Three-stage Supply Chain Research Articles

Production and distribution scheduling optimisation in a three-stage integrated supply chain using genetic algorithm

This paper addresses an integrated P-D scheduling model arising in a three-stage supply chain, which involves three stages: a single vehicle after processing by different suppliers, manufacturers, distributors and consumers located in separate geographic regions. The contribution of this paper is to provide an integrated P-D scheduling under fixed departure times in supply chain with consideration of transportation fleet, integration of supplier-manufacturer. The mathematical model was proposed as a mixed integer programming and solved by CPLEX software in small-scale problems to validate the model. Given the complexity of the solution and NP-hard structure of the proposed problem, genetic algorithm was used to find quality solutions by MATLAB software. Finally, the results of research show that managers of the case study can deliver orders to customer in minimum time and cost. According to the results, the proposed algorithm (GA) has a desired level of quality in solving the P-D real problems.

Production and distribution scheduling optimisation in a three-stage integrated supply chain using genetic algorithm

This paper addresses an integrated P-D scheduling model arising in a three-stage supply chain, which involves three stages: a single vehicle after processing by different suppliers, manufacturers, distributors and consumers located in separate geographic regions. The contribution of this paper is to provide an integrated P-D scheduling under fixed departure times in supply chain with consideration of transportation fleet, integration of supplier-manufacturer. The mathematical model was proposed as a mixed integer programming and solved by CPLEX software in small-scale problems to validate the model. Given the complexity of the solution and NP-hard structure of the proposed problem, genetic algorithm was used to find quality solutions by MATLAB software. Finally, the results of research show that managers of the case study can deliver orders to customer in minimum time and cost. According to the results, the proposed algorithm (GA) has a desired level of quality in solving the P-D real problems.

Multi-objective optimisation framework of genetic programming for investigation of bullwhip effect and net stock amplification for three-stage supply chain systems

Multi-objective optimisation framework of genetic programming for investigation of bullwhip effect and net stock amplification for three-stage supply chain systems

A three-stage supply chain scheduling problem based on the nursing assistants’ daily work in a hospital

In a hospital, the nursing assistants in wards are always responsible for delivering patients to do some medical examinations, and the nursing assistants in the medical departments need take these patients back to the corresponding wards after examinations. We transform this actual problem about nursing assistants’ daily work into a three-stage supply chain scheduling problem if patients are regarded as jobs, the ward as the customer, the nursing assistants as vehicles and the medical department as the processing machine in the factory. The capacity of each vehicle is limited because the nursing assistant only can deliver several patients once. And the transportation schedule back and forth between the customer and the factory are the same in view of the actual arrangement. In order to reduce the average consumption time of patients, the objective is to minimize the total flowtime, that is, the sum of arrival time of every completed job back to the customer. We show that the problem is NP-hard in the strong sense, and provide an approximation algorithm with the performance ratio of 2. Moreover, we study some polynomially solvable cases of the problem.

Optimizing supply chain network for perishable products using improved bacteria foraging algorithm

In a supply chain environment, time delay has a significant impact on the success of perishable products. A major concern is therefore aimed at development of a holistic optimized approach in a supply chain environment for perishable products. Thus, integration of production, inventory and, distribution of perishable products in a supply chain environment are the challenging tasks for practitioners and researchers. In general, the standard optimal supply chain model cannot work for perishable products. There is therefore, a need for a holistic model that focuses on the consolidation of the processes. Shorter product shelf-life, temperature control, requirement of strict tractability, large number of product variants, and a large volume of goods handled are the major challenges in a supply chain environment for perishable products. The present work focuses on the development of a holistic model which uses improved bacteria forging algorithm (IBFA) for solving the formulated model. We have proposed and analyzed some general properties of the model and, finally applied it to a three-stage supply chain problem using an IBFA. Two case studies have been considered for support and demonstration of the integrated perishable supply chain network problem. Results obtained from IBFA reveal that the proposed model is more useful for decision makers while considering optimal supply chain network for perishable products. Finally, validation of results has been carried out using bacteria forging algorithm (BFA). The computational performance of the proposed algorithm proves that IBFA is instrumental in effectively handling the proposed approach.

Revisiting Supply Chain System with Deteriorating Items and Transportation Cost

Supply chain system with deteriorating items and transportation cost with environmental consideration has recently become a popular research stream. This study revisits a supply chain system with deteriorating items and transportation cost. Processing the defective items, which increases cost, affects supply chain decisions. We present an integrated inventory model involving a three-stage supply chain and defective items with no shortage. We then derive the minimal total cost considering supply chain integration and deteriorating items. Numerical examples are provided to illustrate how these models can be applied in practice. Sensitivity analysis is performed to gain more insight on changing parameters in the numerical studies.

Integrated production, inventory, and outbound distribution operations with fixed departure times in a three-stage supply chain

This study considers an integrated production, inventory, and outbound distribution scheduling model that arises in a three-stage supply chain consisting of three parts: a supplier, a manufacturer, and several customers. The manufacturer receives a set of two-stage orders to process a specific set of jobs (products) for the customers. These jobs are first partly processed by the supplier, and delivered to the manufacturer by vehicles immediately at additional delivery costs depending on the delivered jobs. The partly processed jobs are then processed by the manufacturer, and delivered to their respective customers by transporters with fixed delivery departure times. A finished job is either kept in a temporary warehouse before it is delivered at an inventory cost, or delivered to its customer immediately. Each job is associated with a desired due date, and late deliveries are not accepted. The objective is to determine schedules for processing the jobs, and identify delivery schedules from the manufacturer to all customers such that the weighted sum of the weighted number of late jobs, inventory cost, and delivery cost is minimized. For each considered problem, we investigate its computational complexity, and develop pseudo-polynomial or polynomial-time solution algorithm, if viable. Using randomly generated data, we conduct extensive numerical studies to evaluate the performance of the proposed algorithms.

Product quality improvement and air pollutant emission reduction in a mining metal three-stage supply chain under cap-and-trade regulation

Product quality improvement and air pollutant emission reduction in a mining metal three-stage supply chain under cap-and-trade regulation

A hybrid approach for modelling dynamic flows and systemic risks in supply chains

The uncertainty of supply chain operations is becoming more complex with the growth of globalised business collaborations. Temporal business environment fluctuations cause disruptions that require quantitative risk analysis tools to understand and mitigate supply chain risks. This paper presents the formulation and application of the dynamic flow Bayesian networks (DFBNs) and the optimised dynamic flow Bayesian networks (ODFBNs) for a three-stage supply chain. DFBNs are created by integrating dynamic Bayesian networks (DBNs) and system dynamics (SD) to demonstrate the feedback flows of a supply chain with systemic risks considered. ODFBNs that incorporate mathematical optimisation with the original DFBNs are also developed to generate an optimisation-enhanced risk-influenced dynamic flow analysis. DFBN and ODFBN provide supply chain practitioners with a more effective reference for their business strategy. Comparison between the ODFBN and the DFBN is illustrated with a discussion of preliminary modelling results.

Policies Adoption for Supply Disruption Mitigation Based on Customer Segmentation

Supply chain disruptions resulted from various disasters may cause awesome loss; therefore, corresponding operational policies should be designed to mitigate the negative effect. In industrial goods supply chain, customers are segmented and have different contributions and significances to manufacturers, which have to be considered when adopting mitigation policies. From the customer heterogeneity perspective, the models of a three-stage supply chain with a single supplier and two suppliers are established, and sensitivity analysis is carried out to investigate scenario parameters' impact on optimal mitigation policies. The numerical experiments find that safety stocks are suitable for low disruption probability situation. Meanwhile, strategic reserves with lower holding cost are attractive and efficient to handle a fairly highly disruptive situation or high key customer loss. The results also indicate backup supplier should be adopted if there is a low disruption probability or the backup supplier's price is not significantly higher. This paper extends the supply disruption research and leads some managerial implications and insights that might be useful for disruption mitigation in industrial goods supply chain.

Logistics scheduling to minimize the sum of total weighted inventory cost and transport cost

Abstract This paper studies the logistics scheduling problem in the context of a three-stage supply chain comprising a supplier, a manufacturer, and a customer. The supplier supplies raw materials (jobs) to the manufacturer, which processes them into finished products (processed jobs) and transports them to the customer. We analyze the problem using a batching and scheduling model involving both batch supply and batch delivery. The objective is to minimize the sum of the total weighted inventory cost and transport cost. The inventory cost is related to the job flow time, which is the time between the arrival of a job to the manufacturer and the time it is finished processing and leaves the manufacturer. The transport cost is related to the number of batches created. Four cases associated with batch supply and batch delivery, depending on the relative strengths of the arrival cost, inventory cost, and delivery cost are discussed in this paper. We show that all the cases are NP-hard when the number of batches has a constant upper bound, and give a fully polynomial-time approximation scheme for each case.

Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era

In the Big Data era, Data Company as the Big Data information (BDI) supplier should be included in a supply chain. To research the investment decision-making problems of BDI and its effects on supply chain coordination, a three-stage supply chain with one manufacturer, one retailer, and one Data Company was chosen. Meanwhile, considering the manufacturer contained the internal BDI and the external BDI, four benefit models about BDI investment were proposed and analyzed in decentralized and centralized supply chains. Meanwhile, a revenue sharing contract was used to coordinate the decentralized supply chain after investing in BDI. Findings: (1) the Big Data investment threshold of the Data Company was determined by the cost improvement coefficient, meanwhile, Data Company’s benefit was influenced by the consumer preference information conversion coefficient. (2) Whether the manufacturer was suitable to invest in BDI, it was influenced by the cost improvement coefficient. (3) When revenue sharing coefficient could meet a certain range, the revenue sharing contract could make the supply chain coordinate. Moreover, the benefits of supply chain members were same after coordination.

The value of inventory and the direction of the supply chain bullwhip effect

The bullwhip effect (BWE) is the amplification of demand variability in a supply chain and can be quite costly for industry and society. In this empirical investigation of the BWE a three-stage supply chain is examined using monthly industry-level data. Time series regression equations are used to detect the BWE and its direction. Empirical evidence is found suggesting the BWE of a supply chain member is influenced by the BWE of the other members, as well as the values of inventory and inventory to sales ratios of own member and other members of supply chain. In addition, there is evidence that the direction of the BWE is not influenced by the members' horizontal positions in the network. It is shown that the wholesale BWE exhibits heteroscedastic and non-stationary behaviour; thus, making it difficult to predict over a long-horizon time period, unlike the BEW of the manufacturing and retail industries.

Optimising integrated inventory policy for perishable items in a multi-stage supply chain

The value of perishable products is most affected by the time delays in a supply chain. A major issue is how to integrate the existing practices in production, inventory holding and distribution, besides considering the perishable nature of the products, so as to deliver an optimised policy for the perishable commodities. Standard inventory control models are often not adequate for perishable products and there is a need for a new integrated model to focus on consolidation of production, inventory and distribution processes. We develop such a mathematical model to search for an optimal integrated inventory policy for perishable items in a multi-stage supply chain. We specifically assume the exponential deterioration rate so as to be consistent with the growth rate of the micro-organisms responsible for deterioration. We propose and analyse some general properties of the model and apply it to a three-stage supply chain. We show that this integrated model which includes inventory control and fleet selection can be optimised with an evolutionary technique like genetic algorithm. A novel genetic algorithm that avoids revisits and employs a parameter-less self-adaptive mutation operator is developed. The results are compared with those obtained with CPLEX for small-sized problems. We show that our model and optimisation approach gives near optimal results for varied demand scenarios.

Supply chain risk management for critical commodities: A system dynamics model for the case of the rare earth elements

We investigate mitigation strategies that potentially reduce the supply chain risk caused by exogenous interventions for critical commodities. We apply a system dynamics approach to examine the individual company’s reaction in the rare earth elements (REEs) supply chain to China’s introduction of export restrictions over a three-stage supply chain for 11 REEs, 10 semi-finished goods (SFG), and 21 finished goods (FG) between 2004 and 2015. We analyze how FG and SFG substitution, secondary recycling, supply extension and research and development (R&D) affect dynamic variables such as the price level, supply and demand on each stage to determine the effectiveness of each strategy for mining companies, SFG and FG producers.FG and SFG substitution show the highest potential for mitigating supply unavailability. Substitution is especially beneficial for FG producers. It also has the advantage of offering a quick development of alternatives for reducing REE supply unavailability risk. Due to the fact that the necessary infrastructure and technology are not available, secondary recycling shows only a low mitigation potential for FG companies. It was shown that mining companies do not invest proactively in supply capacities beyond China, as our simulation shows that it took 6 years until a new REE mine was opened. R&D is identified as a key element for REE mining companies, SFG and FG producers when it comes to developing alternative products and recycling techniques. Nonetheless, SFG producers face huge difficulties when trying to develop substitutes as alternative materials are not present.

EFFECT OF SUPPLY CHAIN STRUCTURE AND POWER DYNAMICS ON R&D AND MARKET PERFORMANCES

This study is the first investigating the effect of supply chain structure and players’ power dynamics on a supply chain’s R&D and market performances. We consider a three-stage supply chain, consisting of a R&D firm, a manufacturer, and a seller. We develop six supply chain models based on various structures and power dynamics, and reveal important implications by comparing the models. R&D performance is a function of supply chain structure; an integrated supply chain can expect improved performance. The selling price is a function of power dynamics, and is higher when R&D has bargaining power. Market and profit performances are higher in an integrated, manufacturer-led supply chain. The manufacturer’s role must be revisited; due to its location in the middle of the supply chain, the manufacturer can directly control the overall chain. On the other hand, a R&D firm could innately be associated with marginalization issues. Therefore, it is important for the R&D firm to have a systematic understanding of the entire system. This study contributes to the literature and practice by not only bridging the gap in the previous studies but also offering important managerial implications.

A hybrid GA-TLBO algorithm for optimizing a capacitated three-stage supply chain network

A hybrid GA-TLBO algorithm for optimizing a capacitated three-stage supply chain network

Research on Network DEA Model and Efficiency Measurement of Three-stage Dairy Supply Chain

The operation behavior of the supply chain at all stages and the supply chain relationship based on input-output relationship were clarified through analysis of the three-stage supply chain network structure and the operation mode of each stage. In order to measure the efficiency of three-stage dairy supply chain, the network DEA model was constructed, and the supply chain of 29 Chinese dairy enterprises was studied. The total efficiency of three-stage dairy supply chain under DEA model was calculated. The relationship between the total efficiency and the efficiency of each fraction was introduced. By using the envelope model, the frontier was obtained and the corresponding measures were put forward to improve the efficiency of the three-stage dairy supply chain.

Multi-stage supply chain network solution methods: hybrid metaheuristics and performance measurement

ABSTRACTWe study a three-stage supply chain network (SCN) design problem for a single-product system. The SCN is composed of suppliers that provide raw materials for the plants, plants that produce and send the finished products to distribution centres (DCs), and DCs that transport finished products to the customers. The use of different conveyances and step-fixed costs improves the applicability and the results but increases the complexity, generating an NP-hard problem. The overall objective of the problem is to minimise the total cost, which is composed of the opening and transportation costs in all three stages. Two hybrid metaheuristics – genetic algorithm–variable neighbourhood search (GA-VNS) and variable neighbourhood search–simulated annealing (VNS-SA) – are proposed to solve this NP-hard problem. In addition to the novelty of the proposed algorithms, we develop an innovative priority-based decoding method to design chromosomes and solutions related to the nature of the problem. A robust parameter and operator setting is implemented using the Taguchi experimental design method with several random test problems. The performance of these algorithms is evaluated and compared for different problem sizes. The experimental results indicate that the GA-VNS is robust and superior to the other competing methods.

Hybrid Genetic Algorithm and Invasive Weed Optimization via Priority Based Encoding for Location-Allocation Decisions in a Three-Stage Supply Chain

In this paper, location–allocation problem of a three-stage supply chain network, including suppliers, plants, distribution centers (DCs) and customers is investigated. With respect to the total cost, the aim is determining opened plants and DCs and designing transportation trees between the facilities. Considering the capacity of suppliers, plants and DCs are limited and there is a limitation on the maximum number of opened plants and DCs, a mixed-integer linear programming (MILP) model of the problem is presented. Since multi-stage supply chain networks have been recognized as NP-hard problems, applying priority-based encoding and a four-step backward decoding procedure, a meta-heuristic algorithm, namely GAIWO, based on the best features of genetic algorithm (GA) and invasive weed optimization (IWO) is designed to solve the problem. In small size problems, the efficiency of the GAIWO is checked by solutions of GAMS software. For larger size problems, the performance of the proposed approach is compared with four evolutionary algorithms in both aspects of the structure of the GAIWO and the efficiency of the proposed encoding–decoding procedure. Besides usual evaluation criteria, Wilcoxon test and a chess rating system are used for evaluating and ranking the algorithms. The results show higher efficiency of the proposed approach.