Afshin Mehrsai

Integration of supply networks for customization with modularity in cloud and make-to-upgrade strategy

Today, integration of supply networks (SNs) out of heterogeneous entities is quite challenging for industries. Individualized demands are getting continuously higher values in the global business and this fact forces traditional businesses for restructuring their organizations. In order to contribute to new performances in manufacturing networks, in this paper a collaborative approach is recommended out of modularity structure, cloud computing, and make-to-upgrade concept for improving flexibility as well as coordination of entities in networks. A cloud-based framework for inbound and outbound manufacturing is introduced for complying with the production of individualized products in the turbulent global market, with local decision-makings and integrated performances. Additionally, the complementary aspects of these techniques with new features of products are conceptually highlighted. The compatibility of this wide range of theoretical concepts and practical techniques is explained here. A discrete-event simulation out of an exemplary cloud-based SN is set up to define the applicability of the cloud and the recommended strategy.

Modelling and Analysis of Autonomously Controlled Production Networks

Abstract To cope with increasing internal and external dynamics of production networks, a decentralized and flexible autonomous control approach seems to be promising. This paper presents a dynamic model of a production network with geographically dispersed facilities and fixed transport schedules. It investigates the influence of local autonomous control methods on integrated production and transport processes and shows that the application of autonomous control may improve the handling of internal and external dynamics.

Application of learning pallets for real-time scheduling by the use of radial basis function network

The expansion of the scope and scale of products in the current business environments causes a continuous increase in complexity of logistics activities. In order to deal with this challenge in planning and control of logistics activities, several solutions have been introduced. One of the most latest one is the application of autonomy. The paradigm of autonomy in inbound logistics, can be reflected in decisions for real-time scheduling and control of material flows. Integration of autonomous control with material carrier objects can realize the expected advantages of this alternative into shop-floors. Since pallets (bins, fixtures, etc.) are some common used carrier objects in logistics, they have the potential to undertake the responsibility of real-time jobs dispatching to machines in the shop-floor scheduling problem. Hence, the current paper covers the problem of real-time scheduling in a stochastic and complex shop-floor environment, by means of autonomy. Indeed, the sustainments advantage of pallets in manufacturing systems has inspired the idea of developing learning pallets (Lpallets) with the capability of autonomous control in complex and uncertain logistics environment with abrupt changes. Among some intelligent techniques, the artificial neural network (ANN) and, specially, the radial basis function network (RBFN) is selected to transmit the abilities of intelligent decision-making as well as learning to Lpallets in a distributed manner. Some variants in training and RBFN application alternatives are considered to evaluate the competency of RBFN for Lpallets. An Lpallet makes its dispatching and control decision based on its own experience and intelligence about the entire system and local situations in an exemplary hybrid flow-open shop problem. To prove the claimed application of RBFN in autonomous Lpallets a discrete-event simulation model is developed for the assembly scenario.

Bridging lean to agile production logistics using autonomous carriers in pull flow

Since the early 90s the lean manufacturing system has become popular for industries. Following that, agility in production has received great attention. Exploration of any new techniques for bringing these strategic concepts closer to each other has become advantageous for pioneer industries. Accordingly, the new paradigm of individual control, with the progressive interpretation of ‘autonomy’, can contribute to the objectives of the lean and the agile concepts in production and logistics environments. To explain the contributions of the addressed thesis the study describes it in theoretical and empirical forms. The compatibility of these leading-edge concepts to realise the notion of continuous material flow through supply chains and production floors is examined. Simultaneously, the factors of efficiency, effectiveness and responsiveness are considered. This study covers a quick review on the lean and agility techniques and highlights some specific contributions of autonomous control to their targets. The purpose is to clarify the role of the autonomy in compliance with the lean and agility goals. This is inspected through development of a discrete event simulation with some scenarios in a supply network.

Using metaheuristic and fuzzy system for the optimization of material pull in a push-pull flow logistics network

Alternative material flow strategies in logistics networks have crucial influences on the overall performance of the networks. Material flows can follow push, pull, or hybrid systems. To get the advantages of both push and pull flows in networks, the decoupling-point strategy is used as coordination mean. At this point, material pull has to get optimized concerning customer orders against pushed replenishment-rates. To compensate the ambiguity and uncertainty of both dynamic flows, fuzzy set theory can practically be applied. This paper has conceptual and mathematical parts to explain the performance of the push-pull flow strategy in a supply network and to give a novel solution for optimizing the pull side employing Conwip system. Alternative numbers of pallets and their lot-sizes circulating in the assembly system are getting optimized in accordance with a multi-objective problem; employing a hybrid approach out of meta-heuristics (genetic algorithm and simulated annealing) and fuzzy system. Two main fuzzy sets as triangular and trapezoidal are applied in this technique for estimating ill-defined waiting times. The configured technique leads to smoother flows between push and pull sides in complex networks. A discrete-event simulation model is developed to analyze this thesis in an exemplary logistics network with dynamics.

Analysis of learning pallets in flexible scheduling by closed queue network

Within the paper, application of learning pallets in an assembly system for real-time scheduling problems is presented. A specific fuzzy inference system (controller) is adopted to enable the pallets to learn from their experiences regarding some key metrics. In order to analyze the performance of this system and the learning pallets with a robust mathematical analysis, the similarity of this assembly network to the BCMP networks in closed queuing theory, is underlined. The results prove this resemblance and show the suitability of its algorithm for analyzing such closed networks.

Towards learning pallets applied in pull control job-open shop problem

The current paper studies the concept of learning pallets following the autonomy paradigm; in a Conwip control job-shop/ open-shop system. To realize learning capability for pallets several advantages and methodologies can be employed. Among them are the privileges of closed-loops in Conwip system as well as application of evolutionary intelligence for inspiring learning. Specifically, some features of genetic algorithm (GA) can be used to produce new alternatives and avoid local traps in a decentralized approach, though the GA is a global search method. In addition, fuzzy inference system is employed to distinguish the dynamisms of each station as well as of the entire system, concerning vagueness in real time information, and uncertainty in processing sequence and times. It is shown here that learning pallets (Lpallets) are presenting better records in terms of some criteria, e.g., makespan.

Superior Performance of Leagile Supply Networks by Application of Autonomous Control

In the paper, a special approach to supply networks’ material flows is posed. The considered strategy is based on the both principles of Lean and agility, beside push and pull of materials. Here, the trade off between positioning of decoupling point throughout an exemplary network, and reduction of inventory level along throughput time is examined. Moreover, autonomous control for material routing and lot-sizes is taken into account. To do so, a discrete-event simulation model is developed to show the performances.

Decentralized Vs. Centralized Sequencing in a Complex Job-Shop Scheduling

Allocation of jobs to machines and subsequent sequencing each machine is known as job scheduling problem. Classically, both operations are done in a centralized and static/offline structure, considering some assumptions about the jobs and machining environment. Today, with the advent of Industry 4.0, the need to incorporate real-time data in the scheduling decision process is clear and facilitated. Recently, several studies have been conducted on the collection and application of distributed data in real-time of operations, e.g., job scheduling and control. In practice, pure distribution and decentralization is not yet fully realizable because of e.g., transformation complexity and classical resistance to change. This paper studies a combination of decentralized sequencing and central optimum allocation in a lithography job-shop problem. It compares the level of applicability of two decentralized algorithms against the central scheduling. The results show better relative performance of sequencing in stochastic cases.

A fuzzy programming method for optimization of autonomous logistics objects

Recently several studies have explored the realization of autonomous control in production and logistic operations. In doing so, it has been tried to transmit the merit of decision-making from central controllers with offline decisions to decentralized controllers with local and real-time decision makings. However, this mission has still some drawbacks in practice. Lack of global optimization is one of them, i.e., the lost chain between the autonomous decentralized decisions at operational level and the centralized mathematical optimization with offline manner at tactical and strategic levels. This distinction can be reasonably solved by considering fuzzy parameters in mathematical programming to meet the required tolerances for autonomous objects at operational level. This claim is recommended and partially experimented in this paper. An assembly scenario is modeled by a discrete-event simulation, in which autonomous pallets carry products throughout the system. This scenario is optimized with regard to its objectives in a simulation, while fuzzy parameters in optimization programming can consider autonomous decisions done at operational level.