An iterative agent bidding mechanism for responsive manufacturing

In today's market, the global competition has put manufacturing businesses in great pressures to respond rapidly to dynamic variations in demand patterns across products and changing product mixes. To achieve substantial responsiveness, the manufacturing activities associated with production planning and control must be integrated dynamically, efficiently and cost-effectively. This paper presents an iterative agent bidding mechanism, which performs dynamic integration of process planning and production scheduling to generate optimised process plans and schedules in response to dynamic changes in the market and production environment. The iterative bidding procedure is carried out based on currency-like metrics in which all operations (e.g. machining processes) to be performed are assigned with virtual currency values, and resource agents bid for the operations if the costs incurred for performing them are lower than the currency values. The currency values are adjusted iteratively and resource agents re-bid for the operations based on the new set of currency values until the total production cost is minimised. A simulated annealing optimisation technique is employed to optimise the currency values iteratively. The feasibility of the proposed methodology has been validated using a test case and results obtained have proven the method outperforming non-agent-based methods

Agent-based hierarchical production planning and scheduling in make-to-order manufacturing system

PublishedThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Nowadays, manufacturing organisations face increasing pressures from the frequent change in product type, continuous demand fluctuation and unexpected change in customer requirements. In order to survive in the turbulent environment, manufacturing organisations must become flexible and responsive to these dynamic changes in the business environment. This paper presents a hierarchical agent bidding mechanism that is particularly designed for Make-to-Order manufacturing system and attempts to enhance the operational flexibility of manufacturing system in dealing with dynamic changes in the business environment. The novelty of this mechanism is that it enables manufacturing resources to be self-organised cost-efficiently within structural constraints of manufacturing system for fulfilling customer orders. However, when orders cannot be fulfilled within the structural constraints of manufacturing systems, the mechanism can enable manufacturing resources to be regrouped flexibly across system boundaries but with minimum disturbances to existing system structure. Based on an example application to a manufacturing company, this paper demonstrates that the operational flexibility provided by this mechanism is able to help manufacturing system to respond demand fluctuation through balancing the capacity across the entire system. Meanwhile, this mechanism potentially enables manufacturing systems to deal with unexpected changes in product type. As long as the manufacturing system has the technicality required by a new product, this mechanism enables resources across the manufacturing system to be cost-efficiently and flexibly self-organised to fulfil the new product

Agent-based Hierarchical Planning and Scheduling Control in Dynamically Integrated Manufacturing System

It has been broadly recognised that today’s manufacturing organisations face increasing pressures from continuous and unexpected changes in the business environment such as changes in product types, changes in demand pattern, changes in manufacturing technologies etc. To enable manufacturing organisations to rapidly and timely deal with these changes, operational decisions (e.g., process planning and production scheduling) have to be integrated with dynamic system restructure or reconfiguration so that manufacturing organisations do not only use the flexible resource utilisations to deal with these changes, but also can dynamically reconfigure their existing system structures in response these changes. A manufacturing system concept and implementation methodology is proposed by the Exeter Manufacturing Enterprise Centre (XMEC), which is called the Dynamically Integrated Manufacturing System (DIMS). The overall aim of DIMS is to provide a systematic modelling and control framework in which operational decisions can be integrated with the dynamic system restructuring decisions so as to help manufacturing systems to dynamically deal with changes in the business environment. This PhD research is a part of DIMS research, which focuses on the investigation on operational control in DIMS. Based on the established agent-based modelling architecture in DIMS, this research develops two agent bidding mechanisms for the hierarchical control of production planning and scheduling. These two mechanisms work together to assist manufacturing systems in making optimal and flexible operational decisions in response to changes in the business environment. The first mechanism is the iterative agent bidding mechanism based on a Genetic Algorithm (GA) which facilitates the determination of the optimal or near optimal allocation of a production job containing a set of sub-jobs to a pool of heterarchical resources. The second mechanism is the hierarchical agent bidding mechanism which enables product orders to be cost-efficiently and flexibly planned and scheduled to meet the orders’ due dates. The novelty of this mechanism is that it enables orders to be fulfilled within structural constraints of manufacturing systems as far as possible and however enables resources to be regrouped flexibly across system boundaries when orders cannot be fulfilled within structural constraints of manufacturing systems

Market-Based Models for Digital Signage Network Promotion Management

Digital signage network (DSN) is capable of delivering customized content to designated screens in a real-time or near real-time manner, which provides tremendous potential for building dynamic demand stimulation tools. However current DSN media buying process is mainly carried out through manually conducted negotiation between the DSN operator and the advertisers. This practice does not capitalize the unique technology advantage offered by the newly emerged advertising medium. We propose automated DSN media buying models which allow advertisers to customize their promotion schedules in a highly responsive manner. Specifically, we design a direct revelation mechanism and an iterative bidding model for DSN promotion scheduling. We show that the direct revelation mechanism computes optimal solutions. We evaluate the revenue performance of the iterative bidding model through a computational study. The implementation of the iterative bidding mechanism is also described

Distributed scheduling: A review of concepts and applications

Distributed scheduling (DS) is an approach that enables local decision makers to create schedules that consider local objectives and constraints within the boundaries of the overall system objectives. Local decisions from different parts of the system are then integrated through coordination and communication mechanisms. Distributed scheduling attracts the interest of many researchers from a variety of disciplines, such as computer science, economics, manufacturing, and service operations management. One reason is that the problems faced in this area include issues ranging from information architectures, to negotiation mechanisms, to the design of scheduling algorithms. In this paper, we provide a survey and a critical analysis of the literature on distributed scheduling. While we propose a comprehensive taxonomy that accounts for many factors related to distributed scheduling, we also analyse the body of research in which the scheduling aspect is rigorously discussed. The focus of this paper is to review the studies that concern scheduling algorithms in a distributed architecture, not, for example, protocol languages or database architectures. The contribution of this paper is twofold: to unify the literature within our scope under a common terminology and to determine the critical design factors unique to distributed scheduling and in relation to centralised scheduling. © 2010 Taylor & Francis

A multi-agent optimisation model for solving supply network configuration problems

Supply chain literature highlights the increasing importance of effective supply network configuration decisions that take into account such realities as market turbulence and demand volatility, as well as ever-expanding global production networks. These realities have been extensively discussed in the supply network literature under the structural (i.e., physical characteristics), spatial (i.e., geographical positions), and temporal (i.e., changing supply network conditions) dimensions. Supply network configuration decisions that account for these contingencies are expected to meet the evolving needs of consumers while delivering better outcomes for all parties involved and enhancing supply network performance against the key metrics of efficiency, speed and responsiveness. However, making supply network configuration decisions in the situations described above is an ongoing challenge. Taking a systems perspective, supply networks are typically viewed as socio-technical systems where SN entities (e.g., suppliers, manufacturers) are autonomous individuals with distinct goals, practices and policies, physically inter-connected transferring goods (e.g., raw materials, finished products), as well as socially connected with formal and informal interactions and information sharing. Since the structure and behaviour of such social and technical sub-systems of a supply network, as well as the interactions between those subsystems, determine the overall behaviour of the supply network, both systems should be considered in analysing the overall system

Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles

IEEE This article demonstrates the application of a novel multiagent modeling approach to support supply network configuration (SNC) decisions toward addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multistage, multiechelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modeled. In modeling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modeling approach incorporated a reverse-auctioning process to simulate the behavior of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Nondominated Sorting Genetic Algorithm II. Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions

A Free Exchange e-Marketplace for Digital Services

The digital era is witnessing a remarkable evolution of digital services. While the prospects are countless, the e-marketplaces of digital services are encountering inherent game-theoretic and computational challenges that restrict the rational choices of bidders. Our work examines the limited bidding scope and the inefficiencies of present exchange e-marketplaces. To meet challenges, a free exchange e-marketplace is proposed that follows the free market economy. The free exchange model includes a new bidding language and a double auction mechanism. The rule-based bidding language enables the flexible expression of preferences and strategic conduct. The bidding message holds the attribute-valuations and bidding rules of the selected services. The free exchange deliberates on attributes and logical bidding rules for automatic deduction and formation of elicited services and bids that result in a more rapid self-managed multiple exchange trades. The double auction uses forward and reverse generalized second price auctions for the symmetric matching of multiple digital services of identical attributes and different quality levels. The proposed double auction uses tractable heuristics that secure exchange profitability, improve truthful bidding and deliver stable social efficiency. While the strongest properties of symmetric exchanges are unfeasible game-theoretically, the free exchange converges rapidly to the social efficiency, Nash truthful stability, and weak budget balance by multiple quality-levels cross-matching, constant learning and informs at repetitive thick trades. The empirical findings validate the soundness and viability of the free exchange

An Agent-based Approach for Manufacturing Production Scheduling with Emission Consideration

In the current business climate with increasingly changing customer requirements and strong business competition, manufacturing organisations need to enhance their productivity and adaptability in order to survive in the current business environment and raise their competitiveness. As a result, the optimisation of production scheduling in manufacturing systems has attracted increasing attention by manufacturers. The optimisation of manufacturing scheduling can be simplified as an optimisation problem for minimising processing cost and time with a set of constraints reflecting the technical relationships between jobs or job features and the resource capability and capacity. Conventional optimisation approaches including mathematical approaches, dispatching rules, heuristics and meta-heuristics have been applied in this research area but optimal solutions cannot be achieved in a reasonable computational time. In this PhD research, an agent based approach is developed for solving the manufacturing production optimisation problem. There is an agent iterative bidding mechanism coordinated by a Genetic Algorithm (GA) which facilitates the search for optimal routing and sequencing solutions for processing an entire job with shared manufacturing resources. A shop agent in the system works as a mediator which announces bidding operations, collects bids and decides winner machines according to a weight-based function. Machine agents with specific technical capability calculate the total production cost and lead time for job operations according to the predesigned operational sequence, and decide whether to submit their bids based on local utility. Another agent self-adjusting mechanism is employed for resource agents updating the priorities of unprocessed jobs in their buffers. The objective of each machine agent is to maximise local utility, i.e., to increase individual profit. After genetic generations for updating parameters with agent self-adjusting, the near optimal schedule plans can be found. On the other hand, the use of energy in all organisations has become a key issue worldwide. Carbon emissions from manufacturing processes of a company are under the pressure of government and also affect the public opinion. In the previous works from the literature, however, economic and environmental issues are not considered simultaneously in manufacturing production scheduling. Based on the basic agent based optimisation mechanisms, two extensive models with the consideration of the carbon emission during production are built in this research work, where the emission factor is set to be a constraint and another objective respectively. Numerical tests are utilised in order to examine the effectiveness and efficiency of the proposed approaches. Furthermore, two previous approaches from the literature for solving the same problems are rebuilt and results are compared for testing the comparative performance of the proposed approaches. Test results show that near optimal schedule plans can be achieved in a reasonable computational time