John Wassick - Academia.edu (original) (raw)

Papers by John Wassick

Proceedings of the Genetic and Evolutionary Computation Conference, 2017

Supply-chain management problems are common to most industries and they involve a hierarchy of su... more Supply-chain management problems are common to most industries and they involve a hierarchy of subtasks, which must be coordinated well to arrive at an overall optimal solution. Such problems involve a hierarchy of decision-makers, each having its own objectives and constraints, but importantly requiring a coordination of their actions to make the overall supply chain process optimal from cost and quality considerations. In this paper, we consider a specific supply-chain management problem from a company, which involves two levels of coordination: (i) yearly strategic planning in which a decision on establishing an association of every destination point with a supply point must be made so as to minimize the yearly transportation cost, and (ii) weekly operational planning in which, given the association between a supply and a destination point, a decision on the preference of available transport carriers must be made for multiple objectives: minimization of transport cost and maximization of service quality and satisfaction of demand at each destination point. We propose a customized multi-objective bilevel evolutionary algorithm, which is computationally tractable. We then present results on state-level and ZIP-level accuracy (involving about 40,000 upper level variables) of destination points over the mainland USA. We compare our proposed method with current non-optimization based practices and report a considerable cost saving.

2018 17th International Symposium on Parallel and Distributed Computing (ISPDC), 2018

Optimizing physical goods distribution by providing increased visibility to trading partners can ... more Optimizing physical goods distribution by providing increased visibility to trading partners can directly impact product cost. However, current supply chain information systems often lack the ability to cost-effectively relay ground truth information in near real time to all stakeholders and most importantly to the supplier and the customer during the transport of the shipment. This paper presents a solution that addresses this gap through a peer-to-peer architecture that can support the increasing demand for visibility and timely delivery of information during the physical distribution phase of supply chain. Additional features of the proposed solution include scalability, privacy and validity of the information that is delivered to the trading partners. The solution enables small, medium and large businesses to interact in a dynamic and shipment-centric manner through a private blockchain sub-ledger that digitizes the transfer of custody chain for each shipment. Information in this private ledger is augmented by a public event ledger that reflects the movement of the shipment in near real time. Third party monitors are engaged in the validation of the geolocation of the shipments by posting their physical proximity in the form of events to the public ledger.

Turnarounds are huge investments, often in sizable proportion to the annual revenue. Tan and Kram... more Turnarounds are huge investments, often in sizable proportion to the annual revenue. Tan and Kramer [1997] estimate up to $30,000/hr in losses for large companies during turnarounds. Network interactions in integrated chemical sites offer ample scope for planning turnarounds, as demonstrated by Amaran et al. [2015a,b]. In this work, we investigate turnaround perturbation risk to further explore flexibility in strategic planning. sreekanth@cmu.edu CMU-CAPD EWO September 30, 2015 2 / 10 Problem Synopsis Given an integrated chemical sites network with potentially suboptimal turnaround schedules over next 6-9 months, What is the benefit of moving a turnaround (Unit 4) from March (for 14 days) to July (for 17 days)? What is the risk of moving the turnaround? Assume plant reliability data and production-operation cost data is available.

2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), 2017

Affordable and reliable supply chain visibility is becoming increasingly important as the complex... more Affordable and reliable supply chain visibility is becoming increasingly important as the complexity of the network underlying supply chains is becoming orders of magnitudeshigher compared to a decade ago. Moreover, this increase in complexity is starting to reflect on the cost of goods and their availability to the consumers. This paper addresses two key issues in the distribution phase of the supply chain, namely, affordability and pseudo real-time visibility oftruckload activities. The proposed framework creates a digital thread that tracks the pseudo real-time status of the shipment making the physical distribution process completely transparent to the stakeholders. The architecture of the framework is based on a dynamic hybrid peer-to-peer network and a private/public blockchain data model that leverages emergent sensor technologies.

Industrial & Engineering Chemistry Research, 2020

The optimization of batch processes usually relies on the availability of a detailed knowledge-dr... more The optimization of batch processes usually relies on the availability of a detailed knowledge-driven model. However, because of the great varieties of industrial batch processes and their small production rates, a knowledge-driven model might not always be available. In such a case, a data-driven model, developed after a limited number of experiments, is an attractive alternative. Here we apply, in an evolutionary manner, the Design of Dynamic Experiments (DoDE) (Georgakis Ind. & Eng. Chem. Res. 2013, 52 (35), 12369) methodology to model the process behavior and minimize the batch cycle time of an industrial polymerization process. In evolutionary DoDE, the initial design is selected conservatively in the close vicinity of the previous operating conditions to minimize the risk of violating safety constraints of the industrial process. After the initial data-driven model has been estimated using the collected data, an optimal operating condition satisfying process constraints is calculated. In addition, the input domain is enlarged to seek conditions that further optimize the process. The above steps are iterated until the most optimal process performance is achieved. We examine this evolutionary DoDE approach in silico using a detailed simulation of a working polymerization process at Dow to produce that data. After three

Chemical Engineering Science, 2019

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Chemical Engineering Science, 2018

Process optimization and control rely highly on system modeling. A reliable model must be formula... more Process optimization and control rely highly on system modeling. A reliable model must be formulated with estimable parameters in order to closely predict system behavior in the operating domain. This paper focuses on the modeling and parameter estimation of organic solid-liquid reactions in batch reactors with limited lab-scale experimental data and industrial-scale plant data. Two possible mechanisms, shrinking particle model and dissolution model, are reviewed. A uniform dynamic model with a model indicating factor and several lumped parameters is developed for both mechanisms. A Bayesian estimation procedure is discussed and implemented to select an estimable parameter set, simplify the system model, obtain prior information and determine posterior parameter values. The quality of estimation results is analyzed and enhanced by examining the parameter covariance matrix at the optimal point. In the case of multiple candidate process models, model discrimination is then performed to choose the best representative one by comparing posterior probability shares. Finally, the selected model is validated and tested.

Information, 2017

Supply chains (SC) span many geographies, modes and industries and involve several phases where d... more Supply chains (SC) span many geographies, modes and industries and involve several phases where data flows in both directions from suppliers, manufacturers, distributors, retailers, to customers. This data flow is necessary to support critical business decisions that may impact product cost and market share. Current SC information systems are unable to provide validated, pseudo real-time shipment tracking during the distribution phase. This information is available from a single source, often the carrier, and is shared with other stakeholders on an as-needed basis. This paper introduces an independent, crowd-validated, online shipment tracking framework that complements current enterprise-based SC management solutions. The proposed framework consists of a set of private distributed ledgers and a single blockchain public ledger. Each private ledger allows the private sharing of custody events among the trading partners in a given shipment. Privacy is necessary, for example, when trading high-end products or chemical and pharmaceutical products. The second type of ledger is a blockchain public ledger. It consists of the hash code of each private event in addition to monitoring events. The latter provide an independently validated immutable record of the pseudo real-time geolocation status of the shipment from a large number of sources using commuters-sourcing.

AIChE Journal, 2015

This study considers the development of optimization models for grade transition of polyethylene ... more This study considers the development of optimization models for grade transition of polyethylene solution polymerization processes. A detailed mathematical model is developed to capture the dynamics of the solution polymerization process. This includes time delay models for vapor and liquid recycle streams as well as a reduced, yet accurate, vapor-liquid equilibrium (VLE) model derived from rigorous VLE calculations. Simultaneous dynamic optimization approach is applied to solve the optimization problem to reduce off-spec production time and transition time. Two optimization formulations, single stage and multistage, are developed to deal with single-value target and specification bands of product properties, respectively. The results show significant reductions in grade transition time and off-spec production time. In addition, the multistage formulation designed for problems with specification bands outperforms its single stage counterpart. It minimizes transition time and off-spec production directly, and leads to higher performance control profiles.

Proceedings of the 2011 Winter Simulation Conference (WSC), 2011

A generic model is a model that is built for a class of system and can be implemented for a speci... more A generic model is a model that is built for a class of system and can be implemented for a specific system through changes in input data alone, without any structural changes to the model. In this paper, we propose a framework for building such generic model for non-steady state process networks, which are characterized by flow of materials between interconnected nodes. The framework comprises two elements: (1) a generic representation of process network structure using a rule-based queue and (2) a generic representation of non-steady state operations of the network using recipe tables inspired by the Resource-Task Network representation. In this paper, we describe conceptually the data structure and simulation logic that can be used to implement this framework in any simulation software. Examples are provided in the context of batch plant operation.

Computers & Industrial Engineering, 2014

Sustainability plays a key role in the management of a successful and responsible business. When ... more Sustainability plays a key role in the management of a successful and responsible business. When trying to improve the sustainability performance of a business, there are three major challenges that need to be addressed. First, assessment of sustainability requires consideration of not just economic, but also environmental and social impacts. Second, we need to find appropriate sustainability indicators and gather the necessary data in order to quantify sustainability performance. Finally, sustainability has to be seen in the context of the whole system, i.e. it has to include all activities along the supply chain. In this work, we consider all three aspects and propose a multi-objective optimisation framework for the optimisation of a sustainable supply chain. Three sustainability indicators have been considered, namely the total cost, GHG emissions and lead time. We apply this framework to an industrial test case using real-world data drawn from a Dow Chemical business. The results show clear trade-offs between the three different objectives. However, we can also observe that typically a considerable decrease in GHG emissions or lead time can already be achieved by only a relatively small increase in cost. The proposed framework enables us to determine such trade-off relations and consequently make decisions that improve the sustainability performance of the supply chain.

Proceedings of the Winter Simulation Conference 2014, 2014

Production-inventory systems model the interaction of manufacturing processes with internal and e... more Production-inventory systems model the interaction of manufacturing processes with internal and external customers. The role of inventory in these systems is to buffer mismatches between production and demand caused by process uncertainty. Often, production and demand variability is described using simplified probabilistic models that ignore underlying characteristics such as skewness or autocorrelation. These models lead to suboptimal inventory policies that result in higher costs. This work presents a novel analysis of the impact of uncertainty in the performance of production-inventory systems. It quantifies the effect of different probabilistic descriptions of production capacity and demand in systems subject to lost sales or backorders. The analysis is based on the results of discrete-event simulations. The flexibility offered by simulation allows studying diverse conditions that arise in production-inventory systems. The results clearly illustrate the importance of appropriately quantifying variability and performance for inventory management in process networks.

AIChE Journal, 2009

In this article, we consider the risk management for mid-term planning of a global multi-product ... more In this article, we consider the risk management for mid-term planning of a global multi-product chemical supply chain under demand and freight rate uncertainty. A two-stage stochastic linear programming approach is proposed within a multi-period planning model that takes into account the production and inventory levels, transportation modes, times of shipments, and customer service levels. To investigate the potential improvement by using stochastic programming, we describe a simulation framework that relies on a rolling horizon approach. The studies suggest that at least 5% savings in the total real cost can be achieved compared with the deterministic case. In addition, an algorithm based on the multi-cut L-shaped method is proposed to effectively solve the resulting large scale industrial size problems. We also introduce risk management models by incorporating risk measures into the stochastic programming model, and multi-objective optimization schemes are implemented to establish the tradeoffs between cost and risk. To demonstrate the effectiveness of the proposed stochastic models and decomposition algorithms, a case study of a realistic global chemical supply chain problem is presented. 2009 American Institute of Chemical Engineers

Computers & Chemical Engineering, 2008

Chemical supply chain networks provide large opportunities for cost reductions through the redesi... more Chemical supply chain networks provide large opportunities for cost reductions through the redesign of the flow of material from producer to customer. In this paper we present a mixed-integer linear program (MILP) capable of optimizing a multi-product supply chain network made up of production sites, an arbitrary number of echelons of distribution centers, and customer sites. The emphasis of our approach is on the redesign of existing supply chain networks. The model does not lump customer demand into zones, but rather deals with individual customer demand to directly address customer preferred mode of transport at each location. Historical records can be used to fix decision variables in the model so that a base case can be computed to validate the model and contrast it against the optimized network. The details inherent in this approach allow the optimization results to be partitioned and prioritized for implementation. The model results are processed to assign cost components to individual customer records. A simple case study is presented to illustrate the method and actual industrial results are reviewed.

Computer Aided Chemical Engineering, 2013

Abstract The design of efficient supply chains is a major challenge for companies in the process ... more Abstract The design of efficient supply chains is a major challenge for companies in the process industry. Supply chain performance is subject to different sources of uncertainty including reliability of the facilities. Facility disruptions are among the most critical events that supply chains can experience. In order to reduce the undesirable effects of disruptions, these events must be anticipated at the design phase of the supply chain. This work addresses the design of supply chains under the risk of facility disruptions by simultaneously considering decisions on the facility location and the inventory management. The proposed formulation is based on a two-stage stochastic programming framework where the scenarios are determined by the possible combinations of facility disruptions. The first stage decisions include the location of distribution centers and their storage capacity. The second stage decisions involve assigning customer demands to the distribution centers that are available in every scenario. The objective is to minimize the sum of investment cost and the expected cost of distribution during a finite time horizon. The formulation is implemented and compared with the optimal solution of the deterministic design problem though an illustrative example. The results show that the proposed formulation generates supply chain designs with the capability to adjust to adverse scenarios. This flexibility translates into significant savings when disruptions occur in the operation of supply chains.