A two-stage stochastic inventory management model for an intermodal trucking company

Master of ScienceDepartment of Industrial & Manufacturing Systems EngineeringAshesh SinhaIntermodal transportation faces several challenges due to uncertainty in rail schedules and customer demand. However, this uncertainty is rarely considered for determining asset management at the Intermodal rail yards. Typically, each Intermodal rail yard requires certain inventory of chassis to serve the demand for either empty containers or loaded containers. It is crucial for any transportation firm to optimally allocate and move chassis between rail ramps to overcome random demand. This thesis develops a two stage stochastic optimization model to determine the optimal allocation and repositioning decisions for chassis and empty boxes across the rail yards to minimize costs and meet service levels. The first stage formulation contains the initial chassis allocation decisions which are independent from random parameters in the following time periods. The second stage formulation determines the empty boxes and chassis repositining decisions for subsequent time periods when the random demand is realized. This thesis applies the L-Shaped Method to efficiently solve this problem. Using numerical experiments, this thesis analyzes the impact of system parameters on the run time performance. The thesis also analyzes the impact of initial chassis inventory and demand patterns on the optimal decisions. We observe that the higher initial inventory or demand at one location than the other results in an increase in the required repositioning moves and expected cost. Conversely, the model is fairly robust to how inventory and demand values are distributed between resource types

A capacitated facility location model with bidirectional flows

Supply chains with returned products are receiving increasing attention in the operations management community. The present paper studies a capacitated facility location model with bidirectional flows and a marginal value of time for returned products. The distribution system consists of a single supplier that provides one new product to a set of distribution centers (DCs), which then ships to the final retailers. While at the retailers' site, products can be shipped back to the supplier for reprocessing. Each DC is capacitated and handles stocks of new and/or returned products. The model is a nonlinear mixed-integer program that optimizes DC location and allocation between retailers and DCs. We show that it can be converted to a conic quadratic program that can be efficiently solved. Some valid inequalities are added to the program to improve computational efficiency. We conclude by reporting numerical experiments that reveal some interesting properties of the model

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges

Discrete time and continuous time formulations for a short sea inventory routing problem

We consider a fuel oil distribution problem where an oil company is responsible for the routing and scheduling of ships between ports such that the demand for various fuel oil products is satisfied during the planning horizon. Inventory management considerations are taken into account at the demand side only, and consumption rates are given and assumed to be constant. We provide two alternative mixed integer formulations: a discrete time model adapted from the case where the consumption rates are varying and a classical continuous time formulation. We discuss different extended formulations and valid inequalities that allow us to reduce the linear gap of the two initial formulations. A computational study comparing the various models accordingly to their size, linear gap and running time, was conducted based on real small-size instances, using a commercial software

A Liner Shipping Speed Optimization Model to Reduce Bunker Cost and Pollutants Emitted

Environmental impact has become one of the most relevant issues in liner shipping during the recent years. Maritime shipping is responsible for the 2.7 per cent of the world CO2 emissions, of which 25 per cent is attributable to container ships. This business also produces a significant quantity of sulphur, a very dangerous substance for human health, especially if it is emitted in areas next to the coast. At the same time, bunker cost represents the biggest portion of the operational cost of a shipping company. Slow steaming is a cheap and effective strategy from both save pollutants emissions and bunker cost. Moreover, it can be immediately put into practice. This report introduces a Mixed Integer Programming Model to solve the Liner Shipping Routing and Speed Optimization Problem (LSRSOP). The final goal is to find the best route and to optimize the the sailing speed of the vessel considering the Emission Control Areas and maximum transit times between ports. Two Heuristic Methods -the 2-Steps Method and the Simulated Annealingare proposed to solve big instances that would require too much running time to be solved until optimality. Both of them use a Hill-Climbing Algorithm that generates a slight different route from a given one. A Bi-Objective Function Model has been designed for instances whose the optimal solution can be found in reasonable time. It considers the operative cost of the vessel and the external cost of emissions. The results show efficient solutions that are the "golden line" between the most convenient solution for the company and the most sustainable solution

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Stochastic ship fleet routing with inventory limits

This thesis describes a stochastic ship routing problem with inventory management. The problem involves finding a set of least costs routes for a fleet of ships transporting a single commodity when the demand for the commodity is uncertain. Storage at consumption and supply ports is limited and inventory levels are monitored in the model. Consumer demands are at a constant rate within each time period in the deterministic problem, and in the stochastic problem, the demand rate for a period is not known until the beginning of that period. The demand situation in each time period can be described by a scenario tree with corresponding probabilities. Several possible solution approaches for solving the problem are studied in the thesis. This problem can be formulated as a mixed integer programming (MIP) model. However solving the problem this way is very time consuming even for a deterministic problem with small problem size. In order to solve the stochastic problem, we develop a decomposition formulation and solve it using a Branch and Price framework. A master problem (set partitioning with extra inventory constraints) is built, and the subproblems, one for each ship, involve solving stochastic dynamic programming problems to generate columns for the master problem. Each column corresponds to one possible tree of schedules for one ship giving the schedule for the ship for all demand scenarios. In each branch-and-bound node, the node problem is solved by iterating between the master problem and the subproblems. Dual variables can be obtained solving the master problem and are used in the subproblems to generate the most promising columns for the master problem. Computational results are given showing that medium sized problems can be solved successfully. Several extensions to the original model are developed, including a variable speed model, a diverting model, and a model which allows ships to do extra tasks in return for a bonus. Possible solution approaches for solving the variable speed and the diverting model are presented and computational results are given