Applicability of optimal control theory to adaptive supply chain planning and scheduling

Decisions in supply chain planning and control are interconnected and depend on tackling uncertainties and dynamics. From this perspective, control theory (CT) is an interesting research avenue for the supply chain management (SCM). In this paper, the applicability of optimal CT to SCM is investigated. Our analysis is based on the fundamentals of control and systems theory and experimental modeling. The paper describes important issues and perspectives that delineate dynamics in supply chains, identifies and systemizes different streams in application of CT to production, logistics, and SCM in the period from 1960 to 2011. It derives some classifications, performs a critical analysis, and discusses further researches. Some drawbacks and missing links in the literature are pointed out. Several crucial application areas of control theory to SCM are discussed. Subsequently, optimal program control, challenges and advantages of its application in the SCM are addressed. It is shown how optimal program control can be applied to adaptive supply chain planning. In addition, it is concluded that with the help of CT, robustness, adaptability, and resilience of supply chains can be investigated in their consistency with operations planning and execution control within a conceptually and mathematically integrated framework. However, although SCs resemble control systems, they have some peculiarities which do not allow a direct application of CT methods. In this setting, further development of interdisciplinary approaches to supply chain optimization is argued. An extended co-operation between control and supply chain experts may have the potential to introduce more realism to the dynamic planning and models and improve real-time supply chain control policies.     

A survey on control theory applications to operational systems,supply chain management,and Industry 4.0

Modern production and logistics systems, supply chains, and Industry 4.0 networks are challenged by increased uncertainty and risks, multiple feedback cycles, and dynamics. Control theory is an interesting research avenue which contributes to further insights concerning the management of the given challenges in operations and supply chain management. In this paper, the applicability of control theory to engineering and management problems in supply chain operations is investigated. Our analysis bridges the fundamentals of control and systems theory to supply chain and operations management. This study extends our previous survey in the Annual Reviews in Control (Ivanov et al. 2012) by including new literature published in 2012–2018, identifying two new directions of control theory applications (i.e., ripple effect analysis in the supply chains and scheduling in Industry 4.0) and analysis towards the digital technology use in control theoretic models. It describes important issues and perspectives that delineate dynamics in supply chains, operations, and Industry 4.0 networks and identifies and systemizes different streams in the application of control theory to operations and supply chain management and engineering in the period from 1960–2018. It updates the existing applications and classifications, performs a critical analysis, and discusses further research avenues. Further development of interdisciplinary approaches to supply chain optimization is argued. An extended cooperation between control engineers and supply chain experts may have the potential to introduce more realism to dynamic planning and models, and improve performance in production and logistics systems, supply chains, and Industry 4.0 networks. Finally, we analyze the trends towards the intellectualization of control and its development towards supply chain control analytics.     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

Planning tools for managing the supply chain

In this paper, we introduce a new way to manage the supply chain. The proposed solution reduces the problem's complexity using a two-stage hierarchical production planning method and is applicable to realistic transportation optimization problems. The approach is based on planning and operations scheduling models, and is designed to minimize travel and production costs within a flexible organizational network. In the aggregate planning phase, a mathematical model involving an aggregation of products, demand and time periods is solved. It is at this initial stage that the size of the problem is reduced and its output is used as input to the detailed phase in order to improve resolution time. The second stage produces a detailed schedule. It is shown that the proposed approach generates good and feasible solutions to practical problems within a reasonable computational time.     

Optimal harvesting strategies for a multi-cycle and multi-pond shrimp operation: A practical network model

In this paper, we introduce a network formulation of the optimal scheduling model for a multi-cycle and multi-pond shrimp operation grounded on the original optimal harvesting theory for a single production unit. The optimal schedule comprises the harvesting and restocking time that maximizes total profit throughout the planning horizon, bounded by the underlying biological and economic conditions. The model takes into account the information such as harvest size distribution, seasonality of price, temperature and weight-dependent growth, and labor force and market demand constraints. We applied the model to an existing shrimp operation in Hawaii with 40 one-acre ponds and generated the optimal schedule for a year that maximizes overall production. The model schedule is found to be able to increase total production by 5% when compared to the schedule generated using an “educated” trial-and-error procedure currently practiced by this operation. Further insights for this multi-cycle and multi-pond scheduling problem were also generated through several alternate simulations. It is found that labor force and market demand constraints are the key factors in scheduling multi-cycle and multi-pond shrimp operations.     

考虑延时效应和记忆效应的供应链广告策略

研究考虑延时效应和记忆效应的供应链广告策略问题,建立泛函微分方程以刻画广告延时和记忆双重效应对产品品牌信誉变化的影响,运用微分对策理论给出制造商和零售商在分散式决策和集中式决策下的最优广告投入水平、广告排期以及相应的产品品牌信誉,并对不同决策机制下供应链成员的均衡策略进行比较分析.研究表明:集中式决策能激励制造商和零售商的广告投入; 延时效应与记忆效应分别影响供应链成员广告时序策略与广告排期策略的制定,同时,还会影响供应链决策机制的选择.     

An integrated job shop scheduling and assembly sequence planning approach for discrete manufacturing

This paper proposes an integrated job shop scheduling and assembly sequence planning (IJSSASP) approach for discrete manufacturing, enabling the part processing sequence and assembly sequence to be optimized simultaneously. The optimization objectives are to minimize the total production completion time and the total inventory time of parts during production. The interaction effects between the job shop schedule and the assembly sequence plan in discrete manufacturing are analyzed, and the mathematical models including the objective functions and the constraints are established for IJSSASP. Based on the above, a non-dominated sorting genetic algorithm-II (NSGA-Ⅱ) with a hybrid chromosome coding mechanism is applied to solve the IJSSASP problem. Through the case studies and comparison tests for different scale problems, the proposed IJSSASP approach is verified to be able to improve the production efficiency and save the manufacturing cost of the discrete manufacturing enterprise more effectively.     

On the receding horizon hierarchical optimal control of manufacturing systems

This paper concerns the development of a hierarchical framework for the integrated planning and scheduling of a class of manufacturing systems. In this framework, dynamic optimization plays an important role in order to define control strategies that, by taking into account the dynamic nature of these systems, minimize customized cost functionals subject to state and control constraints. The proposed architecture is composed of a set of hierarchical levels where a two-way information flow, assuming the form of a state feedback control, is obtained through a receding horizon control scheme. The averaging effect of the receding horizon control scheme enables this deterministic approach to handle random and unexpected events at all levels of the hierarchy. At a given level, production targets to the subsystems immediately below are defined by solving appropriate optimal control problems. Efficient iterative algorithms based on optimality conditions are used to yield control strategies in the form of production rates for the various subsystems. At the lower level, this control strategy is further refined in such a way that all sequences of operations are fully specified. The minimum cost sensitivity information provided in the optimal control formulation supports a mechanism, based on the notion of a critical machine, which plays an important role in the exploitation of the available flexibility. Finally, an important point to note is that our approach is particularly suited to further integration of the production system into a larger supply chain management framework, which is well supported by recent developments in hybrid systems theory.     

求解IPPS顺序柔性调度问题的模型与集成型调度算法研究

针对工艺规划与调度集成(integrated process planning and scheduling, IPPS)问题中的顺序柔性调度问题,提出了基于简单顺序关系的顺序柔性描述模型及调度模型,并改进遗传算法设计了集成型的顺序柔性调度算法。染色体编码同时采用简单顺序关系编码和基于工序的编码,并为两种编码分别设计了多种交叉和变异操作。为避免遗传算子产生违背工序顺序优先关系的不可行解,提出了顺序约束修正策略;针对遗传算法易过早收敛的缺陷,设计了自适应调节变量以强化种群多样性,并引入变邻域搜索算法改变解的搜索邻域,进一步搜索最优调度方案。三种不同规模的实验仿真验证了问题描述模型及调度算法的有效性。     

基于非线性工艺规划思想的车间动态调度系统

描述了一种基于非线性工艺规划思想的车间动态调度系统的集成框架结构，其核心模块为计划调度模块，该模块调用由遗传算法和启发式调度相结合的调度算法生成动态调度方案。其中所提出的遗传编码的设计充分考虑工艺路径的柔性，并根据此编码提出调度方案的构造方法，同时相应地改进了遗传操作，从而实现了调度的全局最优性和可行性。     

SUPPLY SYSTEM DYNAMICS: DISTRIBUTED CONTROL IN SUPPLY CHAINS AND NETWORKS

The purpose of this article is to make a contribution to a more effective management for supply chains and networks, which we are subsuming under the title supply systems. We conceive of supply systems management as the design, control, and development of logistics along the value chain or in value networks. In this article, we concentrate on control, i.e., the regulation and steering of supply systems from production to customer and vice versa, with the help of system dynamics modeling and simulation. Traditionally, supply chain management has been heavily supported by discrete event simulation and optimization models on very detailed levels. Established tools, e.g., Manufacturing Resources Planning, Enterprise Resources Planning, and Production Planning Systems, have emphasized highly specialized functions, such as the planning for optimal capacity use and achievement of delivery goals, via the sequencing, scheduling, and dispatching of individual orders, or the global allocation of resources but without the possibility to evaluate different outcomes of the allocation process. In the quest for efficiency and effectiveness, new approaches to control, which lead beyond these functions of disposition, are needed. We present a model that combines two systemic methodologies that operate on higher levels of aggregation and complement each other: system dynamics to model and simulate the supply chain and cybernetic control to apply control-theoretical concepts, namely proportional, derivative, and integral control, in a combined mode as well as according to a recursive logic of distributed control. This way, substantial improvements in both efficiency and effectiveness can be achieved, and ultimately organizational viability can be enhanced.     

Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm

Nowadays, scheduling of production cannot be done in isolation from scheduling of transportation since a coordinated solution to the integrated problem may improve the performance of the whole supply chain. In this paper, because of the widely used of rail transportation in supply chain, we develop the integrated scheduling of production and rail transportation. The problem is to determine both production schedule and rail transportation allocation of orders to optimize customer service at minimum total cost. In addition, we utilize some procedures and heuristics to encode the model in order to address it by two capable metaheuristics: Genetic algorithm (GA), and recently developed one, Keshtel algorithm (KA). Latter is firstly used for a mathematical model in supply chain literature. Besides, Taguchi experimental design method is utilized to set and estimate the proper values of the algorithms’ parameters to improve their performance. For the purpose of performance evaluation of the proposed algorithms, various problem sizes are employed and the computational results of the algorithms are compared with each other. Finally, we investigate the impacts of the rise in the problem size on the performance of our algorithms.     

逆向物流供应链最佳供应商选择及订单量分配*

为解决逆向物流供应链中,供应商选择、订单量分配和提货点位置等不确定问题,建立了一个新的模糊多目标数学模型来确定最佳供应商选择、供应量及提货点位置,为避免在解决多目标模型时人为主观赋权,运用基于模糊目标规划的蒙特卡罗仿真模型来求解帕累托(pareto)理想解,采用遗传算法进行求解,并给出了相应优化方案,在此基础上研究讨论了不同权重分配下结果的优劣性及供应商选择风险,最后,针对不同权重分配,比较了遗传算法和Gurobi求解,实验表明,对于该问题模型遗传算法在解的优劣性上优于Gurobi。     

Batch production control in a computer integrated manufacturing environment

Since unit level control systems have already been widely implemented in the chemical industry, it has become abundantly clear that the next levels of application lie in supervisory control and scheduling. This is particularly true for the chemical processing industry, operating batchwise with multi-product and multi-purpose characteristics. A ‘flexible schedule’ (FLESCHE) concept providing the link between operations scheduling and the recipe-driven batch operatioon with its basic instrumentation and control system is being developed and implemented in a computer integrated manufacturing environment. The FLESCHE system incorporates an integrated decision-making framework which includes all the relevant decision functions in a multi-level hierarchy: optimal plant-wide resources coordination, demand management, plant-wide operations scheduling and plant retrofitting on the one hand, as well as master recipe adaptation, batch rerouting and schedule adaptation in response to short-time, unplanned operational disturbances, on the other. A summary of the main characteristics, methodology and present applications of the system is provided. Actual limitations and future extensions are also discussed.     

置换表示方法求解多卫星多地面站调度问题

针对多卫星成像和多地面站数传并存的对地成像调度问题,从置换空间到调度解空间的映射方法和置换空间的搜索算法两方面进行了研究.提出了一种数传时间窗优先的置换序列映射算法,并证明该映射算法可以将置换序列映射到调度解空间上的最优解.提出了一种遗传随机搜索算法,基于有记忆随机邻域搜索,在置换空间上进行搜索.仿真计算表明,随机邻域搜索可以增强遗传算法的局部搜索能力,搜索结果平均获得了4.64%的改进.     

分布式多工厂、多分销商的供应链生产计划模型

描述了分布式多工厂、多分销商的供应链生产 计划,以实现最小化提前/拖期惩罚费用、生产成本、产品运输费用的总额为目标建立了模型 ,通过模型转换,求解得到了其生产计划调度方案.计算结果证明了模型的有效性和可行性.     

流域水电站群经济运行和优化调度系统设计研究

随着越来越多的流域水电站群采用集控中心进行远程监视和控制,如何充分发挥集控中心在流域水库优化调度和电站经济运行中的作用,提高水能资源利用率,充分发挥流域综合经济效益已经成为一个重要课题,本文介绍流域水电站群经济运行和优化调度系统的总体设计要求,提出了优化调度准则和优化凋度算法设计规划。     

Fast reactive scheduling to minimize tardiness penalty and energy cost under power consumption uncertainties

Motivated by the need to deal with uncertainties in energy optimization of flexible manufacturing systems, this paper considers a dynamic scheduling problem which minimizes the sum of energy cost and tardiness penalty under power consumption uncertainties. An integrated control and scheduling framework is proposed including two modules, namely, an augmented discrete event control (ADEC) and a max-throughput-min-energy reactive scheduling model (MTME). The ADEC is in charge of inhibiting jobs which may lead to deadlocks, and sequencing active jobs and resources. The MTME ensures the fulfillment of the innate constraints and decides the local optimal schedule of active jobs and resources. Our proposed framework is applied to an industrial stamping system with power consumption uncertainties formulated using three different probability distributions. The obtained schedules are compared with three dispatching rules and two rescheduling approaches. Our experiment results verify that MTME outperforms three dispatching rules in terms of deviation from Pareto optimality and reduces interrupted time significantly as compared to rescheduling approaches. In addition, ADEC and MTME are programmed using the same matrix language, providing easy implementation for industrial practitioners.     

A new model for the integrated vehicle-crew-rostering problem and a computational study on rosters

Operational planning within public transit companies has been extensively tackled but still remains a challenging area for operations research models and techniques. This phase of the planning process comprises vehicle-scheduling, crew-scheduling and rostering problems. In this paper, a new integer mathematical formulation to describe the integrated vehicle-crew-rostering problem is presented. The method proposed to obtain feasible solutions for this binary non-linear multi-objective optimization problem is a sequential algorithm considered within a preemptive goal programming framework that gives a higher priority to the integrated vehicle-crew-scheduling goal and a lower priority to the driver rostering goals. A heuristic approach is developed where the decision maker can choose from different vehicle-crew schedules and rosters, while respecting as much as possible management’s interests and drivers’ preferences. An application to real data of a Portuguese bus company shows the influence of vehicle-crew-scheduling optimization on rostering solutions.     

Setup Scheduling of Manufacturing Systems as the Art of Juggling

The problem of detailed scheduling of complex flexible manufacturing systems is addressed by optimal flow control. A model problem of scheduling parallel machines is considered to obtain necessary setup conditions. Studying the conditions results in a new solution approach that takes advantage of a juggling analogy of the production/setup scheduling. This analogy is used in the paper to direct construction of a solution method. The method searches for a globally optimal schedule by means of both a juggling strategy and a method of global optimization. The results obtained for a model problem are then generalized to systems with complex production and setup operations. Computational examples demonstrate the validity of the approach.