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Colorectal cancer (CRC) is one of the most prominent malignant diseases, with a high incidence and a dismal prognosis. Metastasis to the liver is the leading cause of death in CRC patients. This study aimed to identify accurate metastatic biomarkers of CRC and investigate the potential molecular mechanisms of liver metastasis of colorectal cancer (LMCRC).Three independent datasets were screened and downloaded from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to identify differentially expressed genes (DEGs) in CRC tissues and liver metastases. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Furthermore, the protein-protein interactions (PPIs) of the DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database, Cytoscape, and Molecular Complex Detection (MCODE). Next, the expression levels and Kaplan-Meier survival analysis of the target gene between normal colon and CRC tissues were performed by UALCAN. The expression of the target gene in tissues and cell lines was verified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR), western blot, and immunohistochemistry (IHC) assay. The impact of the target gene on the proliferation, invasion, and migration ability of COAD cells was exploredA total of 92 common DEGs were found in the three independent datasets. GO/KEGG enrichment analysis showed that the DEGs were mainly involved in 14 different pathways. The protein-protein interaction (PPI) network revealed that complement 5 (C5), the upstream gene of C8A in the complement system, was associated with C8 and other key hub genes. Meanwhile, the online UALCAN resource showed that C5 was up-regulated and facilitated malignant progression in COAD samples. Next, we confirmed that C5 remarkably increased and promoted cell proliferation, migration, and invasion in CRC cell lines, SW620 and SW480. The IHC assay showed C5 was also highly expressed in a majority of LMCRC tissues compared with paired CRC tissues.The findings of our integrated bioinformatics study suggest that complement C5 might serve as a potential therapeutic target in patients with CRC.

Although the treatment of hepatocellular carcinoma (HCC) has been revolutionized by the advent of effective systemic therapies, the prognosis of patients with HCC remains dismal. Herein, we examined the pathophysiological role of PARG and assessed the utility of targeting dePARylation for HCC therapy.The oncogenic function of PARG was evaluated in 2 orthotopic xenograft models and a PargHigh PARG expression was strongly associated with poor HCC prognosis. Hepatocyte-specific PARG deletion significantly impaired liver tumorigenesis. PARG promoted HCC growth and metastasis through DDB1-dependent modulation of c-Myc. Specifically, PARG dePARylated DDB1 and consequently promoted DDB1 autoubiquitination, thus stabilizing the c-Myc protein in HCC cells. PARG downregulation attenuated c-Myc-induced MMR expression and PARG deficiency was correlated with a favorable prognosis in patients with HCC treated with anti-PD-1-based immunotherapy. In addition, PARG inhibitors could act in synergy with anti-PD-1 antibodies in orthotopic mouse models.PARG can act as an oncogene in HCC by modulating PARG/DDB1/c-Myc signaling and could be used as a biomarker to identify patients with HCC who may benefit from anti-PD-1 treatment. Our findings suggest that co-inhibition of PARG and PD-1 is an effective novel combination strategy for patients with HCC.The increase in deaths due to hepatocellular carcinoma (HCC) is a growing concern, with the mechanisms responsible for HCC development still incompletely understood. Herein, we identify a novel mechanism by which the protein PARG contributes to HCC development. Inhibition of PARG increased the efficacy of anti-PD-1 therapy (a type of immunotherapy) in HCC. These findings support the future clinical development of PARG inhibitors, potentially in combination with anti-PD-1 inhibitors.

Autophagy in tumor was also found to influence immune microenvironment. The relation between autophagy and cancer intrinsic PD1 and PD-L1 expression was not clear.With data from TCGA and GTEx databases, mRNA expression levels of autophagy-related genes were compared between tumor samples and normal tissues, which were also correlated with survival status. Expression of autophagy-related genes were also associated with clinical traits in datasets of GSE14520 and ICGC LIRI. Single sample gene set enrichment analysis (ssGSEA) was used to calculate autophagy scores in tumor samples, using signatures from MSigDB database. Lentivirus (PD1 and PD-L1), siRNA (ATG13) and plasmids (LC3A/B) were used to target specific genes in tumor cells; Western blot was used to examine protein expression accordingly. Co-immunoprecipitation was performed to find PD1 or PD-L1 interacting proteins; colony formation and EdU analysis were used to evaluate tumor cell growth abilities.mRNA levels of autophagy markers were increased in tumor and correlated with worse survival of cancer patients. In hepatocellular carcinoma (HCC), high mRNA expression of autophagy markers was related to poor clinical status; increasing LC3 expression in HCC cell lines could promote tumor growth. Tumor intrinsic PD1 or PD-L1 were related to higher autophagy levels in specific tumor types; over-expression of PD1 or PD-L1 could increase autophagy in tumor cells through ATG13 interaction.Autophagy could promote tumor growth in specific cancer types. Tumor intrinsic PD1 or PD-L1 could both increase autophagy through ATG13 interaction.

PurposeThis paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks.Design/methodology/approachA chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance.FindingsThe involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries.Originality/valueThis research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

PurposeFacing the challenge of increasing energy cost and requirement of reducing the emissions, identifying the potential factors of them in the manufacturing factories is an important prerequisite to control energy consumption. This paper aims to present a bi-objective green in-house transportation scheduling and fleet size determination problem (BOGIHTS&FSDP) in automobile assembly line to schedule the material delivery tasks, which jointly take the energy consumption into consideration as well.Design/methodology/approachThis research proposes an optimal method for material handling in automobile assembly line. To solve the problem, several properties and definitions are proposed to solve the model more efficiently. Because of the non-deterministic polynomial-time-hard nature of the proposed problem, a Multi-objective Discrete Differential Evolution Algorithm with Variable Neighborhood Search (VNS-MDDE) is developed to solve the multi-objective problem.FindingsThe performances of VNS-MDDE are evaluated in simulation and the results indicate that the proposed algorithm is effective and efficient in solving BOGIHTS&FSDP problem.Originality/valueThis study is the first to take advantage of the robot's interactive functions for part supply in automobile assembly lines, which is both the challenge and trend of future intelligent logistics under the pressure of energy and resource. To solve the problem, a VNS-MDDE is developed to solve the multi-objective problem.

Energy-awareness in the industrial sectors has become a global consensus in recent decades. Green scheduling is acknowledged as an effective weapon to reduce energy consumption in the industrial sectors. Therefore, this paper is devoted to the green scheduling of flexible manufacturing cells (FMC) with auto-guided vehicle transportation, where conflict-free routing of the vehicles is considered. To deal with this problem, a bi-objective optimization model is proposed to achieve the minimization of the maximum completion time and the total energy consumption in an FMC. The studied problem is an extension of flexible job shop problem which is NP-hard. Thus, an improved bi-objective salp swarm algorithm based on decomposition (IMOSSA/D) is proposed and applied to the problem. The approach is based on the decomposition of the bi-objective problem. Salp swarm intelligence along with three stochastic-distribution-based operators are incorporated into the approach, to enhance and balance its exploring and exploiting ability. Computational experiments are performed to compare the proposed approach with two state-of-the-art algorithms. This study allows the decision makers to better trade-off between energy savings and production efficiency in flexible manufacturing cellular environment.

In recent years, mixed-model assembly lines (MMALs) have been widely adopted by the automotive enterprises to face the requirements of mass customization. Meanwhile, the diversified components of various end products are placing the material feeding processes at great challenges. Under this circumstance, to raise the material feeding efficiency, this paper proposes an automatic monorail shuttle system (AMSS) based on line-integrated supermarkets applied to the MMALs by introducing the load-exchangeable shuttles and crossovers. Considering the importance of cost-control in manufacturing enterprises, we establish a mathematical model with the objective to minimize the total costs of the material feeding system, which includes the installation costs of crossovers and the input and operation costs of load-exchangeable shuttles. Then, due to the NP-hard nature of the proposed problem, a shuffled frog leaping-based hyper heuristic (SBHH) algorithm is developed to determine the allocation and scheduling of the shuttles and crossovers, which adopts the shuffled frog leaping algorithm (SFLA) as the high-level heuristic to select the low-level heuristics. To improve the performance of the algorithm, the concept of ‘dynamic decision unit’ is presented to raise the solution accuracy and the convergence speed. Finally, the simulation results verified the superiority of the proposed SBHH algorithm both in solution quality and convergence speed by comparing with other optimization algorithm in the existing literature.

Since the highly diversified consumer demands and changing market environment pose a great challenge for the automobile industry, this paper presents an extreme learning machine and knowledge base-based dynamic scheduling method to solve the dynamic scheduling problem of material handling for mixed-model assembly lines based on line-integrated supermarkets. First, the dynamic material handling scheduling problem is described and a mathematical model is established to maximize the weight sum of the throughput of the assembly line and the number of logistics workers under the condition of variable product ratio and weights of scheduling criteria, considering the random failure of the equipment and the instability of the cycle time. Subsequently, an extreme learning machine and knowledge base-based dynamic scheduling method is constructed, consisting of a knowledge base-based scheduling rule selection and an extreme learning machine (ELM)-based product mix matching method. Afterward, considering the defects of extreme learning machine, an elite opposition learning self-adaptive differential evolution-based extreme learning machine (EOADE-ELM) is proposed to optimize the parameters of ELM. Elite opposition-based learning and self-adaptive operators are employed to the EOADE-ELM to improve the performance. Finally, the simulation results prove the feasibility and effectiveness of the proposed dynamic scheduling method in the dynamic scheduling process.

PurposeThe use of multiple-capacity rail-guided vehicles (RGVs) has made automated storage and retrieval system (AS/RS) optimization more complex. The paper performs dual-RGV scheduling considering loading/unloading and collision-avoidance constraints simultaneously as these issues have only been considered separately in the previous literature.Design/methodology/approachThis paper proposes a novel model for dual-RGV scheduling with two-sided loading/unloading operations and collision-avoidance constraints. To solve the proposed problem, a hybrid harmony search algorithm (HHSA) is developed. To enhance its performance, a descent-based local search with eight move operators is introduced.FindingsA group of problem instances at different scales are optimized with the proposed algorithm and the results are compared with those of two other high-performance methods. The results demonstrate that the proposed method can efficiently solve realistically sized cases of dual multi-capacity RGV scheduling problems in AS/RSs.Originality/valueFor the first time in the research on dual multi-capacity RGV scheduling in an AS/RS, two-sided loading/unloading operations and collision avoidance constraints are simultaneously considered. Furthermore, a mathematical model for minimizing the makespan is developed and the HHSA is developed to determine solutions.

In recent years, to attain a competitive edge, merely optimizing the production efficiency is no longer the whole story that the manufacturing enterprises concentrate on. Instead, the increasing energy costs are driving manufacturing enterprises to seek an energy-oriented approach further to improve the overall performance of the production systems. Therefore, an opportunistic maintenance policy under energy and quality constraints is developed for a degrading batch production system in this paper. First and foremost, the degradation process of the production system is modeled using the Levy-type processes, meanwhile, the concept of the multielement-dependent degradation model was first put forward to describe the energy and quality degradation in production systems during the work period. Secondly, the degradation constraint threshold, energy constraint threshold, and quality constraint threshold are developed to determine the maintenance groups which include the machines that require two types of maintenance, preventive and corrective. Subsequently, in order not to interrupt the batch production activities, the changeover time between every two batches is regarded as an energy saving window to perform maintenance actions for the integrated maintenance groups. Thirdly, an energy-oriented integrated model that optimizes the total cost rate in consideration of energy and quality is developed, and the optimal maintenance combination scheme can be obtained by the Monte Carlo method. Finally, a numerical example is used to prove the effectiveness of the proposed policy.

Purpose The cross-docking strategy has a significant influence on supply chain and logistics efficiency. This paper aims to investigate the most suitable and efficient way to schedule the transfer of logistics activities and present a meta-heuristic method of the truck scheduling problem in cross-docking logistics. A truck scheduling problem with products time window is investigated with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks. Design/methodology/approach This research proposed a meta-heuristic method for the truck scheduling problem with products time window. To solve the problem, a lower bound of the problem is built through a novel two-stage Lagrangian relaxation problem and on account of the NP-hard nature of the truck scheduling problem, the novel red deer algorithm with the mechanism of the heuristic oscillating local search algorithm, as well as adaptive memory programming was proposed to overcome the inferior capability of the original red deer algorithm in the aspect of local search and run time. Findings Theory analysis and simulation experiments on an industrial case of a cross-docking center with a product’s time window are conducted in this paper. Satisfactory results show that the performance of the red deer algorithm is enhanced due to the mechanism of heuristic oscillating local search algorithm and adaptive memory programming and the proposed method efficiently solves the real-world size case of truck scheduling problems in cross-docking with product time window. Research limitations/implications The consideration of products time window has very realistic significance in different logistics applications such as cold-chain logistics and pharmaceutical supply chain. Furthermore, the novel adaptive memory red deer algorithm could be modified and applied to other complex optimization scheduling problems such as scheduling problems considering energy-efficiency or other logistics strategies. Originality/value For the first time in the truck scheduling problem with the cross-docking strategy, the product’s time window is considered. Furthermore, a mathematical model with objectives of minimizing the total product transshipment time and earliness and tardiness cost of outbound trucks is developed. To solve the proposed problem, a novel adaptive memory red deer algorithm with the mechanism of heuristic oscillating local search algorithm was proposed to overcome the inferior capability of genetic algorithm in the aspect of local search and run time.

Since greening scheduling problems are drawing increasing attention from researchers and modern manufacturing enterprises, and the energy consumption is a substantial problem regarding the greening and sustainability, the aim of this paper is to construct an energy-saving scheduling scheme to carry out the part feeding tasks of mobile robots in the automobile mixed model assembly lines. The objective of minimizing the total energy consumption of mobile robots is jointly incorporated with the operational criterions when implementing part feeding tasks. Due to the NP-hardness nature of the proposed greening problem, a multi-objective disturbance and repair strategy enhanced cohort intelligence (MDRCI) algorithm is established to deal with the multi-objective problem. Computational results indicate that the enhanced strategies are of great significance to the MDRCI algorithm and it outperforms the other benchmark algorithms on both global search capability and search depth. In addition, the energy-saving strategy and disturbance and repair strategy are validated by comparison experiments. Furthermore, managerial insights are illustrated to make trade-offs between the total line-side inventory level and the energy consumption, jointly making it helpful in the greening scheduling process of the practical production. The achievements acquired in this paper may be inspiring for further researches on the energy-related production scheduling problem.

Closed-loop production lines with a constant number of carriers are widely encountered in today's production systems. In this paper, a production performance evaluation and optimization model is proposed for a closed-loop production line taking into account the degradation-level-dependent quality and rework process. Based on a two-machine-one-buffer decomposition method and the system state transition, a production performance evaluation method is presented. Due to the emphasis on quality management, preventive maintenance (PM) is used to ensure the reliability of the machines in the production line, increasing the effective output at minimum cost. A hybrid particle swarm optimization and estimation of distribution algorithm (PSO-EDA) is proposed to efficiently develop the optimal PM strategy. Finally, numerical experiments are performed to verify the effectiveness of the model. With the objective of maximizing the system profit, the optimization of the machines and pallets number is also explored.

Purpose This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks. Design/methodology/approach First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales. Findings Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity. Research limitations/implications This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery. Originality/value Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.

With the wide application of robots in the material distribution process on the assembly lines, single robot scheduling cannot meet the actual production needs. However, the high degree of mechanization also brings about environmental problems. Therefore, this article aims to develop a scheduling methodology to accomplish material supply tasks using multiple robots with energy consumption consideration. Meanwhile, a targeted mathematical model to minimize total weighted penalty costs and total energy consumption is developed. Due to the NP-hard nature of the problem, an adaptive hybrid mutation population extremal optimization multi-objective algorithm based on uniform distribution selection is proposed to solve multi-objective problems. Furthermore, a new coding method for initialization is designed to optimize the whole iterative process. The performance of the proposed algorithm is evaluated by comparing with three benchmark multi-objective algorithms. Computational experiments are represented to prove the validity and feasibility of the proposed algorithm.

Inspired by the manufacturing costs proportion of part feeding in automotive mixed-model assembly lines (MMALs) being up to 20–35%, this paper takes the dynamic scheduling of part feeding for automotive MMALs as a crucial and complex problem. Therefore, a dynamic scheduling mechanism basing on the knowledge base (KB) and fruit fly optimization algorithm (FOA) with variable step sizes and logistic chaos (VSCFOA)-enhanced general regression neural network (VSCFOA-GRNN) is proposed to tackle the real-time part feeding scheduling problem of tow trains under the dynamic manufacturing system. A mathematical model is developed to illustrate the problem, where the throughput of the assembly line and the material delivery distance are determined as components of the objective function. Subsequently, samples of the MMAL are generated by the plant simulation software and used to train the VSCFOA-GRNN model off-line. Afterward, the trained model and KB are adopted in the real-time scheduling process to determine the optimal scheduling rule combination. Finally, the effectiveness, feasibility and accuracy of the novel scheduling mechanism are validated by computational results, especially in dynamic scheduling processes. It can cope well with changes in the dynamic environment, thus effectively realizing the higher productivity of assembly lines and better system performance.

While conventional scheduling researches take production efficiency, cost and quality as objectives, increasingly serious ecological problems and energy shortage have turned scholars’ attention to energy-efficient scheduling. Meanwhile, maintenance activities are of great importance to equipment availability and production continuity. This paper addresses an energy-efficient multi-objective scheduling problem of a serial production line (SPSP) integrating production and maintenance, with the criteria of minimizing the makespan and the total energy consumption simultaneously. The impact of integrating preventive maintenance (PM) is analyzed, and the better interval mode is picked out. An energy-saving strategy combining shutdown windows of PM and energy-saving windows on idle machines is designed to cut down the total energy consumption. In order to tackle this NP-hard problem, a hybrid multi-objective opposite-based learning evolutionary algorithm (HMOLEA) is developed, in which the opposite-based learning and IGD indicator-based evolutionary mechanism are combined together and a special rank assignment is designed to improve the computational efficiency. Furthermore, a self-adaptive weighted mutation operator is fused into the framework of HMOLEA to enhance the exploration of the algorithm. Extensive computational experiments are carried out to verify the effectiveness and superiority of HMOLEA in solving the SPSP.

Purpose This paper aims to investigate the scheduling and loading problems of tow trains for mixed-model assembly lines (MMALs). An in-plant milk-run delivery model has been formulated to minimize total line-side inventory for all stations over the planning horizon by specifying the departure time, parts quantity of each delivery and the destination station. Design/methodology/approach An immune clonal selection algorithm (ICSA) combined with neighborhood search (NS) and simulated annealing (SA) operators, which is called the NSICSA algorithm, is developed, possessing the global search ability of ICSA, the ability of SA for escaping local optimum and the deep search ability of NS to get better solutions. Findings The modifications have overcome the deficiency of insufficient local search and deepened the search depth of the original metaheuristic. Meanwhile, good approximate solutions are obtained in small-, medium- and large-scale instances. Furthermore, inventory peaks are in control according to computational results, proving the effectiveness of the mathematical model. Research limitations/implications This study works out only if there is no breakdown of tow trains. The current work contributes to the in-plant milk-run delivery scheduling for MMALs, and it can be modified to deal with similar part feeding problems. Originality/value The capacity limit of line-side inventory for workstations as well as no stock-outs rules are taken into account, and the scheduling and loading problems are solved satisfactorily for the part distribution of MMALs.

Due to the increasing greenhouse gas emissions and the energy crisis, the manufacturing industry which is one of the most energy intensive sector is paying close attention to the improvement of environmental performance efficiency. Therefore, in this paper the automated assembly line is balanced in a sustainable way which aims to optimize a green manufacturing objective (the total energy consumption) and a productivity-related objective (similar working load) simultaneously. A comprehensive total energy consumption of each processing stage was analyzed and modeled. To make the model more practical, a sequence-based changeover time and robots with different efficiencies and energy consuming rates are considered and optimized. To properly solve the problem, the proposed novel optimal solution takes the well-known MOEA/D as a base and incorporates a well-designed coding scheme and a problem-specific local search mechanism. Computational experiments are conducted to evaluated each improving strategies of the algorithm and its superiority over two other high-performing multi-objective optimization methods. The model allows decision makers to select more sustainable assembly operations based on their decision impacts in both productivity and energy-saving.

This paper considers a joint optimization problem of production and preventive maintenance for a serial production system subject to equipment degradations constrains The system processes different job types to meet different customer demands First a joint optimization problem domain of the system with equipment degradations is formally introduced A mathematic programming model is established with an optimal bi objective function of minimizing the system makespan and preventive maintenance cost Together with introducing an artificial bee colony theory a novel bi objective artificial bee colony algorithm is developed to pursue short production time and low preventive maintenance cost To guarantee the algorithm convergence performance a sorting rule of non dominated sorting genetic algorithm II NSGAII is introduced into the proposed algorithm Local Tabu search technology and probability criteria are involved Finally numerous experiments are conducted to evaluate the performance of the proposed algorithm In comparison with the well known NSGAII the proposed algorithm performs significantly better in terms of finding the spread compromise solutions

Purpose The extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of both workstation time and station area to improve the efficiency and productivity of the robotic assembly lines. A tradeoff was made between two conflicting objective functions, minimizing the number of workstations and minimizing the area of each workstation. Design/methodology/approach This research proposes an optimal method for balancing robotic assembly lines with space consideration and reducing robot changeover and area for tools and fixtures to further minimize assembly line area and cycle time. Due to the NP-hard nature of the considered problem, an improved multi-objective immune clonal selection algorithm is proposed to solve this constrained multi-objective optimization problem, and a special coding scheme is designed for the problem. To enhance the performance of the algorithm, several strategies including elite strategy and global search are introduced. Findings A set of instances of different problem scales are optimized and the results are compared with two other high-performing multi-objective algorithms to evaluate the efficiency and superiority of the proposed algorithm. It is found that the proposed method can efficiently solve the real-world size case of time and space robotic assembly line balancing problems. Originality/value For the first time in the robotic assembly line balancing problems, an assignment-based tool area and a sequence-based changeover time are took into consideration. Furthermore, a mathematical model with bi-objective functions of minimizing the number of workstations and area of each station was developed. To solve the proposed problem, an improved multi-objective immune clonal selection algorithm was proposed and a special coding scheme is designed.

Production system modeling aims to investigate the principles of production procedures and to reveal the relationship between components and systems. Tremendous efforts have been devoted to production system modeling for the serial production system. However, most of the research focuses on the analysis of the systems at the steady state. Due to the emphasis of the quality management, production systems with rework loops are widely used in today’s manufacturing industrials, which the traditional approaches are not applicable to. Since the recent analysis of transients shows significant value and great potential in manufacturing systems, in this article, a new mechanism for rework is introduced based on the principles of quality management and lean production. A novel “Instant-Checking” method is developed to model Bernoulli serial production system considering rework loops. This method overcomes conventional restrictions and limited assumptions, and it extends the problem to systems with complex structures. Meanwhile, the analysis for transients is conducted to demonstrate relationships between component- and system-level characteristics. Finally, numerical experiments are performed to verify the effectiveness of the model.

Purpose The purpose of this paper is to develop novel preventive maintenance (PM) modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure. Design/methodology/approach The system consists of two components and a single repair shop, assuming that the repair shop can only service for one component at a time. Based on semi-Markov theory, transition probabilities between all possible system states are discussed. With the transition probabilities, Markov renewal equations are established at regenerative points. By solving the Markov regenerative equations, the mean time from the initial state to system failure (MTSF) and the steady state availability (SSA) are formulated as two reliability measures for different reliability requirements of systems. The optimal PM policies are obtained when MTSF and SSA are maximized. Findings The result of simulation experiments verifies that the derived maintenance models are effective. Sensitivity analysis revealed the significant influencing factors for optimal PM policy for cold standby systems when different system reliability indexes (i.e. MTSF and SSA) are considered. Furthermore, the results show that the repair for random failure has a tremendous impact on prolonging the MTSF of cold standby system and PM plays a greater role in promoting the system availability of a cold standby system than it does in prolonging the MTSF of system. Practical implications In practical situations, system not only suffers normal deterioration caused by internal factors, but also undergoes random failures influenced by random shocks. Therefore, multiple failure types are needed to be considered in maintenance modeling. The result of the sensitivity analysis has an instructional role in making maintenance decisions by different system reliability indexes (i.e. MTSF and SSA). Originality/value This paper presents novel PM modeling methods for a cold standby system subject to two types of failures: random failure and deterioration failure. The sensitivity analysis identifies the significant influencing factors for optimal maintenance policy by different system reliability indexes which are useful for the managers for further decision making.

With increasing product customization, just-in-time part replenishment has become a significant scheduling problem in the automobile assembly system. This paper investigates a new unrelated parallel machine scheduling problem of an assembly line, where machines are employed to deliver material boxes from an in-house warehouse to workstations. The schedule is to appropriately specify the assignment and sequence of material boxes on each machine for minimizing line-side inventories under no stock-out constraints. By taking advantages of domain properties, an exact algorithm is developed to cope up with small-scale instances. In terms of real-world scale instances, a hybrid teaching–learning-based optimization metaheuristic is established by integrating teaching–learning-based optimization with a beam search technique. Experimental results indicate that the scheduling algorithms are effective and efficient in solving the proposed unrelated parallel machine scheduling.

In modern production systems, an ever-rising product variety has imposed great challenges for in-plant part supply systems used to feed mixed-model assembly lines with required parts. In recent years, many automotive manufacturers have identified the supermarket concept as an efficient part feeding strategy to enable JIT (Just-in-time) deliveries at low costs. This paper studies a discrete supermarket location problem which considers the utilization rate and capacity constraint of the supermarkets simultaneously. Firstly, a mathematical model is developed with the objective of minimizing the total system cost consisting of operating cost and transportation cost. Then, a self-adaptive estimation of distribution algorithm with differential evolution strategy, named DE/AEDA, is proposed to solve the problem. Finally, computational experiments are carried out to analyze the performance of the proposed algorithm compared with the benchmark algorithm by using a non-parametric test method. The results indicate that the proposed algorithm is valid and efficient.

Inspired by the advantages of hybrid intelligent optimization methods, this paper at first proposes a hybrid differential evolution with particle swarm optimization (DEPS) to solve a two-stage hybrid flow shops scheduling problem. On the basis of analyzing the convergence and optimization scheme of DEPS, the Kalman filter algorithm and a multi-stage learning strategy are then creatively fused into DEPS, namely KLDEPS, to enhance the running performance of the algorithm. The introduction of the Kalman filter enriches the diversity of individuals and enhances the neighborhood search ability of the algorithm, and the combination with the multi-stage learning strategy has beneficial effect on jumping out of the local optimal scheme. To make the proposed KLDEPS more suitable for a real manufacturing environment, the constraints of queueing time between two stages, different job sizes and processing time are imposed on the scheduling problem. The performance of the proposed KLDEPS is evaluated by comparing with two other high-performing intelligent optimization algorithms. The computational results reveal that the proposed KLDEPS outperforms the other two algorithms both in solutions’ quality and convergence rate.

This paper investigates a new single machine scheduling problem (SMSP) featuring nonnegative inventory constraints and discretely controllable processing times. Each job contains a single category of item, and the processing of a job would release a predefined number of items into the terminal inventory of the job. A schedule is to properly specify the job and processing time in each operation for minimizing terminal inventories. An exact algorithm and a hybrid metaheuristic are established to solve the problems with different scales. Computational results indicate that the scheduling approaches are effective and efficient in solving the proposed SMSP.

Increasing costs of energy and environmental pollution is prompting scholars to pay close attention to energy-efficient scheduling. This study constructs a multi-objective model for the hybrid flow shop scheduling problem with fuzzy processing time to minimize total weighted delivery penalty and total energy consumption simultaneously. Setup times are considered as sequence-dependent, and in-stage parallel machines are unrelated in this model, meticulously reflecting the actual energy consumption of the system. First, an energy-efficient bi-objective differential evolution algorithm is developed to solve this mixed integer programming model effectively. Then, we utilize an Nawaz-Enscore-Ham-based hybrid method to generate high-quality initial solutions. Neighborhoods are thoroughly exploited with a leader solution challenge mechanism, and global exploration is highly improved with opposition-based learning and a chaotic search strategy. Finally, problems in various scales evaluate the performance of this green scheduling algorithm. Computational experiments illustrate the effectiveness of the algorithm for the proposed model within acceptable computational time.

BackgroundCholangiocarcinoma was a highly malignant liver cancer with poor prognosis, and immune infiltration status was considered an important factor in response to immunotherapy. In this investigation, we tried to locate immune infiltration related genes of cholangiocarcinoma through combination of bulk-sequencing and single-cell sequencing technology.MethodsSingle sample gene set enrichment analysis was used to annotate immune infiltration status in datasets of TCGA CHOL, GSE32225, and GSE26566. Differentially expressed genes between high- and low-infiltrated groups in TCGA dataset were yielded and further compressed in other two datasets through backward stepwise regression in R environment. Single-cell sequencing data of GSE138709 was loaded by Seurat software and was used to examined the expression of infiltration-related gene set. Pathway changes in malignant cell populations were analyzed through scTPA web tool.ResultsThere were 43 genes differentially expressed between high- and low-immune infiltrated patients, and after further compression, PNOC and LAIR2 were significantly correlated with high immune infiltration status in cholangiocarcinoma. Through analysis of single-cell sequencing data, PNOC was mainly expressed by infiltrated B cells in tumor microenvironment, while LAIR2 was expressed by Treg cells and partial GZMB+ CD8 T cells, which were survival related and increased in tumor tissues. High B cell infiltration levels were related to better overall survival. Also, malignant cell populations demonstrated functionally different roles in tumor progression.ConclusionPNOC and LAIR2 were biomarkers for immune infiltration evaluation in cholangiocarcinoma. PNOC, expressed by B cells, could predict better survival of patients, while LAIR2 was a potential marker for exhaustive T cell populations, correlating with worse survival of patients.