Lot Size Research Articles

Assessing the lean-green practices in the automotive industry: Perspectives from academia and industry

Since the emergence of rising environmental issues, firms urgently require a supply chain strategy that will allow them to compete effectively in the marketplace. Implementing lean and green supply chain management strategies have been recommended to achieve sustainability and competitiveness. Yet, there is some ambiguity about how these practices must interact with one another. While lean practices have been around for a while and are widely adopted by businesses, the green movement is still evolving in the supply chains for most industries. This paper aims to identify the most important lean-green practices in supply chain management and their relative importance to firms’ sustainable performance in automotive industry context using Best Worst Method. The results reveal that green related practices are the most important to achieve environmental, economic, and social performance respectively. Whereas most lean practices remain the least important. As mentioned by the results three green practices, namely Environmental Management System (EMS), Green Purchasing (GP), and Reduce, Reuse and Recycle (3R), are the most important, while Small Lot Sizing (SLZ) is the least important lean practice. These practices contribute to the improvement of environmental, economic, and social performance respectively. The findings of this research offer a theoretical and empirical roadmap for decision makers seeking to identify key practices that are most likely to contribute to improving various aspects of sustainable performance in automotive context.

TRIANGLE HEDGING ROBOT FOR EURGBP, EURJPY AND GBPJPY PAIRS ON THE MT4 PLATFORM

The Triangle Hedging Robot or Expert Advisor is designed for the MetaTrader 4 (MT 4) platform. The robot can monitor signals, open positions, close positions both for each currency pair and as a whole. The robot is also designed to be able to run automatically or semi-automatically. The robot is implemented in the foreign exchange (forex) market for the EURJPY, GBPJPY and EURGBP pairs. Position opening is based on technical analysis using the Moving Average (MA) method. A Buy position will be opened if there is an upward cross from the small period MA to the big period, and conversely a Sell position will be opened if there is a downward cross from the small period MA to the big period. The robot works based on two cycles that have been defined, namely the first cycle in the form of buying EURJPY, selling GBPJPY and selling EURGBP; and the second cycle is selling EURJPY, buying GBPJPY and buying EURGBP. Each cycle is a complete position of Triangle Hedging. Robots can also work with user interaction. If the user opens a position with a certain lot size, the robot will perform lot calculations and determine cycles to hedge properly. The results of testing the robot show that the robot works well according to its purpose. It's just that the robot still needs to be tested more intensively to determine whether the robot can provide benefits for the user in the medium or long term.

Mathematical model and adaptive simulated annealing algorithm for mixed-model assembly job-shop scheduling with lot streaming

Mixed-model assembly and lot streaming are two key technologies to improve efficiency and reduce setups in a multi-variety and small-batch discrete manufacturing environment. Yet few studies consider the two technologies in assembly job-shop scheduling problems. Thus, this paper investigates the mixed-model assembly job-shop scheduling problem with lot streaming, which contains two closely coordinated stages with differentiated production lot size: the processing stage with lot streaming and the mixed-model assembly stage with single-piece flow. To tackle this problem, a mathematical model including new constraints i.e. production sequence constraints within/among sublots, sequence-dependent setup constraints between sublots and mixed-model assembly constraints, is developed to establish the temporal and spatial links between the processing and assembly stages. Besides, an adaptive simulated annealing algorithm is proposed. In this algorithm, a sublot sequence rule considering sublot and assembly features is discovered via gene expression programming to promote the performance of initial solutions; a Q-learning-based temperature control mechanism is designed to adaptively adjust the acceptance probability of inferior solutions and accordingly avoid local optima; an adaptive selection mechanism of neighborhood operators is designed to increase the chance of finding potential solutions; an adaptive adjustment mechanism of neighborhood size is put forward to balance computing resources and the coverage of neighborhood space. Experimental results show that both the discovered rule and the adaptive mechanisms are effective, and the developed algorithm is more adapt to fix the studied problem than other state-of-the-art algorithms.

Analysis of Fabric Raw Material Inventory Control in Backpack Products Using the Material Requirements Planning (MRP) Method at CV. Metassa Collection

Inventory of raw materials is an important factor in the company so that the supply of raw materials in CV. Metassa Collection must be sufficient to guarantee the needs for the smooth running of production activities. The purpose of this study is to determine the supply of raw materials with the Material Requirements Planning method. The method used is Material Requirement Planning (MRP). In calculating the MRP, the methods used are Lot For Lot and Economic Order Quantity (EOQ). The results of the research on the smallest forecasting calculation using the Linear Regression method with MAD values ​​of 274.52, MSE 114.536, and MADE Based on MRP calculations with the Lot Sizing EOQ method produce a total cost of Rp. 2,811,032,000. The minimum ordering of raw materials using the EOQ method for controlling raw material inventory at CV. Metassa Collection.

Butterfly Algorithm for Sustainable Lot Size Optimization

The challenges faced by classical supply chain management affect efficiency with regard to business. Classical supply chain management is associated with high risks due to a lack of accountability and transparency. The use of optimization algorithms is considered decision-making support to improve the operations and processes in green manufacturing. This paper suggests a solution to the green lot size optimization problem using bio-inspired algorithms, specifically, the butterfly algorithm. For this, our methodology consisted of first collecting the real data, then the data were expressed with a simple function with several constraints to optimize the total costs while reducing the CO2 emission, serving as input for the butterfly algorithm BA model. The BA model was then used to find the optimal lot size that balances cost-effectiveness and sustainability. Through extensive experiments, we compared the results of BA with those of other bio-inspired algorithms, showing that BA consistently outperformed the alternatives. The contribution of this work is to provide an efficient solution to the sustainable lot-size optimization problem, thereby reducing the environmental impact and optimizing the supply chain well. Conclusions: BA has shown that it can achieve the best results compared to other existing optimization methods. It is also a valuable chainsaw tool.

Improved multi-objective artificial bee colony algorithm for parallel machine lot-streaming scheduling problem with limited and unequal sub-lots

Job splitting is a full-time effective measure used to improve corporate competitiveness, especially when the lot size of jobs is considerably large. This work investigates a parallel machine lot-streaming scheduling problem, where unequal sub-lot is considered and the number of sub-lots is strictly limited by that of cutters. Focus on this problem, a mixed-integer linear programming model is formulated to minimize makespan and due time deviation simultaneously, to scrutinize the coupling relationship among decision variables, i.e., the number and size of sub-lots, the machine allocation, and the processing order of all sub-lots. Then, an improved multi-objective artificial bee colony algorithm (IMOABC) with two categories of improvements is developed to obtain high-quality Pareto front solutions. Specifically, a semi-random initialization is proposed to balance the workload as evenly as possible, which includes a half-normal distribution strategy for determining the number of utilized sub-lots, an equal-probability lot-splitting strategy for determining the size of each sub-lots, and a machine allocation strategy to synchronize the completion time of all machines. Besides, a multipoint preservative crossover is designed to enhance diversity in the employed bee phase, a sub-lot adjustment operator is proposed to implement objective-oriented local search in the onlooker bee phase, and an individual restart mechanism is adopted to avoid being trapped in local optimum in the scout bee phase. Extensive experiment results demonstrate that IMOABC significantly outperforms the other six state-of-the-art algorithms in terms of diversity and convergence.

Sample size planning and the statistical significance of official controls by sampling.

Acceptance sampling is important for food safety and is a relevant tool at production and official control levels, as it helps decision-making processes and verifies quality and food safety management. Generally, sampling plans are hypothesis tests of products that have been submitted for official appraisal and subsequent acceptance or rejection. The sample size is related to the set level of risk, the acceptable precision, and the tolerable misstatement size; therefore, sample size determination has a crucial role in setting up the accepted level of non-compliance and level of error. Using a simple predictive model based on combinatorics, this study showcases how sample size management can change the probability of rejecting good lots and/or accepting bad lots when the acceptance number is 0 (c=0). We showed that when c=0, a very high level of significance of the test corresponds to the high probability of rejecting a lot with an acceptable prevalence of defective items (type II error). We produced tables about the minimum sample size at different significance levels, which can be useful in the field. A paradigmatic example of the role of sample size in the acceptance-sampling plan is represented by the visual inspection for the detection of Anisakid larvae in fishery products. This study investigated this aspect and mainly referred to studies on the prevalence of larvae in farmed fish. We showed that, for lots ≥1000 items, the sample size is not strictly related to the lot size, but to draw a consistent control plan and reduce the variability in the clinical judgment, control authorities require a standardized approach. Because of this, the results on the prevalence of Anisakid larvae in farmed fish, if only based on sampling control plans, do not support a negligible risk statement, despite the claims reported in the EFSA opinion and several other studies.

Dynamic opportunistic maintenance grouping in a lot streaming based job-shop scheduling problem

This paper aims to model maintenance planning with a dynamic opportunistic approach for a job-shop production system. One issue in such a system is the positive or negative economic dependency. That is grouping maintenance activities may decrease or increase system costs. Furthermore, many maintenance models consider the planning of maintenance only based on a long-term horizon. While short-term and real circumstances such as system characteristics and constraints, workload, number of available maintenance teams, and variable maintenance cost and time are almost ignored. To address these issues, a rolling-horizon approach based on a long-term maintenance plan is proposed so that subsequent scheduling of maintenance and production activities are performed as events unfold through the time. Hence, we have developed a mixed-integer nonlinear mathematical model to simultaneously make decisions on maintenance selection, maintenance grouping, lot sizing and production scheduling. The objective function includes the costs of preventive and corrective maintenance activities as well as various production costs such as production and setup, tardiness penalty, and safety stock penalty. A self-adaptive Cuckoo Optimization Algorithm has been used to solve the proposed model. Numerical experiments were conducted to demonstrate the validity of the model and investigate the efficiency and effectiveness of the optimization algorithm.

Analisis Pengendalian Persediaan Bahan Baku Biji Plastik Dengan Metode Probabilistik Di UD. ACC Plastik Sememi, Benowo, Surabaya

UD ACC Plastics is a business in the field of plastic products that are produced, namely Hangers made from PP (polypropylene) plastic. Planning and controlling raw materials is very important for plastic companies with manual cupola especially UD. ACC Plastic. Fluctuating demand from customers resulted in excess and shortage of raw materials at UD. ACC Plastic. For that we need a definite and optimal calculation. In this case, an analysis of the planning and control of raw materials is carried out using the probabilistic method of the P Back Order model. To find out the order time interval, safety stock, lot order size, and inventory costs. Inventory and Control is done by probabilistic calculation of the P Back Order model with the Hadley Within solution by doing iterations and selecting the best solution with the minimum total inventory cost. Calculation results obtained total minimum inventory costs and optimal inventory system. In the raw material inventory of plastic pellets, the total cost of Rp. 204,325,752 with order intervals every 23 days or 0.062 years, order lot sizes of 415 kg, safety stock of 134.59 kg and a maximum order of 550 kg. The service level provided was 99.96%.

Reformulated Silver-Meal and Similar Lot Sizing Techniques

Literature and most textbooks around the world describe Silver-Meal in such a way that periods with zero demand make Silver-Meal suggest a higher frequency of order replenishments than necessary and therefore higher total costs than necessary. Silver-Meal, still the best-known technique, is therefore inferior to other lesser-known techniques when the time interval in the calculations presently is days and not months. The purpose of this article is to show that another mathematical formulation of Silver-Meal avoids this trouble. We also point to characteristics such as Silver-Meal, Least Unit Cost, Part-Period Balancing, and lot-sizing techniques that are available in many textbooks for operations and supply chain management. We illustrate the techniques with different examples of periods without demand, declining demand, and varying demand. We point out possible problems with the different techniques. Literature mostly does not consider periods of zero demand, which was not so important before. Lot-sizing methods must cope with the important performance indicator “Days of inventory”. Numerous practical situations with zero demand periods exist where a lot of sizing techniques help for efficient operations. It is necessary knowledge and a tool for students (future users, performers, and managers). “Lägsta periodkostnad” is a restored and reformulated Silver-Meal, with Silver-Meal’s characteristics already presented in literature, except those difficulties with zero demand periods disappear.

Inventory Model for Order Quantity Optimization with Partial Backlogging on Greater Demand at The Beginning

This article discusses the model of inventory with greater demand at the beginning which allows shortages. During the shortage period, it is assumed that there is a backlogged demand, and the remainder is considered lost sales. This research is completed by using the deterministic inventory model method, namely the EOQ model. The result of using the EOQ method is to determine the inventory lot size and length, with the goal of minimizing the total cost of inventory and generating maximum profits related to the inventory model. An numerical example is given to show the use of this model.

Perencanaan dan Pengendalian Bahan Baku Cup 220 ml Dengan Pendekatan Material Requirement Planning di PT Tirta Sukses Perkasa Sukorejo

PT Tirta Sukses Perkasa is a manufacturing company engaged in the production of plastic for packing CLUB mineral water products. Based on initial observations, this company experienced problems in controlling raw materials, thereby reducing cost effectiveness and efficiency. The purpose of this study is to control and determine the amount of raw materials that must be provided. This study used a descriptive quantitative approach with 25 material populations. This study used purposive sampling with a total sample of 4 materials. Researchers here as research instruments that act as data collectors. The analysis method used is the Material Requirement Planning (MRP) method with the help of QM for Windows V5 software. Based on the research results, the MPS forecasting method uses the Moving Averages technique, because it has smaller MAD, MSE and MAPE values compared to using the Exponential Smoothing method. The most optimal Lot sizing uses the Lot for lot technique compared to using the EOQ method because it has a lower total inventory cost.

Capacitated lot sizing problem with periodic carbon emission constraints and multiple resources

We study the single item capacitated lot sizing problem with multiple resources and periodic carbon emission constraints that impose an upper bound for the average emission per product produced in any period. Although the uncapacitated version of this problem can be solved in polynomial time, generalisation of the problem including the resource capacities is NP-Hard, in general. We present important structural properties for the optimal solutions of the problem. We consider the special cases with two resources and under non-speculative costs, construct the piecewise linear total production cost function when the resource capacities, and the emission and cost parameters are time-invariant, and develop a polynomial time dynamic programming algorithm (DP) to solve them. Then, we generalise the procedure to construct the total production cost function and the DP for the general setting with fixed number of capacitated resources. We test our algorithm for different problem instances, and compare it with a commercial solver and a DP available in the literature for solving the lot sizing problem with piecewise concave production cost functions. The results reveal that our DP outperforms the other one, and it performs better than the commercial solver when the number of breakpoints of the total production cost function is small.

Penerapan Metode Material Requirement Planning (MRP) guna Merencanakan Kebutuhan Bahan Baku (Studi Kasus: CV. AM Nanda Putra Sidoarjo)

Company CV. AM Nanda Putra is located in Sidoarjo and operates in the scaffolding industry. Currently the company is experiencing losses due to a lack of optimization in planning the amount and time of ordering raw materials, which results in shortages and excess material inventory. To overcome this problem, the company uses the MRP (Material Requirement Planning) method in optimizing raw material planning. In terms of the lot sizing approach, the company applies the LFL and EOQ methods. The forecasting methods used are Moving Average (MA), Weight Moving Average (WMA), and Exponential Smoothing (ES). The smallest MAD results were obtained using the Exponential Smoothing (ES) method for all scaffolding products. The forecasting results are obtained to determine the MPS (Master Production Schedule) for the next 10 months. After the determination of MPS, the results of Material Requirement Planning (MRP) were obtained, namely the supply of raw materials for MF 170 AM scaffolding of 35402 units or 17700 sets, MF 170 K1 scaffolding of 28906 units or 14453 sets, MF 190 AM scaffolding of 16250 units or 8125 sets, and MF 190 K1 scaffolding of 7656 units or 3828 sets. From the results of calculating the cost of raw material requirements using the Lot for Lot (LFL) method and the Economic Order Quantity (EOQ) method, it can be seen that the total cost of planning the smallest raw material inventory with an amount of Rp. 12,975,818,022.

A sustainable supply chain coordination model with an investment in green technology and electric equipment

This paper proposes a new mathematical model to examine a solution to a sustainable supply chain problem by investing in green production technology and electrical equipment for transportation and warehousing. This study integrated an inventory model involving a single vendor, a single distributor, and multiple retailers. The carbon emissions involving emissions from a vendor and retailers are incorporated in this paper. An algorithm solution is proposed to minimise the total cost to determine the optimal decision variables, i.e. the lot size, number of deliveries, safety factor, lead-time, and emissions. A numerical example is illustrated to validate the proposed model and provide insight into the relevant industry. The life cycle cost assessment of the electrical equipment is also discussed for managers to inform their decision-making. From the comparison results, the integrated scheme comprising electric-powered with green investment is lower than without green investment, fossil-fuel with green investment, and fossil fuel equipment only with a total cost saving of 0.68%, 3.28%, and 3.59%, respectively; lower-emissions of 39.69%, 42.22%, 27.46%, respectively; and lead-time reduction of 1.80%, 25.23%, 23.78%, respectively.

Synthesis and formation mechanism of HfB2 ultrafine powders with low oxygen via flocculating settling assisted process and carbo/borothermal reduction

In this study, ultrafine HfB2 powders with low oxygen were synthesized by a flocculating settling process which yielded ceramic precursors and subsequent carbo/borothermal reduction of the precursors. The liquid phase precursor method can achieve uniform mixing of components at the molecular level through multiple complexation reactions, and then realize the carbo/borothermal reduction reaction at a lower temperature to obtain ultrapure HfB2 powders. The as-resulted quasi-spherical HfB2 powders under the optimum conditions (atomic molar ratio M:B:C = 1:2.8:10) calcined at 1500 °C for 1 h have an average particle size of 205 nm and an oxygen content of 0.097 wt.%. Detailed analysis of the phase evolution of precursors shows that the formation of HfB2 particles is a mass diffusion mode from the external to internal HfO2 cores. We reveal that below 1300 °C, HfC is not an intermediate product of HfB2 powder during the transition of precursors. Instead, HfC was formed as a by-product at high temperatures in the carbo/borothermal reduction process. The proposed formation mechanism of HfB2 is completely different from the traditional two-step transformation method. After the sintering of the ultrafine powders, the HfB2 ceramics show a relative density of 96.1% and superior mechanical properties compared to other works. Furthermore, by simply replacing the initial metal source, chlorinated group IV and V transitional metals (Ti, Zr, Ta, Nb) can also convert into high-purity and ultrafine diborides. This work shows that flocculating settling assisted carbo/borothermal reduction has potential in lot size production of various high-purity and ultrafine boride powders.

MODEL PERSEDIAAN PADA SUPPLY CHAIN DENGAN MEMPERTIMBANGKAN EMISI KARBON DAN PERMINTAAN YANG BERGANTUNG PADA HARGA JUAL PRODUK

Emission from manufacturing contributes greatly to the increasing og greenhouse gas pollution. One of the activity in manufacturing is the supply chain. To deal with the risk of carbon emission, it is necessary to carry out green supply chain management, which is by implementing hybrid production, by regular production and green production. A company needs to consider the tradeoff between the use of green technology and the benefits that will be obtained. This research was conducted to form a supply chain inventory model by considering carbon policies and productselling price to maximize the joint total profit. The development of this model is carried by set the assumptions, formulating models, and gave the numerical examples by using secondary data. We used algorithm from the formulation model to find the optimal solution. The research is continued with a sensitivity analysis of the proposed scenarios by changing some of the parameter. This sensitivity analysis can be used as a consideration for a manager in making decisions according to those scenarios. The results of this study are the addition of green production facility is proven to reduce carbon emission by considering product selling price, lot size, number of shipping, and safety factor.

New construction heuristic for capacitated lot sizing problems

We consider the classical single-level multi-item capacitated lot-sizing problem (CLSP) which is the core model for production planning. It is NP-hard and if setup operations consume capacity the feasibility problem itself is NP-complete. Several construction heuristics have been proposed in the research literature, but none of them achieves a sufficient solution quality and generality at the same time - meaning that they can be applied to different variations of the problem easily. We propose a general two-step construction heuristic (2-SCH) which sorts the customer orders in the first step and iteratively adds them to a preliminary production plan in the second step. Hence, various problem classes can be solved easily and fast. Computational experiments on the CLSP without setup times show that the 2-SCH outperforms the well-known Dixon–Silver (Dixon & Silver, 1981) and the ABC heuristics Maes and Van Wassenhove (1986) and provides better results than the genetic programming approach proposed recently by Hein et al. (2018). We also apply it to the CLSP with setup times where it outperforms the construction heuristic proposed by Trigeiro (1989). Finally, we show the flexibility of the 2-SCH by applying it to the CLSP with backorders and the multi-level CLSP as well as the single-level CLSP in an online environment.

Price determinants of a graded feeder cattle sale

The objective of this study was to deter- mine how numerous factors, including USDA muscle score, affect auction sale prices for feeder cattle in the US South- east. Data came from a monthly feeder cattle sale in Tennessee from 2019 to 2021, and a hedonic pricing model was estimated that considered how average lot weight, lot size, sex, breed, weaning manage- ment, and corn price influence feeder cattle price. The model shows that aver- age lot weight, total head per lot, corn price, lots with weaned cattle, sex, and breed affected the feeder cattle prices as expected based on the literature. A muscle score of 1 increased the sale price feeder cattle by US$9.51 per hundredweight relative to muscle score 2, which is a key finding of this study. This extends the lit- erature by considering the impact of USDA muscle score on feeder cattle prices for cattle in the Southeastern Unit- ed States. This paper also builds on the growing literature examining how various factors affect Southeastern feeder cattle prices. This estimation is useful to help small and medium-sized producers identify optimal management de- cisions to increase the value of their calves. Additionally, this study demonstrates to producers the added value of grades for cattle.

Meta-inventory

In an Industry 4.0 Factory, physical entities such as humans, machines and materials are digitized into digital twins (DT) with smart IoT (Internet of Things) devices resulting in Cyber-Physical Production Systems (CPPS). Real-time data analytics builds up traceability and visibility, not only in the physical domain but also cyber space. This paper adds a new concept of cyber-physical inventory or simply meta-inventory to Industry 4.0 CPPS. In addition to physical items, their digital twins are considered as part of production inventory. Traceability and visibility enabled by digital twin can significantly reduce complexity and uncertainties (e.g. lead times and variability) while achieving resilience in case of major disturbances. The CPPS factory hedges the risks through meta-inventory without incurring cost for holding inventory digitally. After reflecting upon the developments of production inventory management corresponding to the evolutionary history of manufacturing systems to Industry 4.0, the paper presents the meta-inventory paradigm within a simple Industry 4.0 compliant supply chain. The factory, the supplier, and the transport implement a VMI (vendor-managed inventory) strategy. Two well-known basic EOQ (Economic Order Quantity) and EPQ (Economic Production Quantity or production Lot Sizing) problems are extended to demonstrate and quantify the impacts of using meta-inventory on the supply chain and the member enterprise. The analyses allow us to unfold key perspectives in more complex production and supply chain systems for further research.