Tamás Bányai

Optimization of Consignment-Store-Based Supply Chain with Black Hole Algorithm

The globalization of economy and market led to increased networking in the field of manufacturing and services. These manufacturing and service processes including supply chain became more and more complex. The supply chain includes in many cases consignment stores. The design and operation of these complex supply chain processes can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on metaheuristic algorithms. This research proposes an integrated supply model based on consignment stores. After a careful literature review, this paper introduces a mathematical model to formulate the problem of consignment-store-based supply chain optimization. The integrated model includes facility location and assignment problems to be solved. Next, an enhanced black hole algorithm dealing with multiobjective supply chain model is presented. The sensitivity analysis of the heuristic black hole optimization method is also described to check the efficiency of new operators to increase the convergence of the algorithm. Numerical results with different datasets demonstrate how the proposed model supports the efficiency, flexibility, and reliability of the consignment-store-based supply chain.

Optimization of In-Plant Production Supply with Black Hole Algorithm

Logistic process is a basic factor in the success of manufacturing plants’ operation and has a direct impact on its efficiency, flexibility and reliability. Today’s successful operation of manufacturing processes views logistics as a high priority to ensure maximum utilization of resources. The material supply of manufacturing processes in the automotive industry is usually based on supermarkets and milk runs. This paper proposes an integrated supply model of manufacturing processes, which includes facility location and assignment. After a careful literature review, this paper introduces a mathematical model to formulate the problem of supermarkets and milk run based supply of machines. The model seeks the optimal location of buffers as well as the optimal assignment of buffers and machines so as to minimize the material handling costs while taking into account order limits of machines and capacities of resources. Next, we demonstrate an enhanced black hole algorithm dealing with multi-objective supply chain model to find the optimal structure of the system. Numerical results demonstrate how the proposed model supports the efficiency, flexibility and reliability of the manufacturing process.

Sensor Networks for Smart Manufacturing Processes

Each factory and manufacturing plant needs a flexible and reliable in-plant resource supply to serve production processes efficiently. Manufacturing systems are composed of several numbers of elements, workstations, machines and logistics resources. Production line is a complex system because of the complicated manufacturing process, multiple types, high machining difficulty and many special processes in it. In the Industry 4.0 based on smart manufacturing, it is essential to support the processes with intelligent sensor networks. In this article, we give a brief overview about sensors often used in manufacturing processes. Sensor networks generate a massive and increasing amount of data that needs to be processed. Computationally intensive algorithms are used for the data processing (image, voice and signal processing, different classification functions, numeric optimization routines). Finally, we discuss how GPGPU can improve the real-time processing of data generated by intelligent sensor networks.

Intelligent Transportation Systems to Support Production Logistics

Intelligent transportation systems (ITS) include both the traffic stream control and the intelligent vehicles. Cell phone networks and global positioning systems (GPS) enable the use of geographical information (GI) so that individual vehicles can locate themselves and global transportation systems can be enhanced taking advantages of new information technology solutions and algorithms. One of the major parts of ITS research is the assignment, routing and scheduling of vehicles in global transportation processes. This paper proposes an integrated engineering optimization algorithm to support the solution of assignment and scheduling problems of vehicles in intelligent transportation systems. This novel approach combines the available hardware and software components of an ITS with an algorithm to optimize the transportation processes of a global supply chain. To gain insight into the complexity of the logistic problem, the new model of supply chain including ITS is also described.

Milkrun Based In-plant Supply – An Automotive Approach

The material supply of workplaces in a manufacturing system can be realized in many ways. In the last few years, the milkrun-based in-plant supply is widely spread, especially in the field of the automotive industry. Milkrun supply makes it possible to feed manufacturing and assembly workstations keeping on the 9R rule. The lean approach is a framework for reducing costs and enhancing the quality of products and processes in automotive domain. The design and operation of milkrun based in-plant supply includes a wide range of optimization problems, like location, routing, scheduling, assignment or queuing problems. After a careful literature review, the authors describe the typical milkrun solutions of in-plant supply. The description of milkrun morphology makes it possible to define the most important aspects of milkrun supply solutions. Next, typical milkrun based supply processes are presented and their evaluation method is also described to analyze the efficiency of various solutions.

What Industry 4.0 Means for Just-In-Sequence Supply in Automotive Industry?

The available and future solutions for the digital transformation and use of exponential technologies indicate revolutionary changes in the whole supply chain of manufacturing and service processes. The vertical networking of smart manufacturing systems and the horizontal integration of value-making chains led to a new supply paradigm based on hyperconnected global logistics systems. The goal of the paper is to identify challenges of just-in-sequence supply in the automotive industry from the aspect of Industry 4.0 solutions. The authors introduce readers in both the Industry 4.0 paradigm as well as the just-in-sequence supply. Defining the conception of cyber physical logistics systems (CPLS) authors describe the I4.0 solutions based relations between just-in-sequence supply and Reference Architecture Model Industry 4.0 (RAMI 4.0). The main goal is to define challenges and impacts of Industry 4.0 paradigm on just-in-sequence supply.

Advanced Materials Handling Processes and Devices in the Automotive Industry

Automotive industry is one of the biggest sectors of economy, so any task related to it has also large significance. It is especially valid for the handling solutions used in production procedures of the automotive area. However there are many different handling processes applied suited for the individual production elements. In this paper authors give an overview about the materials handling solutions used in automotive production processes.

Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach

Supply chain management applies more and more Industry 4.0 innovations to increase their availability, elasticity, sustainability, and efficiency. In interconnected logistics networks, operations are integrated from suppliers through 3rd party logistics providers to customers. There are different delivery models depending on the time and cost. In the last few years, a wide range of customers is willing to pay an extra fee for the same delivery or instant delivery. This fact led to the increased importance of the optimized design and control of first mile/last mile (FMLM) delivery solutions. Cyberphysical system-based service innovations make it possible to enhance the productivity of FMLM delivery in the big data environment. The design and operation problems can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on heuristic and metaheuristic algorithms. This research proposes an integrated supply model of FMLM delivery. After a careful literature review, this paper introduces a mathematical model to formulate the problem of real-time smart scheduling of FMLM delivery. The integrated model includes the assignment of first mile and last mile delivery tasks to the available resources and the optimization of operations costs, while constraints like capacity, time window, and availability are taken into consideration. Next, a black hole optimization- (BHO-) based algorithm dealing with a multiobjective supply chain model is presented. The sensitivity of the enhanced algorithm is tested with benchmark functions. Numerical results with different datasets demonstrate the efficiency of the proposed model and validate the usage of Industry 4.0 inventions in FMLM delivery.