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

The Convex Hull of Two Core Capacitated Network Design Problems

The network loading problem (NLP) is a specialized capacitated network design problem in which prescribed point-to-point demand between various pairs of nodes of a network must be met by installing (loading) a capacitated facility. We can load any number of units of the facility on each of the arcs at a specified arc dependent cost. The problem is to determine the number of facilities to be loaded on the arcs that will satisfy the given demand at minimum cost. This paper studies two core subproblems of the NLP. The first problem, motivated by a Lagrangian relaxation approach for solving the problem, considers a multiple commodity, single arc capacitated network design problem. The second problem is a three node network; this specialized network arises in larger networks if we aggregate nodes. In both cases, we develop families of facets and completely characterize the convex hull of feasible solutions to the integer programming formulation of the problems. These results in turn strengthen the formulation of the NLP

Visual analytics for supply network management: system design and evaluation

We propose a visual analytic system to augment and enhance decision-making processes of supply chain managers. Several design requirements drive the development of our integrated architecture and lead to three primary capabilities of our system prototype. First, a visual analytic system must integrate various relevant views and perspectives that highlight different structural aspects of a supply network. Second, the system must deliver required information on-demand and update the visual representation via user-initiated interactions. Third, the system must provide both descriptive and predictive analytic functions for managers to gain contingency intelligence. Based on these capabilities we implement an interactive web-based visual analytic system. Our system enables managers to interactively apply visual encodings based on different node and edge attributes to facilitate mental map matching between abstract attributes and visual elements. Grounded in cognitive fit theory, we demonstrate that an interactive visual system that dynamically adjusts visual representations to the decision environment can significantly enhance decision-making processes in a supply network setting. We conduct multi-stage evaluation sessions with prototypical users that collectively confirm the value of our system. Our results indicate a positive reaction to our system. We conclude with implications and future research opportunities.The authors would like to thank the participants of the 2015 Businessvis Workshop at IEEE VIS, Prof. Benoit Montreuil, and Dr. Driss Hakimi for their valuable feedback on an earlier version of the software; Prof. Manpreet Hora for assisting with and Georgia Tech graduate students for participating in the evaluation sessions; and the two anonymous reviewers for their detailed comments and suggestions. The study was in part supported by the Tennenbaum Institute at Georgia Tech Award # K9305. (K9305 - Tennenbaum Institute at Georgia Tech Award)Accepted manuscrip

An Inexact-Fuzzy-Stochastic Optimization Model for a Closed Loop Supply Chain Network Design Problem

The development of optimization and mathematical models for closed loop supply chain (CLSC) design has attracted considerable interest over the past decades. However, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of the developed tools. The aim of this paper, therefore, is to propose a new mathematical model for designing a CLSC network that integrates the network design decisions in both forward and reverse supply chain networks. Moreover, another objective of this research is to introduce an inexact-fuzzy-stochastic solution methodology to deal with various uncertainties in the proposed model. Computational experiments are provided to demonstrate the applicability of the proposed model in the CLSC network design

Optimal Trees

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Model and Algorithm for Closed-loop Logistics System Considering Time-satisfaction Degree and Returns under E-commerce Environment

Facility location, inventory control and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. At the same time, the customer’s time-satisfaction degree had become one of the important factors for competition .A multi-objective integrated optimization model of location-inventory-routing problem (LIRP) taking the cost of the returned merchandises and time-satisfaction degree into account is proposed for closed-loop logistics system. An improved adaptive genetic algorithm (IAGA) is designed to solve the model. Finally, the real instance is presented to show the effectiveness of the model and algorithm

Reverse Logistics Network Design with Centralized Return Center

Natural resources and landfills have been overused and exhausted, resulting in the necessity of product recovery. Today, as a growing number of producers engage in product recovery, the need for efficient reverse logistics networks has become more significant than ever. An optimization modeling approach is used to develop a generic integrated forward and reverse logistics network for a firm involved in product recovery. The proposed modeling framework demonstrates and compares the performance of centralized return centers (CRC) and conventional collection centers in the reverse logistics network. Several case studies are used to analyze the sensitivity of the network structures and performances to various modeling parameters including product return ratio, product disposition ratios, and processing and handling costs at collection centers. Lastly, recommendations are made to remove model limitations and improve reverse logistics network models