Strategic planning optimization for natural gas to liquid transportation fuel (GTL) systems

A strategic planning optimization model is proposed for a network of natural gas to liquids (GTL) systems, and it is solved using a rolling horizon strategy. The model formulation determines the strategic and tactical decisions of the GTL supply chain over a long time horizon. The decisions to build new GTL refineries may be made over the span of 30 years and their operations cover the span of 60 years. Multiple capacities of GTL refineries (i.e., 1, 5, 10, 50, and 200 thousand barrels per day) that produce gasoline, diesel, and kerosene commensurate to the United States demand ratio may exist in the network. The parameter inputs include the locations, availabilities, and prices of natural gas in the United States discretized by county, the delivery locations of fuel products, and the transportation costs of every input and output of the refinery, defined for each time period. Formulated as a large-scale mixed-integer linear optimization (MILP) model, the problem is solved using a rolling horizon strategy for tractability. Case studies on the state of Pennsylvania are presented for different planning schemes and their impact on the economic performance of the GTL network is discussed.     

Generic mathematical programming formulation and solution for computer-aided molecular design

This short communication presents a generic mathematical programming formulation for computer-aided molecular design (CAMD). A given CAMD problem, based on target properties, is formulated as a mixed integer linear/non-linear program (MILP/MINLP). The mathematical programming model presented here, which is formulated as an MILP/MINLP problem, considers first-order and second-order molecular groups for molecular structure representation and property estimation. It is shown that various CAMD problems can be formulated and solved through this model.     

Multi-objective optimization of hybrid PEMFC/Li-ion battery propulsion systems for small and medium size ferries

Hybrid Polymer Electrolyte Membrane Fuel Cells/Lithium-ion battery powertrains are a promising solution for zero-local-emissions marine propulsion. The present study aims to optimize the design and operation of such hybrid powertrain for small-size passenger ferries, taking into account the performance degradation of both fuel cells and batteries. A Mixed-Integer Linear-Programming approach and a hierarchical method are adopted to concurrently minimize the fuel cells degradation, the capital expenditure and the operating expenditure, while constraints are included in the model to limit the battery degradation. The results show that the proposed multi-objective optimization can lead to a reduction of fuel cells degradation by up to 65% compared to a cost-minimization only. However, this can imply an increase in the battery capacity by up to 136%. The proposed method has general validity, and it is a useful tool for both preliminary design and choice of the optimal energy management strategy for ships energy systems.     

Implementing land-use and ecosystem service effects into an integrated bioenergy value chain optimisation framework

This study presents a multi-objective optimisation model that is configured to account for a range of interrelated or conflicting questions with regard to the introduction of bioenergy systems. A spatial-temporal mixed integer linear programming model ETI-BVCM (Energy Technologies Institute – Bioenergy Value Chain Model) (ETI, 2015b, Newton-Cross, 2015, Samsatli et al., 2015) was adopted and extended to incorporate resource-competing systems and effects on ecosystem services brought about by the land-use transitions in response to increasing bioenergy penetration over five decades. The extended model functionality allows exploration of the effects of constraining ecosystem services impacts on other system-wide performance measures such as cost or greenhouse gas emissions. The users can therefore constrain the overall model by metric indicators which quantify the changes of ecosystem services due to land use transitions. The model provides a decision-making tool for optimal design of bioenergy value chains supporting an economically and land-use efficient and environmentally sustainable UK energy system while still delivering multiple ecosystem services.     

Robust capacitated facility location model for acquisitions under uncertainty

We study a new robust formulation for strategic location and capacity planning considering potential company acquisitions under uncertainty. Long-term logistics network planning is among the most difficult decisions for supply-chain managers. While costs, demands, etc. may be known or estimated well for the short-term, their future development is uncertain and difficult to predict.A new model formulation for the robust capacitated facility location problem is presented to cope with uncertainty in planning. Minimizing the expectation of the relative regrets across scenarios over multiple periods is the objective. It is achieved by dynamically assigning multi-level production allocations, locations and capacity adjustments for uncertain parameter development over time. Considering acquisitions for profit maximization and its supply-chain impact is new as well as the simultaneous decision of capacity adjustment and facility location over time. The solution of the novel robust formulation provides a single setup where good results can be achieved for any realized scenario. Hence, the solution may not be optimal for one particular scenario but may be good, i.e. the highest expected profit to gain, for any highly probable future realization. We show that robust mixed-integer linear programming model achieves superior results to the deterministic configurations in exhaustive computational tests. This dynamic robust formulation allows the supply-chain to favorably adapt to acquisitions and uncertain developments of revenue, demand and costs and hence reduces the potential negative impacts of uncertainty on supply-chain operations.     

Real‐time dynamic hoist scheduling for multistage material handling process under uncertainties

Multistage material handling processes are broadly used for manufacturing various products/jobs, where hoists are commonly used to transport inline products according to their processing recipes. When multiple types of jobs with different recipes are simultaneously and continuously handled in a production line, the hoist movement scheduling should be thoroughly investigated to ensure the operational feasibility of every job inline and in the meantime to maximize the productivity if possible. The hoist scheduling will be more complicated, if uncertainties of new coming jobs are considered, that is, the arrival time, type, recipe, and number of new jobs are totally unknown and unpredictable before they join the production line. To process the multiple jobs already inline and the newly added jobs, the hoist movements must be swiftly rescheduled and precisely implemented whenever new job(s) come. Because a reschedule has to be obtained online without violating processing time constraints for each job, the solution identification time for rescheduling must be taken into account by the new schedule itself. All these stringent requisites motivate the development of real‐time dynamic hoist scheduling (RDHS) targeting online generation of reschedules for productivity maximization under uncertainties. Hitherto, no systematic and rigorous methodologies have been reported for this study. In this article, a novel RDHS methodology has been developed, which takes into account uncertainties of new coming jobs and targets real‐time scheduling optimality and applicability. It generally includes a reinitialization algorithm to accomplish the seamless connection between the previous scheduling and rescheduling operations, and a mixed‐integer linear programming model to obtain the optimal hoist reschedule. The RDHS methodology addresses all the major scheduling issues of multistage material handling processes, such as multiple recipes, multiple jobs, multicapacity processing units, diverse processing time requirements, and even optimal processing queue for new coming jobs. The efficacy of the developed methodology is demonstrated through various case studies. © 2012 American Institute of Chemical Engineers AIChE J, 59: 465–482, 2013     

Petri-net based scheduling strategy for semiconductor manufacturing processes

Semiconductor manufacturing is a highly automated and capital-intensive industrial process. The operating cost of a wafer processing plant is in general closely related to the design and management of its process flows. Traditionally, the task of production scheduling is performed manually on the basis of past experiences. There are thus real incentives to develop a systematic approach to construct a mathematical programming model in order to reduce the chance of human errors and to ensure operational efficiency in implementing the resulting schedules. To this end, the Petri nets are adopted in this work to accurately model the semiconductor manufacturing activities. The token movements in a Petri net are represented with the well-established scheduling model for batch chemical processes, and the optimal schedule of the given semiconductor process can then be determined accordingly. The feasibility and effectiveness of this scheduling strategy is demonstrated in the present paper with three examples, i.e., the final test process, the re-entrant flow process, and the photolithography-etching process.     

The load-balanced multi-dimensional bin-packing problem

The bin-packing problem is one of the most investigated and applicable combinatorial optimization problems. In this paper we consider its multi-dimensional version with the practical extension of load balancing, i.e. to find the packing requiring the minimum number of bins while ensuring that the average center of mass of the loaded bins falls as close as possible to an ideal point, for instance, the center of the bin. We formally describe the problem using mixed-integer linear programming models, from the simple case where we want to optimally balance a set of items already assigned to a single bin, to the general balanced bin-packing problem. Given the difficulty for standard solvers to deal even with small size instances, a multi-level local search heuristic is presented. The algorithm takes advantage of the Fekete–Schepers representation of feasible packings in terms of particular classes of interval graphs, and iteratively improves the load balancing of a bin-packing solution using different search levels. The first level explores the space of transitive orientations of the complement graphs associated with the packing, the second modifies the structure itself of the interval graphs, the third exchanges items between bins repacking proper n-tuples of weakly balanced bins. Computational experiments show very promising results on a set of 3D bin-packing instances from the literature.     

A hybrid hub-and-spoke postal logistics network with realistic restrictions: A case study of Korea Post

Postal logistics has a complex transportation network for efficient mail delivery. Therefore, a postal logistics network consists of various functional sites with a hybrid hub-and-spoke structure. More specifically, there are multiple Delivery & Pickup Stations (D&PSs), multiple Mail Processing Centers (MPCs), and one Exchange Center (EC). In this paper, we develop two mathematical models with realistic restrictions for Korea Post for the current postal logistics network by simultaneously considering locations and allocations. We propose an Integer Linear Programming (ILP) model for transportation network organization and vehicle operation and a Mixed Integer Linear Programming (MILP) model that considers potential ECs for decision making while simultaneously regarding the EC location, transportation network organization, and vehicle operation. We use modified real data from Korea Post. Additionally, we consider several scenarios for supporting EC decision makers. The proposed models and scenarios are very useful in decision making for postal logistics network designers and operators.     

A solution approach to the design of multi-period,multipurpose batch plants

A deterministic model for multipurpose, multiperiod batch plants was presented in a linearized form to predict the future design according to the change of demand by using a modified Benders’ Decomposition. The OSL code offered by the IBM corporation as optimizer was employed for solving several example problems. The decomposition method was successful, showing remarkable reduction in the computing times as compared with those of the direct solution method. Also the heuristic used as a solution approach for the multiperiod model provided an efficient methodology to the block-structured problem by dividing the large overall problem into the manageable single period blocks.