A proposed mathematical model for closed-loop network configuration based on product life cycle

Products may be returned over their life cycle. Industrial experiences show that there are three main return–recovery pairs. Commercial returns are repaired. End-of-use returns often are remanufactured. In addition, end-of-life returns are recycled. However, up to now, no optimization model is proposed for closed-loop configuration based on three return–recovery pairs. The repaired and remanufactured products can be sold in the same or secondary market. In this paper, we design and configure a general closed-loop supply chain network based on product life cycle. The network includes a manufacturer, collection, repair, disassembly, recycling, and disposal sites. The returned products are collected in a collection site. Commercial returns go to a repair site. End-of-use and end-of-life returns are disassembled. Then, end-of-life returns are recycled. The manufacturer uses recycled and end-of-use parts and new parts to manufacture new products. The new parts are purchased from external suppliers. A mixed-integer linear programming model is proposed to configure the network. The objective is to maximize profit by determining quantity of parts and products in the network. We also extend the model for the condition that the remanufactured products are sent to the secondary market. The mathematical models are validated through computational testing and sensitivity analysis

A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return

A closed-loop supply chain (CLSC) network consists of both forward and reverse supply chains. In this paper, a CLSC network is investigated which includes multiple plants, collection centres, demand markets, and products. To this aim, a mixed-integer linear programming model is proposed that minimizes the total cost. Besides, two test problems are examined. The model is extended to consider environmental factors by weighed sums and ε-constraint methods. In addition, we investigate the impact of demand and return uncertainties on the network configuration by stochastic programming (scenario-based). Computational results show that the model can handle demand and return uncertainties, simultaneously

A three-stage model for closed-loop supply chain configuration under uncertainty

In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment

Design and Optimization of Closed-Loop Supply Chain Management

Because of cost and environmental concerns, reverse supply chain (RSC) has received a lot of attention. RSC is defined as the activities of the collection and recovery of product returns in supply chain management. The integration of forward supply chain (FSC) and RSC results in a closed-loop supply chain (CLSC). In this dissertation, FSC, RSC, and CLSC are introduced. Then, the research objectives are mentioned. The objective of this dissertation is to develop effective approaches to support closed-loop supply chain configurations and analyses, especially develop methodologies to examine impacts of multi-objectives, and uncertainty on CLSC. In Chapter 2, literature of CLSC configuration is reviewed including deterministic and uncertain models. In addition, gaps in the literature are mentioned. In Chapter 3, a facility location model is examined. After problem definition, a mixed-integer linear programming model is proposed. Then, the model is developed to consider multi-objectives under uncertain demand and return. In Chapter 4, a CLSC network is examined. In this chapter, an integrated model for CLSC configuration and supplier selection is proposed and a solution approach is developed for the multi-objective model. A numerical example is used to validate the model. In Chapter 5, a three stage model for closed-loop supply chain configuration is proposed based on a general network. It is supposed that demand is an uncertain parameter. Besides, an illustrative example is applied to show the three-stage model. In addition, managerial insights are discussed in this chapter. In Chapter 6, a mixed-integer linear programming model is proposed to configure a CLSC network. The network has been designed based on product life cycle. The objective is to maximize profit by determining quantity of parts and products in the network. We also extend the model for the condition that the remanufactured products are sent to the secondary market. Finally in Chapter 7, conclusions and future works are provided

Analysis of Transportation Modes by Evaluating SWOT Factors and Pairwise Comparisons: A Case Study

Cape Breton Island is one of the most beautiful islands in the World. The island itself has a unique geography and is located in Nova Scotia, Canada. This chapter introduces and summarizes the current transportation modes in Cape Breton Island. The transportation modes discussed include air, rail, water, truck, and intermodal modes. A SWOT matrix is applied to identify the strengths, weaknesses, opportunities, and threats related to the different transportation modes in Cape Breton Island. Then, the factors are evaluated and ranked based on pairwise comparisons in analytic hierarchy process (AHP) method, and the best strategies are defined. This research provides a unique and multidisciplinary overview of transportation modes in the region that is necessary for future quantitative investigations. Furthermore, it introduces the steps to analyze transportation modes of other areas and regions

Selection of Food Items for Diet Problem Using a Multi-objective Approach under Uncertainty

It is a problem that concerns us all: what should we eat on a day-to-day basis to meet our health goals? Scientists have been utilizing mathematical programming to answer this question. Through the use of operations research techniques, it is possible to find a list of foods that, in a certain quantity, can provide all nutrient recommendations in a day. In this research, a multi-objective programming model is provided to determine the selected food items for a diet problem. Two solution approaches are developed to solve this problem including weighted-sums and ε-constraint methods. Two sources of uncertainty have been considered in the model. To handle these sources, a scenario-based approach is utilized. The application of this model is shown using a case study in Canada. Using the proposed model and the solution approaches, the best food items can be selected and purchased to minimize the total cost and maximize health

Coordinating production and recycling decisions with stochastic demand and return

In this paper, the joint production and recycling problem is investigated for a hybrid manufacturing and remanufacturing system where brand-new products are produced in the manufacturing plant and recycled products are remanufactured into as-new products in the remanufacturing facility. Both the brand-new products and remanufactured products are used to satisfy customer demands. Returns of used products that are recycled from customers are assumed to be stochastic and nonlinearly price-dependent. A mathematical model is proposed to maximize the overall profit of the system through simultaneously optimizing the production and recycling decisions, subject to two capacity constraints — the manufacturing capacity and the remanufacturing capacity. Based on Lagrangian relaxation method, subgradient algorithm and heuristic algorithm, a solution approach is developed to solve the problem. A representative example is presented to illustrate the system, and managerial analysis indicates that the uncertainties in demand and return have much influence on the production and recycling policy. In addition, twenty randomly produced examples are solved, and computational results show that the solution approach can obtain very good solutions for all examples in reasonable time

Effects of uncertainty on a tire closed-loop supply chain network

In a closed-loop supply chain (CLSC) network, there are both forward and reverse supply chains. In this research, a tire remanufacturing CLSC network is designed and optimized based on tire recovery options. The objective of the optimization model is to maximize the total profit. The optimization model includes multiple products, suppliers, plants, retailers, demand markets, and drop-off depots. The application of the model is discussed based on a realistic network in Toronto, Canada using map. In addition, a new decision tree-based methodology is provided to calculate the net present value of the problem in multiple periods under different sources of uncertainty such as demand and returns. Furthermore, the discount cash flow is considered in the methodology as a novel innovative approach. This methodology can be applied in comparing the profitability of different design options for CLSCs

Get Your Cyber-Physical Tests Done! Data-Driven Vulnerability Assessment of Robotic Vehicle

The rapid growth of robotic aerial vehicles (RAVs) has attracted extensive interest in numerous public and civilian applications, from flying drones to quadrotors. Security of RAV systems has become increasingly challenging as RAV controller software becomes more complex, exposing a growing attack surface. Memory isolation separates the memory space and enforces memory access control via privilege separation to limit the attacker’s capability so that the attacker cannot compromise other software components by exploiting one memory corruption vulnerability. Memory isolation has been adopted into the resource-constrained systems such as RAVs by lightweight privilege mode switching to meet real-time requirements. In this paper, we propose ARES, a new variable-level vulnerability excavation framework to find deeper bugs from a combined cyber-physical perspective. We present a data-driven method to illustrate that, despite state-of-the-art memory isolation efforts, RAV systems are still vulnerable to adversarial data manipulation attacks. We augment RAV control states with intermediate controller variables by tracing accessible control parameters and vehicle dynamics within the same isolated memory regions. With this expanded state variable space, we apply multivariate statistical analysis to investigate inter-variable quantitative data dependencies and search for vulnerable state variables. ARES utilizes a learning-based method to show how an attacker can exploit memory corruption bugs in a legitimate memory view and elaborately craft adversarial variable values to disrupt a RAV’s safe operations. We demonstrate the feasibility and capability of ARES on the widely-used Ardupilot RAV framework. Our extensive empirical evaluation shows that the attacker may leverage these vulnerable state variables to achieve various RAV failures during its real-time operations, and even evade existing defense solutions

Dynamic capabilities and environmental sustainability for emerging economies’ multinational enterprises

The purpose of this study is to enhance our understanding of how macro (country)—level dynamic capabilities (DC), such as government environmental policies, legal and market requirements, and technological advances, and micro (firm)—level DC, such as organizational size, culture, and managerial characteristics, are related to emerging economies multinationals’ environmental sustainability policies and practices. Limited studies explore linkages between macro-and micro-level DC and environmental sustainability, which urge emerging economies’ multinationals to reconsider their environmental policies and practices in order to compete with enterprises from developed countries. We develop a theoretical framework and offer propositions about the fundamental links between macro and micro DC and emerging economies environmental sustainability efforts. The propositions can be empirically tested in subsequent studies using country-level and firm-level data to examine the interactions between macro-and micro-level capabilities, in relation to sustainable policies and procedures, for multinationals in emerging economies