Multicriterion genetic optimization for due date assigned distribution network problems

This paper focuses on the demand due date factor in multiechelon distribution network problems and its impact on the production scheduling in manufacturing plants. A reliable demand due date is critical in winning of customer orders. However, this may usually require high collaboration among entities in the network. Mismatching of one single schedule may seriously influence the reliability. In this connection, holistically optimizing the schedule of each entity among the network is essential. In addition, on time delivery may induce high operating cost. A trade-off between earliness, on time, and tardiness should also be considered. Hence, a multicriterion genetic optimization methodology is developed to holistically optimize them. It determines the optimized schedule to collaborate each entity to fulfill the demands. For enabling multicriterion decision-making, the proposed algorithm combines analytic hierarchy process with genetic algorithms (GAs). The problem is divided into two parts—(i) demand allocation and transportation problem, and (ii) production scheduling problem. The optimization approach is applied to iteratively optimize part (i), and then part (ii). Three experiments have been carried out, and the computation results show that the effect of due date is critical, and the ability of the proposed algorithms in taking trade-off between earliness and tardiness.     

考虑供应商选择的选址一库存一路径的联合优化

针对装配型制造企业供应链集成优化问题,建立了随机需求情形下整合供应商选择和各层级之间运输方式选择的多层级选址—库存模型。该模型通过对供应商的选择,装配厂和分销中心的选址,相邻两层级之间的分配服务关系及运输方式的确定,实现整体供应链网络成本最小化。为求解此混合整数非线性规划模型,设计了一种矩阵编码的改进自适应遗传算法。仿真实验表明,该算法的解的寻优能力明显优于标准遗传算法,得出了供应链总成本与装配厂的最大提前期存在一定规律性的结论。     

A dual-channel network design model in a green supply chain considering pricing and transportation mode choice

A green supply chain with a well-designed network can strongly influence the performance of supply chain and environment. The designed network should lead the supply chain to efficient and effective management to meet the efficient profit, sustainable effects on environment and customer needs. The proposed mathematical model in this paper identifies locations of productions and shipment quantity by exploiting the trade-off between costs, and emissions for a dual channel supply chain network. Due to considering different prices and customers zones for channels, determining the prices and strategic decision variables to meet the maximum profit for the proposed green supply chain is contemplated. In this paper, the transportation mode as a tactical decision has been considered that can affect the cost and emissions. Lead time and lost sales are considered in the modeling to reach more reality. The developed mathematical model is a mixed integer non-linear programming which is solved by GAMS. Due to NP-hard nature of the proposed model and long run time for large-size problems by GAMS, artificial immune system algorithm based on CLONALG, genetic and memetic algorithms are applied. Taguchi technique is used for parameter tuning of all meta-heuristic algorithms. Results demonstrate the strength of CLONALG rather than the other methods.     

Quantity and Due Date Quoting Available to Promise

The available to promise (ATP) function has increasingly attracted the attention of the supply chain management research community as a tool for enhancing the responsiveness of order promising and the reliability of order fulfillment. It directly links available resources, including both material and capacity, with customer orders and, thus, affects the overall performance of a supply chain. In this paper, a mixed integer programming (MIP) model for a quantity and due date quoting ATP mechanism is presented. This model can provide individual order delivery dates for a batch of customer orders that arrive within a predefined batching interval. In addition, the model allows customized configurations and takes into account a variety of realistic supply chain constraints, such as material compatibility, substitution preferences, capacity utilization, and material reserve. We conclude this paper with sensitivity analysis of performance impacts with respect to batching interval size and material reserve policy.     

整合供应商选择的多层级选址-库存模型

针对多级供应链网络设计中选址和库存一体化决策问题,基于梯级库存策略,建立了整合供应商选择的多层级选址-库存模型。模型以网络中供应商的选择成本、工厂和配送中心的打开成本、层级之间的运输成本、库存成本、采购成本和生产成本之和最小为目标,同时对供应商的选择、工厂和配送中心的选址、配送中心对顾客的分配、层级之间的运输量、工厂和配送中心的订货批量进行决策。为了求解所建立的模型,设计了基于部分编码的粒子群优化算法。20个不同规模的算例测试表明：所建立的模型是有效的,能用于多层级供应链网络的设计;所设计的算法无论是在求解精度,还是在运算速度上都明显优于数学优化软件Lingo 9.0,尤其是当供应链网络中总节点数较大时。     

A genetic algorithm for solving the fixed-charge transportation model: Two-stage problem

Transportation of goods in a supply chain from plants to customers through distribution centers (DCs) is modeled as a two-stage distribution problem in the literature. In this paper we propose genetic algorithms to solve a two-stage transportation problem with two different scenarios. The first scenario considers the per-unit transportation cost and the fixed cost associated with a route, coupled with unlimited capacity at every DC. The second scenario considers the opening cost of a distribution center, per-unit transportation cost from a given plant to a given DC and the per-unit transportation cost from the DC to a customer. Subsequently, an attempt is made to represent the two-stage fixed-charge transportation problem (Scenario-1) as a single-stage fixed-charge transportation problem and solve the resulting problem using our genetic algorithm. Many benchmark problem instances are solved using the proposed genetic algorithms and performances of these algorithms are compared with the respective best existing algorithms for the two scenarios. The results from computational experiments show that the proposed algorithms yield better solutions than the respective best existing algorithms for the two scenarios under consideration.     

Model selection with considering the CO2 emission alone the global supply chain

This study formulates a model for analyzing eco-environmental impact on global supply chain network. The multi-criteria optimization model is applied to seek optimal solutions that not only can achieve predetermined objectives, but also can satisfy constraints for multi-product problems. The overall optimization is achieved using mathematical programming for modeling the supply chain functions such as location, inventory, production, distribution functions and transportation mode selections. Then, the supply chain model is formulated as a minimization problem for costs and environmental impacts. Herein, the solution is the flow of goods in global supply chain environment in different periods of time over one year. Furthermore, the numerical values obtained from a real company are applied to these mathematical formulations to test its usability. The testing is conducted in four different cases that include two combinations, no due date constraint and due date constraint, without connection of distributor and with connection of distributors. The results from these experiments can help in determining the best transportation routes, inventory levels, shipment quantity, and transportation modes. Specifically, the results propose a new configuration for designing global supply chain for the case company that could minimize economical and environmental impacts problems simultaneously.     

Multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times

Supply planning for two-level assembly systems under lead time uncertainties is considered. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. A holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the component lead time is a random discrete variable. The objective is to find the release dates for the components at level 2 in order to minimize the expected component holding costs and to maximize the customer service level for the finished product. For this new problem, we consider two multi-objective approaches, which are both based on genetic algorithms. They are evaluated with a variety of supply chain settings, and their respective performance is reported and commented. These two heuristics permitted to obtain interesting results within a reasonable computational time.     

Robust optimization of supply chain network design in fuzzy decision system

This paper presents a new robust optimization method for supply chain network design problem by employing variable possibility distributions. Due to the variability of market conditions and demands, there exist some impreciseness and ambiguousness in developing procurement and distribution plans. The proposed optimization method incorporates the uncertainties encountered in the manufacturing industry. The main motivation for building this optimization model is to make tools available for producers to develop robust supply chain network design. The modeling approach selected is a fuzzy value-at-risk (VaR) optimization model, in which the uncertain demands and transportation costs are characterized by variable possibility distributions. The variable possibility distributions are obtained by using the method of possibility critical value reduction to the secondary possibility distributions of uncertain demands and costs. We also discuss the equivalent parametric representation of credibility constraints and VaR objective function. Furthermore, we take the advantage of structural characteristics of the equivalent optimization model to design a parameter-based domain decomposition method. Using the proposed method, the original optimization problem is decomposed to two equivalent mixed-integer parametric programming sub-models so that we can solve the original optimization problem indirectly by solving its sub-models. Finally, we present an application example about a food processing company with four suppliers, five plants, five distribution centers and five customer zones. We formulate our application example as parametric optimization models and conduct our numerical experiments in the cases when the input data (demands and costs) are deterministic, have fixed possibility distributions and have variable possibility distributions. Experimental results show that our parametric optimization method can provide an effective and flexible way for decision makers to design a supply chain network.     

Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm

Nowadays, scheduling of production cannot be done in isolation from scheduling of transportation since a coordinated solution to the integrated problem may improve the performance of the whole supply chain. In this paper, because of the widely used of rail transportation in supply chain, we develop the integrated scheduling of production and rail transportation. The problem is to determine both production schedule and rail transportation allocation of orders to optimize customer service at minimum total cost. In addition, we utilize some procedures and heuristics to encode the model in order to address it by two capable metaheuristics: Genetic algorithm (GA), and recently developed one, Keshtel algorithm (KA). Latter is firstly used for a mathematical model in supply chain literature. Besides, Taguchi experimental design method is utilized to set and estimate the proper values of the algorithms’ parameters to improve their performance. For the purpose of performance evaluation of the proposed algorithms, various problem sizes are employed and the computational results of the algorithms are compared with each other. Finally, we investigate the impacts of the rise in the problem size on the performance of our algorithms.     

Multi-objective biogeography-based optimization for supply chain network design under uncertainty

This paper proposes a new two-stage optimization method for multi-objective supply chain network design (MO-SCND) problem with uncertain transportation costs and uncertain customer demands. On the basis of risk-neutral and risk-averse criteria, we develop two objectives for our SCND problem. We introduce two solution concepts for the proposed MO-SCND problem, and use them to define the multi-objective value of fuzzy solution (MOVFS). The value of the MOVFS measures the importance of uncertainties included in the model, and helps us to understand the necessity of solving the two-stage multi-objective optimization model. When the uncertain transportation costs and customer demands have joined continuous possibility distributions, we employ an approximation approach (AA) to compute the values of two objective functions. Using the AA, the original optimization problem becomes an approximating mixed-integer multi-objective programming model. To solve the hard approximating optimization problem, we design an improved multi-objective biogeography-based optimization (MO-BBO) algorithm integrated with LINGO software. We also compare the improved MO-BBO algorithm with the multi-objective genetic algorithm (MO-GA). Finally, a realistic dairy company example is provided to demonstrate that the improved MO-BBO algorithm achieves the better performance than MO-GA in terms of solution quality.     

Evaluating flexibility on order quantity and delivery lead time for a supply chain system

Most of the previous literature on production flexibility is centred on the flexibility of manufacturing systems. However, the manufacturing system is just one of several key components of a supply chain. A supply chain is a network involving all of the activities within individual organisations that link material suppliers, manufacturing factories, distributors, warehouses, retailers and customers. Research into the flexibility of a supply chain therefore extends from the intra-organisational flexibilities to the inter-organisational flexibilities. This article provides a study of examining two aspects of supply chain flexibility: order quantity flexibility and lead time flexibility, which have been clarified as the two most common changes which occur in supply chains. Order quantity flexibility refers to the ability to provide proper order quantity for customer needs. Lead time flexibility allows customers to set the order due date depending on their needs. A simulation model is built to evaluate the performance on different flexibility levels of a supply chain. The experimental results provide interesting insights and can be applied in selecting suppliers with order quantity flexibility and delivery lead time flexibility.     

A best-matching protocol for order fulfillment in re-configurable supply networks

Order fulfillment is a process which encompasses all the activities from the inquiry of goods by the customer to the final delivery of goods to the customer. The most important activity of the order fulfillment process is the selection of the order fulfilling agent in the supply network. The selection of the agent involves multiple criteria based on quantitative and qualitative metrics and requires several self-interested agents and organizations to dynamically form and configure supply chain. This article describes a methodology for selection of an order fulfillment agent in a collaborative, geographically distributed network by developing a Best Matching Protocol (BMP). The BMP developed, enables better matching of fulfillment agents with customers in a given supply network, by determining which agent best satisfies the pre-defined quality and cost requirements of the customer. The protocol enables collaboration between the agents of the Supply Network (SN) and provides a scalable solution for the increasing size of the SN.     

舰载机弹药调运不确定系统的T-S模糊优化模型

具有不确定性的弹药调运过程是影响舰载机作战能力的关键因素.针对弹药调运系统内部和外部不确定性因素的多级供应链网络,研究带有不确定项的弹药动态调运系统的模糊建模和优化问题.利用Takagi-Sugeno模糊方法对系统中不确定项进行非线性建模,基于鲁棒稳定条件,优化设计弹药调运方案.通过与其他模糊控制方法的对比仿真,验证了优化模型的有效性和可靠性,所提方法能够削弱非线性系统内外部不确定项引起的弹药存储量和代价目标的波动,保证弹药持续稳定且及时供应.     

考虑碳排放的冷链物流轴幅式网络多目标优化

针对传统冷链网络优化模型忽视碳排放量的不足,基于绿色物流、共享经济的相关理念,在轴幅式理论下对多个区域的冷链配送进行资源整合后进行共同配送,提高冷链配送车辆的满载率。同时,构建考虑碳排放成本在内的总成本最小和最大化客户满意度的多目标优化模型,达到降低总成本和满足客户最大满意度的目的,实现经济效益和环境效益共赢的状态。以客户满意度来表示物流网络系统的可靠性和服务质量,并结合易腐品的新鲜度对时间的敏感性,引入货损成本。最后,设计粒子群算法对其进行求解。通过算例对比验证了模型与算法的有效性,有效解决冷链物流网络的网点布局和运输配送问题。     

Selection of a dynamic supply portfolio in make-to-order environment withrisks

The problem of a multi-period supplier selection and order allocation in make-to-order environment in the presence of supply chain disruption and delay risks is considered. Given a set of customer orders for finished products, the decision maker needs to decide from which supplier and when to purchase product-specific parts required for each customer order to meet customer requested due date at a low cost and to mitigate the impact of supply chain risks. The selection of suppliers and the allocation of orders over time is based on price and quality of purchased parts and reliability of supplies. For selection of dynamic supply portfolio a mixed integer programming approach is proposed to incorporate risk that uses conditional value-at-risk via scenario analysis. In the scenario analysis, the low-probability and high-impact supply disruptions are combined with the high probability and low impact supply delays. The proposed approach is capable of optimizing the dynamic supply portfolio by calculating value-at-risk of cost per part and minimizing expected worst-case cost per part simultaneously. Numerical examples are presented and some computational results are reported.     

Real-time capacity requirement planning for make-to-order manufacturing with variable time-window orders

In a make-to-stock (MTS) manufacturing environment using material requirement planning (MRP), checking the capacity feasibility of a master production schedule (MPS) requires capacity requirement planning (CRP) that can be easily calculated. The time window of an order is the time interval from its ready date to its due date. In a make-to-order (MTO) manufacturing environment, the CRP method checks whether a set of orders with different time windows can be scheduled for timely completion. This corporate-level CRP problem has long perplexed MTO contract manufacturers, such as those in the fashion industry. This study therefore develops an efficient and effective CRP approach that considers orders with variable time windows. Real-time capacity feasibility can be checked on both the corporate planning and detailed operational scheduling levels by applying the preemptive earliest due date (PEDD) rule to a single machine problem. This simple and efficient dispatching rule can assess the impact on capacity consumption each time an inquiry order is received or select a set of pre-prioritized orders that can be feasibly scheduled. The efficiency of a supply chain network is affected by its overall lead time, which includes time spent on order processing, manufacturing, and transportation. The proposed approach significantly reduces the order processing time and enhances supply chain efficiency.     

Genetic algorithm for supply planning in two-level assembly systems with random lead times

This paper examines supply planning for two-level assembly systems under lead time uncertainties. It is supposed that the demand for the finished product and its due date are known. The assembly process at each level begins when all necessary components are in inventory. If the demand for the finished product is not delivered at the due date, a tardiness cost is incurred. In the same manner, a holding cost at each level appears if some components needed to assemble the same semi-finished product arrive before beginning the assembly at this level. It is assumed also that the lead time at each level is a random discrete variable. The expected cost is composed of the tardiness cost for finished product and the holding costs of components at levels 1 and 2. The objective is to find the release dates for the components at level 2 in order to minimize the total expected cost. For this new problem, a genetic algorithm is suggested. The proposed algorithm is evaluated with a variety of supply chain settings in order to verify its robustness across different supply chain scenarios. Moreover, the effect of a local search on the performance of the Genetic Algorithm in terms of solution quality, convergence and computation time is also investigated.     

Exact and metaheuristic algorithms for the vehicle routing problem with a factory-in-a-box in multi-objective settings

Emergencies, such as pandemics (e.g., COVID-19), warrant urgent production and distribution of goods under disrupted supply chain conditions. An innovative logistics solution to meet the urgent demand during emergencies could be the factory-in-a-box manufacturing concept. The factory-in-a-box manufacturing concept deploys vehicles to transport containers that are used to install production modules (i.e., factories). The vehicles travel to customer locations and perform on-site production. Factory-in-a-box supply chain optimization is associated with a wide array of decisions. This study focuses on selection of vehicles for factory-in-a-box manufacturing and decisions regarding the optimal routes within the supply chain consisting of a depot, suppliers, manufacturers, and customers. Moreover, in order to contrast the options of factory-in-a-box manufacturing with those of conventional manufacturing, the location of the final production is determined for each customer (i.e., factory-in-a-box manufacturing with production at the customer location or conventional manufacturing with production at the manufacturer locations). A novel multi-objective optimization model is presented for the vehicle routing problem with a factory-in-a-box that aims to minimize the total cost associated with traversing the edges of the network and the total cost associated with visiting the nodes of the network. A customized multi-objective hybrid metaheuristic solution algorithm that directly considers problem-specific properties is designed as a solution approach. A case study is performed for a vaccination project involving factory-in-a-box manufacturing along with conventional manufacturing. The case study reveals that the developed solution method outperforms the ε-constraint method, which is a classical exact optimization method for multi-objective optimization problems, and several well-known metaheuristics.     

A First-Order Hybrid Petri Net Model for Supply Chain Management

A supply chain (SC) is a network of independent manufacturing and logistics companies that perform the critical functions in the order fulfillment process. This paper proposes an effective and modular model to describe material, financial and information flow of SCs at the operational level based on first-order hybrid Petri nets (PNs), i.e., PNs that make use of first-order fluid approximation. The proposed formalism enables the SC designer to choose suitable production rates of facilities in order to optimize the chosen objective function. The optimal mode of operation is performed based on the state knowledge of the obtained linear discrete-time, time-varying state variable model in order to react to unpredictable events such as the blocking of a supply or an accident in a transportation facility. A case study is modeled in the proposed framework and is simulated under three different closed-loop control strategies.