Car Sequencing Research Articles

A new hierarchical approach to requirement analysis of problems in automated planning

The use of Knowledge Engineering (KE) processes to analyze and configure domains in automated planning is becoming more appealing since it was noticed that this issue could make a difference to solve real problems. The contrast between a generic domain independent approach, taken as canonical in AI, and alternative processes that include knowledge engineering – eventually adding specific knowledge – has been discussed by Computer and Engineering communities. A big impact has been noticed mainly in the early phase of requirement analysis when KE approach is normally introduced. Requirement analysis is responsible for carrying out the Knowledge modeling of both problem and work domains, which is a key issue to guide different planner algorithms to come out with efficient solutions. Also, there is the scalability issue that appear in most real problems. To face that, hierarchical methods played an important hole in the history of planning and inspired several solutions since the proposal of NONLIN in the 70’s. Since then, the idea of associating hierarchical relational nets with partial ordered actions has prevailed when large systems were considered. However, there is still a gap between the hierarchical approach and the state of art of requirements analysis to allow features anticipated by KE approach to really appear in the requirements of a planning process. This paper proposes a pathway to solve this gap starting with requirements elicitation represented first in the conventional semi-formal (diagrammatic) language – UML – that is translated to Hierarchical Petri Nets (HPNs) by a new enhanced algorithm. The proposed process was installed in a software tool – developed by one of the authors – that analyzes the performance of the KE planning model: itSIMPLE (Integrated Tools Software Interface for Modeling Planning Environment). This tool was initially designed to use classic Place/Transition nets and an old version of UML (2.1). It is now enhanced to use UML 2.4 and a hierarchical Petri Net extension, also developed by the authors. Realistic examples illustrate the process which is now being applied to larger problems related to the manufacturing of car sequencing domain, one of challenge of ROADEF 2005 (French Operations Research & Decision Support Society). Finally, we consider the possibility to introduce another approach to the KE process by using KAOS (Keep All Object Satisfied) to make the planning design more accurate.

Abstract A040: Hijacking CAR19 T-cells for use in targeting diverse hematopoietic and solid tumors

Abstract Introduction: CAR T-cells targeting CD19 (CAR19s) can eradicate B cell leukemias and lymphomas. The effectiveness of CAR19s is linked to their robust expansion properties but also their long-term persistence. Persistence is maintained by normal CD19+ B cells: a non-tumor dependent, self-renewing source of antigen. In this manner, CAR19s are quite unique. We have re-engineered CAR19s to secrete a wide variety of retargeting fusion proteins (FPs) by encoding expression cassettes downstream of the CAR sequence in lentiviral vectors. By hijacking CAR19s, we utilize their inherent persistence properties. By designing multispecific FP, we directly counter the clinically critical issues of tumor heterogeneity and antigen loss. Experimental Procedures: A lentiviral vector with an MCSV promoter was used to express the CAR19 construct and FPs. FPs and multispecific-FPs were designed to encode the extracellular domain of the CD19 protein, followed by one or two scFv sequences, separated from the CAR sequence by a P2A cleavage site, a design termed IMPACTtm (Integrated Modular Proteins for Adoptive Cell Therapy). The FPs therefore consist of the CD19 extracellular domain linked to one or more scFvs. The FPs redirect CAR19 T-cell cytotoxic activity to any tumor antigen of interest by coating that antigen with CD19 via the scFv. Further, multiple antigens can be coated with CD19 by encoding multiple scFv in the FP. Hijacked CAR19s therefore serve as a platform for targeting diverse antigens. Results: Here we describe one example in detail, focusing on Her2+ solid tumors. The CD19/anti-Her2 FP was highly potent in cytotoxicity assays targeting Her2+/CD19- solid tumor cell lines. The concentration of fusion protein required to reduce tumor cell number by 50% was 10 pM (0.7 ng/ml). Primary donor T-cells transduced with the CAR19 - CD19/anti-Her2 FP lentiviral vector secreted > 20 ng/ml of FP in cell culture. Cytotoxic activity of this redirected CAR19 against Her2+ SKOV3 tumor cells was demonstrated in vitro and in vivo. CD19-mediated persistence was demonstrated in serial restimulation assays. A bispecific FP containing CD19 linked to anti-Her2 and anti-EGFR scFv had specific activity against both antigens with a potency of 0.75 pM. For each antigen, the potent cytotoxicity was specifically mediated by the secreted fusion protein. Additional program examples of multispecific targeting for diverse hematologic and solid tumor types will be shown. Conclusions: The IMPACT platform addresses critical issues in cell therapy including CAR persistence, antigen escape and antigen heterogeneity, and provides important solutions for treating both hematologic and solid tumors. The potency of redirected cytotoxicity supports clinical development of CAR19/IMPACT programs, four of which are now ready for IND enabling studies. Citation Format: Paul Rennert, Fay Dufort, Lihe Su, Lan Wu, Alyssa Birt, Christine Ambrose, Roy Lobb. Hijacking CAR19 T-cells for use in targeting diverse hematopoietic and solid tumors [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr A040.

Two-sided assembly line car sequencing with a fuzzy adaptive extended coincidence algorithm

The car sequencing problem is a constraint satisfaction problem that has attracted the attention of academia and practitioners for many years now. In this paper, the industrial version of the car sequencing problem is extended to reflect more real operations in practice by using a two-sided assembly line instead of classical single-sided one. The fuzzy adaptive extended coincidence algorithm (COIN-F) is developed to optimise the multi-objective car sequencing problem in a Pareto sense. The relative performance of COIN-F is compared against COIN-E, NSGA-II and DPSO. Experimental results demonstrate the effectiveness of COIN-F over the contestant algorithms, especially in the search for the approximate true-Pareto frontier.

Two-sided assembly line car sequencing with a fuzzy adaptive extended coincidence algorithm

The car sequencing problem is a constraint satisfaction problem that has attracted the attention of academia and practitioners for many years now. In this paper, the industrial version of the car sequencing problem is extended to reflect more real operations in practice by using a two-sided assembly line instead of classical single-sided one. The fuzzy adaptive extended coincidence algorithm (COIN-F) is developed to optimise the multi-objective car sequencing problem in a Pareto sense. The relative performance of COIN-F is compared against COIN-E, NSGA-II and DPSO. Experimental results demonstrate the effectiveness of COIN-F over the contestant algorithms, especially in the search for the approximate true-Pareto frontier.

Schedule Instability in Automotive Production Networks: The Development of a network-oriented Resequencing Method

Schedule instability in production networks is a highly relevant topic for practitioners and academics lacking research, especially from a network perspective. This paper addresses the phenomenon schedule instability in automotive production networks by proposing a network-oriented partial car sequencing based resequencing method that integrates both, procurement and distribution related information. Based on this network-oriented approach, schedule instability stemming from short-term disruptions is reduced.

Mathematical Model for Proactive Resequencing of Mixed Model Assembly Lines

Abstract Mixed model assembly lines are state of the art for automotive mass production. In the context of mass customization and indi-vidualization the car sequencing recently became more and more challenging due to varying assembly tasks and processing times. Classical principles such as the vehicle pearl chain are thus reaching their limits. Therefore, planned changes in the vehicle sequence during final assembly, Proactive Resequencing, was proposed to optimize utilization segment by segment. The concept leads to a long-term buffer planning problem on a strategic level as well as to an adapted vehicle sequencing problem with enhanced flexibility on an operative level. This paper introduces a mathematical model for the combined buffer planning and order sequencing problem for automotive assembly lines. It also outlines a solution approach for the mentioned production planning challenges separated by their timing preferences.

Automotive Paint Shop 4.0

This paper focuses on the research and development of a new paint shop concept for the automotive industry, based on Industry 4.0 and Digital Factory ideas. The Paint Shop 4.0 project has been carried out on behalf of a German car manufacturer, in cooperation with ProPoint Sp. z o.o. Sp. k. and industrial partners. The main goal of this research is to reduce the primary bottleneck in the operation of a paint shop, which is caused by frequent changeovers of painting guns. A new type of sequencing problem is studied from both an algorithmic and a practical point of view. The original ‘Car Sequencing Problem’ focuses on determining the sequence before production starts, without the possibility of later change to this sequence. The new problem consists of real-time car sequencing using a buffer for the purpose of considering dynamic changes. Work begins with identifying and analyzing the causes of the emerging problem. Then, using tools like Virtual Engineering and Virtual Commissioning, the following tasks are performed: modeling of an existing production line with a buffer, model validation, and the development of methods to improve the sequencing process. Two heuristics based on a set of simple rules and one more advanced heuristic are proposed. Their effectiveness has been tested during simulations, based on real data, and compared with current industry solutions. The extensive computational experiments show that heuristics-based proposal outperforms the methods known from the literature in terms of in terms of matching to industry requirements. This indicates that it is possible to improve the sequencing effectiveness, and as a result a smart buffer control system is proposed. Validating the proposed solution in a virtual environment paves the way to implement the Paint Shop 4.0 concept in a real plant in the future.

Time-clustering behavior and cycles in the time dynamics of car accident sequences in Lebanon

This manuscript analyzes the temporal properties of sequences of car accidents in Lebanon from 2015 to 2018, using data from the Lebanese Road Accident Platform (LRAP). The Allan Factor that is a well suited methodology for investigating the time-clustering in point processes, revealed that the car accidents occurred over the whole Lebanese territory are characterized by a significant scaling behavior for timescales larger than about 2 weeks, with a clear cyclic component at 1 day. The analysis performed on car accident sequences in some specific areas of Lebanon revealed the presence of cycles varying from 1 day to 2 weeks as a function of the distance from Beirut, the capital city of Lebanon.

A rolling horizon optimisation model for consolidated hump yard operational planning

This paper presents an optimisation formulation for consolidated planning of railway hump yard operations. The objective is to minimise the average dwell time of railway cars in the yard, while satisfying constraints related to (i) incoming (outgoing) train arrival (departure) times and composition, (ii) car movement sequencing, and (iii) the limited capacity and number of classification tracks. We present a mixed-integer linear programming formulation that combines three stages of decision making; inbound train humping, railcar classification and outbound train construction. The consolidated approach enables a natural linking between capacity constraints at various stages of the system. However, the scale and complexity of the resulting problem requires some relaxation, with the optimisation model providing a high-level plan, and a low-level heuristic handling the detailed implementation. The high-level optimiser determines hump schedule of the inbound trains, block-to-classification track assignment, and pull-out schedule at coarse level by dividing the planning horizon into discrete time intervals and trains into groups of consecutive cars (segments). The low-level heuristic converts the resulting instructions into fine-grained decisions spatially (at the individual car level) and temporally (the actual duration required for each action). The proposed approach is implemented on a rolling horizon basis, and is used to solve a 42-day, 52246-car example.

Accelerated dynamic programming algorithms for a car resequencing problem in automotive paint shops

• Novel exact and heuristic algorithms for resequencing cars to minimize setup costs in automotive paint shops are proposed. • Incorporating strong lower and upper bounds in the dynamic programming framework reduces computation time significantly. • Proposed algorithms are more efficient than existing approaches and also more practically applicable. • Shows well-tailored exact algorithms can be more efficient than meta-heuristic algorithms. In this paper, a car resequencing problem (CRP) for automotive paint shops is considered, whereby a set of cars conveyed from an upstream shop to one of the multiple conveyors is retrieved sequentially before the painting operation. The aim of the CRP is to find a car retrieval sequence that minimizes the sequence-dependent changeover cost, which is the cost that is incurred when two consecutive cars do not share the same color. For this problem, we propose accelerated dynamic programming (ADP) algorithms that utilize strong combinatorial lower bounds and effective upper bounds in a standard dynamic programming framework, thus outperforming existing exact algorithms. Testing of our algorithms over a wide range of instances confirmed that they are more efficient than the existing approaches and are also more applicable in practice.

Advanced constraint propagation for the combined car sequencing and level scheduling problem

We present an advanced constraint propagation algorithm for the combined car sequencing and level scheduling problem, used within a branch-and-bound framework. The new method solves the only unsolved instance from Drexl et al. (2006) and four of the eight unsolved instances from Yavuz’s (2013) testbeds. The paper also introduces 18 new instances, 9 of which are solved by the algorithm. Optimal sequences of up to 250 cars are obtained.

Komputerowe wspomaganie procesu sekwencjonowania – koncepcja Paint Shop 4.0

Presented is the concept of paint shop operation for the automotive industry – Paint Shop 4.0, based on the ideas of Industry 4.0 and Digital Factory. A new approach to the issue of car body sequencing, taking into account the actual structure of the paint shop department with buffers, has been presented. In the created application, proprietary car body sequencing algorithms were implemented.

Abstract 2569: CAR19 T cells secreting antigen-retargeting fusion proteins have remarkable potency against diverse tumor types

Abstract Introduction. CAR-CD19 T cell therapeutics are approved to treat B cell leukemias and lymphomas. Previously we showed that providing CAR19 T cells with a retargeting fusion protein (FP), consisting of soluble CD19 protein linked to an scFv, redirects CAR19 T cell cytotoxic activity to other tumor antigens. Further, exposure of CAR19 T cells to normal or malignant B cells expressing CD19 improves effector function and phenotype, due to the provision of costimulatory signals. Such signals are missing when CAR-T cells engage antigens on solid tumor cells. In addition, lack of sufficient antigen for expansion and persistence is a critical issue for CAR T therapeutics targeting solid tumors in the clinical setting. We have redirected CAR19 T cells to other tumor antigens via novel FP and bispecific FP (biFP) expression cassettes. CAR19 T cells that express FP or biFP solve critical issues in the cell therapy field by 1) promoting CAR19 T cell expansion, efficacy and persistence by engaging CD19 on B cells; 2) addressing antigen heterogeneity/escape in hematologic malignancies and solid tumors. FP- and biFP-mediated cytotoxicity is extremely potent at pM concentrations. Here we provide examples of in vitro and in vivo modeling to characterize the activity of the CAR19 T cells that are retargeted to kill other tumor types. Experimental Procedures. A CAR-CD19 construct was created as the recipient for FP and biFP expression cassettes, in a lentiviral vector with an MCSV promoter. The CAR consists of the FMC63 scFv, a stalk, TM and CD28 and/or 4-1BB cytoplasmic signaling domains, and the CD3ε cytoplasmic signaling domain. FP and biFP cassettes were designed to encode the extracellular domain of the CD19 protein, followed by an scFv or VHH to one (FP) or two (biFP) antigens, separated from the CAR sequence by a P2A cleavage site. Results. Flow cytometry and binding analyses demonstrated FP and bi-FP-mediated bridging between target tumor cells and CAR19 T cells. In vitro cytotoxicity studies showed robust redirected killing of antigen+ (CD19-) tumor cells by CAR19 T cells. This cytotoxicity is specifically mediated by the secreted FP or biFP and correlated with antigen affinity. The efficiency of cytotoxicity was enhanced by the presence of CD19+ B cells. Tumor xenograft models using Her2+ and CD38+ tumor lines demonstrated effective tumor killing in vivo, with or without the presence of CD19+ cells. Conclusions. Redirected CAR19 T cells potently kill diverse tumor cells in vitro and in vivo. The strategy of redirecting CAR19 T cells to other tumor cell types by encoding novel FP and biFP expression cassettes solves critical issues in cell therapy by supporting CAR19 T cell activation and expansion and by providing a simple and modular multi-antigen targeting system. Further technology refinements include the creation of inducible promoters for FP expression, adding anti-PD-L1 activity, and providing cytokine support. Citation Format: Paul Rennert, Fay Dufort, Lihe Su, Lan Wu, Alyssa Birt, Roy Lobb, Christine Ambrose. CAR19 T cells secreting antigen-retargeting fusion proteins have remarkable potency against diverse tumor types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2569.

การจัดล􀄞ำดับการผลิตรถยนต์แบบมากวัตถุประสงค์บนสายการประกอบผลิตภัณฑ์ผสมแบบ สองด้าน

Car Sequencing on two-sided assembly line is an important problem in an automotive industry. Researchers and practitioners have attempted several approaches to solve this problem aiming at maximum production efficiency. The problem is considered as an “NP-Hard problem”. In this paper, three objective functions are considered including 1) minimizing utility work, 2) reducing the number of violation and 3) decreasing the number of color changes. The expansion of Combinatorial Optimization with Coincidence (COIN-E) algorithm is developed from its original version (i.e. COIN). Several well-known algorithms are compared in solving this problem including Non-dominated Sorting Genetic Algorithms (NSGA-II), Discrete Particle Swarm Optimization (DPSO), Biogeography-based Optimization (BBO) and (COIN). The experimental results indicate that COIN-E is efficient and it obtains the values of convergence = 91.85%, spread = 51.08% and ratio = 57.48%, which are significantly superior to NSGA-II, DPSO, BBO and COIN.

Construction and in vitro verification of a new humanized anti-CD19 CAR-T cells with high affinity

目的构建人源化抗CD19嵌合抗原受体T细胞（CAR-T），通过体外实验验证其杀伤白血病细胞的能力。方法将人CD19的鼠源抗体（FMC63）进行了人源化改造，获得高亲和力的人源化CD19抗体；构建携带人源化CD19 CAR慢病毒载体，感染T细胞获得人源化CD19 CAR-T细胞（hCART19）；按不同效靶比将效应细胞[hCART19、未转染的T细胞（阴性组）及对照病毒转染的T细胞（对照组）]及靶细胞（CHO-K1-CD19及Raji细胞）混合培养，LDH释放实验及ELISA法检测hCART19杀伤白血病细胞的能力及细胞因子释放水平；白血病小鼠模型检测hCART19的杀瘤效果。结果LDH释放实验证实随着效靶比的不断增加，对靶细胞的杀伤率逐渐增加，当效靶比为10∶1时hCART19组的杀伤率最大，在Raji细胞中为（87.56±1.99）%，明显高于阴性组[（19.31±1.16）%]及对照组[（21.35±1.19）%]（P值均<0.001）。ELISA法检测显示Raji细胞作为靶细胞时，hCART19组IL-2水平[（10.56±0.88）pg/ml]及IFN-γ[（199.02±12.66）pg/ml]较阴性组[IL-2：（3.55±0.26）pg/ml；IFN-γ：（37.63±0.85）pg/ml]及对照组[IL-2：（2.92±0.32）pg/ml；IFN-γ：（52.07±3.33）pg/ml]明显升高（P值均<0.001）。以上实验在CHO-K1-CD19细胞作为靶细胞时也出现了相似的结果。给予白血病小鼠模型尾静脉分别注射hCART19、对照病毒转染的T细胞及未转染的T细胞，结果显示hCART19组小鼠存活时间>40 d，另外两组小鼠在20~30 d全部死亡，差异有统计学意义（χ2=11.73，P=0.008）。结论成功构建了具有抗白血病活性的人源化CD19 CAR-T细胞，为下一步的临床研究奠定了基础。

THE ALGORITHMS OF BUFFERS HANDLING IN CAR SEQUENCING PROBLEM PRESENTED ON AN ACTUAL PRODUCTION LINE

The article presents a problem of sequencing a multiversion and multiassortment car production. In particular, the paper focuses on car sequencing in paint shop. An issue of so-called Car Sequencing Problem, widely considered in the literature, is discussed. But it is also noted that the issue presented by researchers is only a reduced problem in comparison with the problem, which occurs in real automotive production. Thus, a new approach to original Car Sequencing Problem is proposed. The presented approach extends the original problem to problem in production line equipped with buffers. This problem is called as Car Sequencing Problem with Buffers. Therefore, firstly, in order to show, how complicated is the real sequencing problem, the article focuses on the issue of buffers construction. In addition, various types of buffer warehouses, used most often in paint shops, are presented. The next section of the article shows, what is the role of use the buffers in production systems both for production process, as well as for sequencing process. At the end of the paper some algorithms to handle selected type of buffers are discussed and the research results are presented.

A hybrid algorithm based on tabu search and large neighbourhood search for car sequencing problem

The car sequencing problem (CSP) concerns a production sequence of different types of cars in the mixed-model assembly line. A hybrid algorithm is proposed to find an assembly sequence of CSP with minimum violations. Firstly, the hybrid algorithm is based on the tabu search and large neighborhood search (TLNS), servicing as the framework. Moreover, two components are incorporated into the hybrid algorithm. One is the parallel constructive heuristic (PCH) that is used to construct a set of initial solutions and find some high quality solutions, and the other is the small neighborhood search (SNS) which is designed to improve the new constructed solutions. The computational results show that the proposed hybrid algorithm (PCH+TLNS+SNS) obtains 100 best known values out of 109 public instances, among these 89 instances get their best known values with 100% success rate. By comparing with the well-known related algorithms, computational results demonstrate the effectiveness, efficiency and robustness of the proposed algorithm.

Car painting process scheduling with harmony search algorithm

Automotive painting program in the process of painting the car body by using robot power, making efficiency in the production system. Production system will be more efficient if pay attention to scheduling of car order which will be done by considering painting body shape of car. Flow shop scheduling is a scheduling model in which the job-job to be processed entirely flows in the same product direction / path. Scheduling problems often arise if there are n jobs to be processed on the machine, which must be specified which must be done first and how to allocate jobs on the machine to obtain a scheduled production process. Harmony Search Algorithm is a metaheuristic optimization algorithm based on music. The algorithm is inspired by observations that lead to music in search of perfect harmony. This musical harmony is in line to find optimal in the optimization process. Based on the tests that have been done, obtained the optimal car sequence with minimum makespan value.

Car sequencing for mixed-model assembly lines with consideration of changeover complexity

Implementing mass customization can inevitably lead to a large product variety, which makes the assembly process in mixed-model assembly lines (MMALs) very complex. In this paper, the concept of product variety induced changeover complexity, as one major source of uncertainty in mixed-model assembly, is proposed. Three types of changeover complexities measured using information entropy are presented. As the negative impact of changeover complexity on the performance of a MMAL can be reduced by selecting a suitable model sequence, a bi-objective car sequencing problem taking it into account is proposed. The problem is aimed at finding a model sequence with the minimum number of violating sequencing rules as a primary criterion, and the minimum level of total changeover complexity as a secondary criterion. A lexicographic approach based on ant colony optimization (ACO) is applied to solve the problem. Computational experiments show that both objectives can be effectively addressed using the presented ACO algorithm.

A column generation approach and new bounds for the car sequencing problem

The subject of the 2005 ROADEF challenge was a variation of the car scheduling problem based on the needs of the Renaultfactories. In this paper we revisit this problem to investigate how further contributions can be made in order to close the gap between exact and heuristic methods that exists for this problem. For the benchmark set used in the final round of the competition we report new lower bounds for 7 out of the 19 instances by using an improved ILP formulation of the problem. We also present a new column generation based exact method for solving the ILP problem which outperforms branch-and-bound in obtaining upper bounds for these problems.