In this paper, the concept of Sustainable Manufacturing (SM) is discussed, and the focus is put on the elimination of wastes in a product’s life cycle. Recent development on enabling technologies to advance SM are surveyed to identify promising research areas. We have found virtualVerification and Validation(V&V) should be promoted to enhance the sustainability especially inSmall and Medium sized Enterprises(SMEs). V&V are typically non-value-added and the effects on V&V should be minimized; while due to lack of expertise, most of SMEs rely heavily on prototyping and physical experiments to evaluate their products. We propose virtual V&V to replace physical experiments to the maximum extent. To show the significance of the proposed concept, a case study of virtual V&V system is developed to reduce the needs of physical prototyping and testing in product development. This benefits greatly to (1) cost savings by replacing numerous physical prototyping and testing by virtual V&V, (2) lead-time reduction for new product to enter emerging markets, and (3) global optimization of product design by analyzing and exploring a large number of design options. The improvement at these aspects contributes to system sustainability significantly. The concept of using virtual V&V to reduce non-value-added prototyping and testing is applicable to manufacturing businesses in most of SMEs who design and test their own products.Note to Practitioners—This work is highly motivated by a number of the authors’ industry projects with regional SMEs who put heavy investments in testing to prove products’ quality to clients before the orders for products can be awarded; manufacturing businesses related to testing are non-value-added that should be minimized from the perspective of sustainability. Virtual V&V is proposed as a vital solution to overcome their dilemma, and the proposed solution has its theoretical and practical significance to reduce wastes, shorten lead-times, and optimize products based on parametric study for a wide scope of design alternatives. This helps to increase the lifespan of SMEs ultimately.

In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

Sustainability issues have been seen as a promising paradigm for achieving a better future. Firms in the logistics service sector are still lacking clear value propositions on sustainable development. While many organizations publish their mission statements publicly as kinds of public information, reviewing mission statements is an appropriate means to evaluate an organization's strategy. This study focuses on the public information such as mission statements of the top 50 global 3-PL companies and the relevant sustainable development. A comprehensive content analysis identified four major content dimensions of mission statements relating to sustainability development. The dimensions are driving forces, approaches, responsibility to stakeholders, and competitive values. This paper offers a good methodological reference for researchers or practitioners managing the public information of organizations. Network analysis reveals that the location of companies has a limited effect on their mission and strategy as they all provide global service.

This paper presents an empirical study on how knowledge management practices and innovation sources affect product innovation performance, among the 152 manufacturers in the low- and high- tech industries in China. The results indicate that external innovation sources are positively correlated with innovation activities and new product performance. Intellectual Property (IP) and knowledge management practices (KMP) are positively correlated with innovation activities, and KMP is positively correlated with innovation sources. The dual effect of KMP shows its indispensable effect on the new product development for both high-tech and low-tech firms, but for low-tech firms, such effect is relatively weak. This empirical study shows that IP management is critical to high-tech but not low-tech firms. We also found that, for innovation activities, low-tech depends on the external sources of innovation whilst high-tech firms do not.

The global wine-making industry has faced challenges due to the increasing demands of consumers, particularly in emerging markets such as China, Brazil, India, and Russia. Controlling the quality during wine production is one of the key challenges faced by global winemakers to produce wine with appropriate sensorial properties tailored to specific markets. The wine production quality is constituted from a number of environmental factors such as climate, soil, and temperature, which affect the sensorial properties and the overall quality. This paper proposed a rule-based quality analytics system (RBQAS) to capture physicochemical data during wine production and to investigate the hidden patterns from the data for quality prediction. It consists of IoT for data capture on a real-time basis, followed by association rule mining to identify relationships between sensorial and physicochemical properties of wine.

The global wine-making industry has faced challenges due to the increasing demands of consumers, particularly in emerging markets such as China, Brazil, India, and Russia. Controlling the quality during wine production is one of the key challenges faced by global winemakers to produce wine with appropriate sensorial properties tailored to specific markets. The wine production quality is constituted from a number of environmental factors such as climate, soil, and temperature, which affect the sensorial properties and the overall quality. This paper proposed a rule-based quality analytics system (RBQAS) to capture physicochemical data during wine production and to investigate the hidden patterns from the data for quality prediction. It consists of IoT for data capture on a real-time basis, followed by association rule mining to identify relationships between sensorial and physicochemical properties of wine.

Recently, tremendous strides have been made in generic object detection when used to detect faces, and there are still some remaining challenges. In this paper, a novel method is proposed named multilevel single stage network for face detection (MSNFD). Three breakthroughs are made in this research. Firstly, multilevel network is introduced into face detection to improve the efficiency of anchoring faces. Secondly, enhanced feature module is adopted to allow more feature information to be collected. Finally, two‐stage weight loss function is employed to balance network of different levels. Experimental results on the WIDER FACE and FDDB datasets confirm that MSNFD has competitive accuracy to the mainstream methods, while keeping real‐time performance.

The modern economy involves a variety of marketplaces, and the Internet has led to the development of a new efficient marketplace-the one-shot multiattribute exchange. It is an important decision problem for a matchmaker (or broker) to achieve the optimal trade matching in one-shot multiattribute exchanges; however, to the best of our knowledge, there has been little work on this issue under fuzzy environments. This paper proposes an optimal matching approach for one-shot multiattribute exchanges with simultaneous fuzzy information and indivisible demand considerations. First, we employ fuzzy set theory to represent the traders' orders with fuzzy information and then put forward a calculation method of the matching degree based on the improved fuzzy information axiom. Second, on the basis of the matching degree, we construct a fuzzy multiobjective programming model for one-shot multiattribute exchanges with indivisible demand. Afterward, the credibility measure is introduced to convert the model into a crisp one. The crisp model belongs to a class of multiobjective nonlinear general assignment problems and has NP-hard complexity. In order to solve the crisp model effectively, we develop a problem-specified metaheuristic algorithm, i.e., multiobjective discrete differential evolution. Finally, we conduct comprehensive computational experiments on numerical examples to illustrate the application and performance of the proposed model and algorithm.

In this paper, another important solution for fuzzy games, called the Banzhaf function, is investigated. First, a simplified expression for the Banzhaf function for games with fuzzy coalition is proposed. The relationship between this simplified expression and two kinds of particular Banzhaf functions are discussed in detail. Furthermore, in the framework of games with fuzzy coalitions, we give a definition of the potential function of fuzzy games, and prove that the marginal values of the potential function are the components of the Banzhaf function. Furthermore, a new axiomatic characterization of the Banzhaf function for games with fuzzy coalitions is provided.

Contemporary information modeling of enterprise systems only focuses on the technical aspect of the systems, though it is known that they are social-technical (socio-tech) systems in essence. In fact, there are many lessons that can be learned from failures in the management of enterprise systems, which range from a small one (e.g., failure to install a printer driver) to a large one (e.g., nuclear power plant post-accident management). This paper, therefore, proposes that the enterprise system should be viewed as a socio-tech system. The paper presents a novel integrated approach to information modeling of socio-tech enterprise systems. In particular, the approach integrates object-orientation, systems-dynamics (as a means to represent high-level dynamics), and aspect-programming. The paper discusses an example to illustrate how the proposed approach works.

Rough set (RS)- and particle swarm optimization (PSO)- based adaptive neuro-fuzzy inference system (ANFIS) approaches are proposed to generate customer satisfaction models in affective design that address fuzzy and nonlinear relationships between affective responses and design attributes. The RS theory is adopted to reduce the number of fuzzy rules generated using ANFIS and simplify the structure of ANFIS. PSO is employed to determine the parameter settings of an ANFIS from which customer satisfaction models with better modeling accuracy can be generated. A case study of mobile phone affec- tive design is used to illustrate the proposed approaches.

The success of a new product is very much related to the customer satisfaction level of the product. Therefore, it is important to estimate the customer satisfaction level of a new product in its design stage. Quality function deployment is commonly used to develop customer satisfaction models for product design. Relationships between customer satisfaction and design attributes are highly fuzzy and nonlinear, but these relationship characteristics cannot be captured by existing customer satisfaction models. In this paper, we propose a novel chaos-based fuzzy regression (FR) approach with which fuzzy customer satisfaction models with second- and/or higher order terms, and interaction terms can be developed. The proposed approach uses a chaos optimization algorithm to generate the polynomial structures of customer satisfaction models. Thereafter, it employs an FR method to determine the fuzzy coefficients of the individual terms of models. To illustrate and validate the proposed approach, it is applied in the development of a customer satisfaction model for a mobile phone design. Five validation tests are conducted to compare modeling results from the chaos-based FR with those from statistical regression, FR, and fuzzy least-squares regression. Results of the validation tests show that the proposed approach outperforms the other three approaches in terms of mean relative errors and variance of errors and customer satisfaction models with second- and/or higher order terms, and interaction terms can be developed effectively using the proposed chaos-based FR approach.

Petri net (PN) is a classical tool for modeling, simulation, and analysis. With the emergence of distributed manufacturing system (DMS), PN has been evolved into different forms such as colored PN (CPN) and timed PN (TPN). To fulfill the practical requirements, CPN has been extended to a larger and more complex model. Meanwhile, DMS becomes an important issue for industry, and how to model a complex manufacturing network for better throughput needs further investigation. A simple single type of PN only cannot fulfill all contemporary requirements at the same time. This paper proposes a new concept for a new PN-hierarchical timed CPN (HTCPN), which can be a powerful tool for modeling, simulation, and analysis for current large complex distributed manufacturing system. This paper presents a general structure of DMS and uses HTCPN to model the manufacturing system as discrete event with dynamic behavior. The proposed approach shows superiority on modeling large complex manufacturing system as it represents the material flow and transitions clearly and provides overview and detailed description for DMS.

Evacuation planning with the contraflow technique is a complex planning problem. The problem is further complicated when more realistic situations such as evacuation priorities and the setup time for the contraflow operation are considered. Such a complex problem has yet to be discussed in the present literature. In this paper, we present a multiple-objective optimization model for this problem and a two-layer algorithm to solve this model. Experiments on three transportation networks with different network scales are presented to show the excellent performance of the proposed model and algorithm.

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs.

This paper presents our study on the emergency resource-planning problem, particularly on the development of a new approach to resource planning through contraflow techniques with consideration of the repair of damaged infrastructures. The contraflow technique is aimed at reversing traffic flows in one or more inbound lanes of a divided highway for the outbound direction. As opposed to the current literature, our approach has the following salient points: (1) simultaneous consideration of contraflow and repair of repair of roads; (2) classification of victims in terms of their problems and urgency in sending them to a safe place or place to be treated; and (3) consideration of multiple destinations for victims. A simulated experiment is also described by comparing our approach with some variations of our approach. The experimental results show that our approach can lead to a reduction in evacuation time by more than 50%, as opposed to the original resource operation on the damaged transportation network, and by about 20%, as opposed to the approach with resource replanning (only) on the damaged network. In addition, the multiobjective optimization algorithm to solve our model can be generalized to other network resource-planning problems under infrastructure damage.

The aircraft arrival sequencing and scheduling (ASS) problem is a salient problem in air traffic control (ATC), which proves to be nondeterministic polynomial (NP) hard. This paper formulates the ASS problem in the form of a permutation problem and proposes a new solution framework that makes the first attempt at using an ant colony system (ACS) algorithm based on the receding horizon control (RHC) to solve it. The resultant RHC-improved ACS algorithm for the ASS problem (termed the RHC-ACS-ASS algorithm) is robust, effective, and efficient, not only due to that the ACS algorithm has a strong global search ability and has been proven to be suitable for these kinds of NP-hard problems but also due to that the RHC technique can divide the problem with receding time windows to reduce the computational burden and enhance the solution's quality. The RHC-ACS-ASS algorithm is extensively tested on the cases from the literatures and the cases randomly generated. Comprehensive investigations are also made for the evaluation of the influences of ACS and RHC parameters on the performance of the algorithm. Moreover, the proposed algorithm is further enhanced by using a two-opt exchange heuristic local search. Experimental results verify that the proposed RHC-ACS-ASS algorithm generally outperforms ordinary ACS without using the RHC technique and genetic algorithms (GAs) in solving the ASS problems and offers high robustness, effectiveness, and efficiency.

Recently, fourth party logistics (4PL) is receiving more and more attentions in manufacturing and retail industries. However, the research on the fourth party logistics routing problems (4PLRP) has just begun. In this paper, the mathematical model of the point to point single task path optimization of 4PLRP with time windows (4PLRPTW) is established based on multi-graph. The objective is to find minimum cost routes from the start node to the destination node within the pre-specified time windows. A recently-developed meta-heuristic optimization method, harmony search, is suggested for solving 4PLRPTW. The results of the numerical experiments demonstrate that the harmony search is effective and could find near optimal solution within the reasonable amount of time and computation.

Recently, there has been an increasing interest in applying genetic algorithms (GAs) in dynamic environments. Inspired by the complementary and dominance mechanisms in nature, a primal-dual GA (PDGA) has been proposed for dynamic optimization problems (DOPs). In this paper, an important operator in PDGA, i.e., the primal-dual mapping (PDM) scheme, is further investigated to improve the robustness and adaptability of PDGA in dynamic environments. In the improved scheme, two different probability-based PDM operators, where the mapping probability of each allele in the chromosome string is calculated through the statistical information of the distribution of alleles in the corresponding gene locus over the population, are effectively combined according to an adaptive Lamarckian learning mechanism. In addition, an adaptive dominant replacement scheme, which can probabilistically accept inferior chromosomes, is also introduced into the proposed algorithm to enhance the diversity level of the population. Experimental results on a series of dynamic problems generated from several stationary benchmark problems show that the proposed algorithm is a good optimizer for DOPs.

Recently, fourth-party logistics (4PL) is receiving considerable attention in the manufacturing and retail industries. However, due to the complexity, the research of routing problem in 4PL is in an initial stage. The existing study does not consider the complicated problem with node-edge property. This paper studies the node-to-node routing problem in 4PL. A mathematical model is set up based on nonlinear integer programming and multigraph. With respect to the problempsilas characteristics a hybrid immune algorithm is designed. The simulation shows that the hybrid immune algorithm is effective for solving the problem and provides an efficient method for making decision on routing in 4PL.

Parallel multi-population Particle Swarm Optimization (PSO) Algorithm using OpenMP is presented for the Uncapacitated Facility Location (UFL) problem. The parallel algorithm performed asynchronously by dividing the whole particle swarm into several sub-swarms and updated the particle velocity with a variety of local optima. Each sub-swarm changes its best position so far of to its neighbor swarm after certain generations. The parallel multi-population PSO (PMPSO) algorithm is applied to several benchmark suits collected from OR-library. And the results are presented and compared to the result of serial execution multi-population PSO. It is conducted that the parallel multi-population PSO is time saving, especially for large scale problem and generated more robust results.

Determination of optimal control strategy is one of the key factors for a successful supply chain management. This paper focuses on the research of an inventory control strategy of a serial supply chain. First, it proposes an optimization model of an inventory control that is based on the combination of a nonlinear integer programming model and a general push/pull control model. Then, the optimal control strategy is obtained by the combination of pheromone evolutionary algorithm and simulation analysis. Case studies demonstrated the effectiveness of the method.

The objective of this research is to optimize the link structure of webpages for an e-supermarket. Customers are looking for greater convenience in shopping when they visit the website of an e-supermarket, while e-supermarket managers prefer webpages that contain information about profitable products to be visited more frequently. In order to balance the interests of both parties and to aid the webmaster in updating the website regularly, we present a mathematical model with the objective of minimizing the overall weighted distances between webpages. An updating algorithm is used to determine the distance between pages. It is proved to be more efficient under certain special circumstances. We propose the statistical Hopfield neural-network and the strategic oscillation-based tabu-search algorithms as solving methods. The former is appropriate for optimizing small-scale problems. The latter is good at solving large-scale problems approximately. The preliminary validity of the model and the performance of the algorithms is demonstrated by experiments on a small website and several large websites, using randomly generated data. The destination pages that customers and website managers preferred are proved to be more accessible after optimization.

Abstract: To cope with the requirements of high dimensional accuracy for injection molding components, it is important to optimize the process parameters in order to sustain the high level dimensional quality of the molded parts. In this respect, a study in the domain of process optimization is of paramount importance in terms of determining the optimal set of injection molding parameters. To this end, a methodology to establish an integrated model which consists of both fuzzy logic reasoning and a genetic algorithm is proposed. These two artificial intelligence techniques can complement each other to form an integrated model which capitalizes on the merits and at the same time offsets the pitfalls of the involved technologies. To validate the feasibility of the proposed model, a case study related to injection molding optimization is also covered in this paper.

Abstract: In today's competitive environment, rapid advances in economic globalization and information technology have forced many organizations to anticipate and respond to increasing volatility and competitive pressures. Numerous researches have focused on developing techniques on the analysis, design and management of whole logistics chains in particular in the domain of data mining, data analysis and data classification. While the data mining techniques and computational intelligence techniques are good independently for solving specific tasks, they can be synergized through the formation of an integrated and unified model, which can take advantage of the goods and offset the flaws of the two techniques. In this paper, an intelligent information infrastructure, which is characterized by its ability to encompass a rich collection of knowledge representation formalisms for dealing with a logistics information flow problem, is presented. Such intelligent information infrastructure includes a two‐step strategy that embraces the combination of online analytical processing and neural networks to support knowledge discovery. In addition, extensible markup language is used to support the overall infrastructure, in order to facilitate the seamless data interchange within an enterprise.

Partner selection is an active research topic in agile manufacturing and supply chain management. In this paper, the problem is described by a 0–1 integer programming with non-analytical objective function. Then, the solution space is reduced by defining the inefficient candidata. By using the fuzzy rule quantification method, a fuzzy logic based decision making approach for the project schedulling is proposed. We then develop a fuzzy decision embedded genetic algorithm. We compare the algorithm with tranditional methods. The results show that the suggested approach can quickly achieve optimal solution for large size problems with high probability. The approach was applied to the partner selection problem of a coal fire power station construction project. The satisfactory results have been achieved.

Presents an investigation of how partner selection problems may be optimized by the use of a precedence network of subprojects. At the start, the problem is described by a model with the subscript type of variables and a non-analytical objective function. It cannot be solved by general mathematical programming methods. By using the fuzzy rule quantification method, a fuzzy logic-based decision-making approach for the project scheduling is proposed. We then develop a fuzzy decision embedded heuristic genetic algorithm (GA/FD) to find the solution for partner selection. The approach was demonstrated by the use of an experimental example drawn from a coal-fired power station construction project. The results show us that the suggested approach can quickly achieve the optimal solution for large-sized problems.