This paper proposes an algorithm for the Unrelated Parallel Machine Scheduling Problem(UPMSP) without setup times, aiming to minimize total tardiness. As an NP-hard problem, the UPMSP is hard to get an optimal solution. Consequently, practical scenarios are solved by relying on operator's experiences or simple heuristic approaches. The proposed algorithm has adapted two methods: a policy network method, based on Transformer to compute the correlation between individual jobs and machines, and another method to train the network with a reinforcement learning algorithm based on the REINFORCE with Baseline algorithm. The proposed algorithm was evaluated on randomly generated problems and the results were compared with those obtained using CPLEX, as well as three scheduling algorithms. This paper confirms that the proposed algorithm outperforms the comparison algorithms, as evidenced by the test results.

This study focuses on a job-shop scheduling problem with the objective of minimizing total tardiness for the job orders that have different due dates and different process flows. We suggest the dispatching rule based scheduling algorithm to generate fast and efficient schedule. First, we show the delay schedule can be optimal for total tardiness measure in some cases. Based on this observation, we expand search space for selecting the job operation to explore the delay schedules. That means, not only all job operations waiting for process but also job operations not arrived at the machine yet are considered to be scheduled when a machine is available and it is need decision for the next operation to be processed. Assuming each job operation is assigned to the available machine, the expected total tardiness is estimated, and the job operation with the minimum expected total tardiness is selected to be processed in the machine. If this job is being processed in the other machine, then machine should wait until the job arrives at the machine. Simulation experiments are carried out to test the suggested algorithm and compare with the results of other well-known dispatching rules such as EDD, ATC and COVERT, etc. Results show that the proposed algorithm, MET, works better in terms of total tardiness of orders than existing rules without increasing the number of tardy jobs.

This paper considers a parallel-machine scheduling problem with dedicated and common processing machines using GA (Genetic Algorithm). Non-identical setup times, processing times and order lot size are assumed for each machine. The GA is proposed to minimize the total-tardiness objective measure. In this paper, heuristic algorithms including EDD (Earliest Due-Date), SPT (Shortest Processing Time) and LPT (Longest Processing Time) are compared with GA. The effectiveness and suitability of the GA are derived and tested through computational experiments.

This paper addresses order-lot pegging issues in the supply chain of a semiconductor business. In such a semiconductor business (memory or system LSI) order-lot pegging issues are critical to achieving the goal of ATP (Available to Promise) and on-time production and delivery. However existing pegging system and researches do not consider capacity limit on bottleneck steps. This paper presents an order-lot pegging algorithm for assigning a lot to an order considering quality constraints of each lot and capacity of bottleneck steps along the entire FAB. As a result, a quick and accurate response can be provided to customer order enquiries and pegged lot lists for each promised orders can be shown transparently and short or late orders can be detected before fixing the order.

Auto part industry supplies production for auto manufacturer and after market. These company have inventory for delivery. High inventory level can be good for delivery, but cost will be increase. Low inventory level can be customer unsatisfaction for delivery late. Low inventory level also is reason of low productivity by decreasing product batch size. These article suggest model for calculation a proper inventory level and prove a effect by simulation of some company.

다른 제조 업체에 부품을 공급하는 부품 기업에서는 고객 수요를 대응하기 위해 운 영하기 위해 완제품이나 반제품 재고를 운영하고 있다. 재고는 많으면 고객 대응도가 좋아질 수 있지만 운영 비용이 늘어나고, 재고가 너무 적으면 고객 대응이 지연되어 고객 불만족을 초래하게 될 것이다. 또한 재고 수준이 적으면 고객 수요에 대한 생산 회수가 증가하여 배치 생산으로 생산성을 높이는 것이 어렵게 된다. 따라서 고객 수요 대응에 대한 만족도를 최대한 유지하고 운영 비용이나 생산 비용을 절감시키는 방안 에 대해서 시뮬레이션을 통해 제시하고자 한다.

Lot-order assignment is the process of assigning items in lots being processed in a production facility to orders to meet due-dates of the orders. In this study, we consider the lot-order assignment problem (LOAP) with the objective of minimizing total tardiness of the orders with distinct due dates. We address similarity relationships between the LOAP and the single machine total tardiness scheduling problem (SMTTSP) and suggest priority rules for the LOAP based on those for the SMTTSP. Performances of the priority rules are compared with each other and with that of the commercial optimization software package in computational experiments.

Customers are generally requiring a variety of products, earlier due date, and lower price. A manufacturing process needs the efficient scheduling to meet those customer's requirements. This study proposes the novel algorithm named MJA(Minimum Job completion time and AGV time) that increases the performance of machines and AGV(Automated Guided Vehicles) in many kinds of job types. MJA optimizes the bottleneck of machines and efficiency of AGV with considering two types of dispatching at the same time. Suggested algorithm was compared with existing heuristic methods by several simulations, it performed better for reducing the time of tardiness.

In case of domestic automobile manufacturers introducing and running a make-to-order production system, JIT system is to provide a necessary number of components at a right place in time which is a specific supply chain management, is different from other occupations. This study is for establishing a efficient production planning and finding a management method to correspond with a manufacturing system and diversified supply chain management and building an information system to support it. For this, we analyze the relevant business process and utilize various informations occur in supply chain of domestic automobile components manufacturer. It will contribute to not only reliability improvement of production management system but also satisfaction for due date of products.

  Exact solutions for practical-size problems in job shop will be highly inefficient. Scheduling heuristics, therefore, are typically found in the literature. If we consider real-life situations such as machine breakdowns, the existing scheduling methods

This paper dealt with a kind of heterogeneous vehicle routing problem with known demand and time deadline of customers. The customers are supposed to have one of tight deadline and loose deadline. The demand of customers with tight deadline must be fulfil

The facility location in designing a supply chain network is an important decision problem that gives form, structure, and shape to the entire supply chain system. Location problems involve determining the location, number, and size of the facilities to be used. The optimization of these location decisions requires careful attention to the inherent trade-offs among service time, inventory costs, facility cost, transportation costs. This paper presents a strategy that provides the best locations of distribution centers using GIS(Geographical Information System) assuming the limitation of delivery time. To get the best strategy of the location of distribution centers, we use the new loss functions as a penalty when the delivery time is violated.

This paper deals with a batch processor model in which the batch processing times depend on the jobs assigned to the batch. Each job has a distinct processing time which is determined as not the exact value but the range from the lower limit to the upper,

This Study will introduce the concept of CTP(Capable-to-Promise) Algorithm, CTP process, and the modeling of algorithm. This research is based on the environment of using Job-Shop method. CTP algorithm model use LPST(Latest Possible Start Time) and EPST(Earliest Possible Start Time) method especially. It is important part of executing CTP system. The CTP modeling and implementing develops to system which is able to implement in the various business environment through additional and continuous research.