不确定环境下跨国供应链生产计划研究

介绍了跨国供应链生产计划研究与不确定规划论在供应链计划研究中的应用;提出了包含模糊需求与随机生产能力的跨国供应链计划模型,模型中将运输成本分配因子与转让价格作为决策变量,根据随机机会约束规划等价类理论,将模型转化为模糊机会约束规划模型,并利用遗传算法与模糊模拟技术相结合的混合算法设计了模型的求解方案;最后,通过数据仿真说明了模型的有效性与适用性。     

Application of a hybrid simulated annealing-mutation operator to solve fuzzy capacitated location-routing problem

In the field of supply chain management and logistics, using vehicles to deliver products from depots to customers is one of the major operations. Before using vehicles, optimizing the location of depots is necessary in a location-routing problem (LRP). Also, before transportation products, optimizing the routing of vehicles is required so as to provide a low-cost and efficient service for customers. In this paper, the mathematical modelling of LRP is developed according to the existing condition and constraint in the real world. Maximum travelling time constraint is added, and we apply fuzzy numbers to determine customer demands, travelling time and drop time. The objective is to open a subset of depots to assign customers to these depots and to design vehicle routes, in order to minimize both the cost of open depots and the total cost of the routes. The proposed problem is modelled as a fuzzy linear programming (FLP), by applying the fuzzy ranking function method; the proposed FLP is converted to an exact linear programming (LP). A Lingo solver is used to solve this LP model in very small size. LRP is an non-deterministic polynomial-time hard (NP-Hard) problem, and because of the limitation of Lingo solver in solving medium, and large-size numerical examples, a hybrid algorithm including simulated annealing and mutation operator is proposed to solve these numerical examples. Also, a heuristic algorithm is proposed to find a suitable initial solution which is used in hybrid algorithm. At the end, a different analysis of the applied algorithm and a proposed model are introduced.     

A decision model for setting target levels in quality function deployment using nonlinear programming-based fuzzy regression and optimization

Quality function deployment (QFD) is a customer-oriented design tool for developing new or improved products and services to increase customer satisfaction. The inherent fuzziness of functional relationships between customer requirements and engineering characteristics, and among engineering characteristics in QFD modeling justifies the use of fuzzy regression. However, when linear programming is used in fuzzy regression, spread values of regression coefficients tend to approach zero. In order to overcome this problem, this paper employs a nonlinear-programming-based fuzzy regression approach to model functional relationships in product planning. Then, a fuzzy mathematical programming model is developed to determine target levels of engineering characteristics using the functional relationships obtained from fuzzy regression. Fuzzy mathematical programming model enables to account for the spread values as well as the center values of the parameter estimates of the functional relationships. A washing machine quality improvement problem is presented to illustrate the application of the proposed approach.     

Solving a new fuzzy multi-objective model for a multi-skilled manpower scheduling problem by particle swarm optimization and elite tabu search

Manpower scheduling is a complicated problem to solve that strives to satisfy employers’ objectives and employees’ preferences as much as possible by generating fairly desirable schedules. But sometimes, objectives and preferences may not be determined precisely. This problem causes manpower scheduling takes the fuzzy nature. This paper presents a new fuzzy multi-objective mathematical model for a multi-skilled manpower scheduling problem considering imprecise target values of employers’ objectives and employees’ preferences. Hence, a fuzzy goal programming model is developed for the presented mathematical model and two fuzzy solution approaches are used to convert the fuzzy goal programming model to two single-objective models. Since the complexity of a manpower scheduling problem is NP-hard, the single-objective models are solved by two meta-heuristics, namely particle swarm optimization and elite tabu search. Eventually, the performance of the proposed algorithms is verified and the results are compared with each other to select the best schedules.     

Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm

Designing and planning a closed-loop supply chain in a comprehensive structure is vital for its applicability. To cope with the design and planning issue of a comprehensive closed-loop supply chain network, this paper develops an extended model, which is multi-echelon, multi-product, and multi-period in a mixed integer linear programming framework. The word “comprehensive,” in our mathematical approach, in designing and planning a closed-loop supply chain problem, can be analyzed from two complementary angles: including all possible entities (facilities) of a real condition and considering minimum limitations on possible flows between entities. In our proposed model, customers can be supplied via manufacturers, warehouses, and distributors, as an example. The proposed model is solved by CPLEX optimization software and by a developed genetic algorithm. During this computational analysis, we compare results of proposed pretuned genetic algorithm with a global optimum of CPLEX solver. Then, a sufficient number of large-size instances are generated and solved by the proposed genetic algorithm. To the best of our knowledge, there has been no similar multi-period multi-product closed-loop supply chain design and planning problem utilizing any kind of meta-heuristics let alone genetic algorithms. Therefore, in this issue, it is an original research, and results prove the acceptable performances of the developed genetic algorithm.     

钢铁工业三级供应链协调生产计划研究

为了研究矿山、选矿厂和炼铁厂所构成的钢铁工业三级供应链中的协调生产计划问题，建立了相应的混合整数非线性规划模型。该模型的特点是：①集成3个环节的生产计划；②集成生产与运输计划。针对该模型的特殊结构，提出了一种将原问题转化为等价的多个线性规划问题，然后使用Excel标准线性规划程序求解的策略。最后，通过案例说明了模型的有效性，并探讨了进一步研究的方向。     

Modeling and solving the bi-objective capacitated location-routing problem with probabilistic travel times

The location-routing problem (LRP) is a relatively new research area within location analysis that concerns simultaneously both the problems of location of facilities and routing of vehicles among the established facilities and the demand points. In this work, we address the capacitated LRP with probabilistic travel times, which may arise in many practical contexts in logistics and supply chain management, and present some bi-objective mathematical programming formulations to model the problem using different stochastic programming approaches. The first objective is to minimize the overall system-wide costs, while the second objective concerns minimization of the maximum delivery time to the customers. In all the cases, the deterministic equivalents of the stochastic models have been extracted. To solve the resulted models, a variable neighborhood descent-based heuristic is proposed and finally computational study is performed and numerical results are reported.     

A new mathematical modeling and a genetic algorithm search for milk run problem (an auto industry supply chain case study)

The idea of Milk Run has been used in the context of logistic and supply chain problems in order to manage the transportation of materials. In this paper, we propose a new Milk Run method, as a mixed integer approach, to manage supply chain problems. Since the resulted problem formulation is NP-Hard, we use some meta-heuristic and compare the results with the optimal solutions of the proposed Milk Run method. The mathematical modeling of this paper is purposely customized for a special case of an auto industry. We implement the mathematical formulation and the meta-heuristic using some actual data and compare the results with the current strategy. The preliminary results indicate that the proposed method could provide a practical tool to significantly reduce the cost of logistic.     

A hybrid heuristic algorithm for the multistage supply chain network problem

In recent years, many developments in logistics were connected to the need for information in an efficient supply chain flow. The supply chain is often represented as a network called a supply chain network (SCN) that is comprised of nodes that represent facilities (suppliers, plants, distribution centers and customers). Arcs connect these nodes along with the production flow. A multistage SCN (MSCN) is a sequence of multiple SCN stages. The flow can only be transferred between two consecutive stages. The MSCN problem involves the choice of facilities (plants and distribution centers) to be opened and the distribution network design must satisfy the demand with minimum cost. In this paper, a revised mathematical model is first proposed to correct the fatal error appearing in the existing models. An efficient hybrid heuristic algorithm (HHA) was developed by combining a greedy method (GM), the linear programming technique (LP) and three local search methods (LSMs) (always used in solving the scheduling problem). The pair-wise exchange procedure (XP), the insert procedure (IP) and the remove procedure (RP) to solve the MSCN problem. Preliminary computational experiments demonstrate the efficiency and performance of the proposed HHA.     

A hybrid heuristic algorithm for the multistage supply chain network problem

In recent years, many developments in logistics were connected to the need for information in an efficient supply chain flow. The supply chain is often represented as a network called a supply chain network (SCN) that is comprised of nodes that represent facilities (suppliers, plants, distribution centers and customers). Arcs connect these nodes along with the production flow. A multistage SCN (MSCN) is a sequence of multiple SCN stages. The flow can only be transferred between two consecutive stages. The MSCN problem involves the choice of facilities (plants and distribution centers) to be opened and the distribution network design must satisfy the demand with minimum cost. In this paper, a revised mathematical model is first proposed to correct the fatal error appearing in the existing models. An efficient hybrid heuristic algorithm (HHA) was developed by combining a greedy method (GM), the linear programming technique (LP) and three local search methods (LSMs) (always used in solving the scheduling problem). The pair-wise exchange procedure (XP), the insert procedure (IP) and the remove procedure (RP) to solve the MSCN problem. Preliminary computational experiments demonstrate the efficiency and performance of the proposed HHA.     

Simulation modeling for comparative evaluation of supply chain management strategies

Supply chain management (SCM) has become one of the most important strategies for achieving competitive advantage in different industries in the last decade. Researchers have investigated various processes in the planning and development of supply chains. However, increasing attention has been placed on performance, design and analysis of supply chain models. A supply chain is a complex model that is very difficult to analyze, in particular, with respect to performance. Simulation is one of the effective tools to evaluate the control mechanism for a supply chain. In this paper, five common supply chain models have been built and tested with the aid of simulation. Various performance measures such as transportation cost, resources utilization, inventory level, and order cycle time will be calculated for comparative indications. The methodology that is presented in this paper can be extended to any real life applications in SCM.     

A fuzzy multi-objective programming model for supplier selection with volume discount and risk criteria

In contemporary supply chain management, the performance of potential suppliers is evaluated against multiple criteria. In this paper, a fuzzy multi-objective programming model is outlined to propose supplier selection taking quantitative, qualitative, and risk factors into consideration. Also quantity discount has been considered to determine the best suppliers and to place the optimal order quantities among them. The mixed integer derivative nonlinear programming is obtained from fuzzy multi-objective programming model by chance-constrained method. To solve this problem, an innovative method is proposed. In addition, several “what if” scenarios are facilitated. Finally, a real-life sample is used to validate the proposed model.     

Modeling and solving a one-supplier multi-vehicle production-inventory-distribution problem with clustered retailers

This paper considers a supply chain management problem which integrates production, inventory, and distribution decisions. The supply chain is composed of one supplier production facility and several retailers located in a given geographic region. The supplier is responsible for the production and the replenishment of the inventory of retailers, in a vendor managed inventory (VMI) context. The distance between retailers is negligible compared to the distance between the supplier and the retailers’ region. Thus, for each vehicle, there is a major fixed cost for traveling to the cluster of retailers and a minor fixed cost for visiting each individual retailer. The problem consists of determining quantities to be produced, quantities to be delivered to retailers, vehicles to be used, and retailers to be serviced by each vehicle. This problem is an extension of the one warehouse multi-retailer problem with the consideration of production planning and storage and vehicle capacity limitations in addition to fixed vehicle utilization costs and retailer servicing costs. The objective is to minimize a total cost composed of production, transportation, and inventory holding costs at the supplier and at the retailers. Two mixed integer linear programming formulations are proposed and six families of valid inequalities are added to strengthen these formulations. Two of these families are new and the others are adapted from the literature. The numerical results show that the valid inequalities considerably improve the quality of the formulations. Moreover, the parameters that influence the most computational times are analyzed.     

A multiagent supply chain planning and coordination architecture

A supply chain is a worldwide network of suppliers, manufactures, warehouses, distribution centers and retailers through which raw materials are acquired, transformed and delivered to customers. In recent years, a new system approach for managing the supply chain at the tactical and operational levels has emerged. It views a supply chain as composed of a set of intelligent (software) agents, who are responsible for one or more activities and interacting with other related agents in planning and executing their responsibilities. This paper presents a multiagent architecture of supply chain integration. Agents coordination using extended contract net protocol is discussed. Two types of bidding approaches, i.e., the customizing-type and webbing-type are introduced into the multiagent supply chain system. Finally, a heuristics and two programming models for the planning and coordination of demand-driven supply chains are suggested.     

Design of a closed-loop supply chain (CLSC) model using an interactive fuzzy goal programming

A closed-loop supply chain (CLSC) network is composed of both forward and reverse flows. An essential issue to be considered in designing any supply chain network is determination of number and locations of facilities in each layer of the network. Such a problem is a challenging job, since it contains sub-problems which are proven to be nondeterministic polynomial time complete. This paper proposes a CLSC distribution network design problem in which reverse flows are imported into forward model proposed by Selim and Ozkarahan (Int J Adv Manuf Technol 36:401–418, 1). Such a model is considered assuming forward covering (model I) and backward covering (model II) objectives, and then results are compared against the model incorporating covering of both forward and backward networks (model III). Our aim is to accentuate on the role of considering backward parameters in design of a CLSC network and to show how results differ from considering sub-problems separately. To model and solve the problem, a fuzzy goal programming approach is developed for network design in an interactive manner between decision maker and the model. To validate the presented model and the proposed solution approach, a test problem is presented and comparison of results is made using this problem. The results show that the proposed model can solve the CLSC problems in a manageable time. Moreover, outputs of the three models differ significantly. Therefore, the role of incorporating backward flows into the network design problem has been shown using our experiments.     

基于启发式思想的供应链产能规划研究

供应链产能规划属于多阶多厂产能规划问题,具有不同于传统的单厂产能规划的复杂特征。分析了与传统产能规划的区别。构建了供应链产能绩效指标体系,提出基于粗糙集理论的评价方法。以此为基础,提出了基于启发式思想的供应链产能规划方法及步骤。给出的案例也证明了所提产能规划方法的有效性。     

跨国供应链环境下的批量订货模型

针对当前供应链批量订货模型研究的不足,提出了跨国供应链环境下的非线性混合整数规划批量订货模型。与以往研究不同,除了传统的决策变量——订货次数外,模型的决策变量还包括转移价格和运输成本分配因子,目标函数为跨国供应链税后利润最大化。由于模型属于NP难题,求解不便,根据问题的具体特点将其转化成为若干个线性规划模型,然后利用MATLAB的优化工具箱进行了数据分析,计算结果表明,转让价格和运输费用分配的调整对跨国供应链最优订货批量及税后利润具有显著影响。     

Dynamic production planning model: a dynamic programming approach

Production planning is one of the most important issues in manufacturing. The nature of this problem is complex and therefore researchers have studied it under several and different assumptions. In this paper, applied production planning problem is studied in a general manner and it is assumed that there exists an optimal control problem that its production planning strategy is a digital controller and must be optimized. Since this is a random problem because of stochastic values of sales in future, it is modeled as a stochastic dynamic programming and then it is transformed to a linear programming model using successive approximations. Then, it is proved that these two models are equivalent. The main objective of the proposed model is achieving optimal decisions using forecasting sales which can be applied in master production schedule, manufacturing resource planning, capacity requirements planning, and job shop/shop floor scheduling.     

Coordination of order and production policy in buyer–vendor chain using PROSA Holonic architecture

Supply chain (SC) coordination can be pursued by adopting a centralized or decentralized decision-making approach. In recent years, new software architecture for managing the supply chain at the tactical and operational levels has emerged. It views the supply chain as a set of intelligent software agents, each responsible to perform one or more activities in the supply chain while interacting and negotiating with other agents in the planning and execution of these responsibilities. This paper describes a practical system framework for coordination activities in a buyer–vendor chain to determine the best decision making in the chain using the holonic paradigm. In the present article, while reference is made to the holonic paradigm, its related concepts as well as the implementation aspects of the mentioned paradigm within a supply chain are discussed. Throughout the article, as we try to cast light on the Product-Resource-Order-Staff Architecture of this approach, attention is paid to the operational advancements of the model in order for the model to be responsive to the needs of coordination of product and information flows within a buyer–vendor chain under present stochastic environments. The proposed holonic model in this article makes use of intelligent agents and mathematical models to build decision-making models in the proposed holons, as well as solution algorithms, negotiation mechanisms, and coordination-expanded policies in the mentioned architecture. Numerical studies are also provided to illustrate the effectiveness of the proposed models and methods.     

A novel hybrid method for supply chain optimization with capacity constraint and shipping option

In a modern market, supply chain network design is considered as a strategic decision that provides the proper platform for cost management and increases the competitive edge of enterprise. In a five-tier supply chain, there are several facilities such as suppliers, manufacturers, warehouses, distribution centers, and retailers or customers. For product transportation from one facility to another, different types of options may be used. These options have variety price and the manager should do the best assignment to reduce the total cost. In this paper, we formulated an integer programming model for a five-tier supply chain with capacitated facility and multiple transportation option with fixed lead time. We also proposed a novel meta-heuristic solution methodology that combines the Taguchi's feature with artificial immune approach in order to solve the proposed model. The performance of the proposed solution methodology has been examined against a set of numeric instances and the obtained results are compared with those provided by hybrid genetic algorithm and Taguchi and artificial immune system. Results indicate that this methodology can make better results than previous solutions effectively.