An integrated weighted fuzzy multi-objective model for supplier selection and order scheduling in a supply chain

This paper presents a new weighted fuzzy multi-objective model to integrated supplier selection, order quantity allocation and customer order scheduling problem to prepare a responsive and order-oriented supply chain in a make-to-order manufacturing system. Total cost and quality of purchased parts as well as the reliability of on-time delivery of customer orders are regarded as the objectives of the model. On the other hand, flexible suppliers can contribute to the responsiveness and flexibility of entire supply chain in the face of uncertain customer orders. Therefore, a mathematical measure is developed for evaluating the volume flexibility of suppliers and is considered as the other objective of the model. Furthermore, by considering the effect of interdependencies between the selection criteria and to handle inconsistent and uncertain judgments, a fuzzy analytic network process method is used to identify top suppliers and consider as the last objective. In order to optimise these objectives, the decision-maker needs to decide from which supplier to purchase parts needed to assemble the customer orders, how to allocate the demand for parts between the selected suppliers, and how to schedule the customer orders for assembled products over the planning time horizon. Numerical examples are presented and computational analysis is reported.     

Two-stage stochastic master production scheduling under demand uncertainty in a rolling planning environment

This paper proposes a scenario-based two-stage stochastic programming model with recourse for master production scheduling under demand uncertainty. We integrate the model into a hierarchical production planning and control system that is common in industrial practice. To reduce the problem of the disaggregation of the master production schedule, we use a relatively low aggregation level (compared to other work on stochastic programming for production planning). Consequently, we must consider many more scenarios to model demand uncertainty. Additionally, we modify standard modelling approaches for stochastic programming because they lead to the occurrence of many infeasible problems due to rolling planning horizons and interdependencies between master production scheduling and successive planning levels. To evaluate the performance of the proposed models, we generate a customer order arrival process, execute production planning in a rolling horizon environment and simulate the realisation of the planning results. In our experiments, the tardiness of customer orders can be nearly eliminated by the use of the proposed stochastic programming model at the cost of increasing inventory levels and using additional capacity.     

面向供应链的大规模定制时间阈值理论研究

针对目前大规模定制管理运作策略中存在的主要问题,以探索提升面向供应链环境下的大规模定制生产过程中的客户服务水平为主线,在对用户订单信息特征分析与合理分类的基础上,通过引入时间阈值概念及相关理论作为协调大规模定制中范围经济同客户定制化服务水平之间矛盾的基本解决思路,并分析了影响时间阈值的各主要因素,同时在时间阈值的前提下,提出了面向供应链的大规模定制管理运作多级优化策略的思想。     

Setting safety stocks in multi-stage inventory systems under rolling horizon mathematical programming models

This paper considers the problem of determining safety stocks in multi-item multi-stage inventory systems that face demand uncertainties. Safety stocks are necessary to make the supply chain, which is driven by forecasts of customer orders, responsive to (demand) uncertainties and to achieve predefined target service levels. Although there exists a large body of literature on determining safety stock levels, this literature does not provide an effective methodology that can address complex multi-constrained supply chains. In this paper, the problem of determining safety stocks is addressed by a simulation based approach, where the simulation studies are based on solving the supply chain planning problem (formulated as a mathematical programming model) in a rolling horizon setting. To demonstrate the utility of the proposed approach, an application of the approach at Organon, a worldwide operating biopharmaceutical company, will be discussed.     

A strategic decision support system for logistics and supply chain network design

This paper aims to develop a strategic decision support system for logistics and supply chain network design of a multi-stage, multi-commodity, and multi-period distribution and transportation system. A mixed integer linear programming model is proposed to tackle the problem while minimizing the operating, transportation and handling cost through all tiers of the supply chain network. A genetic algorithm based method has been proposed to solve the problem in a large scale realistic environment. The efficacy of the developed strategic decision support model in achieving better utilization of network and resources to fulfil the customer demand is demonstrated using illustrative scenarios inspired from the real case of a logistics company.     

Multimodal Fuzzy Downstream Petroleum Supply Chain: A Novel Pentagonal Fuzzy Optimization

The petroleum industry has a complex, inflexible and challenging supply chain (SC) that impacts both the national economy as well as people’s daily lives with a range of services, including transportation, heating, electricity, lubricants, as well as chemicals and petrochemicals. In the petroleum industry, supply chain management presents several challenges, especially in the logistics sector, that are not found in other industries. In addition, logistical challenges contribute significantly to the cost of oil. Uncertainty regarding customer demand and supply significantly affects SC networks. Hence, SC flexibility can be maintained by addressing uncertainty. On the other hand, in the real world, decision-making challenges are often ambiguous or vague. In some cases, measurements are incorrect owing to measurement errors, instrument faults, etc., which lead to a pentagonal fuzzy number (PFN) which is the extension of a fuzzy number. Therefore, it is necessary to develop quantitative models to optimize logistics operations and supply chain networks. This study proposed a linear programming model under an uncertain environment. The model minimizes the cost along the refineries, depots, multimode transport and demand nodes. Further developed pentagonal fuzzy optimization, an alternative approach is developed to solve the downstream supply chain using the mixed-integer linear programming (MILP) model to obtain a feasible solution to the fuzzy transportation cost problem. In this model, the coefficient of the transportation costs and parameters is assumed to be a pentagonal fuzzy number. Furthermore, defuzzification is performed using an accuracy function. To validate the model and technique and feasibility solution, an illustrative example of the oil and gas SC is considered, providing improved results compared with existing techniques and demonstrating its ability to benefit petroleum companies is the objective of this study.     

A bidding decision model in multiagent supply chain planning

The multiagent environment for supply chain planning application is based on a framework unifying the internal behaviour of agents and coordination among agents. This system presents a formal view of coordination using Contract Net Protocol (CNP) that relies on the basic loop of agent behaviours: order receiving, order announcement, bid calculation, and order scheduling followed by order execution. Among these, bid calculation is most difficult. It needs to determine the quantity, cost and time in which a new order can be implemented. Fuzzy programming has made progress in mathematics since Bellman and Zadeh (1970) first studied decision-making in a fuzzy mathematics programming. Currently there are many valuable works in this field (Zimmermann 1983, 1985, Tanaka and Asai 1984) and fuzzy programming has become an effective tool to deal with the decision-making problems in fuzzy systems. Similarly, stochastic programming is a useful tool to treat decision-making problems in a stochastic system (Kolbin 1977, Kall and Wallace 1994). However, in many practical systems, fuzzy factors and random factors arise concurrently, and this problem has not received the attention it deserves. Therefore, there is a need to develop a new kind of optimization technique to make decisions in a fuzzy random system. In this paper, we build the Bid Calculation model, including a random parameter set, the set of product quantity that will be stored to inventory, a fuzzy parameter set, the Maximum Sales Rates (MSR) set, and we discuss an approach to solve the model, as well as present an implementation procedure with the GA method.     

Applied column generation-based approach to solve supply chain scheduling problems

More and more enterprises have chosen to adopt a made-to-order business model in order to satisfy diverse and rapidly changing customer demand. In such a business model, enterprises are devoted to reducing inventory levels in order to upgrade the competitiveness of the products. However, reductions in inventory levels and short lead times force the operation between production and distribution to cooperate closely, thus increasing the practicability of integrating the production and distribution stages. The complexity of supply chain scheduling problems (integrated production and distribution scheduling) is known to be NP-hard. To address the issues above, an efficient algorithm to solve the supply chain scheduling problem is needed. This paper studies a supply chain scheduling problem in which the production stage is modelled by an identical parallel machine scheduling problem and the distribution stage is modelled by a capacitated vehicle routing problem. Given a set of customer orders (jobs), the problem is to find a supply chain schedule such that the weighted summation of total job weighted completion time and total job delivering cost are minimised. The studied problem was first formulated as an integer programme and then solved by using column generation techniques in conjunction with a branch-and-bound approach to optimality. The results of the computational experiments indicate that the proposed approach can solve the test problems to optimality. Moreover, the average gap between the optimal solutions and the lower bounds is no more than 1.32% for these test problems.     

Impact analysis of customized demand information sharing on supply chain performance

In today's dynamic market environment, rapidly changing customer preferences increase the customization of products and the diversification of demand mix. Companies should understand how the demand mix influences supply chain performance and react properly to fulfil customer satisfaction as to what the customers want. This paper investigated the impact of information sharing of the demand mix on the supply chain performance, under increasing product customization, by changing customer demand pattern and production capacity. The results of simulation analysis demonstrated that a demand mix can be determined that produces the best supply chain performance. A real multi-echelon automotive supply chain was analysed for each of six performance measures and the demand mix information significantly influenced five of them. Demand variation and production capacity also resulted in significant impact on the performance measures.     

Fuzzy multi-objective optimisation for master planning in a ceramic supply chain

In this paper, we consider the master planning problem for a centralised replenishment, production and distribution ceramic tile supply chain. A fuzzy multi-objective linear programming (FMOLP) approach is presented which considers the maximisation of the fuzzy gross margin, the minimisation of the fuzzy idle time and the minimisation of the fuzzy backorder quantities. By using an interactive solution methodology to convert this FMOLP model into an auxiliary crisp single-objective linear model, a preferred compromise solution is obtained. For illustration purposes, an example based on modifications of real-world industrial problems is used.     

A dynamic programming approach to integrated assembly planning and supplier assignment with lead time constraints

As manufacturers face fierce competition in the global market, responsiveness has become an important competitiveness factor in addition to quality and cost. One essential responsiveness strategy is to reduce product development and lead times by integrating assembly planning with supplier assignment. This paper addresses the problem of integrated assembly and supply chain design under lead-time constraints by formulating and solving an optimisation problem with minimal total supply chain costs. This new time-constrained joint optimisation problem belongs to an NP-hard resource-constrained scheduling problem. To model this problem effectively, we develop a novel Hyper AND/OR graph and apply it for integrating assembly and supply chain decisions. We also develop a dynamic programming model and associated algorithm in order to solve the integrated optimisation problem with pseudo-polynomial time complexity in practice. Numerical case studies validate that the methods developed can solve the integrated decision-making problem optimally and efficiently. This paper overcomes the limitations of previous studies on concurrent assembly decomposition and supplier selection, which optimises cost without time constraints. The models and results of this research can be applied to a variety of areas including assembly design, maintenance module planning and supply chain restructuring.     

面向产品族的客户需求最佳聚类确定方法

以大规模定制为前提,为实现客户需求指导产品族规划的目的,建立了客户需求广义聚类模型．根据客户需求信息是否存在层次等级关系将其划分为递阶型和等价型,针对目前对递阶型客户需求聚类研究不能充分利用需求信息的问题,提出了基于模糊集的混合型客户需求聚类算法,增强了处理需求信息的能力．为实现产品族的合理规划,综合客户满意度和企业成本理想度建立可行性指标,提出了基于可行性确定客户需求最佳聚类的方法,并以最佳聚类方案指导产品族规划,在满足客户需求的同时保证了企业宽松的资金链．最后结合实例说明了该方法的实用性．     

Monolithic versus hierarchical approach to integrated scheduling in a supply chain

The two approaches, monolithic and hierarchical, with a set of mixed integer programming formulations are proposed and compared for multi-objective integrated scheduling in a customer driven supply chain. The supply chain consists of multiple manufacturers (suppliers) of parts, a single producer of finished products and a set of customers who generate final demand for the products. Each supplier has a number of identical production lines in parallel for the manufacture of parts, and the producer has a flexible assembly line for assembly of products. Given a set of orders, the problem objective is to determine which orders are to be provided with parts by each supplier, find a schedule for the manufacture of parts by each supplier and for the delivery parts from each supplier to the producer, and find a schedule for the assembly of products for each order by the producer, such that a certain performance measure of the supply chain is optimised. The selection of the parts supplier for each order is combined with due date setting for some orders, subject to the suppliers and the producer available capacity. Different objective functions are considered that take into account both customer service level and total manufacturing, delivery and production cost. Numerical examples are presented that are modelled by real-world integrated scheduling in a customer driven supply chain of high-tech products, and some computational results are reported to compare the two approaches.     

Research of Order Allocation of E-commerce Logistics Service Supply Chain Considering Customer Evaluation

Based on the importance of customer evaluation for developing e-commerce enterprises, this paper analyzes the customer evaluation as a fuzzy variable and establishes a multi-objective mixed integer order allocation planning model by considering customer satisfaction, which maximizes customer praise and minimizes procurement cost. As the optimization goal, transaction cost is optimized for the order allocation of the secondary e-commerce logistics service supply chain. In order to defuzzify the customer evaluation, a fuzzy evaluation method is designed to transform the customer evaluation from fuzzy language evaluation to numerical measurement. Finally, the feasibility and effectiveness of the model are verified by using a specific example, and the order is made for the e-commerce enterprise. The allocation provides a theoretical reference.     

e-Commerce and supply chains: Modelling of dynamics through fuzzy enhanced high level petri net

Although information plays a major role in effective functioning of supply chain networks (SCNs), studies that deal specifically with the dynamics of supply chains are few. This problem is relatively new since fast communications and the means to employ it for effective management of supply chains did not exist till recently. In order to provide a vehicle for dynamic modelling and analysis of supply chain operations in vague and uncertain environments, we propose a fuzzy enhanced high level petri net (FEHLPN) model. The proposed model captures the capability of petri nets for graphical and analytical representation of dynamic SCNs with the management of uncertain information provided by fuzzy logic. The dynamics associated with two production planning and control policies are modelled, viz. make-to-stock and assemble-to-order in vague and ambiguous situations in electronic commerce environment. A fuzzy set and fuzzy truth-values are attached to an uncertain fuzzy token to model imprecision and uncertainty. The proposed FEHLPN incorporates essential aspects of rule-based systems, such as conservation of facts, refraction, and closed-world assumption.     

Managing warehousing in an agile supply chain environment: an F-AIS algorithm based approach

In recent years, application of the agile concept in the manufacturing sector has been researched extensively to reduce the varying effect of customer demands. However, most of the research work is focused on the shop floor of different manufacturing processes, while issues concerning the control of warehouse scheduling in a supply chain have been neglected so far. Realising this in the present research an attempt has been made to address the scheduling aspect of a warehouse in an agile supply chain environment. To resolve the warehouse problem in this paper, the authors have proposed a new Fuzzy incorporated Artificial Immune System Algorithm (F-AIS). This algorithm encapsulates the salient features of a fuzzy logic controller and immune system. The proposed algorithm has been compared with genetic algorithm (GA), simulated annealing (SA) and artificial immune system (AIS) algorithm to reveal the efficacy of the proposed F-AIS algorithm.     

Integrating production-transportation planning decision with fuzzy multiple goals in supply chains

This work presents a novel fuzzy multi-objective linear programming (f-MOLP) model for solving integrated production-transportation planning decision (PTPD) problems in supply chains in a fuzzy environment. The proposed model attempts to simultaneously minimise total production and transportation costs, total number of rejected items, and total delivery time with reference to available capacities, labor level, quota flexibility, and budget constraints at each source, as well as forecast demand and warehouse space at each destination. An industrial case demonstrates that the proposed f-MOLP model achieves an efficient compromise solution and overall decision maker satisfaction with determined goal values. Additionally, the proposed model provides a systematic framework that facilitates decision makers to interactively modify the fuzzy data and parameters until a satisfactory solution is obtained. Overall, the f-MOLP model offers a practical method for solving PTPD problems with fuzzy multiple goals, and can effectively improve producer–distributor relationships within a supply chain.     

Managing demand uncertainty through fuzzy inference in supply chain planning

In this paper, we investigate methods for managing the irregular and uncertain demands involved in supply chain planning. We first build a supply chain planning model based on fuzzy linear programming, which defines demand as a fuzzy parameter. Next, we propose a fuzzy inference approach for converting fuzzy demand into crisp demand. In the proposed fuzzy inference-based approach, judgments of upcoming demand from both internal and external experts are used as input variables to reflect the expected demand irregularity. By adopting fuzzy inference, we can compensate for the limitations of the existing demand treatment approaches, which usually demonstrate poor forecasting performance in cases of irregular demand and thus reduce the accuracy of supply chain planning. To verify the feasibility of the proposed approach, we present an illustrative example of a Korean electronics company.     

A three-stage model for closed-loop supply chain configuration under uncertainty

In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment.