Satisfying due-dates in a job shop with sequence-dependent family set-ups

This paper addresses job shop scheduling with sequence dependent family set-ups. Based on a simple, single-machine dynamic scheduling problem, state dependent scheduling rules for the single machine problem are developed and tested using Markov Decision Processes. Then, a generalized scheduling policy for the job shop problem is established based on a characterization of the optimal policy. The policy is combined with a 'forecasting' mechanism to utilize global shop floor information for local dispatching decisions. Computational results show that performance is significantly better than that of existing alternative policies.     

Scheduling policies,batch sizes,and manufacturing lead times

In this paper we examine the impact of scheduling policies on batch sizing decisions in a multi-item production system. We also investigate the joint effect of scheduling policies and batch sizing decisions on production lead times. In particular we compare the performance of a first-come-first-served (FCFS) policy with that of a group scheduling (GS) policy and study the effect of each on the optimal batch size. We show that a GS policy can lead to significant performance gains, as measured by reduced lead times and higher production rates, relative to the FCFS policy, and characterize conditions under which these gains are realized. We also study the impact of the GS policy on other system operating parameters. In particular we find that using a GS policy eliminates the need for batching, preserves system capacity despite the presence of setups, and accommodates higher product mix variety. These results are shown to be very different from those obtained for the FCFS policy and to have important implications for the management and control of multi-item production facilities.     

Revenue sharing in semiconductor industry supply chain: Cooperative game theoretic approach

This paper defines cooperation as the process of coordinating the objectives and activities of supply chain (SC) members. It also focuses on cooperation as a solution for hybrid coordination mechanism to form the basis for semiconductor industry supply chain management. In the complex and competitive environment of semiconductor industry supply chain, independent system members are facing the difficult task of providing/sharing incentives resulting from e-market activities in a fair and equitable manner. So, various other activities are necessary for the e-market to make revenue sharing operations more stable and reliable. In this context, the importance of coalition in enhancing the e-market capability, for revenue generation and sharing, is used to develop a possible mechanism for financial compensation to the supply chain members. Interpreting the supply chain as cooperation, the concepts of the Shapely value are used in this paper for analysing the revenue sharing problem. The motivation behind such a scheme is to align the supply chain members’ cost structure with the bidding value during auction and bargaining for e-procurement. The appropriateness of the Shapely value is verified to ensure that a stable solution exists. The practical implication of this paper is how to make right decisions about revenue sharing. The principal contribution of this approach is for establishing a pooling coalition in order to provide a stable and cooperative solution.     

Order selection and scheduling with leadtime flexibility

In this paper, we study integrated order selection and scheduling decisions, where the manufacturer has the flexibility to choose leadtimes. Our main goal is to provide a mechanism for coordinating order selection, leadtime and scheduling decisions and to determine under what conditions leadtime flexibility is most useful for increasing the manufacturer's profits. Through numerical analyses, we compare and contrast the benefits of leadtime flexibility and the flexibility to partially fulfill orders in different demand environments defined by the congestion level (or demand load), seasonality of the demand and order size. We consider both the cases where the manufacturer has and does not have the flexibility to produce orders early before they are committed.     

Integrated simulation application design for short-term production scheduling

This paper focuses on the development of an integrated short-term production scheduling simulation model for a complex manufacturing operation. The decision making tool has a front-end user data interface built with Microsoft Access and Visual Basic for Applications software. The model assists production planners to make informed decisions on how best to schedule simultaneously two lanes of daily or weekly production on a unique plastic parts manufacturing line. The advantages and disadvantages (model weaknesses) of using the simulation model, especially as a scheduling tool, are discussed. In this particular application, we find that the advantages far outweigh the disadvantages.     

Multicriteria dynamic scheduling methodology for controlling a flexible manufacturing system

A methodology is presented for the dynamic scheduling of flexible manufacturing systems (FMSs). A two-level control hierarchy is suggested. The higher level is used for determining a dominant decision criterion and relevant scheduling rules, based on an analysis of the actual shop status. The lower level uses simulation for determining the best scheduling policy to be selected. Simulation is used to evaluate different control options, and once a control decision is made, it is operated in real time to serve as the FMS controller. The suggested scheduling and control scheme is being developed, implemented and tested in a physical computer integrated manufacturing (CIM)/FMS environment at the CIM and Robotics Lab of the Faculty of Industrial Engineering and Management, Technion. This will serve as a test-bed to study the performance of the FMS under different scheduling rules and control options, and to recommend the best combination of control policies and parameters for specific system conditions and global production objectives.     

Integrated demand-responsive scheduling of maintenance and transportation operations in military supply chains

The management of a military supply chain (SC) involves the integration of production, packaging, warehousing, repair, maintenance and transportation of army supplies. In this paper, we focus on the integrated demand-responsive scheduling of maintenance and transportation operations within the SC. We consider a modular representation of a SC, in which the maintenance and transportation operations constitute the corresponding modules. An efficient integration of these operations is carried out by an additional controlling (commanding) module. We describe and analyse the optimisation problems arising in each module. The main contribution of the paper is that the analysis and optimisation of the controlling module permit to enhance the performance of the entire SC. Computational experiments prove the validity and effectiveness of the suggested models.     

Information management strategies and supply chain performance under demand disruptions

The operations management literature presents inadequate comprehensive understanding on information management strategies of mitigating supply chain disruption risks. By using control theory modelling and simulation, this study compares the disruption mitigation effects of three information management strategies. From the aspect of stability, the existing stability boundaries are revised by a new method in a two-echelon case. It shows that supply chains (SC) with popular information management strategies are not evidently more stable than traditional ones. From the aspect of disruption recovery time, an innovative two-echelon swiftest response problem under these information management strategies is formulated and solved. Results show that a collaborative planning, forecasting and replenishment (CPFR) SC with complete SC information performs the best. However, in a later operational risk mitigation test, an information sharing (IS) SC with partial information has the smallest bullwhip effect. From the aspect of demand amplification and frequency response, an innovative frequency–response plot of order amplification is proposed in a time-continuous SC with moving average forecasts. It implies the best frequency response for concurrently mitigating both operational and disruption risks coming from a CPFR SC. But for a certain SC structure there is still a balance between mitigating bullwhip effect and quick response. Moreover, it also implies that anti-bullwhip should exist in a certain condition, as realised in our numerical experiments.     

Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contracts in the SC as transactions. We develop and test a new model for smart contract design in the SC with multiple logistics service providers and show that this problem can be presented as a multi-processor flexible flow shop scheduling. A distinctive feature of our approach is that the execution of physical operations is modelled inside the start and completion of cyber information services. We name this modelling concept ‘virtual operation’. The constructed model and the developed experimental environment constitute an event-driven dynamic approach to task and service composition when designing the smart contract. Our approach is also of value when considering the contract execution stage. The use of state control variables in our model allows for operations status updates in the Blockchain that in turn, feeds automated information feedbacks, disruption detection and control of contract execution. The latter launches the re-scheduling procedure, comprehensively combining planning and adaptation decisions within a unified methodological framework of dynamic control theory. The modelling complex developed can be used to design and control smart contracts in the SC.     

Minimizing Job Tardiness Using Integrated Preventive Maintenance Planning and Production Scheduling

Production scheduling and preventive maintenance planning decisions are inter-dependent but most often made independently. Given that maintenance affects available production time and elapsed production time affects the probability of machine failure, this inter-dependency seems to be overlooked in the literature. We propose an integrated model that simultaneously determines production scheduling and preventive maintenance planning decisions so that the total weighted tardiness of jobs is minimized. We investigate the benefits of integration through a numerical study of small problems. We compare the integrated solution and its performance with the solutions obtained from solving the production scheduling and preventive maintenance planning problems independently. The numerical results show an average reduction of 30% in expected total weighted tardiness. Finally, we discuss the issues related to solving larger problems and extensions for future study.     

Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach

The purpose of this paper is to develop a data-mining-based dynamic dispatching rule selection mechanism for a shop floor control system to make real-time scheduling decisions. In data mining processes, data transformations (including data normalisation and feature selection) and data mining algorithms greatly influence the predictive accuracy of data mining tasks. Here, the z-scores data normalisation mechanism and genetic-algorithm-based feature selection mechanism are used for data transformation tasks, then support vector machines (SVMs) is applied for the dynamic dispatching rule selection classifier. The simulation experiments demonstrate that the proposed data-mining-based approach is more generalisable than approaches that do not employ a data-mining-based approach, in terms of accurately assigning the best dispatching strategy for the next scheduling period. Moreover, the proposed SVM classifier using the data-mining-based approach yields a better system performance than obtained with a classical SVM-based dynamic dispatching rule selection mechanism and heuristic individual dispatching rules under various performance criteria over a long period.     

Using simulation-based system dynamics and genetic algorithms to reduce the cash flow bullwhip in the supply chain

The bullwhip effect (BWE) is a phenomenon, which is caused by ineffective inventory decisions made by supply chain members. In addition to known inefficiencies caused by the bullwhip effect within a supply chain product flow, such as excessive inventory, it can also lead to inefficiencies in cash flow such as the cash flow bullwhip (CFB). The CFB reduces the efficiency of the supply chain (SC) through heterogeneous distribution of cash among supply chain members. This paper aims to decrease both the BWE and the CFB across a SC through applying a simulation-based optimisation approach, which integrates system dynamics (SD) simulation and genetic algorithms. For this purpose, cash flow modelling is incorporated into the SD structure of the beer distribution game (BG) to develop the CFB function. A multi objective optimisation model is then integrated with the SD-BG simulation model. Finally, a genetic algorithm (GA) is applied to determine the optimal values for the inventory, supply line, and financial decision parameters. Results show that the proposed integrated framework leads to efficient liquidity management in the SC in addition to cost management.     

A job shop distributed scheduling based on Lagrangian relaxation to minimise total completion time

This paper considers a distributed job shop scheduling problem where autonomous sub-production systems share common machines with each other. Each sub-production system is responsible for the scheduling of a set of jobs to minimise the total completion time on shared machines. A sub-production system has ultimate responsibility on maintaining private information such as objective function, processing time and routings on shared machines. Also sub-production systems must cooperate each other in order to achieve a global goal while sharing minimum of private information. In this research, we propose a distributed cooperation method in which sub-production systems and shared machines interact with one another to find a compromised solution between a locally optimised solution and a system-wide solution. We tested the proposed method for small, medium and large size of job shop scheduling problems and compared to a global optimal solutions. The proposed method shows promising results in terms of solution qualities and computational times.     

Distributed scheduling: a review of concepts and applications

Distributed scheduling (DS) is an approach that enables local decision makers to create schedules that consider local objectives and constraints within the boundaries of the overall system objectives. Local decisions from different parts of the system are then integrated through coordination and communication mechanisms. Distributed scheduling attracts the interest of many researchers from a variety of disciplines, such as computer science, economics, manufacturing, and service operations management. One reason is that the problems faced in this area include issues ranging from information architectures, to negotiation mechanisms, to the design of scheduling algorithms. In this paper, we provide a survey and a critical analysis of the literature on distributed scheduling. While we propose a comprehensive taxonomy that accounts for many factors related to distributed scheduling, we also analyse the body of research in which the scheduling aspect is rigorously discussed. The focus of this paper is to review the studies that concern scheduling algorithms in a distributed architecture, not, for example, protocol languages or database architectures. The contribution of this paper is twofold: to unify the literature within our scope under a common terminology and to determine the critical design factors unique to distributed scheduling and in relation to centralised scheduling.     

Decarbonising product supply chains: design and development of an integrated evidence-based decision support system – the supply chain environmental analysis tool (SCEnAT)

Based upon an increasing academic and business interest in greening the industrial supply chains, this paper establishes the need for a state-of-the-art decision support system (DSS) for carbon emissions accounting and management, mainly across the product supply chains by identifying methodological shortcomings in existing tools, and proposing a supply chain (SC) framework which provide businesses with a holistic understanding of their supply chains and ensuring partners within supply chain collaborative networks have a shared understanding of their emissions. It describes the design and development of a DSS now known as supply chain environmental analysis tool (SCEnAT) in detail, putting its unique and innovative features into a comparative perspective vis-à-vis existing tools and software of different types. The methodological framework used to design and develop SCEnAT integrates different individual techniques/methods of supply chain (SC) mapping, SC carbon accounting, SC interventions and SC interventions evaluation on a range of key performance indicators (KPIs). These individual methods have been used and applied innovatively to the challenge of designing SCEnAT within the desired framework. Finally, we demonstrate the application of SCEnAT, especially the advantage of using a robust carbon accounting methodology, to a SC case study. The SCEnAT framework pushes the theoretical boundary by addressing the problems of intra-organisational approach in decision making for lowering carbon along the supply chain; with an open innovation, cutting edge, hybridised framework that considers the supply chain as a whole in co-decision making for lowering carbon along the supply chain with the most robust methodology of hybrid life cycle analysis (LCA) that considers direct and indirect emissions and interventional performance evaluation for low carbon technology investment and business case building in order to adapt and mitigate climate change problems. This research has implications for future sustainability research in SC, decisions science, management theory, practice and policy.     

Procurement decisions and information sharing under multi-tier disruption risk in a supply chain

We consider a manufacturer's procurement decision in a three-tier supply chain (SC) under disruption risk. The manufacturer sources components from a single first-tier supplier (FT). The FT, in turn, sources raw materials from a single second-tier supplier (ST). Suppliers in both tiers are unreliable, i.e. prone to disruption risk. Increasing SC visibility through information sharing is a potential disruption management strategy for the manufacturer. While the manufacturer can obtain disruption risk information for the FT, disruption risk information for the ST is not easily accessible to the manufacturer except through the FT, who may not be willing to share ST information. We study different mechanisms under which the manufacturer can obtain ST information, and its impact on manufacturer's and FT's decisions and potential profits. We show that information sharing makes the manufacturer's procurement decisions more conservative, i.e. carrying more inventories, but the FT's procurement decision is contingent on the ST's reliability; more proactive (conservative) when ST is unreliable (reliable), i.e. carrying less (more) inventories. We demonstrate that there are two ways to induce the FT to share its information, and numerically show that their effectiveness is contingent on multiple factors, including FT and ST reliabilities and information sharing costs.     

Adaptive scheduling and tool flow control in flexible job shops

The recent manufacturing environment is characterized as having diverse products due to mass customization, short production lead-time, and ever-changing customer demand. Today, the need for flexibility, quick responsiveness, and robustness to system uncertainties in production scheduling decisions has dramatically increased. In traditional job shops, tooling is usually assumed as a fixed resource. However, when a tooling resource is shared among different machines, a greater product variety, routing flexibility with a smaller tool inventory can be realized. Such a strategy is usually enabled by an automatic tool changing mechanism and tool delivery system to reduce the time for tooling set-up, hence it allows parts to be processed in small batches. In this paper, a dynamic scheduling problem under flexible tooling resource constraints is studied and presented. An integrated approach is proposed to allow two levels of hierarchical, dynamic decision making for job scheduling and tool flow control in flexible job shops. It decomposes the overall problem into a series of static sub-problems for each scheduling horizon, handles random disruptions by updating job ready time, completion time, and machine status on a rolling horizon basis, and considers the machine availability explicitly in generating schedules. The effectiveness of the proposed dynamic scheduling approach is tested in simulation studies under a flexible job shop environment, where parts have alternative routings. The study results show that the proposed scheduling approach significantly outperforms other dispatching heuristics, including cost over time (COVERT), apparent tardiness cost (ATC), and bottleneck dynamics (BD), on due-date related performance measures. It is also found that the performance difference between the proposed scheduling approach and other heuristics tend to become more significant when the number of machines is increased. The more operation steps a system has, the better the proposed method performs, relative to the other heuristics.     

Integrated maintenance and production control of a deteriorating production system

We consider a make-to-stock production/inventory system consisting of a single deteriorating machine which produces a single item. We formulate the integrated decisions of maintenance and production using a Markov Decision Process. The optimal dynamic policy is shown to have a rather complex structure which leads us to consider more implementable policies. We present a double-threshold policy and derive exact and approximate methods for evaluating the performance of this policy and computing its optimal parameters. A detailed numerical study demonstrates that the proposed policy and our approximate method for computing its parameters perform extremely well. Finally, we show that policies which do not address maintenance and production control decisions in an integrated manner can perform rather badly.     

Collaborative agents for production planning and scheduling (CAPPS): a challenge to develop a new software system architecture for manufacturing management in Japan

Considering the frequent changes in market needs, real-time production management will produce huge additional value for manufacturers. As scheduling of shop floor operations is a local and partial decision in enterprises, the distributed local scheduling problems should be integrated at the enterprise level. Since decisions at the shop floor are very valuable, the next generation of production management will be performed as a decentralized system. This paper proposes architecture for scheduling-intensive production management. In the architecture, referred to as collaborative agents for production planning and scheduling, system integration is only achieved by communication among agents, i.e. a system is composed of loose connections between agents. As an implementation method, the paper introduces a standard specification for the communications among agents. The specification is recommended by PSLX, the Japan-based consortium for production planning and scheduling. The paper also shows some emerging business models on scheduling-intensive production management.     

Investigating the value of integrated operations planning: A case-based approach from automotive industry

During the last decade, many researchers have focused on joint consideration of various operations planning aspects like production scheduling, maintenance scheduling, inventory control, etc. Such joint considerations are becoming increasingly important from the point of view of current advancement in intelligent manufacturing, also known as Industry 4.0. Under the concept of Industry 4.0, advanced data analytics aim to remove human intervention in decision-making. Thus, the managerial level coordination of decisions taken independently by various departments will be out of trend. Therefore, developing an approach that optimises various operations planning decisions simultaneously is essential. Available literature on such joint considerations is more of the exploratory in nature and is limited to simplistic production environments. This necessitates the investigations of value of integrated operations planning for wide range of manufacturing scenarios. Present paper adopts a case-oriented approach to investigate the value of integrated operations planning. First, an integrated approach for simultaneously determining job sequencing, batch-sizing, inventory levels and preventive maintenance schedule is developed. The approach is tested in a complex production environment of an automotive plant and substantial economic improvement was realised. Second, a comprehensive evaluation is performed to study the robustness and implications of proposed approach for various production scenarios. Results of such pervasive performance investigations confirm the value of proposed approach over conventional approaches.