STRATEGIC PLANNING OF CIRCULAR SUPPLY CHAINS WITH MULTIPLE DOWNGRADED MARKET LEVELS: A METHODOLOGICAL PROPOSAL

Recent legislation has recognized the importance of adopting Circular Economy (CE) principles in supply chain (SC) restructuring. The primary objective is to create circular supply chains (CSCs) that effectively reintegrate end-of-life (EOL) products into production networks through processes such as reusing, remanufacturing, and recycling. This paradigm shift toward circularity aims to enhance resource efficiency, extend product lifecycle, and minimise waste, thereby aligning firms with sustainable practices while providing them with a competitive advantage. In line with the goals of the CE, this study focuses on the design and optimisation of strategic decisions within a circular supply chain (CSC). To achieve this aim, a bi-objective mixed-integer linear programming (MILP) model is developed. This model represents a significant contribution as it offers a compact and generalized formulation for dealing with CSC design problems. The proposed MILP model encompasses several key decision variables and considerations. It determines the optimal number of downgraded market levels to be activated, the location of forward and treatment facilities as well as the optimal product flow within the CSC. Furthermore, the model takes into account the cannibalisation effects associated with the demand for both new and recovered products, ensuring a comprehensive analysis of the system dynamics. To solve the complex mathematical model, the augmented epsilon-constraint (AUGMECON2) method is employed. The utilisation of this method enables decision-makers to obtain practical solutions within reasonable time frames. The computational results obtained from applying the MILP model illustrate its encouraging potential and effectiveness in dealing with strategic decision-making problems within CSCs

Emerging Operational Contracts in Competitive Markets.

This dissertation consists of three essays, each dealing with an emerging type of operational contracts. The first essay considers a resource exchange model where the effects of collaboration and competition are intertwined. Exchanging resources often improves utilization and is intended to increase profitability of involved firms. However, it does not guarantee success in competitive settings. More efficient use of resources might actually leads to increased competition. We explore how resource exchange contracts impact the firms and consumers. The results indicate that the resource exchange tends to benefit both firms and the consumers in most situations, except for the extreme situations where simultaneously competition is strong and the purchasing cost is either very low or very high. The second essay focuses on vertical pricing control contracts that manufacturers use to coordinate online and offline retailers. Resale Price Maintenance (RPM) policy requires all retailers to sell at the price suggested by manufacturers. Minimum Advertised Price (MAP) policy is less strict, as it allows retailers to sell at lower prices than the manufacturer suggested, as long as these lower prices are not advertised. This essay studies which of these two policies is more beneficial to each member of the supply chain. We show that manufacturers prefer MAP policy when the customers' valuations vary significantly and the information search requires significant effort. The MAP policy is also favorable to retailers and consumers under similar market conditions. The third essay concerns the contractual issues when energy service companies (ESCOs) provide energy efficiency projects to residential clients. While performance based contracts have been proven successful in public, commercial, and industrial sectors, ESCOs face challenges in the residential sector. Residential clients often change consumption behavior after the project, which makes the real energy savings difficult to measure. Additionally, residential clients are much more risk averse and vulnerable to uncertain outcomes of projects. We show that piecewise linear contracts perform reasonably well. To further improve profitability, ESCOs can either reduce uncertainty of technology involved or develop the ability to verify post-project energy efficiency. We also make recommendations in monetary incentives and regulations from policy makers' perspective.PhDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133457/1/lgding_1.pd

Exploratory research into supply chain voids within Welsh priority business sectors

The paper reports the findings resulting from the initial stages of an exploratory investigation into Supply Chain Voids (SCV) in Wales. The research forms the foundations of a PhD thesis which is framed within the sectors designated as important by the Welsh Assembly Government (WAG) and indicates local supplier capability voids within their supply chains. This paper covers the stages of initial data gathering, analysis and results identified between June 2006 and April 2007, whilst addressing the first of four research questions. Finally, the approach to address future research is identified in order to explain how the PhD is to progress

Analysis of the supply chain design and planning issues: Models and algorithms

Ph.DDOCTOR OF PHILOSOPH

Multiscale modeling in mathematical programming: Application of Clustering

Integration across decision levels of a supply chain is a key point in improving returns on investment. For example, planning and scheduling are usually carried out separately although they are interdependent of each other. Integration of planning and scheduling results in better coordination between decision levels and a reduction in operating costs. Integration of different time scales leads to large scale problems which are usually computationally intractable. Different approaches have been proposed to tackle the problem in terms of modeling and solution methods. However, most of them are problem specific or applicable only to short time horizons. Clustering has the potential to handle such a problem by grouping similar input parameters (like demand or price) together. This will considerably shrink the model size and make it more computationally tractable while at the same time not compromising solution accuracy. Therefore, the aim of this thesis is to develop a new class of clustering algorithms that are based on mathematical programming techniques in order to support the integration of planning applications of different time scales (strategic, tactical, and operational) in process systems engineering. The clustering algorithms were formulated using integer programming with IAE (integral absolute error) as a similarity measure. The initial formulation was a Mixed Integer Nonlinear Program (MINLP) and then reduced to a Mixed Integer Linear Program (MILP) using exact linearization techniques. The model resulted in two different clustering algorithms: normal and sequence clustering. Two case studies were presented to assess outputs and computational performance of the algorithms. Electricity demand and solar radiation data were clustered in these case studies. Both clustering algorithms captured the trend in the data. However, the computational burden of the model was prohibitive to tackle large planning horizons. In order to deal with computational complexity, a heuristic algorithm was developed utilizing an iterative scheme. The heuristic was first applied to clustering the electricity demand in the original cases studied for validation purposes. The quality of the solutions from the heuristic algorithm were checked against the MILP optimal solutions and it was found that the heuristic algorithm is able to provide good quality solutions and even succeeded in finding the optimal solution for simulation runs carried out. The heuristic algorithm was applied to clustering the electricity demand for a whole year with a small computational effort and providing clusters with high intra-cluster similarity and low inter-cluster similarity. In order to illustrate the use of the clustering procedure in solving large scale planning model, the clustered electricity demand was used as input to a Unit Commitment (UC) model with the objective to evaluate the solution quality when clustered demand is applied. The UC problem is a classical problem in electrical power production where the production of a set of electrical generators is coordinated in order to meet the energy demand at minimum cost or maximize revenues from energy production. The results showed a great advantage in term of solution time for the clustering technique compared to the regular solution when no clustering of demand was applied. Moreover, the error of objective function was within 0.5 % of the non-clustered demand for all cases. In addition, a sensitivity analysis study suggested that high quality solutions could still be achieved with smaller number of clusters. The clustering algorithm was extended in order to take into account multiple attributes at the same time such as clustering simultaneously demand for electricity and heat. In this respect, the objective function had different scales due to the different units of measurements of the attributes, and the problem was dealt with as a multi-objective optimization problem. The weighting method was chosen as the optimization approach and to be able to appropriately scale the different attributes. The clustering algorithm was successfully applied to simultaneously cluster hourly electricity and heat demands for the whole year. The Pareto front was captured for all runs with the weight factor combinations considered in this study. The results show that a better objective function is achieved when the number of clusters increases for both normal and sequence clustering. Normal clustering and as expected leads to a better objective function, error average and standard deviation than sequential clustering due to the additional restrictions of sequencing requirements imposed on the model. Clusters that take into account the time of occurrence of events and abide to certain minimum sequencing restrictions are also needed in planning operations in order to minimize the number of set-ups and inconvenience to operators. The statistical analysis of the heat demand was challenging as suggested by the results, due to the huge fluctuation in the heat demand. Moreover, calculations of relative error were problematic for the demand that was close to zero. The results indicated that in the case when operations are flexible or in the case of just classifying demand patterns, normal clustering should be used since it has a major advantage in terms of solution quality over sequence clustering. For the case of simultaneously clustering heat and electricity, it was required to employ many clusters of electricity that sometimes overlap with each other. These clusters could not be merged since they correspond to different days and the clusters of heat demand for these days are different. Nevertheless, the proposed algorithm was able to obtain groups that simultaneously cluster the two attributes and hence can provide computational advantages when solving integrated planning models that deal with more than one demand attribute. The clustered electricity and heat demands were used as inputs to an energy hub model, with the objective of evaluating the solution quality when multiple clustered demand attributes are applied to planning models. The average error of objective function was -1.7 % for normal clustering while for sequence clustering it was -4.2 %. Increasing the number of clusters was found to enhance the solution quality for both normal and sequence clustering. For this particular example, varying the weight factors did not have a drastic effect on the values of the objective function. This is due mainly to a symmetry or inverse similarity in the heat and electricity demands

Department of Defense Dictionary of Military and Associated Terms

The Joint Publication 1-02, Department of Defense Dictionary of Military and Associated Terms sets forth standard US military and associated terminology to encompass the joint activity of the Armed Forces of the United States. These military and associated terms, together with their definitions, constitute approved Department of Defense (DOD) terminology for general use by all DOD components

Developing New Methods for Efficient Container Stacking Operations

Containerized transportation has become an essential part of the intermodal freight transport. Millions of containers pass through container terminals on an annual basis. Handling a large number of containers arriving and leaving terminals by different modalities including the new mega-size ships significantly affects the performance of terminals. Container terminal operators are always looking for new technologies and smart solutions to maintain efficiency. They need to know how different operations at the terminal interact and affect the performance of the terminal as a whole. Among all operations, the stacking area is of special importance since almost every container must be stacked in this area for a period of time. If the stacking operations of the terminal are not well managed, then the response time of the terminal significantly increases and consequently the performance decreases. In this dissertation, we propose, develop, and test optimization methods to support the decisions of container terminal operators in the stacking area. First, we study how to sequence storage and retrieval containers to be carried out by a single or two automated stacking cranes in a block of containers. The objective is to minimize the makespan of the cranes. Finally, we study how to minimize the expected number of reshuffles when incoming containers have to be stacked in a block of containers. A reshuffle is the removal of a container stacked on top of a desired container. Reshuffling containers is one of the daily operations at a container terminal which is time consuming and increases a ship's berthing time