Supply Chain Scheduling Problem Research Articles

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.

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.

A self-adaptive hyper-heuristic based multi-objective optimisation approach for integrated supply chain scheduling problems

Recent global changes have prompted manufacturers to shift their production systems to make-to-order (MTO) supply chain (SC), enabling them to adapt customised customer requirements with their rapidly changing behaviours, reduce inventory costs, and obtain competitive advantages in the market. However, traditional MTO-based scheduling approaches fail to consider all the SC stages required for optimal schedules. This study proposes an integrated SC scheduling problem (ISCSP), where supplier, manufacturer and batching decisions are simultaneously optimised in response to heterogeneous customer requirements with time window constraints. Both economic and environmental sustainability for the supply portfolio is considered while the manufacturer is modelled using the flexible job shop scheduling (FJS) problem. Since the proposed ISCSP is an extension of the FJS problem, this can also be considered an NP-hard problem, which cannot be solved by traditional optimisation techniques, particularly for larger instances. Thus, a self-adaptive multi-operator and multi-objective hyper-heuristic (SA(MO)2H) is designed, where the low-level heuristic utilises strengths of four solution updating heuristics and is intelligently guided by the reinforcement learning, to address the problem. The proposed SA(MO)2H integrates environmental sustainability into the evolutionary process to achieve the best possible supply portfolio, adopting the VIKORSORT approach. Finally, a rigorous experimental study on solving a wide range of instances is conducted to evaluate the performance of SA(MO)2H against its non-intelligent versions and five existing algorithms. Overall, the most beneficial facet of the developed ISCSP and SA(MO)2H is the visibility and meaningful managerial insights provided by the multi-portfolio solutions fostering the responsive relationship among SC stages.

Approximate model and algorithms for precast supply chain scheduling problem with time-dependent transportation times

This paper focuses on the precast supply chain scheduling problem with time-dependent transportation time to minimise the total weighted tardiness (PSCSP_TDT |TWT). In the problem, an order sequence and several job sequences are to be determined simultaneously. At first, through in-depth analysis of problem structure and real data from a precast manufacturer, we approximate the problem into a three-stage order scheduling problem by combining the seven production stages into one differentiation stage, and then explore some useful properties of the schedules for the approximate problem. Subsequently, to solve the small instances for the PSCSP_TDT |TWT, we propose an approximate model-based hybrid dynamic programming and heuristic (AMHDPH) and obtain a lower bound as a by-product of the algorithm. For dealing with medium-or large instances, with considering the complexity of the problem, we propose four approximate model-based hybrid iterated greedy (AMHIG) algorithms by integration of constructive heuristics, structural properties of solutions, an iterated greedy, and a correction heuristic. Comprehensive computational results show that the AMHDPH generates tight lower bounds for small instances and solves the most of small instances to optimality within 60 seconds. Whereas the best AMHIG generates feasible solutions with an average optimality gap below 5 percent for around 70 percent instances.

Swarm intelligent based metaheuristics for a bi-objective flexible job shop integrated supply chain scheduling problems

This study introduces a bi-objective integrated supply chain (SC) scheduling (SCS) model to deal with the challenges of providing highly customized and on-time delivery requirements at the least cost. To address these real-life challenges, the model integrates the supply portfolio into production scheduling with a customer-imposed delivery time window. A set of conflicting factors is considered for the SC in which the manufacturer is modeled using the flexible job shop (FJS) problem to include greater flexibility in process routing. Since the proposed SCS model extends the strongly NP-hard FJS problem, two new meta-heuristic algorithms are developed, enhancing the performance of the multi-objective particle swarm optimization (MOPSO). A Tabu search inspired search mechanism and a problem-specific mutation operator are designed in both algorithms. As inadequate archive diversity leads to premature convergence and unsatisfactory Pareto solutions in many cases, this work employs two practical approaches separately (i.e., crowding distance (CD) and niche count preservation of reference point (RP)) in the proposed MOPSOs, called CD-MOPSO and RP-MOPSO, respectively. For leader selection, CD-MOPSO uses the swarm distance-based Roulette wheel, whereas the concept of RP is proposed in RP-MOPSO to expedite convergence. The performances of the proposed algorithms are validated against four existing algorithms and assessed by utilizing several criteria after solving 45 artificial instances. The simulation results and statistical analysis demonstrate the supremacy of the RP-MOPSO which increases the flexibility for a decision-maker in providing a higher number of Pareto solutions and more diverse and regular frontiers within reasonable computational time. Finally, a sensitivity analysis of the model is conducted, and managerial insights are provided.

Supply Chain Optimization Considering Sustainability Aspects

Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when considering a distributed manufacturing system (DMS). The present study aims to tackle the supply chain scheduling problem in a DMS while considering two essential sustainability aspects, namely environmental and economic. The economic aspect is addressed by optimizing the total delivery time of order, transportation cost, and production cost while optimizing environmental pollution and the quality of products contribute to the environmental aspect. To cope with the problem, it is mathematically formulated as a mixed-integer linear programming (MILP) model. Due to the complexity of the problem, an improved genetic algorithm (GA) named GA-TOPKOR is proposed. The algorithm is a combination of GA and TOPKOR, which is one of the multi-criteria decision-making techniques. To assess the efficiency of GA-TOPKOR, it is applied to a real-life case study and a set of test problems. The solutions obtained by the algorithm are compared against the traditional GA and the optimum solutions obtained from the MILP model. The results of comparisons collectively show the efficiency of the GA-TOPKOR. Analysis of results also revealed that using the TOPKOR technique in the selection operator of GA significantly improves its performance.

A note on the complexity of two supply chain scheduling problems

A note on the complexity of two supply chain scheduling problems

A two-stage stochastic supply chain scheduling problem with production in cellular manufacturing environment: A case study

An integrated decision in supply chain is a significant principle in order to compete in today’s market. This paper proposes a novel mathematical model in a two-stage supply chain scheduling to cooperate procurement and manufacturing activities. The supply chain scheduling along with the production approach of cellular manufacturing under demand, processing time, and transportation time uncertainties makes business environment sustainably responsive to the changing needs of customers. Uncertainties are formulated by queuing theory. In this paper, a new mixed-integer nonlinear programming formulation is used to determine types of vehicles to carry raw materials, suppliers to procure, priority of each part in order to process, and cell formation to configure work centers. The goal is to minimize total tardiness. A linearization method is used to ease tractability of the model. A genetic algorithm is developed due to the NP-hard nature of the problem. The parameters of the genetic algorithm are set and estimated by Taguchi’s experimental design. Numerous test problems are employed to validate the effectiveness of the modeling and the efficiency of solution approaches. Finally, a real case study and a sensitivity analysis are discussed to provide significant managerial insights and assess the applicability of the proposed model.

Two meta-heuristic algorithms for optimizing a multi-objective supply chain scheduling problem in an identical parallel machines environment

Optimizing the trade-off between crucial decisions has been a prominent issue to help decision-makers for synchronizing the production scheduling and distribution planning in supply chain management. In this article, a bi-objective integrated scheduling problem of production and distribution is addressed in a production environment with identical parallel machines. Besides, two objective functions are considered as measures for customer satisfaction and reduction of the manufacturer’s costs. The first objective is considered aiming at minimizing the total weighted tardiness and total operation time. The second objective is considered aiming at minimizing the total cost of the company’s reputational damage due to the number of tardy orders, total earliness penalty, and total batch delivery costs. First, a mathematical programming model is developed for the problem. Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard. A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a non-dominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator. The experiments on MOPSO and NSGA-II are carried out on small, medium, and large scale problems. Moreover, the performance of the two algorithms is compared according to some comparing criteria. The computational results reveal that the NSGA-II performs highly better than the MOPSO algorithm in small scale problems. In the case of medium and large scale problems, the efficiency of the MOPSO algorithm was significantly improved. Nevertheless, the NSGA-II performs robustly in the most important criteria.

Supply Chain Scheduling Using Double Deep Time-Series Differential Neural Network

The purpose of supply chain scheduling is to be able to find an optimized plan and strategy so as to optimize the benefits of the entire supply chain. This paper proposes a method for processing tightly coordinated supply chain task scheduling problems based on an improved Double Deep Timing Differential Neural Network (DDTDN) algorithm. The Semi-Markov Decision Process (SMDP) modeling of the state characteristics and action characteristics of the supply chain scheduling problem is realized, so as to transform the task scheduling problem of the tightly coordinated supply chain into a multi-stage decision problem. The deep neural network model can help fit the state value function, and the unique reinforcement learning online evaluation mechanism can realize the selection of the best action strategy combination, and optimize it under the condition of only the stator processing time. Finally, the optimal action strategy group is obtained.

Fast Algorithms for Basic Supply Chain Scheduling Problems

A basic supply chain scheduling problem in which the orders released over time are to be delivered into the batches with unlimited capacity is considered. The delivery of each batch has a fixed cost D, whereas any order delivered after its release time yields an additional delay cost equal to the waiting time of that order in the system. The objective is to minimize the total delivery cost of the batches plus the total delay cost of the orders. A new algorithmic framework is proposed based on which fast algorithms for the solution of this problem are built. The framework can be extended to more general supply chain scheduling models and is based on a theoretical study of some useful properties of the offline version of the problem. An online scenario is considered as well, when at each assignment (order release) time the information on the next order released within the following T time units is known but no information on the orders that might be released after that time is known. For the online setting, it is shown that there is no benefit in waiting for more than D time units for incoming orders, i.e., potentially beneficial values for T are 0<T<D, and three linear-time algorithms are proposed, which are optimal for both the offline and the online cases when T≥D. For the case 0<T<D an important real-life scenario is studied. It addresses a typical situation when the same number of orders are released at each order release time and these times are evenly distributed within the scheduling horizon. An optimal algorithm which runs much faster than earlier known algorithms is proposed.

An Optimization Model for Closed-Loop Supply Chain Scheduling Problem

In today's complex world and in order to increase competitiveness, planners in the manufacturing systems have focused on product distribution and collection of used products. In this paper, the closed-loop supply chain scheduling problem is investigated for the first time. A comprehensive and integrated model is presented for production scheduling, delivering products to retailers using limited-capacity vehicles, and pick-upping end of life products in order to recycle and reuse in supply chain. The aim of this problem is to minimize maximum tardiness. Due to the fact that this problem is NP-hard, a genetic algorithm is presented to solve the large-size instances by obtaining near-optimal solutions. To illustrate the importance of the problem under consideration, a case study of the motor oil supply chain is presented.

An Optimization Model for Closed-Loop Supply Chain Scheduling Problem

در جهان پیچیده امروزی و به­‌منظور افزایش رقابت‌­پذیری، توجه برنامه­‌ریزان در بخش سیستم­‌های تولیدی به مقوله توزیع محصولات و جمع‌آوری محصولات استفاده‌­شده افزایش یافته است. در این پژوهش، مسئله زمان­بندی زنجیره تأمین حلقه‌­بسته برای نخستین بار بررسی می­‌شود. یک مدل جامع و یکپارچه در خصوص زمان­بندی تولید محصولات، ارسال بسته‌­ای آن­ها به مشتریان توسط وسایل حمل‌ونقل با ظرفیت محدود و جمع­‌آوری محصولات استفاده­‌شده از محل مشتریان و بازگرداندن آن­ها به محل کارخانه برای انجام فرآیند بازیافت ارائه می‌­شود. هدف این مسئله، کمینه‌­کردن حداکثر زمان تأخیر در تحویل محصولات است. با توجه به NP-hard­ بودن مسئله، برای حل مسائل بزرگ یک الگوریتم ژنتیک ارائه می­‌شود که قادر است جواب­‌هایی نزدیک به جواب بهینه ایجاد کند. برای بیان میزان اهمیت مسئله موردبررسی در این پژوهش، یک مطالعه موردی مربوط به زنجیره تأمین روغن‌­موتور ارائه می‌شود.

A green multi-objective integrated scheduling of production and distribution with heterogeneous fleet vehicle routing and time windows

Scheduling the supply chain in an integrated manner is a quite fundamental subject in supply chain management. To consider due-dates with production and distribution times at the same time could lead to reduced costs and, thereby, increased profitability. Integrating the problems dealing with due-date assignment, production/distribution times, and routing, while incorporating environmental considerations, could not only increase the cohesion among different decision-making levels and reduce costs in the long run, it would also contribute to the improvement of environmental conditions and benefit the world population. In this study, the integrated supply chain scheduling problem features assignment of due dates, batch delivery, assignment to multiple heterogeneous vehicles based on their capacity, and delivery of customer orders in time-windows. The objective is to minimize distribution cost, fixed and variable fuel costs, the carbon emitted by the vehicles, total delivery tardiness, and customer dissatisfaction. In this model, the customers are categorized into five clusters based on the length of their association, how recently they began interacting, how often they interact, and the monetary value of their interaction with the organization. The members of these clusters are called core customers, potential customers, new customers, lost customers, and resource-consumption customers. A mixed integer non-linear programming model is introduced for this problem which is solved using three multi-objective metaheuristic algorithms: Multi-Objective Particle Swarm Optimization, Non-dominated Sorting Genetic Algorithm II, and Multi Objective Ant Colony Optimization. A number of performance criteria and statistical tests are used to evaluate the algorithms.

Examining additive manufacturing in supply chain context through an optimization model

This study explores the problem of integrated jobs and vehicles scheduling in a two-stage supply chain, where parts are processed on additive manufacturing (AM) machines and then distributed to customers. In this study, we develop an optimization model for the problem with the objective of makespan minimization. Besides, several structural properties and lower bound formulation are provided to explain the main characteristics of the problem. In this regard, this study also contributes to the existing academic literature by investigating the two-stage supply chain scheduling problem with the additive manufacturing technology. Because the addressed problem belongs to non-deterministic polynomial-time hardness (NP-hard) problem class, a best-fit heuristic-based approach and several selection rules are developed to solve the problem. Each selection rule is designed by considering a distinct structural property of the problem and, thus, each of which is considered to be a different algorithm. A comprehensive experimental study is conducted through random instances, which are generated for small- and large-sized problems by considering real-production data. According to the computational results, the best-fit capacity utilization based selection (BFCUBS) algorithm is superior to others and yields a substantial improvement in the makespan. The reason behind this fact is that the high utilization of capacity enables a large number of jobs to be completed in a short time. Besides, as the number of jobs decreases and the capacity utilization rates increases all algorithms provide better results.

Two heuristic methods based on decomposition to the integrated multi-agent supply chain scheduling and distribution problem

ABSTRACT Supply chain integration has become one of the most attractive topics for researchers in recent years. One of the advantages of this integration is in improving overall profit in comparison to separate decisions. In this study, an integrated scheduling and distribution problem is investigated. One of the contributions of this paper is to study this problem from a multi-agent viewpoint. In this case, each agent has a set of jobs with its own objective and compete with each other to acquire supply chain resources. Here, a two-agent problem is discussed where the objectives of the agents are the minimization of the total tardiness and the total cost of distribution. A mathematical formulation and two heuristics based on decomposition approaches are presented. In the first approach, a modified Benders decomposition is presented. Also, some valid inequalities are introduced to increase the convergence speed of this algorithm. In the second approach, a decomposition and cutting approach is developed. The results represent the good performance of both algorithms in comparison to other exact methods.

Emergency supply chain scheduling problem with multiple resources in disaster relief operations

In emergency logistics, it is important to allocate available assets to operations effectively due to the resource scarcity. This paper considers a supply chain network which includes local and global suppliers of medical relief items, regional and central distribution centers, and many customer demand points. Either pre-positioned resources at distribution centers or new assembly operations at them can fulfill resources demands. Moreover, a capacitated multi-stage operations scheduling problem is presented, in which renewable (medical teams) as well as non-renewable resources (standard or customized medical kits) are required in an emergency supply chain. In addition, a new mixed-integer programming model is presented and solved for small-sized problems. Further, for solving large-sized problems, a particle swarm optimization algorithm is developed. Finally, the performance of the proposed solution method is evaluated, and some sensitivity analyses are conducted on the more important parameters.

A Pareto approach for the multi-factory supply chain scheduling and distribution problem

Integrated decisions in the supply chain are one of the most attractive topics for researchers. But to get closer to the real-world problems, other real assumptions should be considered. One of these assumptions is the multi-agent view in which several sets of customers or agents with their own objective compete with each other to acquire the supply chain resources. Here, an integrated supply chain scheduling problem along with the batch delivery consideration in a series multi-factory environment is investigated and the routing decisions among customers are considered. A mathematical model is presented for this problem. Due to the complexity, a novel ant colony optimization algorithm is developed to obtain Pareto solutions. Also, a simulated annealing based local search is used to improve the quality of solutions. The performance of the algorithm is compared with three well-known multi-objective algorithms. Results show the proper performance of the proposed algorithm compared to the other algorithms.

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.