An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design

This paper addresses an evaluation of new heuristics solution procedures for the location of cross-docks and distribution centers in supply chain network design. The model is characterized by multiple product families, a central manufacturing plant site, multiple cross-docking and distribution center sites, and retail outlets which demand multiple units of several commodities. This paper describes two heuristics that generate globally feasible, near optimal distribution system design and utilization strategies utilizing the simulated annealing (SA) methodology. This study makes two important contributions. First, we continue the study of location planning for the cross-dock and distribution center supply chain network design problem. Second, we systematically evaluate the computational performance of this network design location model under more sophisticated heuristic control parameter settings to better understand interaction effects among the various factors comprising our experimental design, and present convergence results. The central idea of the paper is to evaluate the impact of geometric control mechanism vis-a-vis more sophisticated ones on solution time, quality, and convergence for two new heuristics. Our results suggest that integrating traditional simulated annealing with TABU search is recommended for this supply chain network design and location problem.     

GBOM-oriented management of production disruption risk and optimization of supply chain construction

A generic bill-of-materials (GBOM) describes demand for materials and their proportional relations to a family of products. Supply chain constructed from the perspective of the GBOM is able to respond swiftly to market demand and lean production can be achieved by managing the total cost of supply chain effectively. Based on the GBOM, this paper examines the control of production disruption risk related to supply chain and investigates the uncertainty of production in supply chain enterprises for the purpose of achieving optimal profits in supply chain. As the production disruption risk is controlled at a certain level, the selection model of supply chain partners, which is specific and more feasible, can be constructed. A combination of random simulation and neural network is deployed to approximate uncertain function, and genetic algorithm and simulated annealing arithmetic are also used to approximately achieve the optimal scheme of supply chain construction in the context of uncertainty.     

Robust supply chain design under uncertain demand in agile manufacturing

This paper considers a supply chain design problem for a new market opportunity with uncertain demand in an agile manufacturing setting. We consider the integrated optimization of logistics and production costs associated with the supply chain members. These problems routinely occur in a wide variety of industries including semiconductor manufacturing, multi-tier automotive supply chains, and consumer appliances to name a few. There are two types of decision variables: binary variables for selection of companies to form the supply chain and continuous variables associated with production planning. A scenario approach is used to handle the uncertainty of demand. The formulation is a robust optimization model with three components in the objective function: expected total costs, cost variability due to demand uncertainty, and expected penalty for demand unmet at the end of the planning horizon. The increase of computational time with the numbers of echelons and members per echelon necessitates a heuristic. A heuristic based on a k-shortest path algorithm is developed by using a surrogate distance to denote the effectiveness of each member in the supply chain. The heuristic can find an optimal solution very quickly in some small- and medium-size cases. For large problems, a “good” solution with a small gap relative to our lower bound is obtained in a short computational time.     

Location and transportation planning in supply chains under uncertainty and congestion by using an improved electromagnetism-like algorithm

Supply chain decision makers are constantly trying to improve the customer demand fulfillment process and reduce the associated costs via decision making models and techniques. As two of the most important parameters in a supply chain, supply and demand quantities are subject to uncertainty in many real-world situations. In addition, in recent decades, there is a trend to think of the impacts of supply chain design and strategies on society and environment. Especially, transportation of goods not only imposes costs to businesses but also has socioeconomic influences. In this paper, a fuzzy nonlinear programming model for supply chain design and planning under supply/demand uncertainty and traffic congestion is proposed and a hybrid meta-heuristic algorithm, based on electromagnetism-like algorithm and simulated annealing concepts, is designed to solve the model. The merit of this paper is presenting a realistic model of current issues in supply chain design and an efficient solution method to the problem. These are significant findings of this research which can be interesting to both researchers and practitioners. Several numerical examples are provided to justify the model and the proposed solution approach.     

A steady-state genetic algorithm for multi-product supply chain network design

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management (SCM). The problem is often an important and strategic operations management problem in SCM. The design task involves the choice of facilities (plants and distribution centers (DCs)) to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper presents a solution procedure based on steady-state genetic algorithms (ssGA) with a new encoding structure for the design of a single-source, multi-product, multi-stage SCN. The effectiveness of the ssGA has been investigated by comparing its results with those obtained by CPLEX, Lagrangean heuristic, hyrid GA and simulated annealing on a set of SCN design problems with different sizes.     

A simulated annealing algorithm for multi-manned assembly line balancing problem

Assembly line balancing problems with multi-manned workstations usually occur in plants producing high volume products (e.g. automotive industry) in which the size of the product is reasonably large to utilize the multi-manned assembly line configuration. In these kinds of assembly lines, usually there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. However, owing to the high computational complexity, it is quite difficult to achieve an optimal solution to the balancing problem of multi-manned assembly lines with traditional optimization approaches. In this study, a simulated annealing heuristic is proposed for solving assembly line balancing problems with multi-manned workstations. The line efficiency, line length and the smoothness index are considered as the performance criteria. The proposed algorithm is illustrated with a numerical example problem, and its performance is tested on a set of test problems taken from literature. The performance of the proposed algorithm is compared to the existing approaches. Results show that the proposed algorithm performs well.     

Solving a multi-objective master planning problem with substitution and a recycling process for a capacitated multi-commodity supply chain network

This study focuses on solving the multi-objective master planning problem for supply chains by considering product structures with multiple final products using substitutions, common components, and recycled components. This study considers five objectives in the planning process: (1) minimizing the delay cost, (2) minimizing the substitution priority, (3) minimizing the recycling penalty, (4) minimizing the substitution cost, and (5) minimizing the cost of production, processing, inventory holding and transportation. This study proposes a heuristic algorithm, called the GA-based Master Planning Algorithm (GAMPA), to solve the supply-chain master planning problem efficiently and effectively. GAMPA first transforms the closed-loop supply chain into an open-loop supply chain that plans and searches the sub-networks for each final product. GAMPA then uses a genetic algorithm to sort and sequence the demands. GAMPA selects the chromosome that generates the best planning result according to the priority of the objectives. GAMPA plans each demand sequentially according to the selected chromosome and a randomly-selected production tree. GAMPA tries different production trees for each demand and selects the best planning result at the end. To show the effectiveness and efficiency of GAMPA, a prototype was constructed and tested using complexity analysis and computational analysis to demonstrate the power of GAMPA.     

Mass customization in the product life cycle

This study presents an introduction to mass customization in the product life cycle—the goal of mass customization, mass customization configurations, and new customer integration techniques, modular design techniques, flexible manufacturing systems (FMSs), and supply chain management methods. The study reviews three selected books and twenty-one selected papers—early papers that describe the goal of mass customization, early papers that describe mass customization configurations, and recent papers that describe new customer integration techniques, modular design techniques, FMSs, and supply chain management methods. The study shows that the goal of mass customization is to create individually customized products, with mass production volume, cost, and efficiency, that most companies use ‘assemble-to-order’ configurations to create standardized products, and that more work is needed on interactive customer integration techniques, collaborative modular design techniques, reconfigurable manufacturing systems, and integrated supply chain management methods to achieve the goal of mass customization.     

Web-based modular interface geometries with constraints in assembly models

The use of a modular technique in modeling for assembly can improve design efficiency and reduce the cost of product development. This paper presents an approach to an assembly model that was built using modular interface geometries. This paper proposes a novel, hybrid modular design strategy instead of the traditional, top-down design process. The curve-joint method is used as a simplified process for converting a 3D solid model to a skeleton model with interface geometries in modeling for assembly. This research builds assembly interface geometries with their constraints in the assembly model instead of using information about individual assembly parts for the product. These interface geometries are easy to share, and they deliver the design requirements properly. They also ensure that minimal efforts will be required in the design change process. By implementing this method, the constraints of the features in modular assembly parts can be transferred to interface geometries. Designers can easily add, replace, and delete design parts in the modular product. Module interaction for application programmed interface (MIAPI) is developed using HTML and JavaScript. The module structure of products can be verified via the web-based Internet in VRML format. These simplified assembly models that have fewer constraints allow design project managers to simulate the functioning of the product in the modularized design before the prototype is built. By using the assembly models, customers can easily choose various modules to assemble the exact products they are seeking via the Internet process. A desk lamp model is used as the example for implementation to validate the feasibility of this research.     

An investigation on the impact of product modularity level on supply chain performance metrics: an industrial case study

The benefit of integrating product design decisions and supply chain design decisions has been recognized by researchers. Such integration can facilitate better communication between design teams and operations groups. Consequently, potential supply chain risks can be highlighted and addressed before the launch of a new product. Modularization is one of the most critical elements for both product design and supply chain design decisions as it impacts the assembly sequence and hence the selection of component and module suppliers. However, the impact of modularity level on supply chain performance is still unclear, and thus is the focus of this study. The proposed analytical method incorporates both product design and supply chain design functions, and hence, enables simultaneous consideration of these decisions. The supply chain performances of all two-module and three-module design concepts are fully investigated in an effort to explore the impact of modularity level on supply chain performance. Results show that increased modularity is advantageous for the time-based performance of a supply chain network, whereas decreased modularity yields superiority in terms of cost performance.     

A new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems

This paper presents a new hybrid improvement heuristic approach to simple straight and U-type assembly line balancing problems which is based on the idea of adaptive learning approach and simulated annealing. The proposed approach uses a weight parameter to perturb task priorities of a solution to obtain improved solutions. The weight parameters are then modified using a learning strategy. The maximization of line efficiency (i.e., the minimization of the number of stations) and the equalization of workloads among stations (i.e., the minimization of the smoothness index or the minimization of the variation of workloads) are considered as the performance criteria. In order to clarify the proposed solution methodology, a well known problem taken from literature is solved. A computational study is conducted by solving a large number of benchmark problems available in the literature to compare the performance of the proposed approach to the existing methods such as simulated annealing and genetic algorithms. Some test instances taken from literature are also solved by the proposed approach. The results of the computational study show that the proposed approach performs quite effectively. It also yields optimal solutions for all test problems within a short computational time.     

A heuristic relief transportation planning algorithm for emergency supply chain management

Emergency supply chain operations have to fulfil all the demands in a very short period of time, using the limited resources at its disposal. Mixed integer programming (MIP) is a popular method to solve emergency supply chain planning problems. However, as such problem increases in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. This study proposes a heuristic algorithm, called the Emergency Relief Transportation Planning Algorithm (ERTPA). ERTPA will group and sort demands according to the required products, the imposed due dates, the possible shared capacities, and the distances from the demand nodes to the depots. Then, ERTPA plans the demands individually, using a shortest travelling-time tree and a minimum cost production tree. To show the effectiveness and efficiency of the heuristic algorithm, a prototype was constructed and tested, using complexity and computational analyses.     

Research on pricing and coordination strategy of green supply chain under hybrid production mode

In order to investigate the pricing and coordination issues of single-period green supply chain, we base our work on the market demand that green product and non-green products co-exist with and substitute each other, and examine respectively the equilibrium results for two production modes in cooperative game and non-cooperative game. Theoretical analysis indicates that different production costs will post impact on the choice of production modes by manufacturers when consumers have different valuations of the products. Furthermore, system performance in cooperative game is apparently better than that in non-cooperative game. The cooperative pricing strategy coordinated by Rubinstein bargaining can realize the pareto optimization of supply chain system profits and member profits under different production modes, with a 33.3% increase in system profits from that in non-cooperative game. The numerical examples further validate the validity of this coordinated pricing strategy.     

Supply chain network design under uncertainty with new insights from contracts

In this paper, the classical problem of supply chain network design is reconsidered to emphasize the rolc of contracts in uncertain environments. The supply chain addressed consists of four layers： suppliers, manufacturers, warehouses, and customers acting within a single period. The single owner of the manufacturing plants signs a contract with each of the suppliers to satisfy demand from downstream. Available contracts consist of long-term and option contracts, and unmet demand is satisfied by purchasing from the spot market. In this supply chain, customer demand, supplier capacity, plants and warehouses, transportation costs, and spot prices are uncertain. Two models are proposed here： a risk-neutral two-stage stochastic model and a risk-averse model that considers risk measures. A solution strategy based on sample average approximation is then proposed to handle large scale problems. Extensive computational studies prove the important role of contracts in the design process, especially a portfolio of contracts. For instance, we show that long-term contract alone has similar impacts to having no contracts, and that option contract alone gives inferior results to a combination of option and long-term contracts. We also show that the proposed solution methodology is able to obtain good quality solutions for large scale problems.     

南平市大农业供应链数据系统的设计与实现

本系统依据开发要求主要应用于南平市大农业供应链数据系统,实现对南平市大农业供应链信息的数字化管理。开发本系统可使南平市农产品供应链信息实现实时管理,比较系统地对农产品信息、生产基地及企业信息、农产品供需信息、农产品市场分析等四大类核心数据进行管理,数据具体并全面,同时可以减少劳动力的使用,解决下游农产品种植或生产常常出现无法预计的滞销问题,助力南平市农产品电商行业的发展,提高农产品电商信息化水平。本系统以南平市农产品的优势单品大类及生产大户为核心供应链为重点调查对象,并多方参考,前期充分做好可行性与需求分析,进而做出合理的功能设计,为开发出稳定、高效、安全、符合需求的系统提供强有力的保障,并严格按照软件开发流程进行系统开发。本文以Web技术为支撑,围绕如何实现南平市大农业供应链数据管理信息化,进行需求分析、功能设计、系统开发,研究开发出一套稳定、高效、安全、符合需求的供应链数据系统。     

A supply chain disruption recovery strategy considering product change under COVID-19

A recent global outbreak of Corona Virus Disease 2019 (COVID-19) has led to massive supply chain disruption, resulting in difficulties for manufacturers on recovering their supply chains in a short term. This paper presents a supply chain disruption recovery strategy with the motivation of changing the original product type to cope with that. In order to maximize the total profit from product changes, a mixed integer linear programming (MILP) model is developed with combining emergency procurement on the supply side and product changes by the manufacturer as well as backorder price compensation on the demand side. The model uses a heuristic algorithm based on ILOG CPLEX toolbox. Experimental results show that the proposed disruption recovery strategy can effectively reduce the profit loss of manufacturer due to late delivery and order cancellation. It is observed that the impact of supply chain disruptions is reduced. The proposed model can offer a potentially useful tool to help the manufacturers decide on the optimal recovery strategy whenever the supply chain system experiences a sudden massive disruption.     

A mathematical model and a genetic algorithm for two-sided assembly line balancing

A two-sided assembly line is a type of production line where tasks are performed in parallel at both sides of the line. The line is often found in producing large products such as trucks and buses. This paper presents a mathematical model and a genetic algorithm (GA) for two-sided assembly line balancing (two-ALB). The mathematical model can be used as a foundation for further practical development in the design of two-sided assembly lines. In the GA, we adopt the strategy of localized evolution and steady-state reproduction to promote population diversity and search efficiency. When designing the GA components, including encoding and decoding schemes, procedures of forming the initial population, and genetic operators, we take account of the features specific to two-ALB. Through computational experiments, the performance of the proposed GA is compared with that of a heuristic and an existing GA with various problem instances. The experimental results show that the proposed GA outperforms the heuristic and the compared GA.     

Designing an assembly line for modular products

To respond to the challenge of agile manufacturing, companies are striving to provide a large variety of products at low cost. Product modularity has become an important issue. It allows to produce different products through combination of standard components. One of the characteristics of modular products is that they share the same assembly structure for many assembly operations. The special structure of modular products provides challenges and opportunities for operational design of assembly lines. In this paper, an approach for design of assembly lines for modular products is proposed. This approach divides the assembly line into two subassembly lines: a subassembly line for basic assembly operations and a subassembly line for variant assembly operations. The design of the subassembly line for basic operations can be viewed as a single product assembly line balancing problem and be solved by existing line balancing methods. The subassembly line for the variant operations is designed as a two-station flowshop line and is balanced by a two-machine flowshop scheduling method. A three-station flowshop line for a special structure of modular products is proposed and illustrated with an example.     

多产品相互制约条件下的制造业产品需求预测研究

供应链环境中,产品需求预测对于制造业安排生产计划具有导向作用。对于大多数制造企业,通常生产的同一系列产品下有多种型号产品。由于受产能及市场容量限制,同系列下多型号产品之间对各自产品需求会产生相互影响作用。鉴于此,考虑历史需求数据在时间序列上的相邻关联性,研究在不同时间序列上各型号产品相互制约影响下产生的不同需求形态。同时考虑产品自身属性差异、供应链环境等影响因素,预测出某型号产品未来一段时间内的需求量。通过GRU-BP组合神经网络预测模型,对模型分析求解后证明预测结果的可行性。     

A vendor managed inventory model under contractual storage agreement

Vendor managed inventory (VMI) is a supply chain partnership strategy that allows a supplier to place orders on behalf of its customers. This paper considers a supply chain composed of a single vendor and multiple retailers operating under a VMI contract that specifies limits on retailers' stock levels. We address the problem of synchronizing the vendor's cycle time with the buyers' unequal ordering cycles by developing a mixed integer non-linear program that minimizes the joint relevant inventory costs under storage restrictions. We also propose a cost efficient heuristic to solve the developed optimization problem. We conducted computational experiments to assess the reduction in the total supply chain costs resulting from relaxing the restriction of equal ordering cycles. It is found that the heuristic generates greater cost savings in cases of increased variability in retailers' demand and cost parameters.