On a selection and scheduling problem in automatic storage and retrieval warehouses

Warehousing is one of the main components of the supply chain and its optimisation is crucial to achieve global efficiency. Warehouse operations involve receiving, shipping, storing and order picking, among other things, and the coordinated optimisation of all these different operations is highly complex. This paper examines a real selection and scheduling problem that arises in an automatic storage/retrieval warehouse system involving the scheduling of forklift pickup operations. The objective is to minimise the total loading time of the vehicles performing transportation, while respecting their departure due dates. This complex problem is approached via a two-phase decomposition method, combining both exact and heuristic procedures. The performance of the proposed solution method is evaluated using extensive computational results from several scenarios from a real case study using data from a real mattress warehouse.     

A hierarchical system for effective coordination of available-to-promise logic mechanisms

This paper aims to provide a combinatory approach towards addressing the advanced available-to-promise (ATP) problem, consisting of three deterministic optimisation models that operate on both sides of the Customer Order Decoupling Point. The proposed approach is based on long-term aggregate capacity reservation for periods when increased volatility is expected, while still obtaining production plans that meet the predefined and agreed customer service levels. The three optimisation models together guide a system that helps manufacturers to optimally decide on ATP quantity and due date quoting on the basis of available manufacturing resources. To support this system, a prototype software module was designed and implemented in Java that loosely integrates with the popular Open Source ERP system Compiere2's databases and uses the Linear Programming solver QS-Opt to solve the models developed in this research. The system response times as evidenced in the experiments described in this paper are quite acceptable for real-world operations. The proposed solution of the ATP problem is of great value for all competitive and proactive organisations that need a practical tool to support, in the best possible way and in an almost real-time fashion, their decision on whether to accept or decline an incoming customer order request. It is our belief that an integration of the proposed models into existing ERP systems will enhance their limited ATP functionality and provide management with a powerful decision support tool.     

Risk readiness and resiliency planning for a supply chain

This paper proposes supply chain (SC) risk readiness and resiliency measures and formulates a model for planning and controlling select internal business factors to create desired risk resiliency in order to avert potential risks and mitigate their after-effect. SCs may be exposed to events that affect their business operations, and primarily impact the production processes (i.e. production-related risks), or events (such as natural calamities or terrorism) that affect the way the business interacts with the market, and primarily impact the transportation and distribution processes (i.e. market-related risks). Although a business cannot control such disasters as natural calamities or terrorism, it is possible to identify and control the factors that are responsible for production-related risks and that influence several market-related risks or disasters. The proposed model and the measures will guide SCs through the process of identification, planning and controlling the internal factors that make the chain resilient to these various risks. The resiliency measures and the mixed integer programming model will also enable SCs to conduct what-if analyses of cost and performance trade-off options. A numerical example illustrates the planning in typical scenarios.     

A constrained EPSAC approach to inventory control for a benchmark supply chain system

The design of an appropriate inventory control policy for a supply chain (SC) plays an essential role in tempering inventory instability and bullwhip effect. Several constraints are commonly encountered in actual operations so managers are required to take these physical restrictions into account when designing the inventory control policy. Model predictive control (MPC) appears as a promising solution to this issue, due to its capability of finding optimal control actions for a constrained SC system. Therefore, the inventory control problem for a benchmark SC is solved using the extended prediction self-adaptive control approach to MPC. To extend methodologies in our previous work, the control framework relies on generic process model and incorporates the physical constraints arising from practical operations to form the general constrained optimisation problems. The managers can choose from decentralised and centralised control structures according to specific informational and organisational factors of their SCs. The proposed control schemes in this study may be appropriate for industrial practice because the designed policy can bring a reduction of over 30% in operating cost and a significant increase of customer satisfaction level compared with that of the conventional policy.     

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.     

Integrated low-carbon distribution system for the demand side of a product distribution supply chain: a DoE-guided MOPSO optimiser-based solution approach

This article contributes to distribution system literature on three inter-linked aspects viz. formulation of a novel integrated low-carbon/green distribution system for the demand side of a Supply Chain (SC) with a single product and multiple consumers, i.e. drop-off points, a novel and robust solution approach through a Design of Experiment (DoE)-guided Multiple-Objective Particle Swarm Optimisation (MOPSO) optimiser and exhaustive analysis of the solutions (i.e. prioritisation, ranking and scenario analysis). The total costs, CO₂ emission and the traversed distances of the vehicles during transportation are optimised. The optimisation model for the strategic decision-making is formulated by effectively integrating the 0–1 mixed-integer programming with a green constraint based on Analytic Hierarchy Process. Due to the computationally NP-hard characteristic of the model, a systematic and technically robust DoE-guided solution approach is designed using a commercial solver – modeFRONTIER^®. DoE guides the solution through the MOPSO optimiser in order to eliminate the un-realistic set of feasible and optimal solution sets. A popular multi-attribute decision-making approach, TOPSIS, evaluates the solutions found from the Pareto optimal solution space of the solver. Finally, decision-makers’ preferences are analysed for monitoring the changes in the controlling parameters with respect to the changes in the decisions. A scenario analysis of the events by considering alternative possible outcomes is also conducted. It is found that the implemented methodology successfully routes the vehicles with optimal costs and low-carbon emission thus contributing to greening the environment on the demand side of a SC network.     

Reducing tardy jobs by integrating process planning and scheduling functions

This paper presents a dynamic approach to reduce tardy jobs through the integration of process planning and scheduling in a batch-manufacturing environment. The developed method aims at re-generating a schedule with fewer tardy jobs, step by step, by exploring the process plan solution space of the tardy jobs. The integrated system comprises a process planning module, a scheduling module, and an integrator module. The process planning module employs an optimisation approach in which the entire plan solution space is first generated and a search algorithm is then used to find the optimal plan, while the scheduling module is based on commonly used heuristics. Based on the job tardiness information of the generated schedule, the integrator module automatically issues a modification order to the process plan solution space of the tardy jobs. The process planning and scheduling modules are then re-run to generate a new plan/schedule solution. Through this iterative process, a satisfactory schedule can be gradually achieved. The uniqueness of this approach is characterised by the flexibility of the process planning strategy, which makes full use of the plan solution space intuitively to achieve a satisfactory schedule. Several examples are presented to confirm the efficacy and the effectiveness of the developed integration system.     

Recent advances in research on reconfigurable machine tools: a literature review

A recent trend in research on reconfigurable manufacturing systems is the development of reconfigurable machine tools (RMTs). A RMT can be used as a group of machines through change of its configuration to satisfy different manufacturing requirements. A literature review is provided in this paper to demonstrate the state-of-the-art advances and challenges on research and development of RMTs from the perspectives of architecture design, configuration design and optimisation, and system integration and control. In architecture design, semi-open and open architectures based on modular design approach are often selected to allow different modules of the machine to be added and removed. In configuration design and optimisation, operations of reconfiguration processes are analysed and optimised to achieve variety of configurations with the minimum reconfiguration effort. In system integration and control, transfers of motion, energy and data among different modules of the RMT are carried out. The challenges in research on RMTs are also discussed.     

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.     

Robust production and transportation planning in thin film transistor-liquid crystal display (TFT-LCD) industry under demand and price uncertainties

This paper studies the production and transportation control (PTC) problems under demand and price uncertainties in a multi-product, two-echelon, and hybrid thin film transistor-liquid crystal display (TFT-LCD) production chain. The unique characteristics of the TFT-LCD industry, including quality grades and alternative bill-of-materials (BOMs), are considered in this study. The objective is to determine a production and transportation policy that is robust to price and demand uncertainties. This two-echelon PTC problem is formulated as several two-stage robust optimisation models. An iterative procedure is developed to apply and solve these two-stage models in a TFT-LCD production chain. Our simulation study shows that the proposed method can significantly increase profits while reducing transportation and inventory costs. In addition to improving mean and variance of profits, we use several risk measures to verify the robustness of the iterative optimisation procedure.     

Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

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 methodology for simultaneous optimisation of design parameters for the preventive maintenance and quality policy incorporating Taguchi loss function

The performance of a production system depends on the breakdown-free operation of equipment and processes. Maintenance and quality control play an important role in achieving this goal. In addition to deteriorating with time, equipment may experience a quality shift (i.e. process moves to out-of-control state), which is characterised by a higher rejection rate and a higher tendency to fail. This paper develops an integrated model for joint optimisation of preventive maintenance interval and control parameters incorporating the Taguchi loss function. We consider two types of maintenance policies: minimal corrective maintenance that maintains the state of the equipment without affecting the age and imperfect preventive maintenance that upgrades the equipment in between ‘as good as new’ and ‘as bad as old’ condition. The proposed model enables the determination of the optimal value of each of the four decision variables, i.e. sample size (n), sample frequency (h), control limit coefficient (k), and preventive maintenance interval (t _PM) that minimises the expected total cost of the integration per unit time. A numerical example is presented to demonstrate the effect of the cost parameters on the joint economic design of preventive maintenance and process quality control policy. The sensitivity of the various parameters is also examined.     

Optimal Scheduling of Track Maintenance on a Railway Network

To ensure the safety and continued operation of the railway network system, many maintenance and renewal activities are performed on the track every month. Unplanned maintenance activities are expensive and would cause low service quality. Therefore, the track condition should be monitored, and when it has degraded beyond some acceptable limit, it should be scheduled for maintenance before failure. An optimal timetable of the maintenance activities is needed to be scheduled, planning the monthly workload, to reduce the effect on the transportation service and to reduce the potential costs. Considering the uncertainties of the deterioration process, the safety of transportation service, the lifetime loss of the replaced track, the maintenance cost and the travel cost, this article advances an optimisation model for the maintenance scheduling of a regional railway network. An enhanced genetic algorithm approach is proposed to search for a solution producing maintenance schedule such that the overall cost is minimised in a finite planning horizon. A case study is given to demonstrate the application of the method. The case study results were derived by using an enhanced genetic algorithm method, which is specifically developed to deal with the characteristics of the railway maintenance problem. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal variability sensitive condition-based maintenance with a Cox PH model

Condition based maintenance (CBM) is an important maintenance strategy in practice. In this paper, we propose a CBM method to effectively incorporate system health observations into maintenance decision making to minimise the total maintenance cost and cost variability. In this method, the system degradation process is described by a Cox PH model and the proposed framework includes simulation of failure process and maintenance policy optimisation using adaptive nested partition with sequential selection (ANP-SS) method, which can adaptively select or create the most promising region of candidates to improve the efficiency. Different from existing CBM strategies, the proposed method relaxes some restrictions on the system degradation model and taking the cost variation as one of the optimisation objectives. A real industry case study is used to demonstrate the effectiveness of our framework.     

Availability optimisation for stochastic degrading systems under imperfect preventive maintenance

This paper deals with imperfect preventive maintenance (PM) optimisation problem. The system to be maintained is typically a production system assumed to be continuously monitored and subject to stochastic degradation. To assess such degradation, the proposed maintenance model takes into account both corrective maintenance (CM) and PM. The system undergoes PM whenever its reliability reaches an appropriate value, while CM is performed at system failure. After a given number of maintenance actions, the system is preventively replaced by a new one. Both CM as well as PM are considered imperfect, i.e. they bring the system to an operating state which lies between two extreme states, namely the as bad as old state and as good as new state. The imperfect effect of CM and PM is modelled on the basis of the hybrid hazard rate model. The objective of the proposed PM optimisation model consists on finding the optimal reliability threshold together with the optimal number of PM actions to maximise the average availability of the system. A mathematical model is then proposed. To solve this problem an algorithm is provided. A numerical example is presented to illustrate the proposed maintenance optimisation model.     

The impact of the customer order decoupling point on type and level of supply chain integration

Literature suggests that more supply chain (SC) integration increases of SC performance. However, recent research shows that contextual factors affect the effectiveness of SC integration efforts. Specifically, the location of the customer order decoupling point (CODP) might be such a factor, but there is limited empirical evidence. This study explores the impact of the location of the CODP on SC integration. It empirically investigates upstream, internal and downstream SC integration efforts in twelve metal parts producing Dutch companies to offer a comprehensive view of the effects of the location of the CODP on SC integration. Our results show a clear relationship between SC integration and the location of the CODP. Specifically, make-to-order (MTO), assemble-to-order (ATO) and make-to-stock (MTS) companies show relatively high levels in upstream, internal and downstream SC integration, respectively. Moreover, MTO companies mainly share forecast information and engage in joint R&D with their suppliers, ATO companies focus on internal SC integration using information systems and planning, while MTS companies interact with their customers intensively to exchange forecast information. Our study adds to the insight on the application of SC integration in different situations and offers initial evidence for the effects of the location of the CODP on SC integration.     

GA-based adaptive setup planning toward process planning and scheduling integration

Setup planning of a part for more than one available machine is a typical combinatorial optimisation problem under certain constraints. It has significant impact not only on the whole process planning but also on scheduling, as well as on the integration of process planning and scheduling. Targeting the potential adaptability of process plans associated with setups, a cross-machine setup planning approach using genetic algorithms (GA) for machines with different configurations is presented in this paper. First, based on tool accessibility analysis of different machine configurations, partially sequenced machining features can be grouped into certain setups; then by responding to the requirements from a scheduling system, optimal or near-optimal setup plans are selected for certain criteria, such as cost, makespan and/or machine utilisation. GA is adopted for the combinatorial optimisation, which includes gene pool generation based on tool accessibility examination, setup plan encoding and fitness evaluation, and optimal setup plan selection through GA operations. The proposed approach is implemented in a GA toolbox, and tested using a sample part. The results demonstrate that the proposed approach is applicable to machines with varying configurations, and adaptive to different setup requirements from a scheduling system due to machine availability changes. It is expected that this approach can contribute to process planning and scheduling integration when a process plan is combined with setups for alternative machines during adaptive setup planning.     

Throughput/inventory dollar-days: TOC-based measures for supply chain collaboration

As the semiconductor industry moves away from vertical integration, performance measures play an increasingly important role to ensure effective collaboration. This paper demonstrates that the theory of constraints (TOC)-based measures, Throughput and Inventory Dollar-Days (T/IDD), induce autonomous supply chain (SC) links to function as a synergistic whole and thereby, improve the performance of the whole SC network significantly. We model an SC network of a well-known TOC case study using discrete event simulation and discuss managerial implications of these measures via a set of scenarios. The scenarios explain how these measures – without sharing sensitive financial data – allow members of an SC network to monitor both the effectiveness (TDD) and efficiency (IDD) of SC members and lead them to create win-win solutions following well-known TOC-based planning and control concepts. We conclude this paper by discussing some limitations of the proposed research and provide directions for future theoretical research.     

Many-objective harmony search for integrated order planning in steelmaking-continuous casting-hot rolling production of multi-plants

This paper investigates a challenging problem of integrated order planning (IOP) in steelmaking-continuous casting-hot rolling production of multiple plants with consideration of four conflicting objectives. The objective functions refer to the earliness/tardiness ratio, the non-hot charge ratio and the imbalance ratio of production capacity utilisation corresponding to SCC plants and HR Plants. The IOP guided by the integration strategy, which includes the vertical integration of production stages and the horizontal integration of steel plants, is regarded as a large-scale many-objective optimisation problem. To deal with the difficulty of large-scale decision variables, we introduce a new concept named ‘order-set’ for modelling. In addition, a novel knee point-driven many-objective global-best harmony search (KGHS) algorithm, mainly integrating a KGHS process and a new knee point-driven Pareto optimisation, is developed to tackle this many-objective problem. The proposed model and algorithm were tested with benchmarks and real production data. Experiments demonstrate that the proposed approach generates effective solutions superior to those generated by the other popular many-objective optimisation methods.