by its sales representatives, which may count as a resource allocation problem. We apply stochastic programming methodology to model the agent's multistage decision making in a distribution system with uncertain customer demands, and exemplify a potential improvement in the company's overall performance when mobile facilities are utilized for making decisions. We provide finally an efficient computational algorithm that delivers optimal decision making with and without mobile technologies, and computers the expected overall performance in both cases, for any configuration of a distribution system. Some computational results are presented. --

Operations Research Models for Blood Supply Chain Network Design for Disaster Planning

A blood supply chain (BSC) deals with the collection, processing, storing, and distribution of blood collected from donors and delivered to patients at demand points (DPs) through a network of several temporary mobile blood units (MBUs) and permanent local blood centers (LBCs). In general, the strategic and tactical design of a BSC network determines response time and blood availability and accessibility for various health care services that extend and improve lives. Typically, BSC networks in the context of disaster management fall within the scope of humanitarian logistics networks in which profit, the main objective of commercial supply chains, is replaced by the objective of timely and proper delivery of post-disaster aid. Yet, most of the existing literature on the strategic design of BSCs aims at minimizing costs of the supply chain. Therefore, the main objective of this dissertation is to develop mathematical models to minimize total travel time for the distribution of blood units within a BSC and improve response time to deliver blood to DPs. Moreover, the approach used in this research explicitly incorporates uncertainty associated with disasters in the design and planning of BSCs. First, a deterministic mathematical programming formulation is developed to model the design of a BSC with the specific goal of minimizing travel time to distribute blood to DPs. The model is solved to identify the optimal location and relocation of LBCs and MBUs. Then, the deterministic model is extended into a stochastic programming model by incorporating uncertainty in blood supply, blood demand, and travel times associated with disaster scenarios. The resulting two-stage stochastic programming (TSSP) model is tested on small, medium, and large instances considering several disaster scenarios. Finally, a Sample Average Approximation (SAA) method is utilized to address the computational complexity of the TSSP model and efficiently solve a large-scale BSC network with a large number of scenarios within a reasonable computational time. Based on the insights obtained from computational experiments completed to test key parameters (e.g., donation rates, collection capacities, threshold for demand satisfaction, and facility installation budget) that may affect the BSC network design and the effectiveness of blood distribution, we conclude that there is a trade-off between total travel time and demand satisfaction for all BSC network sizes. Mainly, it was observed that reduced donation rates and collection capacities decreased demand satisfaction while increasing total travel time due to the need for deploying a considerable number of MBUs to collect blood. Another observation is that increasing the threshold for demand satisfaction at DPs considerably increases total travel time given the additional blood that must be distributed to DPs. Further, we noted that although a higher facility installation budget had little effect on reducing total travel time, it allowed for increased demand satisfaction which significantly improved service within the BSC network. Additionally, the value of deploying MBUs along with LBCs is demonstrated in the computational experimentation where optimal BSC networks utilize MBUs more frequently in disaster scenarios to allow for additional blood collection to meet demand in a timely manner. In this regard, we observed that the need for multiple shipments from different LBCs to a single DP reduces when MBUs are deployed. Overall, MBUs have proven to be effective in decreasing total travel time as well as increasing demand satisfaction. Finally, there is evidence that available blood supply had a significant influence on location decisions for LBCs and MBUs more than the location of DPs and their associated demand. Most importantly, the computational experimentation with the two models shows that the stochastic approach consistently outperforms the deterministic approach using the expected values of uncertain parameters. This provides evidence of the value of explicitly accounting for uncertainty when strategically designing a BSC network when planning for disasters

School bus selection, routing and scheduling.

The aim of this thesis is to develop formulations and exact algorithms for the school bus routing and scheduling problem and to develop an integrated software implementation using Xpress-MP/CPLEX and ArcGIS of ESRI, a geographical information system software package. In this thesis, bus flow, single commodity flow, two-commodity flow, multi-commodity flow, and time window formulations have been developed. They capture all of the important elements of the School Bus Routing and Scheduling Problem (SBRSP) including homogeneous or heterogeneous bus fleets, the identification of bus stops from a large set of potential bus stops, and the assignment of students to stops and stops to routes. They allow for the one stop-one bus and one stop-multi bus scenarios. Each formulation of the SBRSP has a linear programming relaxation and we present the relationships among them. We present a Branch-and-Cut exact algorithm which makes use of new linearization techniques, new valid inequalities, and the first valid equalities. We develop an integrated software package that is based on Geographical Information System (GIS) map-based interface, linking to an Xpress-MP/CPLEX solver. The interface between GIS and Xpress-MP is written in VBA and VC++.Dept. of Mathematics and Statistics. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .K4. Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6250. Thesis (Ph.D.)--University of Windsor (Canada), 2005

On modelling planning under uncertainty in manufacturing

We present a modelling framework for two-stage and multi-stage mixed 0-1 problems under uncertainty for strategic Supply Chain Management, tactical production planning and operations assignment and scheduling. A scenario tree based scheme is used to represent the uncertainty. We present the Deterministic Equivalent Model of the stochastic mixed 0-1 programs with complete recourse that we study. The constraints are modelled by compact and splitting variable representations via scenarios

A systems thinking approach for modelling supply chain risk propagation

Supply Chain Risk Management (SCRM) is rapidly becoming a most sought after research area due to the influence of recent supply chain disruptions on global economy. The thesis begins with a systematic literature review of the developments within the broad domain of SCRM over the past decade. Thematic and descriptive analysis supported with modern knowledge management techniques brings forward seven distinctive research gaps for future research in SCRM. Overlapping research findings from an industry perspective, coupled with SCRM research gaps from the systematic literature review has helped to define the research problem for this study. The thesis focuses on a holistic and systematic approach to modelling risks within supply chain and logistics networks. The systems thinking approach followed conceptualises the phenomenon of risk propagation utilising several recent case studies, workshop findings and focus studies. Risk propagation is multidimensional and propagates beyond goods, finance and information resource. It cascades into technology, human resource and socio-ecological dimensions. Three risk propagation zones are identified that build the fundamentals for modelling risk behaviour in terms of cost and delay. The development of a structured framework for SCRM, a holistic supply chain risk model and a quantitative research design for risk assessment are the major contributions of this research. The developed risk assessment platform has the ability to capture the fracture points and cascading impact within a supply chain and logistics network. A reputed aerospace and defence organisation in UK was used to test the experimental modelling set up for its viability and for bridging the gap between theory and practice. The combined statistical and simulation modelling approach provides a new perspective to assessing the complex behavioural performance of risks during multiple interactions within network

Designing robust railroad blocking plans

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1998.Includes bibliographical references (leaves 121-130).On major domestic railroads, a typical general merchandise shipment, or commodity, may pass through many classification yards on its route from origin to destination. At these yards, the incoming traffic, which may consist of several shipments, is reclassified (sorted and grouped together) to be placed on outgoing trains. On average, each reclassification results in an one day delay for the shipment. In addition, the classification process is labor and capital intensive. To prevent shipments from being reclassified at every yard they pass through, several shipments may be grouped together to form a block. The blocking problem consists of choosing the set of blocks to be built at each terminal (the blocking plan) and assigning each commodity to a series of blocks that will take it from origin to destination. It is one of the most important problems in railroad freight transportation since a good blocking plan can reduce the number of reclassifications of the shipments, thus reducing operating costs and delays associated with excess reclassifications. We provide a variety of model formulations that attain the minimum costs for different problem instances. The deterministic model identifies the blocking plan for the problems with certainty in problem inputs. Static stochastic models provide blocking plans that are feasible for all possible realizations of uncertainties in demand and supply. Dynamic stochastic models generate blocking plans that balance flow costs and plan change costs for possible realizations of uncertainties. We adopt Lagrangian relaxation techniques to decompose the resulting huge mixed integer programming models into two smaller subproblems. This reduces storage requirements and computational efforts to solve these huge problems. We propose other enhancements to reduce computational burden, such as adding a set of valid inequalities and using advanced start dual solutions. These enhancements help tighten the lower bounds and facilitate the generation of high quality feasible solutions. We test the proposed models and solution approaches using the data from a major railroad. Compared to current blocking plans, the solutions from our model reduce the total number of classifications significantly, leading to potential savings of millions of dollars annually. We also investigate various problem aggregation techniques to determine the appropriate ways of generating satisfactory blocking plans with different levels of computational resources. We illustrate the benefits of robust planning by comparing the total costs of our robust plans with those of our deterministic plans. The experiments show that the the realized costs can be reduced by around 50% using robust blocking plans.by Hong Jin.Ph.D

Green Logistics Oriented Framework for the Integrated Scheduling of Production and Distribution Networks - A Case of the Batch Process Industry

Nowadays, most consumable goods are produced and transported in batches. Within the globalized environment, the flow of these batches is raising dramatically to satisfy the recurrent demands of the increasing population. Planning the flow of these batches from suppliers to customers, through dynamic logistics systems, has a high degree of uncertainties on supply chain related decisions. In order to respond effectively and efficiently to these uncertainties, the supply chain network has to be redesigned, considering the economic and environmental requirements. To handle these requirements sustainably, green logistics is a promising approach. However, there is a lack of green logistics models which integrate both the production and distribution decisions within the batch process industries. This research develops a green logistics oriented framework in the case of the batch process industry. The framework integrates the tactical and operational levels of planning and scheduling to generate the optimum production and distribution decisions. A two-stage stochastic programming model is formulated to design and manage batch supply chain. This is a mixed-integer linear program of the two-stage stochastic production-distribution model with economic-environmental objectives. The first stage is concerned with optimum schedules of the production and distribution of the required batches. The second stage subsequently generates the optimum delivering velocities for the optimal distribution routes which are resulted from the first stage. Carbon emissions under uncertainties are incorporated as a function of random delivery velocities at different distribution routes within the network of the supply chain. To examine the applicability of the developed framework, the model is verified and validated through four theoretical scenarios as well as two real world case studies of multi-national batch process industries. The results of the analysis provide some insights results into supply chain costs and emissions. Based on the results, savings of about 43 percent of the total related economic and environmental costs were achieved compared to the actual situation at the case study companies. Cost savings mean long-term profitability, which is essential to sustain a worldwide competitive advantage. Furthermore, the stochastic and expected value solutions are compared in several scenarios. The stochastic solutions are consistently better with respect to costs and emissions. Calculations indicate that up to 13 percent of total cost savings are achieved when a stochastic approach is used to solve the problem as opposed to an expected value approach. The proposed framework supports academic green logistics models and real world supply chain decision making in batch process industry. Building such a framework provides a practical tool which links being green and being economically successful