Minimizing total tardiness for the order scheduling problem with sequence-dependent setup times using hybrid matheuristics

This paper aims at presenting a customer order scheduling environment in which the setup times are explicit and depend on the production sequence. The considered objective function is the total tardiness minimization. Since the variant under study is NP-hard, we propose a mixed-integer linear programming (MILP) model, an adaptation of the Order-Scheduling Modified Due-Date heuristic (OMDD) (referred to as Order-Scheduling Modified Due-Date Setup (OMMD-S)), an adaptation of the Framinan and Perez-Gonzalez heuristic (FP) (hereinafter referred to as Framinan and Perez-Gonzalez Setup (FP-S)), a matheuristic with Same Permutation in All Machines (SPAM), and the hybrid matheuristic SPAM-SJPO based on Job-Position Oscillation (JPO). The algorithms under comparison have been compared on an extensive benchmark of randomly generated test instances, considering two performance measures: Relative Deviation Index (RDI) and Success Rate (SR). For the small-size evaluated instances, the SPAM is the most efficient algorithm, presenting the better values of RDI and SR. For the large-size evaluated instances, the hybrid matheuristic SPAM-JPO and MILP model are the most efficient methods.

Analisis Penjadwalan Produksi Berdasarkan Pesanan Menggunakan Metode Asas Prioritas pada CV Davero Cemerlang Indonesia Surabaya

The purpose of this study was to determine the most efficient order scheduling technique on CV. Davero Jaya Shining Indonesia to reduce delays in order fulfillment. The suggested scheduling approach is based on a priority rule system that includes the following criteria: first come, first served, lowest processing time, longest processing time, and earliest due date (first come first served). Davero Jaya Cemerlang Indonesia uses the SPT technique in its manufacturing process, according to their company CV. The company decided to adopt this technique because it seemed reasonable from a consumer's point of view. The downside of this technique is that it often causes delays in completing client orders. Based on the results of data analysis and debates that have been given, the FCFS technique is the most superior method among others. Considering the fact that the findings of the FCFS method effectiveness measure are consistent with the current criteria, which include minimum average completion scores, maximum utility, minimum average delay, and minimum average labor force in the system. Therefore, the researchers gave suggestions to the company CV. Davero Jaya Cemerlang Indonesia to use the FCFS method as an alternative production scheduling method. With the application of the FCFS method in CV. Davero Jaya Cemerlang Indonesia is expected to be able to help resolve production scheduling problems that have been happening so far. So that the problem of delays in completing orders can be minimized. Keywords: Scheduling, Gantt Chart, Priority Principles and CV. Davero Jaya Cemerlang Indonesia

The Application of Order Scheduling Algorithm for Food Delivery Logistics Based on Historical Data

With the improvement of China’s social and economic development, the requirements for delivery efficiency of foreign logistics orders are becoming more and more stringent. Big data technology is the current direction of vigorous development. With the development of the Chinese era, in order to better promote the development of China’s urbanization. With the continuous innovation of big data technology, various industries are undergoing industrial upgrading and format conversion. This article mainly discusses the order scheduling algorithm of takeaway logistics based on historical data. Through in-depth research and analysis of the algorithm, the researched scheduling algorithm is reasonably applied to the delivery industry of takeaway logistics, and the scheduling algorithm of big data technology is used for accurate calculation. The supply-demand relationship between food delivery and customers in historical data and the optimal path of actual logistics delivery provide algorithmic support for the selection of time and path of food delivery. Making good use of big data technology can not only accurately predict customers’ habits of ordering food, but also play a role in optimizing delivery. With this technology, the automation level of food delivery can be effectively improved, and foreign media can be delivered to customers in a more timely manner. In its hands, it highlights the application value of big data technology and promotes the development of China’s modern logistics industry, making it possible to develop in the direction of intelligence and automation. The experimental results show that the food delivery industry has great convenience and higher efficiency for ordering and transportation of food delivery under the condition of combining big data technology.

Analisa Penjadwalan Order Distribusi Produk di PT. Indofood CBP Sukses Makmur Medan Menggunakan Algoritma Genetika

The existence of computers has even replaced many human workers, both in business and industry. This cannot be faulted because it is proven that the computerized system is much more efficient and profitable for the company than the manual system (with human labor). Almost every company, especially those that are developing and advancing, are currently competing to computerize their company's systems, to improve work efficiency and effectiveness. At PT. Indofood CBP Sukses Makmur Medan, especially those that produce goods, always occurs in the process of producing, storing, and selling products. With the application design carried out in the making of this research is the storage of input and output of data from existing goods, namely by managing all data on goods in the warehouse in a computerized system. Then the arrangement of incoming goods and outgoing goods in the warehouse rack pallets is arranged as optimally as possible by using the Genetic Algorithm method. The scheduling at PT. Indofood CBP Sukses Makmur Medan, which is a schedule of orders given to sales that can be on that day. Next, distribution orders are scheduled on the day, hour, and sales working. In optimizing the preparation of the distribution order schedule using the genetic algorithm, four parameters are needed, including the number of goals, the number of sales, the order crossover and mutation probability. In designing a product distribution order scheduling system at PT. Indofood CBP Sukses Makmur Medan consists of a login form, order data, sales data, destination data, time data, genetic algorithm processing and logout.

Empirical Study of Foundry Efficiency Improvement Based on Data-Driven Techniques

In this paper, a data-driven approach was applied to improve a furnace zone of a foundry in Taiwan. Improvements are based on the historical production records, order-scheduling, and labor-scheduling data. To resolve the bottleneck provided by the company, historical data were analyzed, and the existence of large variance in the process was found. Statistical analysis was performed to identify the primal factors causing the variance, and suggestions were made and implemented to the production line. As a result, daily production increased steadily to more than 30 pots of molten metal, while the original production was 20–30 pots of molten metal and are not controllable. Such significant improvement was mainly made by standardizing the input and reducing the variance of processes. The average cycle time of each pot of molten metal was reduced from 219 min to 135 min. Our suggested improvements also reduced the foundry’s electricity consumption cost by almost $240,000NT per month. In summary, data analysis can help traditional industries in identifying the main factors causing the bottleneck.

The design of a memory controller for DDR SDRAM

Memory system performance is an important factor in determining overall system performance. The design of key components of the memory system, such as the memory controller, becomes more important as memory performance becomes a limiting factor in high performance computing. This work focuses on the design of a unit which sends control signals to Double Data Rate Synchronous Dram (DDR SDRAM). The design is based on established concepts such as access reordering. A novel, adaptive page policy based on a machine learning algorithm has been developed in this work and evaluated with traditional page policies. the work illustrates some of the design trade-offs in a memory controller and the performance of the designs when using real application address traces.The results show that access reordering improves the performance of DDR SDRAM compared to in-order scheduling (up to 50% improvement) and that scheduling multiple requests can result in latency hiding. The dynamic page policy approximates the best static page policy in most cases.

The design of a memory controller for DDR SDRAM

Memory system performance is an important factor in determining overall system performance. The design of key components of the memory system, such as the memory controller, becomes more important as memory performance becomes a limiting factor in high performance computing. This work focuses on the design of a unit which sends control signals to Double Data Rate Synchronous Dram (DDR SDRAM). The design is based on established concepts such as access reordering. A novel, adaptive page policy based on a machine learning algorithm has been developed in this work and evaluated with traditional page policies. the work illustrates some of the design trade-offs in a memory controller and the performance of the designs when using real application address traces.The results show that access reordering improves the performance of DDR SDRAM compared to in-order scheduling (up to 50% improvement) and that scheduling multiple requests can result in latency hiding. The dynamic page policy approximates the best static page policy in most cases.

Customer order scheduling with job-based processing on a single-machine to minimize the total completion time

This study considers a customer order scheduling (COS) problem in which each customer requests a variety of products (jobs) processed on a single flexible machine, such as the computer numerical control (CNC) machine. A sequence-independent setup for the machine is needed before processing each product. All products in a customer order are delivered to the customer when they are processed. The product ordered by a customer and completed as the last product in the order defines the customer order’s completion time. We aim to find the optimal schedule of the customer orders and the products to minimize the customer orders’ total completion time. We have studied this customer order scheduling problem with a job-based processing approach in which the same products from different customer orders form a product lot and are processed successively without being intermingled with other products. We have developed two mixed-integer linear programming models capable of solving the small and medium-sized problem instances optimally and a heuristic algorithm for large-sized problem instances. Our empirical study results show that our proposed tabu search algorithm provides optimal or near-optimal solutions in a very short time. We have also compared the job-based and order-based processing approaches for both setup and no-setup cases and observed that the job-based processing approach yields better results when jobs have setup times.