Multi-variety and small-batch production mode enables manufacturing industries to expeditiously satisfy customers' personalised demands, where a large amount of identical jobs can be split into several sublots, an...
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Multi-variety and small-batch production mode enables manufacturing industries to expeditiously satisfy customers' personalised demands, where a large amount of identical jobs can be split into several sublots, and be processed by reconfigurable machines with multiple machining technics. However, such highly flexible manufacturing environments bring some intractable problems to the production scheduling. Mathematical programming and meta-heuristic methods become less efficient when a scheduling problem contains both discrete and continuous optimisation attributes. Therefore, matheuristic, which combines advantages of the two methodologies, is regarded as a promising solution. This paper investigates a flexible job shop scheduling problem with lot-streaming and machine reconfigurations (FJSP-LSMR) for the total weighted tardiness minimisation. First, a monolithic mixed integer linear programming (MILP) model is established for the FJSP-LSMR. Afterwards, a matheuristic method with a variable neighbourhood search component (MH-VNS) is developed to address the problem. The MH-VNS adopts the classical genetic algorithm (GA) as the framework, and introduces two MILP-based lot-streaming optimisation strategies, LSO1 and LSO2, to improve lot-sizing plans with varying degrees. Four groups of instances are extended from the well-known Fdata benchmark to evaluate the performance of proposed MILP model, LSO1 and LSO2 components, and MH-VNS. Numerical experimental results suggest that LSO1 and LSO2 are efficient in different scenarios, and the proposed MH-VNS can well balance the solution quality and computational costs for reasonably integrating the GA- and MILP-based local search strategies. In addition, a complicated FJSP-LSMR case is abstracted from a real-world shop floor for processing large-sized structural parts to further validate the MH-VNS.
In this paper, we introduce the multiple Traveling Salesman Problem with Drone Stations (mTSP-DS), which is an extension to the classical multiple Traveling Salesman Problem (mTSP). In the mTSP-DS, we have a depot, a ...
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In this paper, we introduce the multiple Traveling Salesman Problem with Drone Stations (mTSP-DS), which is an extension to the classical multiple Traveling Salesman Problem (mTSP). In the mTSP-DS, we have a depot, a set of trucks, and some packet stations that host a given number of autonomous vehicles (drones or robots). The trucks start their mission from the depot and can supply some packet stations, which can then launch and operate drones/robots to serve customers. The goal is to serve all customers either by truck or by drones/robots while minimizing the makespan. We formulate the mTSP-DS as a mixed integer linear programming (MILP) model to solve small instances. To address larger instances, we first introduce two variants of a decomposition-based matheuristic. Afterwards, we suggest a third approach that is based on populating a solution pool with several restarts of an iterated local search metaheuristic, which is followed by determining the best combination of tours using a set-partitioning model. To verify the performance of our algorithms, we conducted extensive computational experiments. According to the numerical results, we observe that the use of drone stations leads to considerable savings in delivery time compared to traditional mTSP solutions. Furthermore, we investigated the energy consumption of trucks and drones. Indeed, depending on the energy consumption coefficients of trucks and drones as well as on the distance covered by drones, the mTSP-DS can also achieve energy savings in comparison to mTSP solutions.(c) 2022 Elsevier B.V. All rights reserved.
One of the important alternatives to conventional fossil fuel vehicles in the transportation sector is hydrogen fuel cell vehicle (HFCV) technology. One of the most significant obstacles to the widespread use of these...
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One of the important alternatives to conventional fossil fuel vehicles in the transportation sector is hydrogen fuel cell vehicle (HFCV) technology. One of the most significant obstacles to the widespread use of these vehicles is the hydrogen supply chain network (HSC) infrastructure. In the design of this network, the harm caused by the network to the environment and the security risks that may arise are as important as the associated cost of building it. In this study, an HSC design that will minimize cost, carbon emission and security risk for Turkey is proposed. The problem is modeled using a mixed integer linear programming (MILP). Five different optimization cases are studied when the penetration rate of HFCV is 25%. In the first three cases, the objectives are independently optimized. The multi-objective optimization is addressed in Case 4 and Case 5. Case 4 is solved with epsilon constraint method by employing the results of the first three cases. The most balanced solution found is 88%, 10% and 2% away from the best cost, carbon emission and risk values, respectively. It is observed that the proposed solution has a decentralized network structure where steam methane reforming (SMR) and electrolysis (ELE) production plants are established. In Case 5, the weighted sum method (another multi-objective optimization method) is used and those which gave the closest results to that of the epsilon constraint method are chosen as the associated weights of three objectives. Using these weights, 10 different demand scenarios are studied. It is observed that the HSC has a decentralized structure under almost all demand scenarios. (c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
In this article we explore a symplectic packing problem where the targets and domains are 2n-dimensional symplectic manifolds. We work in the context where the manifolds have first homology group equal to Z(n) , and w...
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In this article we explore a symplectic packing problem where the targets and domains are 2n-dimensional symplectic manifolds. We work in the context where the manifolds have first homology group equal to Z(n) , and we require the embeddings to induce isomorphisms between first homology groups. In this case, Miller Maley, Mastrangeli, and Traynor showed that the problem can be reduced to a combinatorial optimization problem, namely packing certain allowable simplices into a given standard simplex. They designed a computer program and presented computational results. In particular, they determined the simplex packing widths in dimension four for up to k = 12 simplices, along with lower bounds for higher values of k. We present a modified algorithmic approach that allows us to determine the k-simplex packing widths for up to k = 13 simplices in dimension four and up to k = 8 simplices in dimension six. Moreover, our approach determines all simplex-multisets that allow for optimal packings.
We study a delivery strategy for last-mile deliveries in urban areas which combines freight transportation with mass mobility systems with the goal of creating synergies contrasting negative externalities caused by tr...
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We study a delivery strategy for last-mile deliveries in urban areas which combines freight transportation with mass mobility systems with the goal of creating synergies contrasting negative externalities caused by transportation. The idea is to use the residual capacity on public transport means for moving freight within the city. In particular, the system is such that parcels are first transported from origins (central distribution centers) to drop-in stations, which are stop locations on public vehicle itineraries. Then, they are transported on public vehicles to drop-out stations, from where they are delivered to destination by green vehicles (such as bikes, drones, porters, etc.). The system is known as Freight-On-Transit (FOT). In this paper, we focus on the strategic decisions related to defining the public transportation network that will take part in the delivery system, i.e., which public vehicle lines and stop locations will be included (and thus equipped for the service). We propose different formulations for the problem and effective heuristic solution approaches based on column generation. We perform exhaustive tests aimed at providing managerial insights on the performance and the efficiency of the system.
The rapid development of intelligent warehouse systems is resulting in the realization of automation in warehouse activities and raising awareness of decarbonization, particularly the need to reduce carbon emissions f...
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The rapid development of intelligent warehouse systems is resulting in the realization of automation in warehouse activities and raising awareness of decarbonization, particularly the need to reduce carbon emissions from electricity consumption. Driven by the decarbonization trend, microgrid systems with rooftop photovoltaic panels are becoming more popular in warehouses and are providing zero-carbon electricity for warehouse operations. How to make better use of microgrid systems and reduce the consumption of electricity generated from traditional energy sources is becoming increasingly important in warehouse systems. This paper investigates an operational problem in a warehouse system equipped with a shuttle-based storage and retrieval system, in which a microgrid system acts as the main electricity source. Power-load management is applied to avoid peaks of energy consumption, and a mixedlinearprogramming model is developed to optimize task sequencing and scheduling with decarbonization awareness. To solve the proposed problem, a data-driven variable neighbourhood search algorithm is built. Numerical experiments are conducted to validate the model and algorithm. Sensitivity analysis shows the effectiveness of power-load management and the influence of system configuration on energy consumption.
For past several years the resiliency of the power grid is severely challenged by extreme events. Black start and restoration scheme (BS & RS) is critical to enhance distribution grid resiliency. Variability in th...
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For past several years the resiliency of the power grid is severely challenged by extreme events. Black start and restoration scheme (BS & RS) is critical to enhance distribution grid resiliency. Variability in the power supply capacity of distributed energy resources (DERs) and load demand stand against the efficiency of sequential black start restoration of an unbalanced distribution system. An efficient black start restoration scheme must consider forecasted generation and demand profile to assess resource participation and generate valid switching sequence during sequential restoration of distribution grid. This work proposes a novel model predictive control (MPC) based efficient BS & RS strategy for reconfigurable distribution grid with DERs. The proposed algorithm generates optimal switching sequence by coordinating smart switches, black start DERs (BS-DERs) and energy storage systems (ESSs). Active power-frequency droop, reactive power-voltage droop and ramp rate constraints are assigned to BS-DERs for practical purposes. The non-convexity of sequential service restoration problem due to operational constraints of distribution grid, line switches and dynamics of ESS are linearized and solved as a mixed integer linear programming problem (MILP). The proposed scheme is also extended to incorporate errors associated with forecasting, DER capacity and load demand. Results of IEEE 123-bus and 1069-bus systems indicate effectiveness of the proposed approach.
BackgroundDiscontinuous transcription allows coronaviruses to efficiently replicate and transmit within host cells, enhancing their adaptability and survival. Assembling viral transcripts is crucial for virology resea...
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BackgroundDiscontinuous transcription allows coronaviruses to efficiently replicate and transmit within host cells, enhancing their adaptability and survival. Assembling viral transcripts is crucial for virology research and the development of antiviral strategies. However, traditional transcript assembly methods primarily designed for variable alternative splicing events in eukaryotes are not suitable for the viral transcript assembly problem. The current algorithms designed for assembling viral transcripts often struggle with low accuracy in determining the transcript boundaries. There is an urgent need to develop a highly accurate viral transcript assembly *** this work, we propose Cov-trans, a reference-based transcript assembler specifically tailored for the discontinuous transcription of coronaviruses. Cov-trans first identifies canonical transcripts based on discontinuous transcription mechanisms, start and stop codons, as well as reads alignment information. Subsequently, it formulates the assembly of non-canonical transcripts as a path extraction problem, and introduces a mixed integer linear programming to recover these non-canonical *** results show that Cov-trans outperforms other assemblers in both accuracy and recall, with a notable strength in accurately identifying the boundaries of transcripts. Cov-trans is freely available at https://***/computer-Bioinfo/***.
In this article, we consider the problem of optimizing the connectivity of a landscape under a budget constraint, by improving habitat areas and ecological corridors between them. We model this problem as a discrete o...
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In this article, we consider the problem of optimizing the connectivity of a landscape under a budget constraint, by improving habitat areas and ecological corridors between them. We model this problem as a discrete optimization problem over graphs, in which vertices represent the habitat areas and arcs represent the connections between them. We propose a new flow-based integerlinearprogramming formulation that improves upon the existing models for this problem. By following an approach similar to Catanzaro et al. for the robust shortest path problem, we design an improved preprocessing algorithm that reduces the size of the graphs on which we compute generalized flows. Computational experiments show the benefits of both contributions, by enabling to solve instances of the problem larger than previous models. These experiments also show that several versions of greedy algorithms perform relatively well in practice, while returning arbitrarily bad solutions in the worst case.
Increasing product variety and fluctuating demand have led to the need of assembly systems that can adapt to multiple different products as the return of investment for dedicated assembly lines is more and more diffic...
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Increasing product variety and fluctuating demand have led to the need of assembly systems that can adapt to multiple different products as the return of investment for dedicated assembly lines is more and more difficult to achieve. In response to this challenge, the paradigm of reconfigurable assembly systems has emerged. However, configuring and optimizing these systems still pose challenges in the industry. This paper proposes a new simple optimization approach for the configuration analysis and optimization of a reconfigurable multi-product assembly system in the automotive industry, using configuration selection, task allocation, and sequencing. Its effectiveness is validated throughout three real industrial study cases in the automotive supplier industry.
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