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

Robust Cross-dock Location Model Accounting for Demand Uncertainty

The objective of this thesis was to develop optimization models to locate cross-docks in supply chain networks. Cross-docks are a type of intermediate facility which aid in the consolidation of shipments, in which the goods spend little or no time in storage. Instead, the goods are quickly and efficiently moved from the inbound trucks to the outbound docks. Two deterministic facility location models were developed. One followed the p-median facility problem type, where only p facilities were opened in order to minimize total network costs. In the second model, as many cross-docks as necessary were opened and facility location costs were considered while minimizing total network costs. In order to account for uncertainty in demands, a robust optimization model was created based on the initial deterministic one. Robust counterparts were developed for each equation that contained the demand term. The robust model allowed for the creation of a network with the ability to handle variations in demand due to factors such as inclement weather, seasonal variations, and fuel prices. Numerical analysis was performed extensively on both the deterministic and robust models, following the p-median facility problem type, using three networks and parameters coherent with industry standards. The results showed that accounting for uncertainty in demands had a real effect on the facilities which were opened and total network costs. While the deterministic network was less expensive, it was unable to handle increases in demand due to uncertainty, whereas the robust network had no capacity shortages in any scenario. Simple demand inflation, along with the use of a robust model for baseline comparison, also proved to be a legitimate strategy to account for uncertainties in demand among small freight carriers

Optimal Supply Network with Vendor Managed Inventory in a Healthcare System with RFID Investment Consideration

Supply Chain Management in the healthcare sector faces several significant challenges, including complexity in healthcare systems, high supply chain costs, balancing quality and costs, delay in delivery, product availability from vendors, inventory waste, and unpredictability and uncertainty. Among those challenges, having an effective inventory management system with an optimal supply network is important to improve the match between supply and demand, which would improve the performance of for healthcare firms. Vendor Managed Inventory (VMI) system is a replenishment solution in which the vendor monitors and decides the time and the quantity of the inventory replenishment of their customers subject to their demand information exchange. A VMI contract in the location-inventory assignment problem is a decision tool for management in the healthcare industry, in which it enables the management to have a cost and service effective decision tool to critically re-evaluate and examine all areas of operations in a SC network looking for avenues of optimization. This dissertation is based on a real-world problem arising from one of the world\u27s leading medical implant supply company applied to a chain of hospitals in the province of Ontario. The chain of hospitals under study consists of 147 hospitals located in Ontario, Canada. The vendor is a supplier of three types of medical implants (a heart valve, an artificial knee, and a hip). In Chapter 2 of this dissertation, we present an optimal supply healthcare network with VMI and with RFID consideration, in which we shed light on the role of the VMI contract in the location-inventory assignment problem and integrate it with both the replenishment policy assignment and the Radio Frequency Identification (RFID) investment allocation assignment in healthcare SC networks using both VMI and direct delivery policies. A numerical solution approach is developed in the case of the deterministic demand environment, and we end up with computational results and sensitivity analysis for a real-world problem to highlight the usefulness and validate the proposed model. We extend our research of integrating the VMI contract in the location-inventory assignment problem with the replenishment policy assignment under a deterministic demand environment to include the stochastic demand environment. The impact of the uncertainty of the demand as a random variable following two types of distributions, normal and uniform distributions, is studied in Chapter 3. Motivated by the lack of investigations and comparative studies dealing with the preference of dealing with VMI contracts to other traditional Retailer Managed Inventory (RMI) systems, we provide in Chapter 4 of this dissertation a comparative study in which we compare the total cost of the VMI system with another two situations of traditional RMI systems: first, a traditional RMI system with a continuous replenishment policy for all hospitals and with assigned storage facilities and second, a traditional RMI system with a direct delivery policy for all hospitals without assigning a storage facility. Computational results, managerial insights, sensitivity analysis, and solution methodologies are provided in this dissertation. Keywords: Vendor Managed Inventory, healthcare system, location-inventory, RFID technology, supply-chain network, stochastic demand, location-inventory assignment problem, and retailer managed Inventory

Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method