製造拠点と顧客の交渉による顧客ニーズを考慮した日程計画及び在庫引当て計画の同時最適化に関する研究

In re-entrant flow shop, an optimal resource allocation becomes more difficult as the number of product types, equipment, and repetition of processes increases. Therefore, there is need to consider a new robust method to resolve this resource allocation problem. In this paper, we propose an optimization method that uses combinatorial auction to search solution space efficiently. The proposed method is a high-speed method based on a bidder’s utility to solve the resource allocation problem in practical calculating time. Here, the utility means the levels on degree of content obtained by bids and is used to make the neighborhood efficiently in the combinatorial auction. We have applied this method to two objective problems and have obtained quasi-optimal solutions. Computation time of a model has reduced from 56 to 77 % and that of the other has reduced 91 %. This indicates that a combinatorial auction with utility is efficient method to search solution space.

In the closing chapter of this book we would like to share with our readers our view about the future of the customer centric enterprise and the enabling strategies of mass customization, customer integration, and personalization. We will also comment on fields for further research necessary for the development of new processes, tools and programs for integrating the customer into value creating activities, both on the technological and the operational process side. We identify six areas where - from our perspective and within our field of knowledge - more research is needed most. These are also fields which we think require special attention when implementing a mass customization system: (1) Issues concerning the design of products and product architectures. (2) Consumer behavior with customer interaction tools. (3) Drivers of customer satisfaction and dissatisfaction with custom made products. (4) Impact of integrating a user and customer into value creation on knowledge management and information management. (5) Capability analysis and systems engineering for concurrency in value chains. (6) Measurement of value contribution in mass customization systems.

Port berth allocation and microgrid cluster joint optimization scheduling based on master-slave game

Since the last decades, companies have been increasing product variety, thus forcing manufacturers to create more and more customized products. To manage such contexts, manufacturing companies are adopting mass customization, i.e., a manufacturing strategy that aims to offer customized goods at low cost. Recently, advancements in information system technologies provide new opportunities for the manufacturing sector. In particular, the concept of Industry 4.0, i.e., the application of the concepts of smartness and networking to the manufacturing environment, is providing tools to reduce the complexity of managing production systems. Despite the relevance of both areas, how mass customization can be integrated with Industry 4.0 concept and what are the benefits of such an integration are still open issues. Therefore, this study investigates how to implement the Industry 4.0 concept for the specific case of mass customization industry and, by using modeling and simulation, proposes a quantification of the benefits of such implementation. Implementing Industry 4.0 solutions requires high level of investments, and there is a great need of research that outlines its quantifiable benefits to justify the investments. To the aim of the research, two conceptual models, one integrating Industry 4.0 and mass customization and one featuring only mass customization, have been developed. Afterward, these two models have been simulated in FlexSim software in order to measure the performance. The results obtained seem to be extremely favorable for the implementation of Industry 4.0 solutions on mass customization systems. Significant improvement in product completion rate on time, customer satisfaction rate, utilization of equipment, and waiting time in queues has been observed. This study will help mass customization industries to understand the opportunities and criticalities concerned with the implementation of this concept.

Mass customisation (MC) is considered to be an important competitive tool to increase the performance of manufacturers. Due to the importance of MC as part of the manufacturing process, there is an increasing amount of research devoted to exploring the nature of MC and how to effectively implement MC processes. Based on the theory of complementary assets, this study investigates the relationships between different MC practices and the effects of these practices on the firm's financial performance. In this study, simultaneous equation modelling and hierarchical regression analysis are applied to data collected from a large-scale survey in China. The results show that different MC practices positively affect each other, and that the interactions between the MC practices positively influence a firm's financial performance. This indicates that the complementary adoption of MC practices is important for a firm's success, requiring the simultaneous deployment of elicitation, process-flexible technology and logistics practices.

Tractable combinatorial auctions and b-matching

The problem of context, a fundamental aspect of dealing with built environments, has not been adequately addressed by mass customization systems so far, which has limited their scope of application.The aim of the present article is to evaluate the adequacy of existing methods of producing as-built plans of rooms by non-expert users for the automatic generation and production of partition walls for building renovation.This paper highlights criteria to develop appropriate methods of capturing context for mass customization construction systems.

Ambient energy harvesting has emerged as a promising technique to improve the energy efficiency and reduce the total greenhouse gas emissions for green wireless communications. Energy management for throughput maximization under random energy arrivals has been studied extensively in energy harvesting relay communication networks with either finite-size data buffer or finite-size energy storage. However, the problem is still open when the energy harvesting relay node is subject to both finite-size data and energy storage. In this paper, we study the transmission policy of joint time scheduling and power allocation under a transmission deadline, which maximizes the end-to-end system throughput in a two-hop relay communication network where the energy harvesting relay node is equipped with finite-size data buffer and battery. In particular, we first formulate the throughput maximization problem as a convex optimization problem under an offline optimization framework, and obtain the optimal offline time scheduling and power allocation by the Karush–Kuhn–Tucker conditions based on the full knowledge of energy arrivals and channel states. Then, we formulate the throughput maximization problem as a stochastic dynamic programming problem under the online optimization framework, and obtain the optimal online time scheduling and power allocation by solving a series of convex optimizations based on the casual knowledge of energy arrivals and channel states. Finally, to reduce the computation complexity, we further propose two suboptimal online transmission policies. Numerical results show the impacts of battery capacity and buffer size on the maximum throughput of the proposed policies, as well as the balance between the spectrum efficiency and the delay sensitivity.

The growth in product variety has been accelerating in almost all product categories over the last four decades. Providing high levels of variety and customisation is challenging for many producers. Here the range and diversity of order fulfilment approaches available to producers to respond are examined critically. A continuum of six fulfilment approaches is presented, encompassing both traditional approaches and those that have emerged more recently. The new integrated perspective focuses on how a producer can respond to provide relevant product variety and customisation. Demand-side and supply-side factors influencing the need for greater flexibility in order fulfilment systems are identified and the need to balance both is emphasised. Two specific approaches – mass customisation and open pipeline systems – are analysed in detail. The range of order fulfilment approaches is considered in the context of postponement and production system decoupling. Challenges in two sectors – the clothing sector and the automotive sector – are analysed. The factors affecting the choice of order fulfilment approach are discussed. Producers experiencing the competitive pressures to provide greater levels of variety and customisation need to consider the full range and diversity of order fulfilment approaches discussed here.

In recent years, auction theory has been extensively studied and many state-of-art solutions have been proposed aiming at allocating scarce resources (e.g. spectrum resources in wireless communications). Unfortunately, most of these studies assume that the auctioneer is always trustworthy in the sealed-bid auctions, which is not always true in a more realistic scenario. On the other hand, performance guarantee, such as social efficiency maximization, is also crucial for auction mechanism design. Therefore, the goal of this work is to design a series of strategyproof and privacy preserving auction mechanisms that maximize the social efficiency. To make the designed auction model more general, we allow the bidders to express their preferences about multiple items, which is often regarded as the multi-unit auction. As computing an optimal allocation in multi-unit auction is NP-hard, we design a set of near optimal allocation mechanisms with privacy preserving separately for: (1) The auction aims at identical multi-items trading; and (2) The auction aims at distinct multi-items trading, which is also known as combinatorial auction. To the best of our knowledge, we are the first to design strategyproof multi-unit auction mechanisms with privacy preserving, which maximize the social efficiency at the same time. The evaluation results corroborate our theoretical analysis, and show that our proposed methods achieve low computation and communication complexity.

We study a one-hop device-to-device (D2D)-assisted wireless caching network, where popular files are randomly and independently cached in the memory of end users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through a one-hop D2D transmission, or from the base station. We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex, and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link-scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link-scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link-scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of the arts.

Operational Classification and Method for Reconfiguration & Recommissioning of Changeable Manufacturing Systems on System Level

With the increase of vehicular broadband services, network slicing is regarded as a promising technology to meet the various requirements. In the Internet of Vehicles (IoV) enabled by network slicing, both the resource allocation and task scheduling algorithms have received extensive attentions. However, most of the existing works on the joint optimization of inter-slice resource allocation and intra-slice task scheduling ignore the power consumption. In this regard, this paper further considers the queuing latency and the power consumption in the uplink transmission. Three sub Markov decision processes (MDP) are modeled and a layered deep reinforcement learning (DRL) based algorithm is designed for the joint optimization. The simulation consists of two parts, firstly the cumulative reward of different task scheduling algorithms are compared and analyzed, then the performance of all solutions are evaluated under different task densities. The results show that the proposed algorithm can significantly reduce the queuing latency while the power consumption is comparable to that of second best algorithm.

QoS-Oriented joint optimization of resource allocation and concurrent scheduling in 5G millimeter-wave network

The use of a combinatorial auction is believed to be an effective way to distribute spectrum to buyers who have diversified valuations for different spectrum combinations. However, the allocation of spectrum with combinatorial auctions mainly aims at optimizing over certain utility functions, e.g., social welfare, but ignores individual preferences of buyers and sellers, who have incentives to deviate from globally optimal allocation results to improve their own utility. In this paper, we explore the possibility of designing a new stable matching algorithm for combinatorial spectrum allocations. Starkly different from existing efforts on spectrum matching mechanism design, our proposed combinatorial spectrum matching framework not only allows buyers to express preferences towards spectrum combinations (rather than individual channels), but also computes the payment that should be transferred from buyers to sellers. Payment determination, while essential in spectrum exchange, has never been addressed in existing spectrum matching frameworks. We design a novel algorithm to achieve a stable combinatorial spectrum matching and to compute the corresponding payment profiles. We conducted an extensive array of experiments to compare the performance of stable combinatorial spectrum matching with spectrum auctions. It is shown that the combinatorial spectrum matching sacrifices little allocation efficiency in terms of social welfare and spectrum utilization, but achieves a much higher individual buyer utility, which will incentivize buyers to participate and comply with the allocation results.