Data-driven Product Functional Confguration:Patent Data and Hypergraph

The product functional confguration(PFC)is typically used by frms to satisfy the individual requirements of customers and is realized based on market analysis.This study aims to help frms analyze functions and realize functional confgurations using patent data.This study frst proposes a patent-data-driven PFC method based on a hypergraph network.It then constructs a weighted network model to optimize the combination of product function quantity and object from the perspective of big data,as follows:(1)The functional knowledge contained in the patent is extracted.(2)The functional hypergraph is constructed based on the co-occurrence relationship between patents and applicants.(3)The function and patent weight are calculated from the patent applicant’s perspective and patent value.(4)A weight calculation model of the PFC is developed.(5)The weighted frequent subgraph algorithm is used to obtain the optimal function combination list.This method is applied to an innovative design process of a bathroom shower.The results indicate that this method can help frms detach optimal function candidates and develop a multifunctional product.

Four development stages of collective intelligence

The new generation of artificial intelligence(AI)research initiated by Chinese scholars conforms to the needs of a new information environment changes,and strives to advance traditional artificial intelligence(AI 1.0)to a new stage of AI 2.0.As one of the important components of AI,collective intelligence(CI 1.0),i.e.,swarm intelligence,is developing to the stage of CI 2.0(crowd intelligence).Through in-depth analysis and informative argumentation,it is found that an incompatibility exists between CI 1.0 and CI 2.0.Therefore,CI 1.5 is introduced to build a bridge between the above two stages,which is based on biocollaborative behavioral mimicry.CI 1.5 is the transition from CI 1.0 to CI 2.0,which contributes to the compatibility of the two stages.Then,a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is given.The meta-synthesis of wisdom,as an improvement of crowd intelligence,is an advanced stage of bionic intelligence,i.e.,CI 3.0.It is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0,and some elaboration is made.As a result,we propose four development stages(CI 1.0,CI 1.5,CI 2.0,and CI 3.0),which form a complete framework for the development of CI.These different stages are progressively improved and have good compatibility.Due to the dominant role of cooperation in the development stages of CI,three types of cooperation in CI are discussed:indirect regulatory cooperation in lower organisms,direct communicative cooperation in higher organisms,and shared intention based collaboration in humans.Labor division is the main form of achieving cooperation and,for this reason,this paper investigates the relationship between the complexity of behavior and types of labor division.Finally,based on the overall understanding of the four development stages of CI,the future development direction and research issues of CI are explored.

Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints

We investigate a kind of vehicle routing problem with constraints(VRPC)in the car-sharing mobility environment,where the problem is based on user orders,and each order has a reservation time limit and two location point transitions,origin and destination.It is a typical extended vehicle routing problem(VRP)with both time and space constraints.We consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user(or passenger)experience.To solve the scheduling model more accurately,a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer,and a spatiotemporal distance embedded hybrid ant colony algorithm(HACA-ST)is proposed.The algorithm can be divided into two stages.First,through spatiotemporal clustering,the spatiotemporal distance between users is the main measure used to classify customers in categories,which helps provide heuristic information for problem solving.Second,an improved ant colony algorithm(ACO)is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling route.Computational analysis is carried out based on existing data sets and simulated urban instances.Compared with other heuristic algorithms,HACA-ST reduces the length of the shortest route by 2%–14%in benchmark instances.In VRPC testing instances,concerning the combined cost,HACA-ST has competitive cost compared to existing VRP-related algorithms.Finally,we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

Firefly algorithm with division of roles for complex optimal scheduling

A single strategy used in the firefly algorithm(FA)cannot effectively solve the complex optimal scheduling problem.Thus,we propose the FA with division of roles(DRFA).Herein,fireflies are divided into leaders,developers,and followers,while a learning strategy is assigned to each role:the leader chooses the greedy Cauchy mutation;the developer chooses two leaders randomly and uses the elite neighborhood search strategy for local development;the follower randomly selects two excellent particles for global exploration.To improve the efficiency of the fixed step size used in FA,a stepped variable step size strategy is proposed to meet different requirements of the algorithm for the step size at different stages.Role division can balance the development and exploration ability of the algorithm.The use of multiple strategies can greatly improve the versatility of the algorithm for complex optimization problems.The optimal performance of the proposed algorithm has been verified by three sets of test functions and a simulation of optimal scheduling of cascade reservoirs.

A PRODUCTION OPTIMIZATION MODEL OF SUPPLY-DRIVEN CHAIN WITH QUALITY UNCERTAINTY

Supply-driven chain＇s production is different from traditional demand-driven production because its supplies must guide the full production flow toward the markets and respond actively to customer demand. According to the control tlaeory, a novel multi-variable operation model of supply-driven chain is discussed here, integrating suppliers, manufacturers, distributors and market demands. Especially the coordination problem between suppliers and manufacturers is discussed where suppliers play more important role than manufacturers. Because defect is common in real production system, the production operation of supply-driven chain with imperfect quality is described on the basis of fuzzy set to model the ambiguity of quality and to provide appropriate supply coordination mechanism. In a designed numerical example, it is apparent that both response and robustness performances of supply-driven production system on demand with imperfect quality are improved by a fuzzy proportional-integral- differential regulator. The proposed model may apply to similar productions with imoerfect quality.

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades.It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial.Moreover,many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment.In this study,based on binary wolf pack algorithm(BWPA),combining with flexible population updating strategy,a flexible binary wolf pack algorithm(FWPA)is proposed.Then,FWPA is used to solve a set of static multidimensional knapsack benchmarks and several dynamic multidimensional knapsack problems,which have numerous practical applications.To the best of our knowledge,this paper constitutes the first study on the performance of WPA on a dynamic combinatorial problem.By comparing two state-of-the-art algorithms with the basic BWPA,the simulation experimental results demonstrate that FWPA can be considered as a feasibility and competitive algorithm for dynamic optimization problems.