In this paper, a brief overview aimed to introduce the literature in the areas of lean and green manufacturing is given. The overview presented is intended to provide a glimpse of the types of work that exists in lean and green manufacturing and to provide a useful starting point for researchers interested in exploring this area in greater depth.

There are different types of inventories in production systems including raw material, work-in-process (WIP) inventory and finished products, and each of them causes associated costs in the production system. In this paper, we analyze work-in-process inventories using a control engineering approach. We apply the Automotive Pipeline Inventory and Order Based Production and Control System (APIOBPCS) as a control system with demand updating, finished products recovery and WIP inventory adjustment as control parameters. We analytically prove that levels of WIP inventory in the system follow Little's law, where the average number of WIP inventory in the system is equal to the production quantity multiplied by production leadtime. Furthermore, we validate this result showing system responses for different inputs such as step function and sinusoidal function. The results shows that WIP behaves the same with the production quantity of the production system, and hence an analysis of WIP can be substituted by an analysis of production quantity.

In this paper, we consider a form of multi-item inventory management known as the Joint Replenishment Problem (JRP). In our model, the target warehouse sells multiple items to meet the retailer's demand and replenishes these items from the supplier. At each replenishment, the warehouse decides which items should be ordered and how much should be ordered. Carriers with a finite capacity transport items from the supplier to the warehouse with a fixed lead-time. The fixed ordering cost of each carrier is charged according to the number of carriers used, not the order volume. Because of the stepwise ordering cost, full-load replenishments reduce the cost, even if an unscheduled item is ordered. In addition, if a relation exists among the items demanded, the can-order policy is more suitable. Under the can-order policy for JRP, some items are re-ordered when the inventory volume is below the re-order level and any items with an inventory volume below the can-order level can be included in the order. Several studies have considered how to set the parameters of the can-order policy. But, correlated demands have not received sufficient attention in the literature because it is difficult to find the optimal parameters. In this paper, we propose to find the optimal parameters of the can-order policy, the can-order level and the order-up-to level for each correlated demand item by applying a genetic algorithm. The main objectives in our model are minimizing item storage, stock-outs and carrier fees. In numerical experiments, we simulated inventory movement and ordering items while considering the capacity of the carriers. The experimental results of our proposed model are compared with the results of other models.

This paper considers a risk-aversion approach for an inventory model considering standard and high qualities of a product under a competitive store. In the case the competitive store sells a standard product, another store must decide the purchase volumes, prices and ordering quantities of standard and high quality products in order to manage a loss risk as well as maximize the total profit. In this paper, a mathematical programming problem of the proposed model is proposed considering these conditions and uncertainty of consumer demand based on risk measure such as conditional Value-at-Risk. Furthermore, in order to obtain the optimal prices and ordering quantities, a scenario-based approach is developed.

Since the automotive sector is a particularly powerful driver of growth for the semiconductor industry, some semiconductor companies are focusing on the automotive industry as a key market. This research gives an introduction to semiconductor products and their production. The current business conditions and challenges in a company at the time of research are presented, and the causes and effects on the semiconductor supply chain in terms of semiconductor inventory management are examined. This research focuses on problem formulation and solution procedures for inventory planning using Markov decision process models to recommend the decisions regarding finished goods inventory in a supply chain with stochastic demand. We first identify a Markov decision problem for an inventory model considering the chain's state space, transition probabilities and cost structure, and then obtain the optimal control policy that minimizes the expected cost under stochastic variability.

In this research, a four-echelon supply chain network (SCN) comprised of suppliers producing different modules, a product assembler, warehouses, and markets is considered. When designing an SCN, it is important to assess non-dominated solutions. Various methods used to solve such problems place emphasis on objectives either at the beginning of the solution process or interactively during the solution process. However, assigning weights in the beginning may lead to skipping some valuable solutions. To address this problem, Pokharel (2008) proposed a two-objective linear programming model for decision-making in a supply chain using a STEP method. Depending on the decision-maker's preferences, the STEP method generates non-dominated solutions. However, the preferences of the decision-maker do not necessarily generate a new solution. This paper develops a solution procedure for obtaining Pareto optimal solutions for Pokharel's SCN model.

The aim of this study is to design a global supply chain network by selecting suppliers and determining locations for assembly factories while satisfying the demands of different markets for finished assembly products not only in consideration of parts procurement, production, and logistics costs, but also material-based CO₂ emissions. First, the global supply chain network is modeled with material-based CO₂ emissions and the costs, and is formulated as a mixed integer programming (MIP) problem. Second, numerical experiments are conducted where the parts and the products with material-based CO₂ emissions and costs based on a bill of materials are transported between two countries. Additionally, the numerical experiment is shown by varying the target reduction ratio of the environmental impact. Finally, the results of the proposed design method are compared with a previous study, and the effect of the facility cost is discussed.

This paper proposes an approach to effectively reuse experiential knowledge by integrating current and past knowledge in future business system designs. First, this study discusses the business system transformation model (BSTM) as a framework for accumulating and utilizing information about the transformation process of business systems, focusing primarily on experiential knowledge. Next, three types of knowledge are established, such as using BSTM to modify and use past experiences (past experiential knowledge: PEK), using it in similar cases (similar experiential knowledge: SEK), and finally using it for a current business design (current discussed knowledge: CDK). This paper then describes the relationship between the three types of knowledge, and proposes two utilizations to demonstrate the process of accumulating and utilizing information. These proposed utilizations are examined in two actual cases where users identify a clear purpose for the accumulation and utilization of experiential knowledge. Finally, operational problems are claried and solutions for solving these problems are also discussed.

Finite element analysis (FEA) is frequently employed by researchers to investigate the mechanics and/or thermal behaviors of high-power LEDs. However, after performing FEA, only a few people continue to discuss the life and reliability of the LED. In this study, a relationship between the junction temperature and the life of a LED is first established based on real test data. FEA is then employed to find the junction temperature of the LED. By using the numerical results of FEA as the input for the relationship mentioned above, one can predict the life of the LED. However, the life is a fixed value under a certain condition, which cannot truly reflect the discrete characteristic in real life testing. Furthermore, it cannot provide extra information such as the reliability and the failure rate of the LED. To the end, this study further accounts for uncertainties coming from parameters such as convection coefficient and photoelectric conversion efficiency, and regards them as random variables. The Monte-Carlo method is used to simulate samples of these random variables when performing FEA and predicting life of the LED. Scattered lives indicating a random sample out of the studied LED are then obtained even under the same temperature and environmental condition. By using the probability plot and statistical analysis, one can find the life distribution and reliability-related quantities of the LED.

This paper presents an analytical model for analyzing the competition between biofuels and food. The model is based on the Cournot model in which two representative consumers of corn, one each for bioethanol and food, decide the consumption so as to maximize their utility. Comparing the equilibrium consumption with the optimal consumption shows that the market equilibrium where corn is used for both bioethanol and food is inefficient. The effects of a tax and a subsidy on consumption are then discussed to examine whether or not the optimal consumption can be achieved. The results demonstrate that the use of a tax can achieve the optimal consumption, whereas the use of a subsidy cannot.

The objective of this paper is to explore the current situation regarding routes along the East-West Economic Corridor (EWEC) from the viewpoint of regional logistics networks used in transportation of international trade from the Greater Mekong Subregion (GMS) countries to east Asian markets and the Americas. An analytic hierarchy process (AHP) method is applied to select a potential route to transport export products from the northeastern region of Thailand. Travel distance, travel time, and logistics cost including multimodal transportation and cross-border processes are calculated and compared with a conventional exporting route. The results and recommendations are also presented.

We introduce an estimation method for calculating operation durations using regression analysis and a mixed-integer programming model for the operating room (OR) scheduling problem. We develop an OR scheduling system and apply it to OR scheduling at Aichi Medical University Hospital. The goals of scheduling are to reduce staff overtime, schedule changes, and OR reassignment. We attain this by scheduling according to predicted operation durations. The resulting schedule makes highly efficient utilization of the OR. We confirm the efficiency of the schedules obtained by comparing them to schedules created manually.