Optimisation of traceability and operations planning: an integrated model for perishable food production

The food industry has been under pressure regarding improving food safety, implementing efficient risk management, and managing quality ‘from farm to fork’. Individual parties in a food supply chain must make appropriate strategic and operational adjustments, enhance product quality and increase operational efficiency and profits. In this paper, an optimisation model is developed integrating traceability initiatives with operation factors to achieve desired product quality and minimum impact of product recall in an economic manner. A case study with numerical example is provided to illustrate the theory. Sensitivity analyses are conducted. A comparison between the proposed model and the existing model operating in the case company is also presented.     

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.     

Optimisation of cutting parameters using a multi-objective genetic algorithm

This paper presents a new optimisation technique based on genetic algorithms (GA) for determination of cutting parameters in machining operations. The cutting parameters considered in this study are cutting speed, feed rate and cutting depth. The effect of these parameters on production time, production cost and roughness is mathematically formulated. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. Experimental results show that the proposed algorithm is both effective and efficient, and can be integrated into an intelligent process planning system for solving complex machining optimisation problems.     

中央厨房冷链质量安全标准及可追溯信息技术应用

从食品冷链现代化和食品安全出发,建立中央厨房冷链物流质量管理的理论框架,把质量管理的领域拓展到产品整个生命周期.依据冷链物流质量安全管理理论,建立明确的质量安全标准,并通过可追溯信息化技术的应用,协助中央厨房获取冷链质量安全管理需求,并以更有效的方式快速地响应需求,支持中央厨房通过实现产品创新、管理创新、服务创新来提高顾客的满意度,从而保持并扩大企业产品的市场份额,提高企业的综合竞争力.     

Minimisation of non-machining times in operating automatic tool changers of machine tools under dynamic operating conditions

In many optimisation studies, it is assumed that problem related data does not change once the generated solution plan or schedule is currently in use. However, majority of real-life manufacturing problems are time-varying in their nature due to unpredictable events such as changes in lot sizes, fluctuating capacities of manufacturing constraints, changes in costs or profits. A problem, which contains at least one of these feature is referred as dynamic optimisation problem (DOP) in the related literature. The present study introduces a practical industrial application of a DOP, emerging particularly in flexible manufacturing systems (FMSs), where numerically controlled machine tools with automatic tool changers are employed. It is already known in FMSs that minimisation of non-machining times is vital for an efficient use of scarce resources. Therefore, fast response to possible changes in production is crucial in order to attain flexibility. In this context, first, a benchmarking environment is created by making use of already published problems and by introducing dynamic events. Next, effective strategies, including simulated annealing (SA) algorithm along with SA with multiple starts are developed for the introduced problem. Numerical results show that the developed SA with multiple starts is a promising approach for the introduced problem.     

A data-driven approach for the optimisation of product specifications

In order to develop the profit-maximising, market share-maximising or cost-minimising bundle of product engineering specifications with proper performance levels, an optimisation model driven by operating data is proposed. The operating data are input as the sources to conduct the optimisation and a data-based customer satisfaction function can be formed. Then, a customer choice model developed from the customer satisfaction is constructed to estimate the customer choice probability. The expected market share (EMS) then can be derived from the choice probability. After all, a multi-objective model is constructed to maximise the EMS and minimise the total engineering cost. The candidate Pareto-optimal solutions can be obtained by solving the optimisation model. Then a membership function is defined to select the optimal solution from the Pareto-optimal solutions. A case study for optimising the smartphone’s specifications is conducted to demonstrate the effectiveness of the newly developed approach. Compared with the commonly used Conjoint Analysis (CA) method in determining the most desired levels for product specifications, the proposed data-driven method can avoid the situation where the user’s preferences are irrational, making the proposed method be more practical in measuring customer preferences than the utility-based model.     

An optimisation model and its effective beam search heuristics for floor-storage warehousing systems

This paper presents an optimisation model and its effective solution technique using beam search heuristic for floor-storage warehousing systems. For a floor-storage system, storage can be accessed from the top of stacks only. The objective is to minimise the number of re-handling operations by optimally determining the storage location and by grouping products for each customer that fit a given sequence for receiving and retrieving operations. An integer programming model is formulated and an approximate solution technique based on the beam search method is proposed to solve the problem by incorporating effective heuristics to reduce the search space using future receiving and retrieving requests. The effectiveness of the proposed method is demonstrated for industrial warehousing problems in a steel plant with 58 storage areas involving more than 3000 retrieving operations. The proposed solution method is shown to be more efficient than the traditional branch-and-bound method for solving integer programming problems.     

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.     

Mitigating supply chain disruptions through interconnected logistics services in the Physical Internet

This paper investigates the resilience of inventory models using interconnected logistics services in the Physical Internet (PI). With traditional supply chain network design, companies define and optimise their own logistics networks, resulting in current logistics systems being a set of independent heterogeneous logistics networks. The concept of PI aims to integrate independent logistics networks into a global, open, interconnected system. Prior research has shown that new inventory models enabled by and applied to PI could help reduce inventory levels thanks to its high flexibility. Continuing along these lines, this paper examines how inventory models applying PI deal with disruptions at hubs and plants. To attain this, a single product inventory problem with uncertain demands and stochastic supply disruptions is studied. A simulation-based optimisation model is proposed to determine inventory control decisions. The results suggest that the PI inventory model, with greater agility and flexibility, outperforms the current classic inventory models in terms of resilience. Moreover, the difference in performance increases when the product value, penalty costs and disruption frequency increases. This paper indicates a novel approach to build a resilient supply network.     

Optimum Patch Repair Shapes for Cracked Members

In the present paper the problem of finding the optimal patch repair shape in a cracked structural component with respect to a given objective function, is investigated by using a biological-based procedure known as Genetic Algorithm (GA) in conjunction with a penalty constraint. The search for an optimal patch shape, which can be regarded also as an optimal topology problem, is obtained by determining the corresponding patch’s continuous material’s density distribution: the design optimisation considered in the present study takes into account the minimisation of some mechanical desired performance of the structure’s while keeping constant the total available patched area (optimal constrained problem). The proposed optimisation model is implemented in a F.E. code and some numerical simulations are performed in order to assess its reliability in optimal topology design of patch repairs, applied to mode I cracked members, with respect to some expected performances. For the considered problems, the optimisation algorithm allows a quite important improvement (in reducing the stress intensity factor) with respect to cracked components repaired with simple shape (i.e. rectangular) bonded patches.     

Optimisation of product configuration in consideration of customer satisfaction and low carbon

Product configuration is one of the key technologies in the environment of mass customisation, and it has been emphasised and concerned by much research. However, previous studies mostly focus on the cost or the customer utility, but ignore the environmental concern which becomes an important design criterion due to the rising awareness of environmental protection. Moreover, various preferences of customers are also not considered. In this paper, we develop a new bi-objective optimisation model with simultaneous consideration of customer satisfaction and the environmental impact in product configuration. Two objectives of our model are the customer satisfaction index (CSI) and greenhouse gas (GHG) emissions of products. The CSI is presented for the evaluation of customer satisfaction and the GHG emission model is developed to assess the environmental impact of the product. Essential constraints, such as selection, cost and compatibility, are also considered in the model. In addition, a two-phase approach is proposed to solve the optimisation model. Finally, the effectiveness of the proposed method is demonstrated through a case study.     

Smart box-enabled product–service system for cloud logistics

Modern logistics takes significant progress and rapid developments with the prosperity of E-commerce, particularly in China. Typical challenges that logistics industry is facing now are composed by a lack of sharing, standard, cost-effective and environmental package and efficient optimisation method for logistics tasks distribution. As a result, it is difficult to implement green, sustainable logistics services. Three important technologies, Physical Internet (PI), product–service system (PSS) and cloud computing (CC), are adopted and developed to address the above issues. PI is extended to design a world-standard green recyclable smart box that is used to encapsulate goods. Smart box-enabled PSS is constructed to provide an innovative sustainable green logistics service, and high-quality packaging, as well as reduce logistics cost and environmental pollution. A real-time information-driven logistics tasks optimisation method is constructed by designing a cloud logistics platform based on CC. On this platform, a hierarchical tree-structure network for customer orders (COs) is built up to achieve the order-box matching of function. Then, a distance clustering analysis algorithm is presented to group and form the optimal clustering results for all COs, and a real-time information-driven optimisation method for logistics orders is proposed to minimise the unused volume of containers. Finally, a case study is simulated to demonstrate the efficiency and feasibility of proposed cloud logistics optimisation method.     

A block transportation scheduling system considering a minimisation of travel distance without loading of and interference between multiple transporters

Material transportation scheduling problems concerning scheduling optimisation have been extensively investigated by researchers in such fields as industrial engineering and management science. Various algorithms have been proposed to solve such problems. However, the majority of these algorithms cannot be applied to a block transportation problem when a shipyard that uses a transporter, a large vehicle employed for moving weight, is considered. In this study, a hybrid optimisation algorithm is proposed for solving a block transportation problem when multiple transporters are used. With regards to the transporters, a minimisation of the travel distance without loading of and interference between the transporters is considered. A block transportation scheduling system is then developed based on the proposed algorithm. The developed system is applied to an actual block transportation scheduling problem of a shipyard. From the attained results, we demonstrate that the proposed algorithm has the ability to effectively solve the block transportation scheduling problems of a shipyard.     

Rack shape and energy efficient operations in automated storage and retrieval systems

Energy efficiency has become a primary goal to be pursued for sustainable logistics. In automated storage and retrieval systems this leads to revise the traditional control policies aimed at picking time minimisation and to pay more attention to rack configuration, which has been not a research concern from the time-based perspective. Proper models for energy calculation should be developed by introducing new factors neglected in time analysis, such as the weight of unit loads and the differentiation of shifts along the horizontal and vertical axis as regard energy requirements, due to different contribution of gravity, inertia and friction. In this study, a classification of racks based on system height is proposed in order to select the proper crane specifications needed to compute the torque to be overcome by motors to serve a given location within a rack. An overall optimisation model based on Constraint Programming hybridised with Large Neighborhood Search is developed, allowing the joint application of the best control policies for storage assignment and sequencing both for time and energy-based optimisation, as well as the introduction of multiple weight unit loads and energy recovery. Simulations analysis is performed in order to assess the impact of the rack shape on energy saving. Results show how, regardless the demand curve and the optimisation objective, the best performances in terms of energy efficiency are reached by the intermediate height rack shapes, while the lower ones outperform when considering travel time performance.     

Solving the blocking flow shop scheduling problem with makespan using a modified fruit fly optimisation algorithm

The flow shop scheduling problem with blocking has important applications in a variety of industrial systems but is under-represented in the research literature. In this paper, a modified fruit fly optimisation (MFFO) algorithm is proposed to solve the above scheduling problem for makespan minimisation. The MFFO algorithm mainly contains three key operators. One is related to the initialisation scheme in which a problem-specific heuristic is adopted to generate an initial fruit fly swarm location with high quality. The second is concerned with the smell-based search in which a neighbourhood strategy is designed to generate a new location. To further enhance the exploitation of the proposed algorithm considered, a speed-up insert-neighbourhood-based local search is applied with a probability. Finally, the last is for the vision-based search in which an update criterion is proposed to induce the fruit fly into a better searching space. The simulation experimental results demonstrated the efficiency of the proposed algorithm, in spite of its simple structure, in comparison with a state-of-the-art algorithm. Moreover, new best solutions for Taillard’s instances are reported for this problem, which can be used as a basis of comparison in future studies.     

Multi-site production planning integrating procurement and distribution plans in multi-echelon supply chains: an interactive fuzzy goal programming approach

Motivated by a real case of an automobile company, this study proposes a multi-objective, multi-site production planning model integrating procurement and distribution plans in a multi-echelon supply chain network with multiple suppliers, multiple manufacturing plants and multiple distribution centres. The model incorporates four important conflicting objectives simultaneously: minimisation of the total cost of logistics, maximisation of the total value of purchasing, minimisation of defective items and minimisation of late deliveries subject to some realistic constraints. Due to the imprecise/fuzzy nature of the objectives’ aspiration levels and some critical data, an interactive fuzzy goal programming formulation is first developed. Then, a novel fuzzy approach is proposed to convert the FGP model into an auxiliary crisp formulation to find an efficient compromise solution. The proposed model and solution method are validated through some numerical tests. Computational results indicate the practicality and tractability of the proposed model and also the superiority of the proposed auxiliary crisp formulation in contrast to the current alternative fuzzy approaches.     

An efficient method for dynamic-demand joint replenishment problem with multiple suppliers and multiple vehicles

How to improve competitive edges to meet rapidly changing market environment and dynamic customer needs is critical for the survival and success of firms these days. A good supply chain and inventory management is a necessity in the intensive competitive market. This paper considers a dynamic-demand joint replenishment problem with multiple vehicle routing. The problem is first formulated as a mixed integer programming model with an objective to minimise total costs, which include ordering cost, purchase cost, production cost, transportation cost and holding cost, under a prerequisite that inventory shortage is prohibited in the system. A particle swarm optimisation model is proposed next to solve large-scale problems which are computationally difficult. A case study of a touch panel manufacturer is presented to examine the practicality of the models.     

Optimisation of maintenance strategy for rural road network using genetic algorithm

The objective of this paper is to develop an optimised maintenance strategy for the rural road network of Kerala state. This is accomplished with the development of a bi-objective deterministic optimisation model which simultaneously satisfies the objectives of both minimisation of total maintenance cost and maximisation of performance of the road network. The model is capable of planning the maintenance activities over a multi-year planning period. The performance of the road network is accounted using the composite index, namely Pavement Condition Index. The constraint-based genetic algorithm was used as the optimisation tool since it very well takes care of the combinatorial nature of the network-level pavement maintenance programming. The applicability of the model is illustrated using a case study for the rural road network of Kerala state in India. The effort made through this research work to develop a suitable Pavement Maintenance and Management System for rural road network can lead to the proper maintenance and upkeep of the rural roads, thereby triggering a positive impact on the Indian economy.     

Simulation-based optimisation of logistics distribution system for an assembly line with path constraints

The design of logistics distribution system for an assembly line with given layout is usually constrained by various factors such as the vehicles for the distribution of assembly components and the paths on the shop floor for the vehicle movement. Since design optimisation of these production systems with such constraints is hard to solve by mathematic approaches, simulation-based approach is mostly used. In this paper, simulation-based analysis and optimisation of a logistic distribution system is performed combined with a heuristic algorithm to obtain a solution that most suits the practical requirements of an assembly line. The proposed simulation-based method builds a model that satisfied various complicated real-world problems at the work site of an investigated factory. Meanwhile, a dynamic dispatching method for vehicle movement is also presented which can adjust the control strategy and decision parameters dynamically in the process of simulation and contributes significantly to form a more fruitful design scheme. Experimental results show that proposed method performs better than the analytical model-based approach and some other methods, and the dynamic vehicle dispatching method presented performs better than some existing common strategies used in literature.     

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.