Multi-product Supply Chain Research Articles

A multi-level multi-product supply chain network design of vegetables products considering costs of quality: A case study.

Effective logistics management is crucial for the distribution of perishable agricultural products to ensure they reach customers in high-quality condition. This research examines an integrated, multi-echelon supply chain for perishable agricultural goods. The supply chain consists of four stages: supply, processing, storage, and customers. This study investigates the quality-related costs associated with product perishability to maximize supply chain profitability. Key factors considered include the network design, location of processing and distribution centers, the ability to process raw products to minimize post-harvest quality degradation, the option to sell the excess produce to a secondary market due to unpredictable yields, and the decision not to fulfill demand from distant customers where significant quality loss and price drops would be involved, instead diverting those products to the aforementioned secondary market. Quantitative methods and linear mathematical programming are employed to model and validate the proposed supply chain using actual data from a real-world case study on vegetable supply chains. The main contribution of this research is the incorporation of quality costs into the objective function, which allows the supply chain to prioritize meeting nearby customers' demands with minimal quality loss over serving distant customers where high quality loss is unavoidable. Additionally, deploying a faster transportation fleet can significantly improve the overall profitability of the perishable product supply chain.

Production Planning Optimization in a Two-Echelon Multi-Product Supply Chain with Discrete Delivery and Storage at Manufacturer’s Warehouse

In today’s competitive world, customers expect their demands to be met at the shortest possible time, while manufacturers aspire to deliver the orders within a convenient time and at a minimum cost. Thus, manufacturers are compelled to seek ways of lowering the costs of their services in order to satisfy customers and survive the competition in their respective industries. This research paper investigates a multi-product problem in a two-echelon supply chain consisting of a single manufacturer and several retailers. The main objective of this research is to develop and present a multi-product optimization model in which retailers receive their orders through discrete delivery and surplus manufactured goods are stored in the manufacturer’s warehouse. The objective function of the mathematical model in the economic dimension includes the minimization of the total supply chain costs and the maximization of profit. The retailers in this model place new orders when their inventory level drops to zero, and the manufacturer responds to the retailers’ orders at the same time as it begins processing each product. After delivering the last set of orders, the manufacturer stores surplus items in its warehouse in case the retailers place new orders. This optimization problem is modeled using mixed integer nonlinear programming, while numerical scenarios are coded using the MATLAB software which helps estimate the total cost within a short time. Finally, a sensitivity analysis is performed to determine the effects of a number of factors on the total cost, including problem parameters, demand and production rates, the production quantity, and the number of times the manufacturing machines are operated at each production cycle.

Enhancing supply chain resilience facing partial and complete disruptions: The application in the cooking oil industry

Nowadays, food supply chains are more vulnerable to disruptions due to competition, uncertain environments, and global catastrophes such as the recent pandemic. Due to the importance of addressing disruption risks, this study develops a hybrid mechanism to enhance resilience in a multi-product supply chain. The proposed mechanism improves supplier resilience in a Cooking Oil Supply Chain (COSC) network design. First, we identify elements that affect supplier resilience and present a mechanism based on the Analytical Network Process (ANP) to evaluate resilience. In addition, several strategies are determined to enhance network resilience. Moreover, a stochastic mathematical formulation is proposed to utilize suppliers’ scores and a combination of identified strategies to strengthen supply chain resilience. The developed stochastic model encompasses three objectives and considers possible partial and complete disruptions of the supply chain network. Furthermore, we develop a robust counterpart formulation to handle the disruption uncertainty of the COSC and utilize the Normalized Normal Constraint (NNC) method to generate Pareto optimal solutions. Lastly, we put the implemented mechanism into practice in a real-world case to demonstrate its effectiveness in enhancing supply chain resilience. Likewise, we unveil that the implementation of suitable resilience strategies within the supply chain can lead to substantial improvements in performance metrics. For example, the results obtained illustrate that by embracing resilience strategies, the proposed mechanism has the potential to reduce the overall cost of the supply chain by as much as 56%.

Optimal resilience strategy for manufacturers to deal with supply disruptions: Investment in supply stability versus dual sourcing

Enhancing supply chain resilience is fundamental to the stable development of manufacturing enterprises. By comparing three resilience strategies (investment in fixed supplier for stable supply, dual sourcing by adding backup suppliers, and hybrid strategy), we investigate their effects on improving supply chain resilience under supply disruption and selecting resilience strategies for manufacturing enterprises in a complex supply chain environment. We then propose a corresponding management approach for manufacturers. This study reveals the following: (1) Compared with the non-resilience strategy, the investment strategy for supply stability and hybrid strategy may adversely impact the manufacturer’s profits while concurrently enhancing expected sales when the manufacturer’s investment cost is high, whereas the dual sourcing strategy is an effective strategy to increase the manufacturer’s expected profit. (2) Compared with investment and dual sourcing strategies, the hybrid strategy has a lower wholesale price but not always a lower sale price. (3) Manufacturers who prefer supply stability always adopt a hybrid strategy to ensure maximum expected sales volume; manufacturers who prefer profit adopt a hybrid strategy only when the investment cost is within a certain range; otherwise, they adopt a dual sourcing strategy to obtain higher profits. (4) Under an asymmetric sourcing strategy, when suppliers’ costs of production differ significantly, manufacturers adopting dual sourcing and hybrid strategies do not always increase sales, and adopting an investment strategy for supply stability is a more sales-resilient strategy. (5) The results of manufacturer resilience strategy selection under a multi-product supply chain are similar to those for a single product.

Decomposition heuristics for multiobjective problems. The Food bank network redesign case

Large MO-MILP problems are often very hard to solve by exact methods. In this study we consider the Food bank network redesign (FBNR) problem to introduce three decompose-and-fix heuristics that successfully solve large real-life based instances of the problem. The FBNR problem is modeled as a multi-period, multi-product supply chain redesign problem that accounts for economic, environmental and social objectives in three distinct functions. Each new heuristic decomposes the FBNR problem into two MO-MILP problems, which are sequentially solved following the lexicographic concept. Decomposition observe the nature and terms of the decisions involved. The first MO-MILP problem concerns only the longer term decisions. After the corresponding decisions are fixed, the second MO-MILP problem referring to shorter term decisions is solved respecting the ranking of objectives followed for each solution obtained in the first problem. The difference among the heuristics lies in what is considered as longer or shorter term decisions. Besides solving large instances of the problem, which the exact method used could not do, CPU time savings generated by the heuristics range from 80% to 97% of the time required by the exact method. Compromise between the computational effort and the quality of solutions obtained by each heuristic is discussed. The solving methodology proposed in this study can be adapted to other large multiobjective problems.

Supply Chain Optimization under Risk and Uncertainty using Nondominated Sorting Genetic Algorithm II for Automobile Industry

In recent decades, the use of supply chain management is essential for developing new technologies and setting the ground in expanding major global markets to integrate suppliers, manufacturers, warehouses, and stores effectively. In addition, the growing competition in the modern business environment has created an increasing trend of new products and improved product quality for attracting more consumers. However, an increase in the related costs and uncertainty in these innovations requires the development of algorithms to solve optimization problems. Therefore, this study is aimed to implement a multi-product supply chain including raw material suppliers, factories, distributors, and customers to maximize the quality and minimize costs. To this aim, supplier quality, distribution centers, and manufacturing products were considered for the quality model, while warehousing costs, product production, transportation, defective raw materials, and the like were regarded for the cost model. Then, the NSGAII algorithm was used for solving the created optimization problem, and accordingly, the optimal Pareto points were calculated. Based on the results, the proposed model can give the manufacturer the ability to decide on a multi-product and multi-time supply chain by involving cost and quality variables. Thus, the owner can manage the supply chain of the factory effectively.

A robust fuzzy-stochastic optimization model for managing open innovation uncertainty in the ambidextrous supply chain planning problem

This paper studies the effect of the open innovation concept in the product design process and supply chain master planning. Complex uncertainties caused by using outbound resources within co-design processes, financial challenges between the collaborating parties, and integrating outbound innovative designs with supply chain tactical planning problem are taken into account. To this end, a robust fuzzy-stochastic optimization model is proposed which can integrate the product design with the main activities of a multi-product supply chain. The proposed model is able to cope with different type of uncertainties including random, epistemic and deep uncertainties. Integrating the financial and physical flows, using a novel pricing mechanism, and considering the outside-in innovations in the product design process are the outstanding contributions of the proposed model. Furthermore, to cover both the short-term and long-term success criteria, ambidexterity of the studied supply chain is taken into account via two conflicting explorative and exploitive objectives. Results indicate the superiority of the presented model and its ability in supporting managerial decisions in the mid-term planning process.

Multi product supply chain scheduling method based on hybrid genetic algorithm

Multi product supply chain scheduling method based on hybrid genetic algorithm

Computationally efficient approximate dynamic programming for multi-site production capacity planning with uncertain demands

With globalization and rapid technological-economic development accelerating the market dynamics, consumers' demand is becoming more volatile and diverse. In this situation, capacity adjustment as an operational strategic decision plays a major role to ensure supply chain responsiveness while maintaining costs at a reasonable norm. This study contributes to the literature by developing computationally efficient approximate dynamic programming approaches for production capacity planning considering uncertainties and demand interdependence in a multi-factory multi-product supply chain setting. For this purpose, the k-Nearest-Neighbor-based Approximate Dynamic Programming and the Rolling-Horizon-based Approximate Dynamic Programming are developed to enable real-time decision support while ensuring the robustness of the outcomes in stochastic decision environments. Given the market volatilities in the Thin Film Transistor-Liquid Crystal Display industry, a real case from this sector is investigated to evaluate the applicability of the developed approach and provide insights for other industry situations. The developed method is less complex to implement, and numerical experiments showed that it is also computationally more efficient compared to Stochastic Dynamic Programming.

Integrated design of sustainable supply chain and transportation network using a fuzzy bi-level decision support system for perishable products

This study introduces a fuzzy bi-level Decision Support System (DSS) to optimize a sustainable multi-level multi-product Supply Chain (SC) and co-modal transportation network for perishable products distribution. To this end, two integrated multi-objective Mixed Integer Linear Programming (MILP) models are proposed to formulate the problem. On-time delivery is taken into account as the main factor that determines model performance due to perishability of products. Optimizing the design of SC network using the first level of the proposed DSS, the transportation network configuration is provided optimally in the second level considering different modes and options. In order to contribute to the literature, mainly by addressing uncertainty and perishability, a hybrid solution technique based on possibilistic linear programming and Fuzzy Weighted Goal Programming (FWGP) approach is developed to accommodate our suggested bi-level model. This technique can deal with problem uncertainty while also ensuring the sustainability of the overall system. Lp-metric method is implemented along with three well-known quality indicators to assess the performance of the proposed solution method and quality of obtained solutions. Finally, three illustrative numerical examples are provided using the CPLEX solver to showcase the applicability of the proposed methodology and discuss the complexity of the model. Results demonstrate the efficiency of the proposed methodology in finding optimal solutions compared to Lp-metric method, such that it is able to treat a problem with more than 2.2 million variables and 1.3 million constraints in 1093.08 s.

Spatio-temporal economic properties of multi-product supply chains

In this work we present a generalization of market coordination to multi-product supply chains and use this framework to analyze their spatio-temporal economic properties. We interpret a supply chain as a market consisting of independent stakeholders (suppliers, consumers, transportation, and technology providers) who bid into a coordination system that is managed and cleared by an independent entity to obtain product allocations and prices. The proposed model provides a general graph representation of spatio-temporal product transport that helps capture geographical transport, time delays, and storage (temporal transport) in a unified and compact manner. This representation allows us to establish fundamental economic properties for the supply chain (revenue adequacy, cost recovery, and competitiveness) and to establish bounds for space-time prices. To illustrate these concepts, we consider a case study in which organic waste is used for producing biogas and electricity. Our market model shows that incentives for waste storage emerge from electricity demand dynamics and illustrates how space-time price dynamics for waste and derived products emerge from geographical transport and storage.

Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: Pareto-based algorithms

This study develops a novel mathematical model to design a sustainable mask Closed-Loop Supply Chain Network (CLSCN) during the COVID-19 outbreak for the first time. A multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to address the locational, supply, production, distribution, collection, quarantine, recycling, reuse, and disposal decisions within a multi-period multi-echelon multi-product supply chain. Additionally, sustainable development is studied in terms of minimizing the total cost, total pollution and total human risk at the same time. Since the CLSCN design is an NP-hard problem, Multi-Objective Grey Wolf Optimization (MOGWO) algorithm and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are implemented to solve the proposed model and to find Pareto optimal solutions. Since Meta-heuristic algorithms are sensitive to their input parameters, the Taguchi design method is applied to tune and control the parameters. Then, a comparison is performed using four assessment metrics including Max-Spread, Spread of Non-Dominance Solution (SNS), Number of Pareto Solutions (NPS), and Mean Ideal Distance (MID). Additionally, a statistical test is employed to evaluate the quality of the obtained Pareto frontier by the presented algorithms. The obtained results reveal that the MOGWO algorithm is more reliable to tackle the problem such that it is about 25% superior to NSGA-II in terms of the dispersion of Pareto solutions and about 2% superior in terms of the solution quality. To validate the proposed mathematical model and testing its applicability, a real case study in Tehran/Iran is investigated as well as a set of sensitivity analyses on important parameters. Finally, the practical implications are discussed and useful managerial insights are given.

Optimization of resource flows across the whole supply chain. Application to a case study in the dairy industry

This paper deals with the problem of optimally planning the flows of raw materials and products across a linear multiproduct supply chain. Given a known series of products demands along a planning period, the problem consists of optimizing procurement, inventory, production, and distribution decisions for each time-period of the planning horizon. A decomposition procedure is developed to solve the optimization problem in reasonable computational times. The proposal first generates both a set of pickup routes for raw material collection and a set of delivery routes for product distribution through the Column Generation method. Then, these routes are feed into a Mixed-Integer Linear Program modelling the integrated problem. The scope of the optimization approach covers the entire supply chain from sources of raw materials to the end-customers, providing a useful computational tool for assessment of linear supply chains. The aim is to maximize the profit of the company that fabricates and distributes the products. The usefulness and effectiveness of the proposed solution strategy is demonstrated by solving an extensive set of realistic instances for a dairy industry.

Matheuristics for the Design of a Multi-Step, Multi-Product Supply Chain with Multimodal Transport

Supply-chain network design is a complex task because there are many decisions involved, and presently, global networks involve many actors and variables, for example, in the automotive, pharmaceutical, and electronics industries. This research addresses a supply-chain network design problem with four levels: suppliers, factories, warehouses, and customers. The problem considered decides on the number, locations, and capacities of factories and warehouses and the transportation between levels in the supply chain. The problem is modeled as a mixed-integer linear program. The main contribution of this work is the proposal of two matheuristic algorithms to solve the problem. Matheuristics are algorithms that combine exact methods and heuristics, attracting interest in the literature because of their fast execution and high-quality solutions. The matheuristics proposed to select the warehouses and their capacities following heuristic rules. Once the warehouses and their capacities are fixed, the algorithms solve reduced models using commercial optimization software. Medium and large instances were generated based on a procedure described in the literature. A comparison is made between the algorithms and the results obtained, solving the model with a time limit. The algorithms proposed are successful in obtaining better results for the largest instances in shorter execution times.

Robust simulation optimization for supply chain problem under uncertainty via neural network metamodeling

Real-world supply chain management problems are highly complicated such that their optimization procedure is computationally expensive due to the extensive dimensions and uncertainty of their critical variables. Simulation optimization is a commonly applied technique to determine the optimal variables since the problem is too complex. Due to the uncertain nature of the real-world systems, it is also worthy to consider the robustness of the optimal solutions. To address this issue, this study investigates the problem of determining near-optimal safety stock levels in a multi-product supply chain with regard to deviations of its overall cost. A new framework is proposed to define the decision as well as environmental variables. This novel framework results in a significant reduction of the solution space while maintains the essential supply chain control parameters. The prediction performance of artificial neural networks (ANNs) with different structural settings are compared, and the best-fitted ANNs are selected to obtain the robust solutions. Consequently, we employ a robust metamodel-based simulation optimization approach based on Taguchi’s view and optimize the multi-objective supply chain problem with respect to supply chain operational costs and customer satisfaction criteria.

Sustainable EOQ and EPQ models for a two-echelon multi-product supply chain with return policy

Sustainable EOQ and EPQ models for a two-echelon multi-product supply chain with return policy

Optimizing the Combined Problem of Facility Location and Multi-Objective Supplier Selection Using a Comprehensive Benchmarking Method

초록·키워드 목차 오류제보하기 The selecting suppliers is one of the most important decisions in any supply chain, which, if properly selected, will reduce the costs of supply chain production. On the other hand, in a company"s logistics decisions, facility location has a great impact on supply chain design. Therefore, in this study, the facility location problem, supplier selection and order allocation in a multi-cycle and multi-product supply chain is studied in which economic goals and network reliability are considered as a two-objective optimization / decision-making problem. To evaluate the mathematical model, the model has been implemented in GAMS software by LP metric method with different P values. Because of solving the mathematical model, the selected location of the facility, the best suppliers to purchase products and the optimal allocation to each supplier are determined. The results show that the LP metric method has a good performance in finding optimal solutions to the problem and can be efficient in searching for optimal solutions. #Supplier Selection #Facility Location #Multi-Objective Mathematical Model #LP Metric Method ABSTRACT1. INTRODUCTION2. PROBLEM STATEMENT AND MATHEMATICAL MODEL3. SOLUTION METHOD4. RESULTS5. CONCLUSIONREFERENCES

A Multi-Objective Model for Inventory Management of the Integrated Multi-Product Supply Chain Considering Central Warehouse and Potential Demand

In this research, an inventory management model is developed by the seller for a three-echelon supply chain, which also includes a central warehouse, along with order constraints. In this method, the seller monitors the level of inventory of the retailer so that it determines the time and amount of products for the seller and the retailer and reduces the inventory costs for the seller and the retailer. Sales inventory management in the supply chain has an integrated approach to planning and controlling materials and information in the production and distribution chain, transportation, warehouses and customers. One of the objectives of the proposed mathematical model is to determine the inventory of retailers, vendors, central warehouse and the optimal refund rate for the seller and central warehouse, which ultimately leads to a reduction in the cost of the entire system. In this study, after presenting a three-objective mathematical model to study the three-level supply chain by considering several retailers and multiple vendors and the central warehouse, the model is solved using GAMS software by the Epsilon constraint method and to test. The validation of the proposed model is examined by providing an example of the proposed model.

Modeling and Solving the Cross-Docking Centers Location and Vehicle Scheduling Problem in a Multi-Product Supply Chain with Discrete Pick-up and Delivery

در این پژوهش مسائل مکان‌یابی انبارهای متقاطع، مسیریابی و زمان‌بندی وسایل نقلیه را به‌طور هم‌زمان در یک زنجیره تأمین سه‌­سطحی با امکان برداشت و تحویل گسسته، با هدف کمینه‌سازی مجموع هزینه‌ها (هزینه احداث انبارهای متقاطع، هزینه‌های ثابت و متغیر حمل‌ونقل و جریمه تأخیر و تعجیل)، موردمطالعه قرار گرفته و یک مدل برنامه‌ریزی ترکیبی عدد صحیح غیرخطی برای آن ارائه شده است. در این مدل تصمیم‌گیری در خصوص تخصیص وسایل نقلیه ناهمگن به فرآیند برداشت و تحویل و انتخاب مکان و تعداد انبارهای متقاطع برای احداث از میان مکان‌های بالقوه موجود پس از حل مدل صورت می‌گیرد. فرض چندمحصولی‌­بودن شامل تک‌تک تأمین‌کنندگان، انبارهای متقاطع و مشتریان می‌شود. برای تحویل هر نوع از کالاها در محل هر یک از مشتریان یک پنجره زمانی نرم در نظر گرفته شده است و علاوه بر جریمه تأخیر، جریمه تعجیل در تحویل کالاها متناسب با مدت‌زمان و مقدار کالای مواجه‌­شده با تأخیر/ تعجیل محاسبه می­شود. سه دسته مسئله در ابعاد کوچک، متوسط و بزرگ به‌صورت تصادفی تولید و با استفاده از الگوریتم شبیه‌سازی تبرید حل شده‌اند. برای مسائل کوچک، جواب حاصل از روش‌های حل دقیق با نتایج الگوریتم شبیه‌سازی تبرید مقایسه شده است.

Significance of multi-objective optimization in logistics problem for multi-product supply chain network under the intuitionistic fuzzy environment

Determining the methods for fulfilling the continuously increasing customer expectations and maintaining competitiveness in the market while limiting controllable expenses is challenging. Our study thus identifies inefficiencies in the supply chain network (SCN). The initial goal is to obtain the best allocation order for products from various sources with different destinations in an optimal manner. This study considers two types of decision-makers (DMs) operating at two separate groups of SCN, that is, a bi-level decision-making process. The first-level DM moves first and determines the amounts of the quantity transported to distributors, and the second-level DM then rationally chooses their amounts. First-level decision-makers (FLDMs) aimed at minimizing the total costs of transportation, while second-level decision-makers (SLDM) attempt to simultaneously minimize the total delivery time of the SCN and balance the allocation order between various sources and destinations. This investigation implements fuzzy goal programming (FGP) to solve the multi-objective of SCN in an intuitionistic fuzzy environment. The FGP concept was used to define the fuzzy goals, build linear and nonlinear membership functions, and achieve the compromise solution. A real-life case study was used to illustrate the proposed work. The obtained result shows the optimal quantities transported from the various sources to the various destinations that could enable managers to detect the optimum quantity of the product when hierarchical decision-making involving two levels. A case study then illustrates the application of the proposed work.