Extending the solid step fixed-charge transportation problem to consider two-stage networks and multi-item shipments

This paper develops a new mathematical model for a capacitated solid step fixed-charge transportation problem. The problem is formulated as a two-stage transportation network and considers the option of shipping multiple items from the plants to the distribution centers (DC) and afterwards from DCs to customers. In order to tackle such an NP-hard problem, we propose two meta-heuristic algorithms; namely, Simulated Annealing (SA) and Imperialist Competitive Algorithm (ICA). Contrary to the previous studies, new neighborhood strategies maintaining the feasibility of the problem are developed. Additionally, the Taguchi method is used to tune the parameters of the algorithms. In order to validate and evaluate the performances of the model and algorithms, the results of the proposed SA and ICA are compared. The computational results show that the proposed algorithms provide relatively good solutions in a reasonable amount of time. Furthermore, the related comparison reveals that the ICA generates superior solutions compared to the ones obtained by the SA algorithm

Order fulfillment in online retailing : what goes where

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2005.Includes bibliographical references (p. 139-146).We present three problems motivated by order fulfillment in online retailing. First, we focus on one warehouse or fulfillment center. To optimize the storage space and labor, an e-tailer splits the warehouse into two regions with different storage densities. One is for picking customer orders and the other to hold a reserve stock that replenishes the picking area. Consequently, the warehouse is a two-stage serial system. We investigate an inventory system where demand is stochastic by minimizing the total expected inventory- related costs subject to a space constraint. We develop an approximate model for a periodic review, nested ordering policy. Furthermore, we extend the formulation to account for shipping delays and advance order information. We report on tests of the model with data from a major e-tailer. Second, we focus on the entire network of warehouses and customers. When a customer order occurs, the e-tailer assigns the order to one or more of its warehouses and/or drop- shippers, so as to minimize procurement and transportation costs, based on the available current information. However, this assignment is necessarily myopic as it cannot account for any subsequent customer orders or future inventory replenishments.(cont.) We examine the benefits from periodically re-evaluating these real-time assignments. We construct near- optimal heuristics for the re-assignment for a large set of customer orders by minimizing the total number of shipments. Finally, we present saving opportunities by testing the heuristics on order data from a major e-tailer. Third, we focus on the inventory allocation among warehouses for low-demand SKUs. A large e-tailer strategically stocks inventory for SKUs with low demand. The motivations are to provide a wide range of selections and faster customer fulfillment service. We assume the e-tailer has the technological capability to manage and control the inventory globally: all warehouses act as one to serve the global demand simultaneously. The e-tailer will utilize its entire inventory, regardless of location, to serve demand. Given we stock certain units of system inventory, we allocate inventory to warehouses by minimizing outbound transportation costs. We analyze a few simple cases and present a methodology for more general problems.by Ping Josephine Xu.Ph.D

Essays in Measuring, Controlling, and Coordinating Supply Chain Inventory and Transportation Operations

Supply chain collaboration programs, such as continuous replenishment program (CRP), is among the most popular supply chain management practices. CRP is an arrangement between two partners in a supply chain to share information on a regular basis for lowering logistics costs while maintaining or increasing service levels. CRP shifts the replenishment responsibility to the upstream partner to avoid the bullwhip effect across the supply chain. This dissertation aims to quantify, measure, and expand the benefits of CRP for the purpose of reducing logistics cost and improving customer service. The developed models in this dissertation are all applied in different case studies supported by a group of major healthcare partners. The first research contribution, discussed in chapter 2, is a comprehensive data-driven cost approximation model that quantifies the benefits of CRP for both partners under three cost components of inventory holding, transportation and ordering processing without imposing assumptions that normally do not hold in practice. The second contribution, discussed in chapter 3, is development of a verifiable efficiency measurement system to ensure the benefits of CRP for all partners. Multi-functional efficiency metrics are designed to capture the trade-off in gaining efficiency between multiple functions of logistics (i.e. inventory efficiency, transportation efficiency, and order processing efficiency). In addition, a statistical process control (SPC) system is developed to monitor the metrics over time. We discuss suitable SPC systems for various time series behaviors of the metrics. The third contribution of the dissertation, discussed in chapter 4, is development of a multi-objective decision analysis (MODA) model for multi-stop truckload (MSTL) planning. MSTL is becoming increasing popular among shippers while is experiencing significant resistance from carriers. MSTL is capable of reducing the shipping cost of shippers substantially but it can also disrupt carriers’ operations. A MODA model is developed for this problem to incorporate the key decision criteria of both sides for identifying the most desirable multi-stop routes from the perspective both decision makers

Optimization Models for Cost Efficient and Environmentally Friendly Supply Chain Management

This dissertation aims to provide models which will help companies make sustainable logistics management and transportation decisions. These models are extensions of the economic lot sizing model with the availability of multiple replenishment modes. The objective of the models is to minimize total replenishment costs and emissions. The study provides applications of these models on contemporary supply chain problems. Initially, the impact of carbon regulatory mechanisms on the replenishment decisions are analyzed for a biomass supply chain under fixed charge replenishment costs. Then, models are extended to consider multiple-setups replenishment costs for age dependent perishable products. For a cost minimization objective, solution algorithms are proposed to solve cases where one, two or multiple replenishment modes are available. Finally, using a bi-objective model, tradeoffs in costs and emissions are analyzed in a perishable product supply chain

Electricity and Fuel Consumption in a Lean Energy Supply Chain

Human activities are the main sources of environmental pollution. Awareness about this fact, motivated us to make changes in different paradigms of our lives including industrial or personal activities. Environmental activities assumed to have conflict with financial objectives, in this study we try to align business requirements with environmental concerns. Among all human activities, generating energy has the most negative impact on the environment. The major part of the generated energy will be consumed in transportation and industrial demand which makes them the most effective targets for the reduction of greenhouse gas emission. In a lean environment, small batch sizes increase the number of set-ups and consequently, energy consumption in manufacturing. On the other hand, small batch sizes increase the delivery rates and complexity of transportation. Therefore, the focus of this study will be on reducing the environmental impact of human activities in transportation and industrial loads as a part of lean supply chain. The focus in transportation will be on trucking with gasoline or diesel as the source of energy. In industrial loads, the emerging opportunities after deregulation of the electricity market and incentive programs toward cleaner productions encouraged us to focus on electrical demand in the industry. Despite motivations for reducing emissions in supply chain management, lack of knowledge and expertise in measuring, modeling and optimizing energy consumption is a barrier in production section. In this dissertation, a framework of a power measurement and simulation will be introduced. In the next section, a production planning model incorporating energy will be developed considering different states of electricity consumption (idle, startup, etc.). As the next segment of the supply chain, a method for optimal carrier selection and routing will be developed and tested based on real world data. This model can use the advantage of geographically distributed carriers while utilizing private fleet at an acceptable level. Based on the insight developed in transportation and industrial loads, an experience based performance measure will be developed to quantify the performance and associated energy consumption in the supply chain

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Tabu search for ship routing and scheduling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 20/12/2006.This thesis examines exact and heuristic approaches to solve the Ship Routing and Scheduling Problem (SRSP). The method was developed to address the problem of loading cargos for many customers using heterogeneous vessels. Constraints relate to delivery time windows imposed by customers, the time horizon by which all deliveries must be made and vessel capacities. The objective is to minimise the overall operation cost, where all customers are satisfied. Two types of routing and scheduling are considered, one called single-cargo problem, where only one cargo can be loaded into a ship, and the second type called multi-cargo problem, where multiple products can be carried on a ship to be delivered to different customers. The exact approach comprises two stages. In the first stage, a number of candidate feasible schedules is generated for each ship in the fleet. The second stage is to model the problem as a set partitioning problem (SPP) where the columns are the candidate feasible schedules obtained in the first stage. The heuristic approach uses Tabu Search (TS). Most of the TS operations, such as insert and swap moves, tenure, tabu list, intensification, and diversification are used. The results of a computational investigation are presented. Solution quality and execution time are explored with respect to problem size and parameters controlling the tabu search such as tenure and neighbourhood size. The results showed that the average of the solution gap between TS solution and SPP solution is up to 28% (for small problems) and up to 18% for large problems. However, obtaining an optimal solution requires a large amount of computer time to produce the solution compared to obtaining approximate solutions using the TS approach. The use of Tabu Search for SRSP is novel and the results indicate that it is viable approach for large problems

Mixing quantitative and qualitative methods for sustainable transportation in Smart Cities

L'abstract è presente nell'allegato / the abstract is in the attachmen

Development of a robust and resilient Supply Chain System for selected companies in Gauteng

Abstract: These days, in the extremely competitive nature of business, nearly every big business has to reap the benefits of investing in improvements of its supply chain. The beginning of the upgrades is considered together with the examination concerning profits and most organisations have addressed measures that a supply chain execution and monitor changes in order to drive the benefits of their business. While execution estimation is basic, most organisations either measure excessively or pay little attention to supply chain. Different weaknesses may incorporate; an excessive number of measurements, disconnected measurements, clashing measurements, obsolete measurements, temperamental information, and absence of possession, among others. Some organisations measure incorrect variables in their pursuit of their objectives. This is detrimental to the realisation of these objectives and this affects the organisation. Framework estimations lead to improved framework. "Estimation is the initial step that prompts control and in the long run to progress. In the event that you can't gauge something, you can't get it. On the off chance that you can't get it, you can't control it. On the off chance that you can't control it, you can't improve it" (Harrington, 2012)...M.Ing. (Quality and Operations Management