Multi-stage Supply Chain Research Articles

Multistage supply chain inventory model for controllable deterioration and imperfect production with carbon emissions regulations under Stackelberg game approach

Multistage supply chain inventory model for controllable deterioration and imperfect production with carbon emissions regulations under Stackelberg game approach

A novel fuzzy finite-horizon economic lot and delivery scheduling model with sequence-dependent setups

This paper addresses the economic lot and delivery scheduling problem (ELDSP) within three-echelon supply chains, focusing on the complexities of demand uncertainty, limited shelf-life of products, and sequence-dependency of setups. We develop a novel mixed-integer non-linear programming (MINLP) model for a supply chain comprising one supplier, multiple manufacturers with flexible flow shop (FFS) production systems, and multiple retailers, all operating over a finite planning horizon. The common cycle (CC) strategy is adopted as the synchronization policy. Our model employs fuzzy set theory, particularly the “Me measure,” to effectively handle the retailers’ demand uncertainty. Our findings indicate that total supply chain costs escalate with an increase in demand, final components’ holding costs, and sequence-dependent setup costs, but decrease with increasing production rates. Furthermore, while total costs are significantly sensitive to changes in demand, they are relatively insensitive to fluctuations in sequence-dependent setup times. The models developed offer valuable managerial insights for optimizing costs in synchronized multi-stage supply chains, aiding managers in making informed decisions about production lot sizes and delivery schedules under both deterministic and fuzzy demand scenarios. Additionally, the proposed models bridge key research gaps and provide robust decision-making tools for cost optimization, enhancing supply chain synchronization in practical settings.

Resilient and robust management policy for multi‐stage supply chains with perishable goods and inaccurate forecast information: A distributed model predictive control approach

AbstractAn efficient supply chain (SC) management requires that decisions are taken to minimize the effects of parametric uncertainties and unpredictable external disturbances. In this article, we consider this problem with reference to a multi‐stage SC (MSSC) whose dynamics is characterized by the following elements of complexity: perishable goods with uncertain perishability rate, an uncertain future customer demand that is only known to fluctuate inside a given compact set. The problem we face is to define a resilient and robust Replenishment Policy (RP) such that at any stage the following requirements are satisfied: the fulfilled demand is maximized, overstocking is avoided, the bullwhip effect (BE) is mitigated. These objectives should be pursued despite the mentioned uncertainties and unexpected customer demand behaviors violating the bounds of the compact set. Robustness is here intended with respect to uncertainty on the perishability rate, and resiliency as the ability to quickly react to the mentioned unforeseen customer demands. We propose a method based on a distributed resilient robust model predictive control (DRRMPC) approach. Each local robust MPC (RMPC) involves solving a Min‐Max constrained optimization problem (MMCOP). To drastically reduce the numerical complexity of each MMCOP, we parametrize its solution by means of B‐spline functions.

A breakthrough in circular economy: Using a closed-loop framework to assess the circularity of supply chains

The concept of the circular economy (CE) proposes eco-friendly principles for dealing with circularity problems in production systems and supply chains (SCs). To achieve circularity goals in SCs, CE principles assist decision-makers with reusing, remanufacturing, and recycling initiatives in the production process. However, previous meso- and macro-level circularity investigations reveal shortcomings in practical closed-loop solutions. This study aims to develop a closed-loop framework for assessing the circularity of sustainable SCs using network data envelopment analysis (NDEA). The closed-loop framework is created utilizing real-life indices of sustainable SCs, such as "recyclable undesirable outputs". To make the appraisal more realistic and enhance the validity and reliability of the results, the circularity results of SCs are assessed under data uncertainty. This allows SCs to be ranked based on their real circularity level, identifying sustainable and unsustainable SCs. The proposed framework offers decision-makers a practical evaluation tool to identify the highly circular SCs and establish circularity benchmarks for inefficient SCs. The proposed approach is scalable and applicable to real-world circularity evaluations in multistage SCs and production systems. The study concludes with a two-stage case study to show the practicality of the new framework.

Multistage Supply Chain Channel Principal-Agent Model in the Context of e-Commerce With Fairness Preference.

This research aims to investigate information asymmetry in e-commerce supply chain channels and the impact of the fair preference model on the behavior and returns of channel members. Therefore, by contrasting it with the model in the completely rational case, this research establishes a more realistic principal-agent model and incorporates the fair preference model into the e-commerce supply chain channel. According to the model's analysis, the effort level of the retailer at each stage is positively correlated with the e-commerce efficiency coefficient, and the incentive coefficient of manufacturers is positively correlated with the e-commerce efficiency coefficient in the case where all rationality is assumed. Manufacturing companies' anticipated profits are positively correlated with the e-commerce efficiency coefficient. According to the fair preference model, retailers will put forth more effort to sell products when their fixed income from manufacturers is higher and their optimal effort level is positively correlated with that income. When e-commerce's efficiency coefficient is higher than 1, the retailer's revenue and effort exceeded those of traditional channels. Manufacturers and retailers both experience Pareto improvements in their earnings after the fair preference model is introduced.

Intelligent E-commerce Logistics Supply Chain Management and Scheduling Optimisation

Abstract In the digital era, logistics supply chain scheduling has become the key to enhancing the competitiveness of e-commerce, and the fine management and scheduling optimization of the e-commerce logistics supply chain is particularly important. Based on the three-layer supply chain scheduling model, the study combines the production cost, transportation cost, and transportation time of each member of the supply chain with other influencing indexes. It establishes a multi-stage supply chain scheduling model (SCISM) with dual-objective. The rotational algorithm in linear programming is used to solve the model after it has been constructed. The effectiveness of the SCISM model for intelligent e-commerce logistics supply chains is explored through algorithmic and arithmetic validation of the SCISM model. The results show that the SCISM model algorithm determines the optimal solution σ∗ = (1,2,4,6,3,5) and the optimal objective generalized function value J(σ∗) = −49328 in only 0.95 seconds, which significantly improves the solution efficiency. Compared to Genetic Algorithm (GA) and Particle Swarm Algorithm (PSO), the SCISM algorithm quickly achieves the global optimal solution with the minimum number of iterations (100).

A novel variational inequality approach for modeling the optimal equilibrium in multi-tiered supply chain networks

We present a novel variational inequality model (VIM) to capture the complex real decision-making process in multi-tiered supply chain networks (MSCN) without strictly limiting the features of related functions. The VIM is formulated with the equilibrium conditions on links as the optimization goal and the flow conservation condition as the main constraints. We transform the VIM into a series of equivalent Non-Linear Programming Models (NLPMs) to solve. To address this challenge, we propose a novel population-based heuristic algorithm called the Multiscale Model Learning Algorithm (MMLA). The MMLA is inspired by the learning behavior of individuals in a group and can converge to an optimal equilibrium state of the MSCN. The MMLA has two key operations: zooming in on the search field and learning search in a learning stage. The excellent performers, called medalists, are imitated by other learners. With the increase in learning stages, the learning efficiency is improved, and the searching energy is concentrated in a more promising area. We employ sixteen benchmark optimization problems and two supply chain networks to demonstrate the effectiveness of the MMLA and the rationality of the equilibrium models. The results obtained by MMLA for the NLPM show that the MMLA can solve the equilibrium model effectively, and multiple optimal equilibrium states may exist for an MSCN. The flexibility of the NLPM makes it possible to consider more complicated decision-making mechanisms in the model.

Supply Chain Planning for IC Design House Back-End Production Network With Turnkey Service

Owing to global competition, integrated circuit (IC) design houses are facing challenges in the optimal use of capacity and resources to fulfill customer demands and maximize profit. A hybrid make-to-stock (MTS) and make-to-order (MTO) model is generally used. Wafer procurement is made according to customers’ long-term demand forecast based on MTS. Limited wafer fab resources and back-end capacity are first allocated to on-hand orders and then to forecast demand. In production planning, several factors need to be considered to determine the production quantity and inventory level, such as the material, capacity, substitute, technical constraint, and order tardiness. This study considers a typical multi-stage supply chain network of semiconductor back-end production with turnkey service for IC design houses. The objective is to maximize the total profit with the constraints of equipment capacity, the flexibility of substitutes, and the usage of the turnkey mode. The problem is formulated with a mixed-integer programming model and solved using two novel heuristic algorithms: a product-based decomposition (PD_SCP) with profit-based criteria and a Lagrangian decomposition combined with the product-based decomposition (LD-SCP) method. Using the LD-SCP algorithm takes a long time but results in a great solution. Sensitivity analysis is conducted to investigate the impact of different parameters on production cost and total profit.

Multi-objective optimization of renewable fuel supply chains regarding cost, land use, and water use

Renewable fuels contribute to net zero emissions by replacing fossil fuels in the transportation sector. Nevertheless, there are significant environmental concerns associated with renewable energy development, including land and water usage. This research explores the use of land and water related to large-scale renewable fuel production. We develop a multi-objective mathematical model to determine the optimal supply chain design for the EU's future renewable fuel demand in the transportation sector. We propose a flexible, multi-period, and multi-stage supply chain design that can accommodate multiple feedstocks and multiple products and that takes the seasonality of the resources into account. We consider economic and environmental impacts by minimizing total system costs, land use, and water use. Based on the analysis of the results, we gain insights into conflicting objective functions and the associated trade-offs and synergies to assist decision-makers in selecting an appropriate framework and gaining a better understanding of future opportunities and impacts of renewable fuel production. The outcome demonstrates that, despite not being a desirable alternative in terms of water and land usage, the utilization of energy crops as raw material has economic benefits. As shown by the results, the burden of such a supply chain lies with renewable electricity requirements that could be the capacity bottleneck and require investments. We find that we could achieve a nearly optimal value of land use and water use by increasing total cost by only 10%.

(Un)Learning sustainability practices in a multi-tiered supply chain: an interpretive study

PurposeAlthough the number of studies that investigate supply chain sustainability learning has increased, little is known about the way sub-suppliers build knowledge and learn sustainability practices. Thus, moving beyond merely investigating the accumulation of knowledge, this research explores sub-suppliers’ knowing that supports the learning of sustainability practices in a multi-tiered food supply chain.Design/methodology/approachIn the conduct of this interpretive research in South Brazil, two ethnographies were completed during 74 days of observations to understand similarities and differences between certified and non-certified sub-suppliers with respect to sustainability practices. As part of our research conducted in the context of poultry production, secondary data and data gathered through semi-structured interviews with representatives of the buyer and first-tier supplier firms were used to provide a better comprehension of the multi-tiered supply chain context. Then, we executed an interpretive textual analysis.FindingsOur investigation explored six vignettes to reveal ways of learning sustainability practices in terms of waste management, biosecurity and animal welfare. Although the buyer firm requested these practices, we noted that the first-tier supplier was responsible for translating the practices to sub-suppliers. Moreover, we found that sustainability learning was shaped by the sub-supplier context embodied in knowledge through knowing. The ways of learning were related to sharing knowledge between experts and novices with the support of material practices; however, knowledge was also gained by unlearning some knowledge shared by the supplier. Sustainability practice learning, thus, was performed in a space of learning via knowledge creation among practitioners.Practical implicationsRecognising how sustainability learning happens in a multi-tiered supply chain context can help managers to develop plans to implement sustainability practices that will broaden their sustainability knowledge.Originality/valueUnlike previous studies on supply chain sustainability learning, we reveal ways that sustainability practices emerge from knowledge that results from sub-suppliers’ knowing. We also explain how unlearning can consciously occur in several situations of sustainability learning.

A moderated-mediation analysis of supply chain efficiency, flexibility, integration, and risk management

The preparedness and stability of the defense-industry supply chains have been challenged by significant disruptions and risks that can be critical to national security. Despite increasing importance, limited studies have explored the underlying factors (i.e., efficiency, flexibility, integration, and risk management) impacting the supply chain operations of national defense companies. Hence, this study aims at exploring the relational impact of supply chain risk management (SCRM) practices and organizational performance by hypothesizing a moderated-mediation model involving operational efficiency, flexibility, and supply chain integration (SCI). Using the information processing theory, the survey-based data of 2017 respondents was collected from defense-related national institutions in Pakistan. Empirical findings based on structural equation modeling validated a significant positive SCRM è Organizational Performance relationship. According to the findings, supply chain risk management is strongly connected to operational efficiency and flexibility. Additionally, the study also substantiates the mediating effects of operational performance (i.e., efficiency and flexibility), along with the significant moderation by supply chain integration in the relationship between risk management and operational efficiency. However, the results do not support the role of operational flexibility in mediating the relationship between SCRM and OP. Furthermore, the role of SCI as a moderator between SCRM è operational flexibility was not established. This research is the first to introduce an empirically validated framework of supply chain performance in the defense industry, besides conceptualizing the direct and indirect roles of SCRM, efficiency, flexibility, and integration. This study also navigates some of the critical issues and obstacles faced by the national defense industry and highlights a holistic model that facilitates a multistage supply chain decision-making.

Algae-Based Two-Stage Supply Chain with Co-Products

The last years have seen the emergence of the bioeconomy. Assessment of these new technologies is a significant challenge. We develop a unique dynamic programming framework to assess the value of the investment in a multi-stage supply chain with the production of bio-feedstock and its processing into multiple outputs. The system allows for adaptive learning in all supply chain stages, which creates a positive learning effect of co-outputs. We apply the framework to macroalgae (seaweed) farming and biorefinery processing into proteins and sugars for the Philippines and Ireland as representatives of developing and developed economies with emerging supply chains. We run Monte Carlo simulations to analyze the uncertainty of learning and prices. The key results indicate that the macroalgae sector that builds on traditional technologies is quite viable. Developing a new algae industry that generates proteins and other high-value products requires significant investment and depends on the dynamics of learning and prices. Even though the production of high-value chemicals is not yet viable, it gains profitability potential from learning of feedstock farming that is currently produced for the lower value application. The learning is much more valuable in feedstock production and processing into proteins than low-value chemicals currently produced (carrageenan).

Effectiveness of a Multi-Agent Cooperation Game in a Multi-Stage Supply Chain

Effectiveness of a Multi-Agent Cooperation Game in a Multi-Stage Supply Chain

A Study on Behavioral Simulation of Single Chained Multi-Stage Supply Chain Planed by Advance Demand Information

A Study on Behavioral Simulation of Single Chained Multi-Stage Supply Chain Planed by Advance Demand Information

Integrated multistage supply chain inventory model of multiple retailers with imperfect production and inspection systems

An integrated multistage supply chain inventory model containing a single manufacturer and multiple retailers is proposed to consider deteriorating raw materials and finished products with imperfect production and inspection systems. The main purpose is to jointly determine the manufacturer’s production and delivery strategies and the retailers’ replenishment strategies to maximize the integrated total profit. First, the individual total profit functions of the manufacturer and multiple retailers are established and are integrated to form the total profit function of the supply chain system. Then, to address the model complexity, an algorithm is proposed to obtain the optimal solution. Several practical numerical examples are presented to demonstrate the solution procedure, and a sensitivity analysis is performed on the major parameters. From the numerical results, several findings that differ from those in the previous literature were observed. First, retailers with larger market scale, better cost control, and/or inspection capabilities guarantee higher individual and integrated total profits. Second, increasing the deterioration rates of raw materials and finished products has different effects on the order quantity of raw materials. Third, the manufacturer’s shipping strategy is rigid and not easily changed except for significant changes (increase or decrease by 20%) to the production rate or selling price. The performance of the proposed model has several meaningful management implications.

An integrated multitiered supply chain network model of competing agricultural firms and processing firms: The case of fresh produce and quality

In this paper, we develop an integrated multitiered competitive agricultural supply chain network model in which agricultural firms and processing firms compete to sell their differentiated products. The focus here is on fresh produce and minimally processed such agricultural products, with quality also captured. The competition among agricultural firms and processing firms is studied through game theory, where the governing Cournot–Nash equilibrium conditions correspond to a variational inequality problem. The algorithm, at each iteration, yields explicit closed form expressions for the agricultural product path flows, the agricultural product shipments from agricultural firms to the processing firms, and the Lagrange multipliers. A numerical study consisting of several supply chain disruption scenarios demonstrates the applicability of our modeling framework.

A mathematical model for managing the multi-dimensional impacts of the COVID-19 pandemic in supply chain of a high-demand item.

The COVID-19 pandemic has wreaked havoc across supply chain (SC) operations worldwide. Specifically, decisions on the recovery planning are subject to multi-dimensional uncertainty stemming from singular and correlated disruptions in demand, supply, and production capacities. This is a new and understudied research area. In this study, we examine, SC recovery for high-demand items (e.g., hand sanitizer and face masks). We first developed a stochastic mathematical model to optimise recovery for a three-stage SC exposed to the multi-dimensional impacts of COVID-19 pandemic. This allows to generalize a novel problem setting with simultaneous demand, supply, and capacity uncertainty in a multi-stage SC recovery context. We then developed a chance-constrained programming approach and present in this article a new and enhanced multi-operator differential evolution variant-based solution approach to solve our model. With the optimisation, we sought to understand the impact of different recovery strategies on SC profitability as well as identify optimal recovery plans. Through extensive numerical experiments, we demonstrated capability towards efficiently solving both small- and large-scale SC recovery problems. We tested, evaluated, and analyzed different recovery strategies, scenarios, and problem scales to validate our approach. Ultimately, the study provides a useful tool to optimise reactive adaptation strategies related to how and when SC recovery operations should be deployed during a pandemic. This study contributes to literature through development of a unique problem setting with multi-dimensional uncertainty impacts for SC recovery, as well as an efficient solution approach for solution of both small- and large-scale SC recovery problems. Relevant decision-makers can use the findings of this research to select the most efficient SC recovery plan under pandemic conditions and to determine the timing of its deployment.

Optimal Policies for a Multi-Echelon Inventory Problem with Service Time Target and Expediting

Problem definition: We study the optimal inventory ordering, expediting, and allocation decisions in a multiechelon supply chain over a finite horizon, in which customer orders are quoted with a fixed fulfillment time window, termed the service time target (STT). Academic/practical relevance: Service time target is commonly used as a marketing strategy to increase customer satisfaction and strengthen firms’ competitive edge. However, how to efficiently manage a multistage supply chain to meet the target has received relatively scant attention in the literature. Our study fills this gap. Methodology: We use dynamic programming to characterize the optimal policy. Results: We show that an echelon base stock policy and a rationing policy are optimal for inventory ordering and inventory allocation/expediting, respectively. We also develop a polynomial-time algorithm to compute the optimal policy. To derive these results, we uncover a new functional property named the decomposable of degree 2 property, which is a nontrivial generalization of the celebrated Clark-Scarf decomposition. This property further allows us to derive induced penalty and compensation to coordinate a decentralized serial system with STT and expediting. Managerial implications: Our result provides an efficient decision tool for managing centralized and decentralized serial supply chains with STT and expediting. Our model can be used to quantify the tradeoff between marketing and operational decisions, such as the impact of a marginal reduction in STT on system cost and expedition frequency, as explored in our numerical studies.

Active management strategy for supply chain system using nonlinear control synthesis.

Nonlinear dynamical behaviours with chaotic phenomena are commonly observed in a typical logistics model and supply chain system. Bullwhip effect has been widely recognized as one of the main issues on affecting the supply chain management. In essence, this phenomenon will lead to unnecessary consumption and waste of natural and social resources by demand variability amplification as moving up in the supply chain networks. However, traditional modelling approaches may become complicated in dealing with uncertainty and chaotic behaviour that are prevalent in real supply chains. System dynamics theory has been employed as a potentially effective strategy to cope with chaotic supply chains which are unpredictable behaviours in time. Four-dimensional differential equations which exhibit chaotic behaviours are constructed to describe a multi-echelon supply chain with bullwhip effect. Furthermore, modern control theory is applied to deal with the multi-stage supply chain optimization problems against disruptions. Specifically, the novel fractional order adaptive sliding mode control (FO-ASMC) algorithm has been implemented for ensuring efficient supply chain management. In addition, the chaos synchronization scheme is implemented in an attempt to regulate the supply chain systems under the impact of extensive uncertainties caused by tumultuous real market. It is found that the chaos synchronization is effectively realised by new FO-ASMC theory to manage advanced supply chain networks. Finally, this advanced management optimization offers a new class of intelligent applications that connects demand to supply and planning to execution across the entire supply chains.

A multi-stage supply chain disruption mitigation strategy considering product life cycle during COVID-19.

The global pandemic of COVID-19 has caused severe damage to the supply chain such that manufacturers may face long-term supply disruptions. In this paper, a disruption recovery strategy of a supply chain system is investigated from the perspective of product change, in which the life cycle and design change time of a new product are both considered in order to minimize the losses of manufacturer after disruptions. A mixed-integer linear programming (MILP) model is presented to address the disruption recovery problem for this multi-period, multi-supplier, and multi-stage supply chain system. A two-stage heuristic algorithm is designed to solve the problem. Experimental results show that the proposed disruption mitigation strategy can effectively reduce the profit loss of manufacturer due to supply disruption, and demonstrate the impact of product life cycle in the selection of new product design planning. A sensitivity analysis is performed to ensure the applicability of the model in the actual environment, which illustrates the effect of different parameter changes on the results. This work can help manufacturers establish an optimal recovery strategy whenever the supply chain system experiences supply disruptions.