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The purpose of this study is to develop a novel and practical series-parallel inventory-redundancy allocation system in a green supply chain including a single manufacturer and multiple retailers operating in several positions without any conflict of interests. The manufacturer first produces multi-product and then dispatches them to the retailers at different wholesale prices based on a common replenishment cycle policy. In contrast, the retailers sell the purchased products to customers at different retail prices. In this way, the manufacturer encounters a redundancy allocation problem (RAP), in which the solution subsequently enhances system production reliability. Furthermore, to emphasize on global warming and human health concerns, this paper pays attention both the tax cost of industrial greenhouse gas (GHG) emissions of all produced products and the limitation for total GHG emissions.

The manufacturer intends not only to maximize the total net profit but also to minimize the mean time to failure of his production system using a RAP. To achieve these objectives, the max-min approach associated with the solution method known as the interior point method is utilized to maximize the minimum (the worst) value of the objective functions. Finally, numerical experiments are presented to further demonstrate the applicability of the proposed methodology. Sensitivity analysis on the green supply chain approach is also performed to obtain more insight.

The computational results showed that increasing the number of products and retailers might lead into a substantial increase in the total net profit. This indicated that the manufacturer would feel adding a new retailer to the green supply chain strongly. Moreover, an increase in the number of machines provides significant improvement in the reliability of the production system. Furthermore, the results of the performed sensitivity analysis on the green approach indicated that increasing the number of machines has a substantial impact on both the total net profit and the total tax cost. In addition, not only the proposed green supply chain was more efficient than the supply chain without green but also the proposed green supply chain was very sensitive to the tax cost of GHG emission rather than the number of machines.

In summary, the motivations are as follows: the development of a bi-objective series-parallel inventory-RAP in a green supply chain; proposing a hybrid inventory-RAP; and considering the interior point solution method. The novel method comes from both theoretical and experimental techniques. The paper also has industrial applications. The advantage of using the proposed approach is to generate additional opportunities and cost effectiveness for businesses and companies that operate utilizing the green supply chain under an inventory model.

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.

The production-distribution (P-D) problems are two critical problems in many industries, in particular, in manufacturing systems and the supply chain management. In previous researches on P-D planning, the demands of the retailers and their inventory levels have less been controlled. This may lead into huge challenges for a P-D plan such as the bullwhip effects. Therefore, to remove this challenge, the purpose of this paper is to integrate a P-D planning and the vendor-managed inventory (VMI) as a strong strategy to manage the bullwhip effects in supply chains. The proposed P-D-VMI aims to minimize the total cost of the manufacturer, the total cost of the retailers, and the total distribution time simultaneously.

This paper presents a multi-objective non-linear model for a P-D planning in a three-level supply chain including several external suppliers at the first level, a single manufacturer at the second level, and multi-retailer at the third level. A non-dominated sorting genetic algorithm and a non-dominated ranking genetic algorithm are designed and tuned to solve the proposed problem. Then, their performances are statistically analyzed and ranked by the TOPSIS method.

The applicability of the proposed model and solution methodologies are demonstrated under several problems. A sensitivity analysis indicates the market scale and demand elasticity have a substantial impact on the total cost of the manufacturer in the proposed P-D-VMI.

Although the P-D planning is a popular approach, there has been little discussion about the P-D planning based on VMI so far. The novelty comes from developing a practical and new approach that integrates the P-D planning and VMI.

The purpose of this paper is to investigate optimal decision methods under a cooperative situation in two-echelon logistic models.

The authors propose the optimal strategies of exporters in the three types of rival game behaviors: Stackelberg, Collusion, and Cournot, each of which provides the optimal decision for the duopolistic shippers and the oligopolistic forwarders in each scenario.

From the empirical studies the paper finds that among three scenarios, the oligopolistic treatment of forwarders’ actions demonstrates that Stackelberg behavior can carry out the maximum profit, and Collusion game can achieve the maximum profit for the shippers.

Proposed an optimal decision methods in two-echelon logistic models.

To establish a strategic resource allocation model to capture and encapsulate the complexity of the modern global supply chain management problem.

A mathematical model was constructed to describe the stochastic multiple‐period two‐echelon inventory with the many‐to‐many demand‐supplier network problem. Genetic algorithm (GA) was applied to derive optimal solutions through a two‐stage optimization process. A practical example and its solution were included to illustrate the GA‐based solution procedure.

The model simultaneously constitutes the inventory control and transportation parameters as well as price uncertainty factors.

The model can be utilized as a collaborative supply chain strategic planning tool to determine efficiently the appropriate inventory allocation and effectively manage the distribution/re‐distribution process according to real‐time demand.

The yield of defective items and emissions of greenhouse gases in supply chains are areas of concern. Organizations try to reduce the yield defective items and emissions. In this paper, a constrained optimization model is developed with consideration of the yield of defective items and strict carbon cap policy simultaneously and then optimized. Further, sensitivity analyses have been carried out to draw different managerial insights. Precisely, we have tried to address the following research questions: (1) how to optimize the cost for a two-echelon supply chain considering yield of defective items and strict carbon cap policy, (2) how the total expected cost and total expected emissions act with changing parameters.

The mathematical modeling approach has been adopted to develop a model and further optimized it with optimization software. Costs and emissions from different areas of a supply chain have been derived and then the total cost and total emissions have been formulated mathematically. One constrained mixed-integer nonlinear programming (MINLP) problem has been formulated and solved considering emissions-related, velocity and production related-constraints. Further, different sensitivity analyses have been derived to draw some managerial insights.

In this paper, many decision variables have been calculated with a set of basic values of other parameters. It has been found that both cost and emissions can be controlled by controlling different parameters. It has been also found that some parameters have very little or no influence either on cost or emissions. In most cases, originations may exhaust the given limit of carbon cap to optimize their costs.

In spite of my sincere efforts, no paper has been found that has considered the yield of defective items and strict carbon cap policy simultaneously. In this paper, it is assumed that both demand and defect rates are random in nature. The model, presented in this paper may give insights to develop different supply chain models with consideration of both defective items and strict carbon cap policy. Sensitivity analyses, drawn in this paper may give deep insights to managers and carbon regulatory bodies.

With the increasing competition among the industries, they remain under pressure as how to select the best set of suppliers for the competitive edge. Often, it has been challenging to develop an effective set of suppliers due to varied and asymmetric mode of criteria. The purpose of this paper is to develop a responsive chain under original equipment manufacturer (OEM).

This study proposes a responsive chain under a two-echelon system (TES) of OEM, which needs to collaborate with a set of suppliers at each echelon through an integrated methodology of AHP and TOPSIS. According to the OEM’s criteria, demands and suppliers’ capacity vary with time, therefore they are not static for a longer period. Hence, supplier selection (SS) problem possesses dynamicity in real practice. For this, MILP is used for finding optimal order quantities based on the optimal ranking at each echelon in the multi-period scenario. Subsequently, sensitivity analysis (SA) is conducted through Taguchi method of parameter design (TMPD) to achieve an optimal ranking in the TES.

This study suggests optimal criteria’s weight, percentage contribution, and flexibility for the suppliers and manufacturers involving through maximum demand strategy at each echelon of OEM. It also provides robust group of suppliers and manufacturers in the TES through optimal ranking and simultaneously in the order allocations. Furthermore, it restricts the number of suppliers and manufactures at each echelon through proposed methodology to obtain the solution in a very short running time.

To validate this model, a real data set for the case of chain conveyor company is used. This adopted methodology can suggest the organization that how the approach should be implemented.

This paper aims to discuss a simulation study for a multi‐product, two‐echelon inventory replenishment system. The paper compares a one‐warehouse N‐retailer replenishment system to a two‐warehouse, N‐retailer system with cost per unit of distribution and delivery lead‐times as the performance measures. The purpose is to demonstrate that under specific circumstances a two warehouse N‐retailer inventory replenishment system provides better customer service without significant changes in the cost.

Mathematical modeling and simulation methodology is used to test the performance of the proposed two warehouse N‐retailer system and statistical analysis is used to compare the performance of several scenarios.

The two warehouse replenishment system indeed reduces delivery lead‐times, used as a measure of customer service, under specific conditions such as controllable freight costs.

Caution should be exercised when interpreting these findings as the historical data used was from a single source. The paper did not investigate the effects of variable shipping costs from the manufacturing plant, warehouse and retailer. Future research could also consider multiple second level warehouses.

The findings provide a persuasive argument for manufacturers struggling with performance issues and channel relationships. Moreover, in addition to contributing to efficiency of distribution, two level systems can also enhance ability to adapt to local market conditions and to unexpected demand variations.

The model examined in this paper addressed a specific case for one company. While freight costs and warehousing costs will vary across companies, the cost represented here may be used as a gauge for evaluating systems with cost structures in the vicinity of those for the company represented in this paper. Additionally, the model is amenable to substitution of other firms' cost structures.

While in some districts having drinking water is a given reality, there are others where there is a lack of access to this resource. Unfortunately, even today, 10.2% of the world population lives this situation and it could be worse in the coming years, according to UNICEF. Inhabitants in Pamplona Alta at southern Lima, Peru, daily suffer this harsh reality. This social challenge study attempts to define a methodology for an effective logistic planning of water distribution in Torres Minas. Currently, they obtain it from unsanitary and informal vendors. This chapter provides the basis of a new layout of the water distribution network based on clusters to efficiently satisfy water demand. Specifically, we propose the use of orderly delivery points called “bus-stops of water” in a two-echelon distribution system, whose optimization relies on a mixed-integer linear programming (MILP) technique. The objective of these guidelines is minimizing the transactional and transportation cost, while increasing the bargaining power of the community. Results showed a reduction of 52.67% and 26% in transactional and transportation costs, respectively, and a reduction of the associated risks of shortage and contamination of a tight delivery of water. Moreover, we foster the application of this methodology in other similar situations to produce sustainable growth for human settlements; regardless, there is a lack of access to water or a steep geography.

The purpose of this study is to solve the problem of healthcare waste management in developing countries. The buildup of medical waste has attracted the attention of all spheres of society due to the expanding population and developing economy. Timely collection and processing of medical waste are extremely important due to its potential hazards. Although the problem of planning medical waste management has been addressed in developed countries, it persists in several developing countries. This research is motivated by an example of a city in India characterized by a dense population, abundant health-care facilities and a lack of planning for managing large medical waste generated daily.

The authors address the problem of designing the network of collection and processing facilities for medical waste and optimizing the vehicle route that collects and transfers the waste between facilities. Due to distinct topographic restrictions in the considered city, the collection and transfer process needs to be conducted in two echelons – from hospitals to collection centers using smaller vehicles and then to the processing facilities using trucks. This work addresses these two problems as a two-echelon location-routing problem.

A mixed-integer programming model is developed to minimize the cost of opening the facilities and transporting medical waste. Several managerial insights are drawn up to assist planners and decision-makers.

This study follows a case study approach to provide a descriptive and prescriptive approach to hospital waste management in the ancient city of Varanasi. The city has witnessed unplanned growth over the years and is densely populated. The health-care facilities in the city have a large catchment area and attract patients from neighboring districts. The situation analysis based on secondary data and unstructured interviews of the stakeholders suggests that the ad hoc approach prevails in present hospital waste management in the city.