Special Issue "Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain"

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: 31 December 2019

Special Issue Editors

Guest Editor
Prof. Dr. Biswajit Sarkar

Department of Industrial and Management Engineering, Hanyang University, Ansan, Gyeonggi-do, 15588, South Korea
Website | E-Mail
Interests: operations research; nonlinear optimization; Boolean algebra; graph theory; inventory management; reliability; advanced manufacturing system; production planning and control; supply chain management; control theory; logistics
Guest Editor
Dr. Mitali Sarkar

Department of Industrial and Management Engineering, Hanyang University, Ansan, Gyeonggi-do, 15588, South Korea
Website | E-Mail

Special Issue Information

Dear Colleagues,

Under any competitive scenario, the application of optimization in any field of production, inventory management, supply chain management, and logistics is really important. The main task for the industries is to obtain more and more profit or to reduce costs more. Optimization may not always be linear for all fields: it can sometimes be a nonlinear but classical, or metaheuristic, or simulation technique. Those can be used to solve the research models related to abovementioned field. In any kind of production, the basic needs are human labor and machines to continue production. The main contemporary issue of the production system is that the revolution of industry effects a lot to change in the traditional production to smart or additive production management, the direction of sustainable smart production, and, finally the formation of a smart supply chain management using the perfect logistics management. Sustainability issues mainly relate to the optimum values of economic, social, and environmental effects and the logistics management is a subpart of supply chain management, which gives details of organization and the implementation of a business operation. Thus, these optimizations cannot always be improved through an analytical procedure. That can be solved by a simulation or metaheuristic approach. These recent issues should be solved under the framework of the present Special Issue.

Prof. Dr. Biswajit Sarkar
Dr. Mitali Sarkar
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nonlinear optimization in any production management
  • classical, heuristic, and metaheuristic optimization approach in a supply chain and logistics management
  • optimum solution in any smart production
  • optimization in third party in the logistics management
  • cost optimization or profit maximization in an inventory management
  • application of graph theory in real life

Published Papers (15 papers)

View options order results:
result details:
Displaying articles 1-15
Export citation of selected articles as:

Research

Open AccessFeature PaperArticle
A Single-Stage Manufacturing Model with Imperfect Items, Inspections, Rework, and Planned Backorders
Mathematics 2019, 7(5), 446; https://doi.org/10.3390/math7050446
Received: 14 April 2019 / Revised: 15 May 2019 / Accepted: 16 May 2019 / Published: 19 May 2019
PDF Full-text (847 KB)
Abstract
Each industry prefers to sell perfect products in order to maintain its brand image. However, due to a long-run single-stage production system, the industry generally obtains obstacles. To solve this issue, a single-stage manufacturing model is formulated to make a perfect production system [...] Read more.
Each industry prefers to sell perfect products in order to maintain its brand image. However, due to a long-run single-stage production system, the industry generally obtains obstacles. To solve this issue, a single-stage manufacturing model is formulated to make a perfect production system without defective items. For this, the industry decides to stop selling any products until whole products are ready to fulfill the order quantity. Furthermore, manufacturing managers prefer product qualification from the inspection station especially when processes are imperfect. The purpose of the proposed manufacturing model considers that the customer demands are not fulfilled during the production phase due to imperfection in the process, however customers are satisfied either at the end of the inspection process or after reworking the imperfect products. Rework operation, inspection process, and planned backordering are incorporated in the proposed model. An analytical approach is utilized to optimize the lot size and planned backorder quantities based on the minimum average cost. Numerical examples are used to illustrate and compare the proposed model with previously developed models. The proposed model is considered more beneficial in comparison with the existing models as it incorporates imperfection, rework, inspection rate, and planned backorders. Full article
Open AccessArticle
A Two-Echelon Supply Chain Management With Setup Time and Cost Reduction, Quality Improvement and Variable Production Rate
Mathematics 2019, 7(4), 328; https://doi.org/10.3390/math7040328
Received: 7 December 2018 / Revised: 26 January 2019 / Accepted: 11 February 2019 / Published: 3 April 2019
PDF Full-text (512 KB) | HTML Full-text | XML Full-text
Abstract
This model investigates the variable production cost for a production house; under a two-echelon supply chain management where a single vendor and multi-retailers are involved. This production system goes through a long run system and generates an out-of-control state due to different issues [...] Read more.
This model investigates the variable production cost for a production house; under a two-echelon supply chain management where a single vendor and multi-retailers are involved. This production system goes through a long run system and generates an out-of-control state due to different issues and produces defective items. This model considers the reduction of the defective rate and setup cost through investment. A discrete investment for setup cost reduction and a continuous investment is considered to reduce the defective rate and to increase the quality of products. Setup and processing time are dependent on lead time in this model. The model is solved analytically to find the optimal values of the production rate, safety factors, optimum quantity, lead time length, investment for setup cost reduction, and the probability of the production process going out-of-control. An efficient algorithm is constructed to find the optimal solution numerically and sensitivity analysis is given to show the impact of different parameters. A case study and different cases are also given to validate the model. Full article
Figures

Figure 1

Open AccessArticle
Constrained FC 4D MITPs for Damageable Substitutable and Complementary Items in Rough Environments
Mathematics 2019, 7(3), 281; https://doi.org/10.3390/math7030281
Received: 2 January 2019 / Revised: 25 February 2019 / Accepted: 27 February 2019 / Published: 19 March 2019
PDF Full-text (1584 KB) | HTML Full-text | XML Full-text
Abstract
Very often items that are substitutable and complementary to each other are sent from suppliers to retailers for business. In this paper, for these types of items, fixed charge (FC) four-dimensional (4D) multi-item transportation problems (MITPs) are formulated with both space and budget [...] Read more.
Very often items that are substitutable and complementary to each other are sent from suppliers to retailers for business. In this paper, for these types of items, fixed charge (FC) four-dimensional (4D) multi-item transportation problems (MITPs) are formulated with both space and budget constraints under crisp and rough environments. These items are damageable/breakable. The rates of damageability of the items depend on the quantity transported and the distance of travel i.e., path. A fixed charge is applied to each of the routes (independent of items). There are some depots/warehouses (origins) from which the items are transported to the sales counters (destinations) through different conveyances and routes. In proposed FC 4D-MITP models, per unit selling prices, per unit purchasing prices, per unit transportation expenditures, fixed charges, availabilities at the sources, demands at the destinations, conveyance capacities, total available space and budget are expressed by rough intervals, where the transported items are substitutable and complementary in nature. In this business, the demands for the items at the destinations are directly related to their substitutability and complementary natures and prices. The suggested rough model is converted into a deterministic one using lower and upper approximation intervals following Hamzehee et al. as well as Expected Value Techniques. The converted model is optimized through the Generalized Reduced Gradient (GRG) techniques using LINGO 14 software. Finally, numerical examples are presented to illustrate the preciseness of the proposed model. As particular cases, different models such as 2D, 3D FCMITPs for two substitute items, one item with its complement and two non substitute non complementary items are derived and results are presented. Full article
Figures

Figure 1

Open AccessArticle
A Decision Support System for Dynamic Job-Shop Scheduling Using Real-Time Data with Simulation
Mathematics 2019, 7(3), 278; https://doi.org/10.3390/math7030278
Received: 26 February 2019 / Revised: 12 March 2019 / Accepted: 14 March 2019 / Published: 19 March 2019
PDF Full-text (5523 KB) | HTML Full-text | XML Full-text
Abstract
The wide usage of information technologies in production has led to the Fourth Industrial Revolution, which has enabled real data collection from production tools that are capable of communicating with each other through the Internet of Things (IoT). Real time data improves production [...] Read more.
The wide usage of information technologies in production has led to the Fourth Industrial Revolution, which has enabled real data collection from production tools that are capable of communicating with each other through the Internet of Things (IoT). Real time data improves production control especially in dynamic production environments. This study proposes a decision support system (DSS) designed to increase the performance of dispatching rules in dynamic scheduling using real time data, hence an increase in the overall performance of the job-shop. The DSS can work with all dispatching rules. To analyze its effects, it is run with popular dispatching rules selected from the literature on a simulation model created in Arena®. When the number of jobs waiting in the queue of any workstation in the job-shop falls to a critical value, the DSS can change the order of schedules in its preceding workstations to feed the workstation as soon as possible. For this purpose, it first determines the jobs in the preceding workstations to be sent to the current workstation, then finds the job with the highest priority number according to the active dispatching rule, and lastly puts this job in the first position in its queue. The DSS is tested under low, normal, and high demand rate scenarios with respect to six performance criteria. It is observed that the DSS improves the system performance by increasing workstation utilization and decreasing both the number of tardy jobs and the amount of waiting time regardless of the employed dispatching rule. Full article
Figures

Figure 1

Open AccessArticle
Low Carbon Supply Chain Coordination for Imperfect Quality Deteriorating Items
Mathematics 2019, 7(3), 234; https://doi.org/10.3390/math7030234
Received: 19 January 2019 / Revised: 18 February 2019 / Accepted: 20 February 2019 / Published: 5 March 2019
PDF Full-text (2727 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, many countries have implemented carbon pricing policies. Hence, the industry adapts to this policy while striving for its main goal of maximizing financial benefits. Here, we study a single manufacturer–retailer inventory decision considering carbon emission cost and item deterioration for an imperfect [...] Read more.
Nowadays, many countries have implemented carbon pricing policies. Hence, the industry adapts to this policy while striving for its main goal of maximizing financial benefits. Here, we study a single manufacturer–retailer inventory decision considering carbon emission cost and item deterioration for an imperfect production system. This study examines two models considering two cases of quality inspection. The first is when the buyer performs the quality inspection, and the second is when the quality inspection becomes the vendor’s responsibility so that no defective products are passed to the buyer. Carbon emission costs are incorporated under a carbon tax policy, and we consider the carbon footprint from transporting and warehousing the items. The objective is to jointly optimize the delivery quantity and number of deliveries per production cycle that minimize the total cost and reduce the total carbon emissions. This study provides solution procedures to solve the models, as well as two numerical examples. Full article
Figures

Figure 1

Open AccessArticle
Effects of Preservation Technology Investment on Waste Generation in a Two-Echelon Supply Chain Model
Mathematics 2019, 7(2), 189; https://doi.org/10.3390/math7020189
Received: 27 December 2018 / Revised: 11 February 2019 / Accepted: 13 February 2019 / Published: 17 February 2019
PDF Full-text (5823 KB) | HTML Full-text | XML Full-text
Abstract
This study develops an integrated production-inventory model for a two-echelon supply chain network with controllable probabilistic deterioration. The investment in preservation technology is considered a decision variable to control the deteriorated quantity of an integrated system. The objective of the study is to [...] Read more.
This study develops an integrated production-inventory model for a two-echelon supply chain network with controllable probabilistic deterioration. The investment in preservation technology is considered a decision variable to control the deteriorated quantity of an integrated system. The objective of the study is to optimize preservation investment, the number of shipments and shipment quantity, so that the total cost per unit of time of the supply chain is minimized. The study proposes a solution method, and the results show that investment in preservation technology reduces the total supply chain cost by 13%. Additionally, preservation increases the lot size, thus increasing the production cycle length, which reduces the ordering cost of the system. Furthermore, this study shows that preservation leads to a reduction of solid waste from deteriorated products. Total deteriorated products reduced to 8 units from 235 units, hence, preservation generates positive environmental benefits along with economic impacts. The robustness of the proposed model is illustrated with a numerical example, sensitivity analysis, and graphical representations. Moreover, comparative study and managerial insights are given to extract significant insights from the model. Full article
Figures

Figure 1

Open AccessArticle
Interactive Fuzzy Multi Criteria Decision Making Approach for Supplier Selection and Order Allocation in a Resilient Supply Chain
Mathematics 2019, 7(2), 137; https://doi.org/10.3390/math7020137
Received: 24 December 2018 / Revised: 21 January 2019 / Accepted: 25 January 2019 / Published: 1 February 2019
Cited by 1 | PDF Full-text (1394 KB) | HTML Full-text | XML Full-text
Abstract
Modern supply chains are vulnerable to high impact, low probability disruption risks. A supply chain usually operates in such a network of entities where the resilience of one supplier is critical to overall supply chain resilience. Therefore, resilient planning is a key strategic [...] Read more.
Modern supply chains are vulnerable to high impact, low probability disruption risks. A supply chain usually operates in such a network of entities where the resilience of one supplier is critical to overall supply chain resilience. Therefore, resilient planning is a key strategic requirement in supplier selection decisions for a competitive supply chain. The aim of this research is to develop quantitative resilient criteria for supplier selection and order allocation in a fuzzy environment. To serve the purpose, a possibilistic fuzzy multi-objective approach was proposed and an interactive fuzzy optimization solution methodology was developed. Using the proposed approach, organizations can tradeoff between cost and resilience in supply networks. The approach is illustrated using a supply chain case from a garments manufacturing company. Full article
Figures

Graphical abstract

Open AccessArticle
Application of Optimization to Select Contractors to Develop Strategies and Policies for the Development of Transport Infrastructure
Mathematics 2019, 7(1), 98; https://doi.org/10.3390/math7010098
Received: 9 December 2018 / Revised: 4 January 2019 / Accepted: 12 January 2019 / Published: 18 January 2019
Cited by 3 | PDF Full-text (1223 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Many factors influence the efficiency and quality of transport works. In particular, consultants and construction contractors of these works play important roles, and critical factors directly affect the quality of traffic works. If the quality of consultancy and construction is good, the project [...] Read more.
Many factors influence the efficiency and quality of transport works. In particular, consultants and construction contractors of these works play important roles, and critical factors directly affect the quality of traffic works. If the quality of consultancy and construction is good, the project will reduce the total investment; if the contractor is good, the completion time of the new project is guaranteed, thus reducing construction costs. The longer the construction time is, the higher the cost of the project. In this study, the authors used optimal algorithms to evaluate past, present, and future contractors’ technical, technological, and performance effectiveness. Research results show that bidders are divided into three groups: highly effective bidders, stable contractors, and inefficient groups. Research results for this subject will help the government, regulatory agencies, and investors select good contractors as the basis for developing strategies and policies for the development of transport infrastructure. Full article
Figures

Figure 1

Open AccessArticle
Effect of Time-Varying Factors on Optimal Combination of Quality Inspectors for Offline Inspection Station
Mathematics 2019, 7(1), 51; https://doi.org/10.3390/math7010051
Received: 29 November 2018 / Revised: 24 December 2018 / Accepted: 29 December 2018 / Published: 7 January 2019
Cited by 1 | PDF Full-text (1090 KB) | HTML Full-text | XML Full-text
Abstract
With advanced manufacturing technology, organizations like to cut their operational cost and improve product quality, yet the importance of human labor is still alive in some manufacturing industries. The performance of human-based systems depends much on the skill of labor that varies person [...] Read more.
With advanced manufacturing technology, organizations like to cut their operational cost and improve product quality, yet the importance of human labor is still alive in some manufacturing industries. The performance of human-based systems depends much on the skill of labor that varies person to person within available manpower. Much work has been done on human resource and management, however, allocation of manpower based on their skill yet not investigated. For this purpose, this study considered offline inspection system where inspection is performed by the human labor of varying skill levels. A multi-objective optimization model is proposed based on Time-Varying factors; inspection skill, operation time and learning behavior. Min-max goal programming technique was used to determine the efficient combination of inspectors of each skill level at different time intervals of a running order. The optimized results ensured the achievement of all objectives of inspection station: the cost associated with inspectors, outgoing quality and inspected quantity. The obtained results proved that inspection performance of inspectors improves significantly with learning and revision of allocation of inspectors with the proposed model ensure better utilization of available manpower, maintain good quality and reduce cost as well. Full article
Figures

Figure 1

Open AccessArticle
Sustainable Lot Size in a Multistage Lean-Green Manufacturing Process under Uncertainty
Mathematics 2019, 7(1), 20; https://doi.org/10.3390/math7010020
Received: 10 November 2018 / Revised: 18 December 2018 / Accepted: 21 December 2018 / Published: 25 December 2018
Cited by 1 | PDF Full-text (2487 KB) | HTML Full-text | XML Full-text
Abstract
Optimal lot sizing is the primary tool applied by lean practitioners to reduce inconsistency in the manufacturing system to cut down inventories, which are often considered as a type of waste in the lean culture. Managers attempt to consider environmental impacts of the [...] Read more.
Optimal lot sizing is the primary tool applied by lean practitioners to reduce inconsistency in the manufacturing system to cut down inventories, which are often considered as a type of waste in the lean culture. Managers attempt to consider environmental impacts of the manufacturing system and find ways to reduce these effects while making efforts to achieve environmental protection. From a sustainability standpoint, carbon emissions are the major source of environmental contamination and degradation. In this context, this research provides an economic production quantity model with uncertain demand and process information in a multistage manufacturing process. This imperfect manufacturing process produces defective products at an uncertain rate, and is reworked to convert them into perfect quality products and reduce wastages. To control this uncertainty in the manufacturing process, the decomposition principle and the signed distance method of fuzzy theory are applied. The manufacturing process is analyzed with regard to environmental concerns, and a sustainable lot size is obtained through an interactive Weighted Fuzzy Goal Programming (WFGP) approach for the simultaneous achievement of economic and environmental sustainability. An experimental study is performed to verify the practical implication of the model, and results are evaluated through a sensitivity analysis. Important managerial insights and graphical illustrations are provided to elaborate the model. Full article
Figures

Figure 1

Open AccessArticle
Imperfect Multi-Stage Lean Manufacturing System with Rework under Fuzzy Demand
Mathematics 2019, 7(1), 13; https://doi.org/10.3390/math7010013
Received: 19 November 2018 / Revised: 19 December 2018 / Accepted: 19 December 2018 / Published: 24 December 2018
Cited by 2 | PDF Full-text (3150 KB) | HTML Full-text | XML Full-text
Abstract
Market conditions fluctuate abruptly in today’s competitive environment and leads to imprecise demand information. In particular, market demand data for freshly launched products is highly uncertain. Further, most of the products are generally manufactured through complex multi-stage production systems that may produce defective [...] Read more.
Market conditions fluctuate abruptly in today’s competitive environment and leads to imprecise demand information. In particular, market demand data for freshly launched products is highly uncertain. Further, most of the products are generally manufactured through complex multi-stage production systems that may produce defective items once they enter the out-of-control state. Production management of a multi-stage production system in these circumstances requires robust production model to reduce system costs. In this context, this paper introduces an imperfect multi-stage production model with the consideration of defective proportion in the production process and uncertain product demand. Fuzzy theory is applied to handle the uncertainty in demand information and the center of gravity approach is utilized to defuzzify the objective function. This defuzzified cost objective is solved through the analytical optimization technique and closed form solution of optimal lot size and minimum cost function are obtained. Model analysis verifies that it has successfully achieved global optimal results. Numerical experiment comprising of three examples is conducted and optimal results are analyzed through sensitivity analysis. Results demonstrate that larger lot sizes are profitable as the system moves towards a higher number of stages. Sensitivity analysis indicates that the processing cost is the most influencing factor on the system cost function. Full article
Figures

Figure 1

Open AccessArticle
Product Channeling in an O2O Supply Chain Management as Power Transmission in Electric Power Distribution Systems
Mathematics 2019, 7(1), 4; https://doi.org/10.3390/math7010004
Received: 14 November 2018 / Revised: 14 December 2018 / Accepted: 18 December 2018 / Published: 20 December 2018
Cited by 1 | PDF Full-text (1571 KB) | HTML Full-text | XML Full-text
Abstract
With the aim of delivering goods and services to customers, optimal delivery channel selection is a significant part of supply chain management. Several heuristics have been developed to solve the variants of distribution center allocation and vehicle routing problems. In reality, small-scale suppliers [...] Read more.
With the aim of delivering goods and services to customers, optimal delivery channel selection is a significant part of supply chain management. Several heuristics have been developed to solve the variants of distribution center allocation and vehicle routing problems. In reality, small-scale suppliers cannot afford research and development departments to optimize their distribution networks. In this context, this research work develops a model for an online to offline (O2O) supply chain management network of a small-scale household electric components manufacturer for delivering goods to its distribution centers and retailers. Retailers are acquired by the company through investment in the O2O channel of e-commerce. Electric power transmission and distribution is considered as representative of the product distribution network. A model is developed using a combination of the supply chain management technique and power transmission terminologies. The constrained linear programming model is solved through the linear programming tool of the LINGO optimization software and the global optimum results for the proposed quantity allocation problem are achieved. A numerical experiment is provided to illustrate the practical applicability of the model and the optimal results are analyzed for model robustness. Full article
Figures

Figure 1

Open AccessArticle
A Model and an Algorithm for a Large-Scale Sustainable Supplier Selection and Order Allocation Problem
Mathematics 2018, 6(12), 325; https://doi.org/10.3390/math6120325
Received: 14 November 2018 / Revised: 11 December 2018 / Accepted: 11 December 2018 / Published: 13 December 2018
Cited by 3 | PDF Full-text (895 KB) | HTML Full-text | XML Full-text
Abstract
We consider a buyer’s decision problem of sustainable supplier selection and order allocation (SSS & OA) among multiple heterogeneous suppliers who sell multiple types of items. The buyer periodically orders items from chosen suppliers to refill inventory to preset levels. Each supplier is [...] Read more.
We consider a buyer’s decision problem of sustainable supplier selection and order allocation (SSS & OA) among multiple heterogeneous suppliers who sell multiple types of items. The buyer periodically orders items from chosen suppliers to refill inventory to preset levels. Each supplier is differentiated from others by the types of items supplied, selling price, and order-related costs, such as transportation cost. Each supplier also has a preset requirement for minimum order quantity or minimum purchase amount. In the beginning of each period, the buyer constructs an SSS & OA plan considering various information from both parties. The buyer’s planning problem is formulated as a mathematical model, and an efficient algorithm to solve larger instances of the problem is developed. The algorithm is designed to take advantage of the branch-and-bound method, and the special structure of the model. We perform computer experiments to test the accuracy of the proposed algorithm. The test result confirmed that the algorithm can find a near-optimal solution with only 0.82 percent deviation on average. We also observed that the use of the algorithm can increase solvable problem size by about 2.4 times. Full article
Figures

Figure 1

Open AccessArticle
Sustainable Supplier Selection Process in Edible Oil Production by a Hybrid Fuzzy Analytical Hierarchy Process and Green Data Envelopment Analysis for the SMEs Food Processing Industry
Mathematics 2018, 6(12), 302; https://doi.org/10.3390/math6120302
Received: 12 November 2018 / Revised: 3 December 2018 / Accepted: 3 December 2018 / Published: 4 December 2018
Cited by 4 | PDF Full-text (1145 KB) | HTML Full-text | XML Full-text
Abstract
Today, business organizations are facing increasing pressure from a variety of sources to operate using sustainable processes. Thus, most companies need to focus on their supply chains to enhance sustainability to meet customer demands and comply with environmental legislation. To achieve these goals, [...] Read more.
Today, business organizations are facing increasing pressure from a variety of sources to operate using sustainable processes. Thus, most companies need to focus on their supply chains to enhance sustainability to meet customer demands and comply with environmental legislation. To achieve these goals, companies must focus on criteria that include CO2 (carbon footprint) and toxic emissions, energy use and efficiency, wastage generations, and worker health and safety. As in other industries, the food processing industry requires large inputs of resources, which results in several negative environmental effects; thus, decision-makers have to evaluate qualitative and quantitative factors. This work identifies the best supplier for edible oil production in the small and medium enterprise (SME) food processing industry in Vietnam. This study also processes a hybrid multicriteria decision-making (MCDM) model using a fuzzy analytical hierarchy process (FAHP) and green data envelopment analysis (GDEA) model to identify the weight of all criteria of a supplier’s selection process based on opinions from company procurement experts. Subsequently, GDEA is applied to rank all potential supplier lists. The primary objective of this work is to present a novel approach which integrates FAHP and DEA for supplier selection and also consider the green issue in edible oil production in uncertain environments. The aim of this research is also to provide a useful guideline for supplier selection based on qualitative and quantitative factors to improve the efficiency of supplier selection in the food industry and other industries. The results reveal that Decision-Making Unit 1 (DMU 1), DMU 3, DMU 7, and DMU 9 are identified as extremely efficient for five DEA models, which are the optimal suppliers for edible oil production. The contributions of this research include a proposed MCDM model using a hybrid FAHP and GDEA model for supplier selection in the SME food processing industry under a fuzzy environment conditions in Vietnam. This research also is part of an evolution of a new hybrid model that is flexible and practical for decision-makers. In addition, the research also provides a useful guideline in supplier selection in the food processing industry and a guideline for supplier selection in other industries. Full article
Figures

Figure 1

Open AccessArticle
Supply Chain with Customer-Based Two-Level Credit Policies under an Imperfect Quality Environment
Mathematics 2018, 6(12), 299; https://doi.org/10.3390/math6120299
Received: 8 November 2018 / Revised: 23 November 2018 / Accepted: 25 November 2018 / Published: 3 December 2018
Cited by 1 | PDF Full-text (4691 KB) | HTML Full-text | XML Full-text
Abstract
The present model develops a three-echelon supply chain, in which the manufacturer offers full permissible delay to the whole seller, while the latter, in turn, adopts distinct trade credit policies for his subsequent downstream retailers. The type of credit policy being offered to [...] Read more.
The present model develops a three-echelon supply chain, in which the manufacturer offers full permissible delay to the whole seller, while the latter, in turn, adopts distinct trade credit policies for his subsequent downstream retailers. The type of credit policy being offered to the retailers is decided on the basis of their past profiles. Hence, the whole seller puts forth full and partial permissible delays to his old and new retailers respectively. This study considers bad debts from the portion of new retailers who fail to make up for the delayed part of the partial payment. The analysis shows that it is beneficial for the whole seller to make shorter contracts, particularly with new retailers, along with the fetching of a higher fraction of initial purchase cost from them. In addition to the above-described scenario, the lot received by the whole seller from the manufacturer is not perfect, and it contains some defects for which he employs an inspection process before selling the items to the retailers. In order to make the study more realistic, Type-I, as well as Type-II misclassification errors, and the case of out-of-stock are considered. The impact of Type-I error has been found to be crucial in the study. The present paper determines the optimal policy for the whole seller by maximizing the expected total profit per unit time. For the optimality of the solution, theoretical results are provided. Finally, a numerical example and a sensitivity analysis are done to validate the model. Full article
Figures

Figure 1

Mathematics EISSN 2227-7390 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top