Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Multiobjective scheduling for semiconductor manufacturing plants

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Design of Closed Loop Supply Chains

Increased concern for the environment has lead to new techniques to design products and supply chains that are both economically and ecologically feasible. This paper deals with the product - and corresponding supply chain design for a refrigerator. Literature study shows that there are many models to support product design and logistics separately, but not in an integrated way. In our research we develop quantitative modelling to support an optimal design structure of a product, i.e. modularity, repairability, recyclability, as well as the optimal locations and goods flows allocation in the logistics system. Environmental impacts are measured by energy and waste. Economic costs are modelled as linear functions of volumes with a fixed set-up component for facilities. We apply this model using real life R&D data of a Japanese consumer electronics company. The model is run for different scenarios using different parameter settings such as centralised versus decentralised logistics, alternative product designs, varying return quality and quantity, and potential environmental legislation based on producer responsibility.supply chain management;reverse logistics;facility location;network design;product design

Competition in network industries

A wave of privatization is sweeping the globe, affecting about 100 countries and adding up to an average of more than $60 billion a year in business in the past decade. The challenge is to ensure that privatization yields clear benefits. Empirical studies suggest that ownership change by itself will often yield results, especially when it reduces government interference. But the regulation required in areas of natural monopoly can become overly intrusive and undermine progress. Real competition is required to generate sizable and lasting welfare improvements. But in infrastructure sectors, the introduction of competition is complicated by the existence of complex transport and communications networks. Debate about whether and how to introduce competition in network industries is sometimes heated. Certain questions recur: Will continuing regulation be needed? Whether and at what terms will private finance be forthcoming? The author argues that policymakers need to understand how competitive forces can be brought to bear in network industries. He explains the following: 1) common principles that are often lost in"technical"debates about specific sectors; 2) various methods for introducing competition in network industries; 3) competition for the market, and bidding for franchises; 4) options for competition for existing networks; 5) options for expanding competitive systems by decentralizing investment in new network capacity; 6) the option of allowing competition among multiple networks; and 7) the implications of these options for the sectors and for financing industry expansion. In case of doubt, he contends, policymakers should not restrict the entry of competitive firms in such networks. If they do, entry restrictions should be subject to an automatic test after a set period, and reviewed for costs and benefits.Economic Theory&Research,Decentralization,Markets and Market Access,Environmental Economics&Policies,Labor Policies,Education for the Knowledge Economy,Economic Theory&Research,Access to Markets,Markets and Market Access,Environmental Economics&Policies

Priority allocation decisions in large scale MTO/MTS multi-product manufacturing systems : Technical report

In this paper, the authors consider a single stage multi-product manufacturing facility producing a large number of end-products for delivery within a service constraint for the customer lead-time. The manufacturing facility is modeled as a multi-product, multi-priority queuing system. In order to reduce inventory costs, an e±cient priority allocation between items consists in producing some items according to a Make-To-Stock (MTS) policy and others according to a Make-To-Order (MTO)policy epending on their features (costs, required lead-time, demand rates). The authors propose a general optimization procedure that gives a near-optimal °ow control (MTO or MTS) to associate with each product and the corresponding near-optimal priority strategy. We illustrate e±ciency of our procedure via several examples and by a numerical analysis. In addition, we show numerically that a small number of priority classes is su±cient to obtain near-optimal performances.Make-to-Stock (MTS); Make-to-Order (MTO); Priority allocation; Scheduling rule; Heterogeneous multi-product queuing system

Restless bandit marginal productivity indices II: multiproject case and scheduling a multiclass make-to-order/-stock M/G/1 queue

This paper develops a framework based on convex optimization and economic ideas to formulate and solve approximately a rich class of dynamic and stochastic resource allocation problems, fitting in a generic discrete-state multi-project restless bandit problem (RBP). It draws on the single-project framework in the author's companion paper "Restless bandit marginal productivity indices I: Single-project case and optimal control of a make-to-stock M/G/1 queue", based on characterization of a project's marginal productivity index (MPI). Our framework significantly expands the scope of Whittle (1988)'s seminal approach to the RBP. Contributions include: (i) Formulation of a generic multi-project RBP, and algorithmic solution via single-project MPIs of a relaxed problem, giving a lower bound on optimal cost performance; (ii) a heuristic MPI-based hedging point and index policy; (iii) application of the MPI policy and bound to the problem of dynamic scheduling for a multiclass combined MTO/MTS M/G/1 queue with convex backorder and stock holding cost rates, under the LRA criterion; and (iv) results of a computational study on the MPI bound and policy, showing the latter's near-optimality across the cases investigated

Stochastic make-to-stock inventory deployment problem: an endosymbiotic psychoclonal algorithm based approach

Integrated steel manufacturers (ISMs) have no specific product, they just produce finished product from the ore. This enhances the uncertainty prevailing in the ISM regarding the nature of the finished product and significant demand by customers. At present low cost mini-mills are giving firm competition to ISMs in terms of cost, and this has compelled the ISM industry to target customers who want exotic products and faster reliable deliveries. To meet this objective, ISMs are exploring the option of satisfying part of their demand by converting strategically placed products, this helps in increasing the variability of product produced by the ISM in a short lead time. In this paper the authors have proposed a new hybrid evolutionary algorithm named endosymbiotic-psychoclonal (ESPC) to decide what and how much to stock as a semi-product in inventory. In the proposed theory, the ability of previously proposed psychoclonal algorithms to exploit the search space has been increased by making antibodies and antigen more co-operative interacting species. The efficacy of the proposed algorithm has been tested on randomly generated datasets and the results compared with other evolutionary algorithms such as genetic algorithms (GA) and simulated annealing (SA). The comparison of ESPC with GA and SA proves the superiority of the proposed algorithm both in terms of quality of the solution obtained and convergence time required to reach the optimal/near optimal value of the solution