基于低碳理念的多目标闭环供应链网络设计

针对低碳环境下的供应链网络设计,研究具有供应商、制造商、消费者、配送中心、回收中心及处理中心的多层级闭环供应链网络。考虑网络设施约束、碳排放、顾客服务等因素的影响,以网络运营成本、碳排放成本和顾客需求响应时间成本为目标建立多目标规划模型,解决网络内设施建设、选址、设施间流量分配等网络资源优化配置问题。利用Lingo11.0软件对模型求解,通过算例分析设施能力限制、碳税税率以及时间延迟成本对设施建设方式和网络成本的影响,为企业提供决策支持并在生产实践中发挥指导作用。     

A strategic model for exact supply chain network design and its application to a global manufacturer

This paper presents a comprehensive model that captures significant strategic decisions involved in designing or redesigning high-performance supply chains from the perspective of the manufacturer. The problem considers deterministic demand by multiple clients, for multiple products, over the periods of a long-term horizon. The design decisions involve selection of suppliers, establishment or resizing of production facilities and distribution centres, possible subcontracting of related activities, and selection of transportation modes and routes. The problem is formulated by a Mixed Integer Linear Programming model. Its objective is to minimise the overall costs associated with procurement, production, inventory, warehousing, and transportation over the design horizon. Appropriate constraints model the complex relationships among the links of the supply chain. The proposed model has been applied to a large case study of a global manufacturing firm, providing valuable insights into the transformation of the firm’s current supply chain network, as well as into the potential of the proposed approach.     

The design of Make-to-Order supply networks under uncertainties using simulation and optimisation

This paper addresses the problem of designing Make-to-Order (MTO) driven supply networks as is faced by producers of industrial goods. A major challenge in MTO network design is to estimate the operational performance of candidate networks. In particular, the stochastic and dynamic nature of order arrivals and fulfilment processes as well as the need to design a network that enables a timely delivery of ordered products complicate the decision-making. In this paper, a solution approach is presented where simulation is used for assessing the operational performance of candidate networks. The proposed simulation model captures multiple sources of uncertainties and incorporates fundamental control policies for reflecting the autonomous decision-making processes of operational planners. A Variable Neighbourhood Search (VNS) method is presented to guide the search for good network designs. Experiments are conducted on a set of multi-stage networks, where complex products are manufactured in MTO fashion and delivered to customers within a promised order lead time. The results show that our approach effectively produces supply networks that are able to cope with challenges arising from a strong customer orientation.     

Integrated inventory valuation in multi-echelon production/distribution systems

The pressure to reduce inventory has increased as competition expands, product variety grows, and capital costs increase. This investigation addresses the problem of inventory quantification and distribution within multi-echelon supply chains under market uncertainty and management flexibility. This approach is based on an optimisation model emphasising demand uncertainty and the relevant dimensions of network design as number of echelons, lead time, service level, and cost of processing activities. Overstock quantification enables the understanding of inventory level sensitivity to market uncertainty. A comparison among production sites and storage facilities revealed that higher downstream overstock levels decrease upstream echelons of uncertainty exposition. The contribution of this study relies on management's ability to establish inventory targets for each stocking point according to risk exposure and to promote the optimisation of working capital. Overall, this investigation increases knowledge related to the treatment of demand uncertainty in flexible and integrated supply chains     

Applying the TOC thinking process in manufacturing: A case study

A case study is presented to illustrate the application of the Theory of Constraints thinking process logic tools in a manufacturing environment. The study firm performs design activities related to meeting future product requirements while concurrently meeting existing production schedules for the current design of the product. Current approaches to managing the firm's limited productive capacity do not allow for both design and production activities to occur simultaneously while meeting the customer's current product delivery schedule. Thus, despite their desire to satisfy their customer's future design requirements, management uses the majority of its production capacity to meet its customer's current product delivery schedule. This case study demonstrates how a team of employees used thinking process logic diagrams to document reality, identify a core conflict and problem, develop proposed changes to address the core problem, and create several detailed action plans to implement changes within the study organization. Initially, scenarios associated with some undesirable effects are used to understand how prevailing policies and behaviours result in less than desired production line performance. Then, a current reality tree is constructed to link the core problem or system constraint with the previously identified undesirable effects. Next, two major injections are developed to address the core problem in managing the production line as logically documented in a future reality tree. Finally, three transition trees are presented to guide the implementation of change at the study organization.     

Joint replenishment and manufacturing activities control in a two stage unreliable supply chain

This paper deals with dynamic stochastic situations faced by supply chains. In this context, various interactions, disparate decisions and random phenomena must be considered. These issues are considered in this paper through a two stage supply chain control problem. The supplier and the transformation stage are both subject to random events such as periods of unavailability due to internal difficulties or market constraints. Our objective is to find information sharing control policies for the supply and production activities that minimises the expected discounted cost of ordering, inventories/backlog and transformation over an infinite horizon. This is an optimal control problem with state constraints and hybrid dynamics of the production and replenishment activities. It is shown that, from a mathematical point of view, the considered problem is difficult to tackle and it calls upon optimal and impulsive control theory notions. A dynamic stochastic model is thus proposed. The existence of an optimal control policy and Hamilton-Jacobi-Bellman optimality condition in terms of the value function of the problem are derived and discussed. A numerical schema is then proposed to solve the obtained optimality conditions equations. A complete control policy is finally developed. The confirmation of such a policy structure is illustrated through sensitivity analysis. Some particular cases are also presented and discussed.     

Antecedents and enablers of supply chain agility and its effect on performance: a dynamic capabilities perspective

This paper investigates the fundamental building blocks of supply chain agility, which are conceptualised as supply- and demand-side competence. While the former refers to production and supply management related activities, the latter refers to distribution and demand management related activities. The model further assesses the influence of supply chain agility on operational performance, as well as its mediating role in the relationship between supply- and demand-side competence and performance. Within this framework, process compliance, i.e. how well supply chain management processes are internally executed by the firm's employees, is viewed as an enabler (moderator) on the relationship between supply chain competencies and supply chain agility. Theoretical substantiation is provided by the resource-based view of the firm augmented with the dynamic capabilities perspective. The model is tested with data from 121 supply chain management professionals. Implications for both academic theory development and supply chain and production management practice are provided.     

Optimal acquisition policy with quantity discounts and uncertain demands

We study the acquisition policy decision problem for a supply network involving one manufacturer and multiple suppliers. The manufacturer produces multiple products under uncertain demands and each supplier provides price discounts. The problem is to determine the manufacturer's acquisition policy and production levels so as to maximise the manufacturer's expected profit, subject to both the manufacturer's and suppliers’ capacities. We present a mixed integer nonlinear programming (MINLP) formulation of the problem, for both single- and multiple-sourcing procurement policies. General algebraic modeling system (GAMS) and its solvers, combining external integration functions, are employed to solve the complex MINLP problem. The preliminary computation results and managerial analysis are reported.     

A blockchain-based approach for a multi-echelon sustainable supply chain

Blockchain technology is destined to revolutionise supply chain processes. At the same time, governmental and regulatory policies are forcing firms to adjust their supply chains in response to environmental concerns. The objective of this study is therefore to develop a distributed ledger-based blockchain approach for monitoring supply chain performance and optimising both emission levels and operational costs in a synchronised fashion, producing a better outcome for the supply chain. We propose the blockchain approach for different production allocation problems within a multi-echelon supply chain (MESC) under a carbon taxation policy. As such, we couple recent advances in digitalisation of operations with increasingly stringent regulatory environmental policies. Specifically, with lead time considerations under emission rate constraints (imposed by a carbon taxation policy), we simultaneously consider the production, distribution and inventory control decisions in a production allocation-based MESC problem. The problem is then formulated as a Mixed Integer Non-Linear Programming (MINLP) model. We show that the distributed ledger-based blockchain approach minimises both total cost and carbon emissions. We then validate the feasibility of the proposed approach by comparing the results with a non-dominated sorting genetic algorithm (NSGA-II). The findings provide support for policymakers and supply chain executives alike.     

A design model and a production–distribution and inventory planning model in multi-product supply chain networks

Supply chain network (SCN) design implicates decision-making at a strategic level. That includes selecting the right suppliers and determining the number and the location of plants, distribution centres and retailers. An apt design model of the supply chain is imperative for the proper function of the supply chain and consequently for making better operational decisions in an attempt of a continuous improvement. In this paper, we propose two models. The first model is a mixed-integer linear programming model which is concerned with the SCN design problem, whereas the second operational model is a mixed-integer non-linear programming model in respect to the production–distribution and inventory planning problem in a supply chain network. The number of customers and suppliers as well as their demand and capacities are assumed to be known in both models. Two steady-state genetic algorithms were implemented in MATLAB in order to solve both the design and the operational model. The results were compared with GAMS. Some examples were devised in order to demonstrate potential ways of use for the designer of the supply chain network, as well as for the supply chain manager.     

A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systems

This research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the same workstation multiple times and dynamic dispatching oftentimes aims to dynamically assign different priorities to various job groups to minimise weighted cycle time or maximise throughput. MDP is an effective tool for dynamic production control, but it suffers from two major challenges in dynamic control problems. First, the curse of dimensionality limits the computational performance of solving large MDP problems. Second, a different model should be built and solved after system configuration is changed. DNN is used to overcome both challenges by learning directly from optimal dispatching policies generated by MDP. Results suggest that a properly trained DNN model can instantly generate near-optimal dynamic control policies for large problems. The quality of the DNN solution is compared with the optimal dynamic control policies through the standard K-fold cross-validation test and discrete event simulation. On average, the performance of the DNN policy is within 2% of optimal in both tests. The proposed artificial intelligence algorithm illustrates the potential of machine learning methods in manufacturing applications.     

Modelling a complex supply chain: understanding the effect of simplified assumptions

The benefits of coordinating activities and consolidating distribution points in supply chains are well highlighted and intuitively logical. However, the impact of these decisions on the overall performance of a complex supply chain may not be as obvious as usually perceived. This study models a relatively complex supply chain and evaluates the impact of simplifying demand and lead time assumptions under various supply chain configurations. Of particular interest is the investigation of the effect of risk pooling and the synchronization of production cycles in a multi-level multi-retailer supply chain under the influence of various parameters such as batch size, delivery frequency and ordering cycle. This study highlights the extent of complicated interaction effects among various factors exist in a complex supply chain and shows that that the intricacy of these effects can be better understood with a simulation model.     

非粮质生物能源供应链优化设计研究

开发和利用生物能源可以在一定程度上缓解我国面临的能源紧张的局面。为了实现对生物能源的精益开发,有必要研究生物能源供应链的优化设计问题。在考虑原材料产量约束、精炼厂生产能力约束和需求满足约束的基础上,建立了以追求生物能源供应链总成本最小化为目标的整数规划模型,通过CPLEX软件进行求解,最终决策精炼厂的选址、精炼厂的规模设计及其年产量,并确定原材料产地、精炼厂和需求地三者之间的供应关系和配送量。以辽宁省的数据为实际算例,验证了模型的有效性。在总成本中,精炼厂的生产成本比重最大,约为总成本的59%,而运输成本最小约为8%。在此基础之上进行了灵敏性分析,讨论了该模型在价格和需求发生变化时的不同表现。     

Agent-based simulation and analysis of demand-driven production strategies in the timber industry

This paper addresses the generic problem of production planning in a divergent timber production environment. It aims at analysing the performance of various demand-driven production strategies of a timber production system. This analysis is performed using a simulation platform built on an agent-based advanced planning system. Nine production strategies configurations are evaluated under six scenarios in order to carry out a complete mixed level design of 54 simulation runs. Each of these configurations is a combination of a decoupling point position and a level of capacity that is committed to contracts with customers. Accordingly, the six scenarios are designed as a combination of supply type (i.e. log diameter distribution) and timber market prices. Production processes and co-production yields are based on a real manufacturing system from eastern Canada. Performance is evaluated from the logistic and economic points of view. Results demonstrate that demand-driven planning approaches that propagate demand information upstream the supply chain have the potential to improve planned customer service and reduce planned inventories. Results also show that timber companies need to receive a premium from their customers in order to compensate from the loss of potential value resulting from a more constrained planning environment.     

Creation Of A Regional Hub For Flexible Production

Since the late 1980s, Singapore has emerged as a major regional hub for magnetic hard disk drive (HDD) assembly operations, accounting for about half of the world's total HDD output. This paper examines how this came about by analyzing the supply infrastructure needed to support the competitive requirements for flexible production capability in the HDD industry. The paper suggests that government policies have contributed towards reinforcing the agglomeration economies of HDD assembly activities.     

Buyer behaviours and the performance of the supply chain: An international exploration

This paper studies the effects of buyer?supplier interaction in a supply chain environment. It focuses on the effects of buyer behaviours (such as supplier selection, number of suppliers, percentage of outsourcing, and frequency of production changes) on the performance of the supply chain. Using data collected from machine tool manufacturers around the world, the various behaviours of buyers are examined to see how they induce or mitigate the degree of uncertainty experienced by suppliers and thus affect suppliers' delivery performance. In addition, it was investigated how such buyer behaviours could further affect the supply chain's ability to perform as expected. The statistical results suggest that many buyer behaviours that have no direct effect on buyer's performance can have a significant effect on their suppliers' performance. Specifically, buyer behaviours directly manifest in supplier performance and only indirectly manifest in their own performance. This can give the buyer the false impression that the supply base is harming performance, when the real problem is the way the buyer manages the supply chain. The results vary by region of the world, suggesting that any theory that links buyer behaviours to supplier performance will need to consider a firm location.     

SPACE–TIME SPECTRAL ELEMENT METHOD FOR OPTIMAL SLEWING OF A FLEXIBLE BEAM

A new computational approach to modelling and control of a flexible beam is proposed. The structural modelling and the control design problems are formulated in a unified mathematical framework that allows simultaneous structural and control design iterations that result in an optimal overall system performance. The method employs the space–time spectral elements for simultaneous space and time discretizations of a Timoshenko beam model. Dimensionless equations of motion are derived using Hamilton's principle of variable action and an integral formulation in the framework of space–time spectral elements is introduced. An optimal control problem formulated for the continuum model is transformed by the space–time spectral element formulation into an optimization problem in a finite-dimensional parameter space. Dynamic programming is then used to obtain both open and closed loop control laws. A simulation study shows good performance of the control law applied to the nominal model. It is also demonstrated that proper discretization yields performance robustness of the system with respect to modal truncation.     

A decision model for corrective maintenance management

Proper management of maintenance offers many companies significant potential for improving productivity and profitability. Traditional management thinking regards maintenance costs as accidental, rather than planned and controllable. Additionally, research in maintenance management has focused on preventive maintenance and has ignored corrective maintenance even though the latter is also considered to be a critical activity in industry. This study proposes a decision model that could assist in a comparative evaluation of alternative corrective maintenance policies. This decision model consists of a simulation model and economic analysis. The simulation model predicts inventory costs and delivery performance of a corrective maintenance policy in various production systems. Based on simulation results, an economic analysis, consisting of a net present value model and breakeven models, determines the economic value of alternative maintenance policies. A detailed example is offered to evaluate two particular correciive maintenance policies (machine redundancy and worker flexibility) although the decision model can be applied to other options. The results of the example demonstrate the decision model's capability to assist managers in selecting the best corrective maintenance policy.     

Operational and environmental decisions for a two-stage supply chain under vendor managed consignment inventory partnership

Supply-chain-based organisations are nowadays facing intense pressure to abide to environmental regulatory requirements while they are striving to be responsive to customers’ needs at the least cost possible. As supply chain activities are among the top contributors to carbon emissions, several recent research works have investigated the impact of carbon regulation policies on economic and environmental supply chain performance. This paper presents integrated economic and environmental models for the one-vendor one-buyer supply chain problem under a vendor managed consignment inventory (VMCI) arrangement. Through the developed models, we study the impacts of two carbon reduction policies, namely carbon cap and carbon tax policies, on supply chain wide costs and carbon emissions. We first provide a much simpler and more compact formulation for the basic single-vendor single-buyer supply chain under VMCI agreement. We also present an environmental-based VMCI model where reduction of carbon footprint is considered as the only objective function. We then extend these two basic models to include each of the two carbon emissions reduction policies. We identify structural properties for the optimal solutions of the two hybrid economic and environmental models and propose algorithms to generate optimal solutions. The results of the computational experiments reveal that the implementation of carbon reduction policies, through carbon cap and carbon tax, may call for the adjustment of the vendor’s production and buyer’s delivery quantities to reduce carbon footprint without significantly increasing the operational costs.     

Optimal long-term design,rehabilitation and upgrading of water distribution networks

Given a limited budget, the choice of the best water distribution network upgrading strategy is a complex optimization problem. A model for the optimal long-term design and upgrading of new and existing water distribution networks is presented. A key strength of the methodology is the use of maximum entropy flows, which reduces the size of the problem and enables the application of linear programming for pipe size optimization. It also ensures the reliability level is high. The capital and maintenance costs and hydraulic performance are considered simultaneously for a predefined design horizon. The timing of upgrading over the entire planning horizon is obtained by dynamic programming. The deterioration over time of the structural integrity and hydraulic capacity of every pipe are explicitly considered. The upgrading options considered include pipe paralleling and replacement. The effectiveness of the model is demonstrated using the water supply network of Wobulenzi town in Uganda.