Supply Chain Scheduling Research Articles

Innovative Resource Distribution Through Multi-Agent Supply Chain Scheduling Leveraging Honey Bee Optimization Techniques

Many people and things involved in the supply chain can be represented by a network of linked agents in a multi-agent system. To achieve distribution and manufacturing targets efficiently, actions in a supply chain must be scheduled by tracking and organizing the allocation of resources. Healthcare, energy, aerospace, agriculture, and manufacturing are some of the industries that make use of it to better allocate and coordinate resources in complex networks. The goal of multi-agent supply chains is to lower costs and meet demand in dynamic contexts through efficient resource allocation and planning. Traditional approaches, such as linear programming and heuristic algorithmic approaches, have problems with adaptation and scalability. This study offers a novel scheduling method, Resource Distribution by Honey Bee Optimization (RDHBO), to circumvent these issues. It is based on a tactic that the honey bee uses when foraging. In this model, scout bees and foragers take on the role of actors, investigating and making the most of potential new ways to divide up resources by employing HBO strategies. The foraging agents look for places that have been profitable in the past, and the scouting agents carefully examine the supply chain to find places where resources might be available. The proposed RDHBO architecture allows for better supply chain scheduling by encouraging effective utilization and investigation, better communication, dynamic adaptability, autonomous decision-making, and efficiency of complicated systems. This method leads to an adaptable supply chain, lower operational costs, and more scalability and adaptability. This paper presents a formidable alternative to conventional optimization methods by demonstrating an efficient approach to dealing with complicated supply chain challenges.

A Dynamic Scheduling Method for Logistics Supply Chain Based on Adaptive Ant Colony Algorithm

To reduce the dynamic scheduling cost of logistics supply chain and improve customer satisfaction, this paper proposes a dynamic scheduling method for logistics supply chain based on adaptive ant colony algorithm. First, determine the goal of dynamic scheduling in the logistics supply chain. Second, considering supplier satisfaction, transportation costs, and maximum delivery distance constraints, a dynamic scheduling model for logistics supply chain is constructed. Then by smoothing the pheromones and designing a transition function, adjusting factors are introduced to update the pheromone rules. Finally, based on the adaptive ant colony algorithm, the solution of the dynamic scheduling function of the logistics supply chain is solved to achieve the dynamic scheduling of the current logistics supply chain. The experimental results show that after 19 iterations, the method can search for the optimal route A1 group with a length of 33.85 km, with fewer iterations and shorter paths. The total cost is 114,290 yuan, and the degree of cargo loss is low, with a maximum of only 0.14%. The task completion time is short, customer satisfaction is above 0.85, and the scheduling accuracy is 99.9%. It can effectively control costs, improve customer satisfaction, and accurately arrange logistics supply chains.

Supply chain digitalization and sports industry performance in Chinese context: a mediated moderated model

PurposeIn era of industry 4.0 all the organizations are investing their resources so they can adopt the latest technologies with no exception of supply chain. Accordingly, the present study attempted to examine the influence of digitalization of supply chain and performance in context of Chinese sports industry. Additionally, it has considered the supply chain integration and time management as a mechanism along with boundary condition of supply chain resilience.Design/methodology/approachThe study followed positivism philosophy and adopted quantitative methods. The data were collected by using the questionnaire from the Chinese sport industry organizations’ professionals related to logistics and supply chain. A total 746 questionnaires were distributed and 570 of them subjected to SPSS and Smart PLS for data analysis. Hypotheses were tested using PLS-SEM through the Smart-PLS 4.FindingsThe results of the study revealed that supply chain digitalization positively influences the both internal and external supply chain integration. Moreover, supply chain planning and scheduling influenced by both internal and external supply chain integration lead by supply chain digitalization. Ultimately resulting in improved sports industry performance. However, the supply chain resilience did not found as a significant moderator.Originality/valueThe study is few of the attempts that have tested a comprehensive mechanism through which the supply chain digitalization lead towards the higher performance. Also, the study results signify that the digitization can help the organizations to accomplish superior performance specifically in sports industry.

Metaheuristic Algorithms for the Optimization of Integrated Production Scheduling and Vehicle Routing Problems in Supply Chains

Metaheuristic Algorithms for the Optimization of Integrated Production Scheduling and Vehicle Routing Problems in Supply Chains

Waveless Picking in Supply Chain Performance of Food and Beverages Processing Firms in Kenya

The purpose of this study was to analyze the role of waveless picking in supply chain performance of food and beverages manufacturing firms in Kenya. The research concentrated on the 134 food and beverage manufacturers that are operating in Nairobi City County besides being registered with Kenya Association of Manufacturers. The study adopted a mixed research design with both qualitative and quantitative approaches. The target population of the study was the 134 food and beverages manufacturing firms in Nairobi County. A sampling frame of this study included a list of the 134 manufacturing companies in Nairobi County that are members of the Kenya Association of Manufacturers. The study utilized simple random sampling. A sample size of 100 was selected with the aid of Yamane 1967 formula. Both primary and secondary data was collected using a questionnaire. The questionnaire was tested pilot at 10 food and beverages manufacturing companies in Kiambu county. These pilot study questionnaires were filled out by warehouse managers. The statistical package for social sciences (SPSS) version 25 was used to analyze the data. Using content analysis, the qualitative data was analyzed. Quantitative data was analyzed using statistical methods involving descriptive and inferential data. A multiple linear regression model was applied to analyze the relationship between the variables. Analysis was also performed on the correlation. In this study, the findings were presented using tables and graphs. Data presentation made use of percentages, frequencies, means and other means of central tendencies. The study on revealed several ways to improve operational efficiency and productivity. Most enterprises have not reduced warehouse travel time, indicating potential for improvement. The study recommended reducing warehouse travel time, implementing batch picking, designating picking zones, increasing product forecasting accuracy, improving cash flow management and supply chain scheduling, accepting technology and automation, and promoting continuous learning and development.

Supply chain scheduling with deteriorating jobs and delivery times

Supply chain scheduling with deteriorating jobs and delivery times

Optimal scheduling method of low-carbon energy supply chain based on machine learning and game theory

Aiming at the quality and stability of a low-carbon new energy supply chain, this paper puts forward an optimal scheduling method for a low-carbon energy supply chain based on machine learning and game theory. By establishing an index and preprocessing keywords, the similarity between the index and the data in the platform database is calculated, and the data-sharing result of the low-carbon energy supply chain is obtained. This paper analyzes the game benefits of each subject in the optimization of the low-carbon energy supply chain, obtains the game matrix, and realizes the optimal scheduling of the low-carbon energy supply chain. The experimental results show that the scheduling convergence error of the low-carbon energy supply chain is low, the accuracy is high, and the load supply equipment can run stably.

A simheuristic approach towards supply chain scheduling: Integrating production, maintenance and distribution

This study attempts to integrate production, maintenance, and delivery operations among supply chain members. Despite numerous studies in the field of supply chain management, researchers have often overlooked crucial aspects, such as uncertainties in demand and production. For instance, the significant impact of maintenance activities on production flow has been underrepresented in supply chain management literature. This study investigates these gaps in the context of a fertilizer producer case study, which is characterized by seasonal demand and the functional silos syndrome due to old-fashioned management approaches. This study proposes a mathematical model and two multi-objective simheuristics for the Integrated Production, Maintenance, and Distribution Scheduling Problem (IPMDSP) considering demand variation for multiple products and product delivery time-windows using a heterogeneous fleet of vehicles. The IPMDSP is solved using the ϵ-constraint method and simheuristics linking the simulation model to customized and tuned versions of Particle Swarm Optimization (MOPSO) and the Non-dominated Sorting Genetic Algorithm (NSGA-II). The optimization objectives include minimizing maintenance duration, distribution costs, and customer dissatisfaction due to delivery tardiness. The results demonstrate the superiority of the simheuristic empowered by NSGA-II over the MOPSO in solving the IPMDSP. The comparison between the performance of deterministic and stochastic approaches in addressing the problem reveals that neglecting uncertainty caused by maintenance activities can lead to an increase in optimization objectives. Furthermore, the proposed simheuristics achieved significant improvements in minimizing objectives in solving the fertilizer producer case study.

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).

Order assignment and two-stage integrated scheduling in fruit and vegetable supply chains

Fruit and vegetable supply chains require tight integration due to freshness-keeping requirements during the post-harvest, production, and distribution stages. This paper addresses the scheduling problem of a two-stage flow shop in the fruit and vegetable supply chain involving procurement, production, distribution, and order assignment. The supply chain consists of contract growers, processors, and demand points. Growers and processors maintain limited capacity. Growers have processing lines to sort and grade fruits and vegetables, which are then shipped to processors for further processing. The first stage of the problem involves determining the production and distribution schedules of growers to processors. The second stage concerns assigning demand points to processors, scheduling production at processors, and distributing from processors to demand points. This paper considers its two sub-problems and proposes a mixed-integer linear programming problem: growers’ and processors’ production and distribution scheduling with order assignment. Also, we present two respective strongly polynomial algorithms for solving the two sub-problems. Besides, we develop a two-phase heuristic algorithm for the integrated problem by solving sub-problems together. We conduct computational experiments to justify the proposed algorithms and integration value. Computational results reveal that the proposed algorithms outperform the CPLEX on large-scale problem instances. Finally, we analyze the impacts of waste reduction, unit processing time and cost, and inbound and outbound shipping rates on supply chain performance, along with managerial insights.

A Lagrangian heuristic for integrated production planning, material ordering and investment multi-project scheduling in a project-driven supply chain

Effective decision making in large project planning presents a significant challenge. Despite progress in developing mathematical models, few studies comprehensively address project and supply-chain management aspects in an integrated framework. This article presents an integrated approach for multi-project resource investment, material ordering and production planning in a project-driven supply chain. A mixed-integer programming model is developed to optimize the total cost of renewable resource investment, production, transportation, and material ordering and holding, with an opportunity to share mobile renewable resources across multiple projects. A Lagrangian relaxation heuristic algorithm is proposed to determine the upper and lower bounds for the objective function, with an additional set of valid cuts to reduce the distance between bounds. The results of numerical experiments demonstrate the favourable performance of the proposed algorithm compared to the GAMS solver. This article provides insights into supply-chain participant expenses and activity scheduling based on these findings.

A hybrid metaheuristic with learning for a real supply chain scheduling problem

In recent decades, research on supply chain management (SCM) has enabled companies to improve their environmental, social, and economic performance.This paper presents an industrial application of logistics that can be classified as an inventory-route problem. The problem consists of assigning orders to the available warehouses. The orders are composed of items that must be loaded within a week. The warehouses provide an inventory of the number of items available for each day of the week, so the objective is to minimize the total transportation costs and the costs of producing extra stock to satisfy the weekly demand. To solve this problem a formal mathematical model is proposed. Then a hybrid approach that involves two metaheuristics: a greedy randomized adaptive search procedure (GRASP) and a genetic algorithm (GA) is proposed. Additionally, a meta-learning tuning method is incorporated into our hybridized approach, which yields better results but with a longer computation time. Thus, the trade-off of using it is analyzed.An extensive evaluation was carried out over realistic instances provided by an industrial partner. The proposed technique was evaluated and compared with several complete and incomplete solvers from the state of the art (CP Optimizer, Yuck, OR-Tools, etc.). The results showed that our hybrid metaheuristic outperformed the behavior of these well-known solvers, mainly in large-scale instances (2000 orders per week). This hybrid algorithm provides the company with a powerful tool to solve its supply chain management problem, delivering significant economic benefits every week.

Supply Chain Scheduling Method for the Coordination of Agile Production and Port Delivery Operation

The cost-reducing potential of intelligent supply chains (ISCs) has been recognized by companies and researchers. This paper investigates a two-echelon steel supply chain scheduling problem that considers the parallel-batching processing and deterioration effect in the production stage and sufficient vehicles in the port delivery stage. To solve this problem, we first analyze several sufficient and necessary conditions of the optimal scheme. We then propose a heuristic algorithm based on a dynamic programming algorithm to obtain the optimal solution for a special case where the assignment of all ingots to the soaking pits is known. Based on the results of this special case, we develop a modified biased random-key genetic algorithm (BRKGA), which incorporates genetic operations based on the flower pollination algorithm (FPA) to obtain joint production and distribution schedules for the general problem. Finally, we conduct a series of computational experiments, the results of which indicate that BRKGA-FPA has certain advantages in solving quality and convergence speed issues.

Hierarchical Reinforcement Learning for Crude Oil Supply Chain Scheduling

Crude oil resource scheduling is one of the critical issues upstream in the crude oil industry chain. It aims to reduce transportation and inventory costs and avoid alerts of inventory limit violations by formulating reasonable crude oil transportation and inventory strategies. Two main difficulties coexist in this problem: the large problem scale and uncertain supply and demand. Traditional operations research (OR) methods, which rely on forecasting supply and demand, face significant challenges when applied to the complicated and uncertain short-term operational process of the crude oil supply chain. To address these challenges, this paper presents a novel hierarchical optimization framework and proposes a well-designed hierarchical reinforcement learning (HRL) algorithm. Specifically, reinforcement learning (RL), as an upper-level agent, is used to select the operational operators combined by various sub-goals and solving orders, while the lower-level agent finds a viable solution and provides penalty feedback to the upper-level agent based on the chosen operator. Additionally, we deploy a simulator based on real-world data and execute comprehensive experiments. Regarding the alert number, maximum alert penalty, and overall transportation cost, our HRL method outperforms existing OR and two RL algorithms in the majority of time steps.

A gamified teaching–learning based optimization algorithm for a three-echelon supply chain scheduling problem in a two-stage assembly flow shop environment

The supply chain scheduling problem, which is an integrated production scheduling and distribution through the batch delivery system, has been studied by researchers for more than two decades. So far, all researches in this regard have studied the two-echelon supply chain network. The first novelty of this research is that, for the first time, the three-echelon supply chain scheduling problem, including supplier, manufacturer and customer, is investigated considering two separated batch delivery systems for transportation, simultaneously. To do so, a two-stage assembly flow shop environment is used. In the first stage, there are a number of decentralized suppliers who are responsible for manufacturing components. The components are dispatched to the second stage (assembly stage) with the first batch delivery system and after assembly, the orders are dispatched to the customer through secondary batch delivery system. The purpose of this problem is to minimize the cost of total completion time plus batch delivery costs. First, a mixed integer linear programming model (MILP) is presented that is able to solve small-size instances in a logical time. The second novelty of this research is that, to solve large-size instances, a novel gamified teaching–learning based optimization (GTLBO) algorithm is developed with applying a number of dominance rules and gamification tools. The computational results indicate the superiority of the new TLBO variant over its standard form and two well-known methods, i.e. GA and PSO, especially by applying the dominance rules.

Significance of Linear Programming for Optimization

Linear programming (LP) is a powerful mathematical technique that has revolutionized optimization problems across various fields. This abstract highlights the significance of linear programming in optimization and its profound impact on decision-making, resource allocation, and efficiency enhancement. The primary objective of linear programming is to maximize or minimize a linear objective function, subject to a set of linear constraints. LP provides a systematic framework for modeling and solving complex optimization problems, enabling decision-makers to make informed choices and allocate resources optimally. The significance of linear programming lies in its ability to address a wide range of real-world challenges efficiently and effectively. In the realm of operations research, linear programming has found extensive applications in supply chain management, production planning, scheduling, and logistics optimization. By formulating decision problems into mathematical models, LP allows organizations to streamline their processes, minimize costs, optimize resource utilization, and maximize overall productivity. Linear programming has also made significant contributions in the field of economics. It has enabled economists to analyze and optimize economic systems, such as determining the optimal production levels for different goods, optimizing resource allocation in a market, and solving complex pricing problems. LP provides a quantitative framework for economic decision-making, facilitating the identification of optimal solutions and supporting policy development. Furthermore, linear programming plays a crucial role in the field of transportation and network optimization. It aids in solving problems related to route optimization, facility location, network design, and capacity planning. By efficiently allocating resources and optimizing transportation networks, LP helps reduce costs, improve service levels, and enhance overall efficiency in the transportation sector. In summary, the significance of linear programming for optimization is evident in its wide-ranging applications and its ability to tackle complex decision problems in various domains. By employing mathematical modeling and optimization techniques, LP allows decision-makers to make informed choices, allocate resources optimally, and achieve desired objectives. The ongoing advancements in LP algorithms and computing technologies further enhance its applicability and make it an indispensable tool for optimization in today's dynamic and competitive world.

Hybrid Optimization of Green Supply Chain Network and Scheduling in Distributed 3D Printing Intelligent Factory

Considering the advantages of 3D printing, intelligent factories and distributed manufacturing, the 3D printing distributed intelligent factory has begun to rise in recent years. However, because the supply chain network of this kind of factory is very complex, coupled with the impact of customized scheduling and environmental constraints on the enterprise, the 3D printing distributed intelligent factory is facing the great challenge of realizing green supply chain networks and optimizing production scheduling at the same time, and thus a theoretical gap appears. This paper studies the hybrid optimization of green supply chain networks and scheduling of the distributed 3D printing intelligent factory. Firstly, according to the green supply chain network architecture of the distributed 3D printing intelligent factory, the cost minimization model is constructed. Secondly, mathematical software is used to solve the model, and the scheduling plan can be worked out. Finally, through the simulation analysis, it is concluded that the influencing factors such as demand, factory size and production capacity complicate the production distribution, and it can be observed that the carbon emission cost has gradually become the main factor affecting the total cost. The study has a reference value for the management decision making of the distributed 3D printing intelligent factory under the background of carbon emissions.

Guarantee Stability of Supply Chain with Dual Channels under Supply Failure of Goods

The supply chain is an extremely important and complex system, which is widely related to the social system and the management system. The hierarchical scheduling and production planning of a dynamic supply chain can enable enterprises in the supply chain to obtain, and maintain, stable and lasting competitive advantages; thus, improving the overall competitiveness of the supply chain. Simultaneously, research on supply chains is also a hotspot in system engineering. At present, research on complex supply chain systems with dual channels is insufficient, and analysis of dynamic supply chains with supply failure of goods is rare. In the epidemic era, dynamic supply chain management was affected by the epidemic and became a very challenging topic. The control of the supply chain system under the impact of an epidemic situation is particularly important. This paper focused on H∞ the fault-tolerant control of a complex supply chain system with dual channels. First, from the perspective of engineering practice and green production, we established a new green supply chain model. This supply chain model includes the dual-channel supply of goods and also considers the supply failure of goods. At the same time, production delay and supply delay are also considered in this model. Second, a supply scheduling scheme was designed through cybernetics to ensure the stability of a supply chain with dual channels under supply failure of goods, and we completed supply chain management. Finally, the effectiveness of our results was verified by specific Chinese enterprise data.

Book Reviews

In Book Reviews, we review an extensive and diverse range of books. They cover theory and applications in operations research, statistics, management science, econometrics, mathematics, computers, and information systems. In addition, we include books in other fields that emphasize technical applications. The editor will be pleased to receive an email from those willing to review a book with an indication of specific areas of interest. If you are aware of a specific book that you would like to review or that you think should be reviewed, please contact the editor. The following books are reviewed in this issue of INFORMS Journal on Applied Analytics: The Spirit of Green: The Economics of Collisions and Contagions in a Crowded World (author: William D. Nordhaus; reviewer: Amitrajeet A. Batabyal) and Supply Chain Scheduling (authors: Zhi-Long Chen and Nicholas G. Hall; reviewer: Chelliah Sriskandarajah).

Reshore or not Reshore: A Stochastic Programming Approach to Supply Chain Optimization

This paper presents a scenario-based stochastic mixed integer programming model for risk-neutral or risk-averse optimization of supply chain reshoring to domestic region, under the ripple effect propagated from a foreign disruption source region. The reshoring decisions with respect to tier-one suppliers of parts and tier-zero OEM (Original Equipment Manufacturer) assembly plants are considered under disruptions in supply, manufacturing, logistics and demand rippling across the entire supply chain. The proposed innovative approach integrates strategic supply chain reshoring and operational supply chain scheduling, which allows the decision maker to evaluate the operational impact of the strategic decision. Results of computational experiments, partially modeled after a supply chain reshoring problem in the smartphone industry, are provided. The findings indicate that reshoring decisions are strongly dependent on the level of government subsidy for capital expenditure and for risk-neutral reshoring, a portfolio of supply chain nodes with positive expected net savings can be considered only. In general, the reshored supply chain can better meet domestic market demand. Moreover, full reshoring of a supply chain improves its business as usual performance and even partial reshoring mitigates the impact of the ripple effect. However, for risk-averse decision-making, if reshoring is incapable of reducing worst-case cost, in particular, worst-case lost sales, no reshoring is selected.