基于约束规划的一类排序问题通用求解方法

多约束排序问题是生产调度中常遇到的问题,传统的优化模型及方法在适应约束改变等方面存在诸多不足。鉴于此,将多约束排序问题定义为约束满足问题,系统设计时将模型定义与求解算法分离,利用约束规划平台的基本约束构建特定领域的抽象约束库,形成可重构的多约束排序问题通用求解框架。应用时,根据问题需求不同可利用抽象约束库快速重构优化模型,针对重构的优化模型配置相应的求解算法即可实现问题求解。应用结果表明,提出的方法通用性强,可满足实际应用的要求。     

Algorithms for propagating resource constraints in AI planning and scheduling: Existing approaches and new results

This paper summarizes the main existing approaches to propagate resource constraints in Constraint-Based scheduling and identifies some of their limitations for using them in an integrated planning and scheduling framework. We then describe two new algorithms to propagate resource constraints on discrete resources and reservoirs. Unlike most of the classical work in scheduling, our algorithms focus on the precedence relations between activities rather than on their absolute position in time. They are efficient even when the set of activities is not completely defined and when the time window of activities is large. These features explain why our algorithms are particularly suited for integrated planning and scheduling approaches. All our algorithms are illustrated with examples. Encouraging preliminary results are reported on pure scheduling problems as well as some possible extensions of our framework.     

约束满足技术在板坯排序中的应用

热轧调度中的板坯排序问题是一类特殊的排序问题,具有约束条件复杂、NP难特点。为了简化问题,将板坯排序问题转化为一个约束满足问题处理。给出板坯排序问题的约束满足模型,设计了基于约束满足和启发式混合求解算法。用3组实际生产数据对算法性能进行验证,说明了算法的有效性。     

约束规划方法在原油混输调度中的应用

应用约束规划方法建立炼厂原油混输模型,并通过搜索求解获得可操作的调度方案。约束规划允许以逻辑约束表达混输过程中的操作规则,使模型简洁直观,而对整型变量及非线性约束不敏感,则降低了模型的求解难度。选取了一个时间跨度为300小时实际调度问题作为案例,模型中包含约8000个变量和14000条约束,在普通PC机上计算求解,能够在30秒内获得调度方案,方案具有可操作性且优于人工方案,表明以约束规划方法解决原油混输调度问题是可行的。     

约束规划在原油调度中应用的探索

针对数学规划方法应用在原油调度中对非线性约束和连续变量处理的局限性,通过分析约束规划方法解决问题的原理、建模方式和搜索机制,指出了应用约束规划方法解决原油调度问题的可能性和优势;并针对实际问题,分别应用两种方法进行建模、求解．通过对结果的分析得出,约束规划方法建模简单,可直接处理非线性问题,对连续变量和整型变量都可以很快求得可行解。     

On the complexity of interval scheduling with a resource constraint

We consider a scheduling problem where jobs have to be carried out by parallel identical machines. The attributes of a job j are: a fixed start time s_j, a fixed finish time f_j, and a resource requirement r_j. Every machine owns R units of a renewable resource necessary to carry out jobs. A machine can process more than one job at a time, provided the resource consumption does not exceed R. The jobs must be processed in a non-preemptive way. Within this setting, the problem is to decide whether a feasible schedule for all jobs exists or not.We discuss such a decision problem and prove that it is strongly NP-complete even when the number of resources are fixed to any value R≥2. Moreover, we suggest an implicit enumeration algorithm which has O(nlogn) time complexity in the number n of jobs when the number m of machines and the number R of resources per machine are fixed.The role of storage layout and preemption are also discussed.     

REDOM: an OO language to define and on-line manipulate regulations in the resource (re)scheduling problem

In the class of (re)scheduling problems where humans constitute the main resource, the scheduling process is influenced by a great number of complex and frequently changing regulations. The complexity and the dynamic nature of these regulations impose the need for an efficient, flexible and user-friendly way to express and manage them. A solution to this problem, in the form of an object-oriented high-level language with semantics highly-tailored to the user needs, is presented. The language, called REDOM, can be applied to different scheduling application domains with a minimum degree of effort, because it is based on a generic meta-model of the resource scheduling problem. An application programming interface facilitates REDOM integration into existing scheduling systems. REDOM is currently being utilised by the DAYSY resource management system, that is implemented as a constraint satisfaction system based on a partial test-and-generate approach. The combination of REDOM and CHIP (Constraint Handling In Prolog), which was used for the implementation of the solution generation subsystem, resulted in a highly-efficient and flexible (re)scheduling system, well-accepted by users. © 1997 John Wiley & Sons, Ltd.     

Combined planning and scheduling in a divergent production system with co-production: A case study in the lumber industry

Many research initiatives carried out in production management consider process planning and operations scheduling as two separate and sequential functions. However, in certain contexts, the two functions must be better integrated. This is the case in divergent production systems with co-production (i.e. production of different products at the same time from a single product input) when alternative production processes are available. This paper studies such a context and focuses on the case of drying and finishing operations in a softwood lumber facility. The situation is addressed using a single model that simultaneously performs process planning and scheduling. We evaluate two alternative formulations. The first one is based on mixed integer programming (MIP) and the second on constraint programming (CP). We also propose a search procedure to improve the performance of the CP approach. Both approaches are compared with respect to their capacity to generate good solutions in short computation time.     

A hybridization of genetic algorithms and fuzzy logic for the single-machine scheduling with flexible maintenance problem under human resource constraints

This work focuses on the problem of scheduling jobs on a single machine that requires flexible maintenance under human resource competence and availability constraints. To solve the problem we developed two fuzzy genetic algorithms that are based on respectively the sequential and total scheduling strategies. The one respecting the sequential strategy consists in two phases. In the first phase, the integrated production and maintenance schedules are generated. In the second one, the human resources are assigned to maintenance activities. The second algorithm respecting a total strategy consists in generating the integrated production and maintenance schedules that explicitly satisfy the human resource constraints. In regard to these two different strategies, we studied then two integrated fuzzy genetic algorithms that use the fuzzy logic framework to deal with the uncertain nature of both production and maintenance data. The proposed genetic algorithms have been implemented and applied to non-standard test problems which integrate production, maintenance and human resource data. The experimental results show that the consideration of human resource constraints and uncertainties allows to get more realistic and applicable solutions. Moreover, the comparison between the two proposed algorithms shows that the one based on the total strategy outperforms the one based on the sequential strategy regarding the objective functions’ optimization. However, this latter is better in terms of computational times.     

Application of a hybrid intelligent decision support model in logistics outsourcing

Outsourcing is an increasingly important issue pursued by corporations seeking improved efficiency. Logistics outsourcing or third-party logistics (3PL) involves the use of external companies to perform some or all of the firm's logistics activities. This paper proposes an intelligent decision support framework for effective 3PL evaluation and selection. The proposed framework integrates case-based reasoning, rule-based reasoning and compromise programming techniques in fuzzy environment. This real-time decision-making approach deals with uncertain and imprecise decision situations. Furthermore, the integration of different methodologies takes the advantage of their strengths and complements each other's weaknesses. Consequently, the framework leads to a more accurate, flexible and efficient retrieval of 3PL service providers (alternatives) that are most similar and most useful to the current decision situation. Finally, a real industrial application is given to demonstrate the potential of the proposed framework.     

Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption

Green transportation has recently been the focus of the transportation industry to sustain the development of global economy. Container terminals are key nodes in the global transportation network and energy-saving is a main goal for them. Yard crane (YC), as one type of handling equipment, plays an important role in the service efficiency and energy-saving of container terminals. However, traditional methods of YC scheduling solely aim to improve the efficiency of container terminals and do not refer to energy-saving. Therefore, it is imperative to seek an appropriate approach for YC scheduling that considers the trade-off between efficiency and energy consumption. In this paper, the YC scheduling problem is firstly converted into a vehicle routing problem with soft time windows (VRPSTW). This problem is formulated as a mixed integer programming (MIP) model, whose two objectives minimize the total completion delay of all task groups and the total energy consumption of all YCs. Subsequently, an integrated simulation optimization method is developed for solving the problem, where the simulation is designed for evaluating solutions and the optimization algorithm is designed for exploring the solution space. The optimization algorithm integrates the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, where the GA is used for global search and the PSO is used for local search. Finally, computational experiments are conducted to validate the performance of the proposed method.     

Resource‐constrained project scheduling through the goal programming model: integration of the manager's preferences

The aim of this paper is to present a multi‐objective project scheduling approach to help project managers when deciding on a baseline schedule. The concepts of satisfaction functions and goal programming are incorporated to generate this baseline schedule that represents the best compromise among a set of conflicting project objectives. An efficient computerized procedure based on the tabu search algorithm is proposed and enables the handling of large planning and scheduling projects.     

Improving the delivery efficiency of the customer order scheduling problem in a job shop

The focus of this paper is customer order scheduling (COS) problem, where each order consists of a set of jobs that must be shipped as one batch at the same time. In COS each job is part of a customer order and the make-up of the jobs in the order are pre-specified. Most of the existing research deals with COS in a single machine or in a parallel machine shop for developing an optimal solution. COS is common in a normal job shop, and the more complex the shop, the more complex the scheduling. Most existing research has focused on trying to reduce the completion time of the batch. That is, the focus is only on the point in time the last job is finished, while ignoring the actual duration of the jobs within the same order. The longer it takes to complete all the jobs within an order the more it increases the stock of finished goods and the more it deteriorates the efficiency of the logistics and the supply chain management.A new dispatching rule, referred to as Minimum Flow Time Variation (MFV), has been proposed for COS in a normal job shop, in order to reduce the total time it takes to complete all jobs within the same order. That is, the individual completion times of all jobs for the same customer order will be controlled in order to improve the shipping performance. In the simulation test and statistical analysis, the level of work in process (WIP) under the MFV rule in the finished goods warehouse is reduced by more than 70% compared to any other method. The MFV method will efficiently reduce the stock level of finished goods, and controls the waiting time required before they can be shipped. Depending on the environmental factors, the performance of our proposed method will become increasingly significant the more complex the system.     

A hybrid VNS approach for the short-term production planning and scheduling: A case study in the pulp and paper industry

Mathematical formulations for production planning are increasing complexity, in order to improve their realism. In short-term planning, the desirable level of detail is particularly high. Exact solvers fail to generate good quality solutions for those complex models on medium- and large-sized instances within feasible time. Motivated by a real-world case study in the pulp and paper industry, this paper provides an efficient solution method to tackle the short-term production planning and scheduling in an integrated mill. Decisions on the paper machine setup pattern and on the production rate of the pulp digester (which is constrained to a maximum variation) complicate the problem. The approach is built on top of a mixed integer programming (MIP) formulation derived from the multi-stage general lotsizing and scheduling problem. It combines a Variable Neighbourhood Search procedure which manages the setup-related variables, a specific heuristic to determine the digester's production speeds and an exact method to optimize the production and flow movement decisions. Different strategies are explored to speed-up the solution procedure and alternative variants of the algorithm are tested on instances based on real data from the case study. The algorithm is benchmarked against exact procedures.     

A dual‐response strategy for global logistics under uncertainty: a case study of a third‐party logistics company

This paper examines global logistics problems experienced by a third‐party logistics (3PL) company that is responsible for transporting goods from one country to another by road, as well as warehousing goods in two countries. Because of the limited fleet capacity, the logistics company has to hire additional trucks from two countries in advance. However, customer's shipment information is uncertain, and the accurate shipment notice is only available on the shipping day. This paper proposes a dual‐response logistics strategy to be as responsive as possible for coping with short shipment notice time and uncertainty involved. A two‐stage stochastic mixed 0–1 programming model is formulated to satisfy customer demand while minimizing the total logistics cost. A series of experiments demonstrate the effectiveness of the proposed stochastic model. Compared to the corresponding expected value model, the stochastic model provides a more responsive and less expensive global logistics system.     

Quantifying low back peak and cumulative loads in open and senior sheep shearers in New Zealand: Examining the effects of a trunk harness

Sheep shearing requires shearers to adopt sustained flexed postures for prolonged periods of time and has been associated with an increased risk of developing low back pain (LBP). However, these postures do not generally result in acute compressive values at L4/L5 exceeding the action limit proposed by the National Institute for Occupational Safety and Health, despite the high prevalence of LBP in this occupation. Therefore, it may not be peak loading that is responsible for LBP in this occupation but instead it may be the effect of cumulative loading over the course of a workday. The primary purpose of this research was to quantify the low back cumulative load exposure in 12 sheep shearers with and without the aid of a commercial trunk harness. Results revealed a significant reduction in the magnitude of cumulative compression with the use of the trunk harness and therefore its use may potentially reduce the risk of injury. The use of the trunk harness also reduced the time spent in axially twisted postures, which have been associated with LBP. However, using the trunk harness also resulted in increased time spent in laterally bent postures, which has been associated with increased risk for pain and injury.     

Bicriteria scheduling of a material handling robot in an m-machine cell to minimize the energy consumption of the robot and the cycle time

This study considers a flowshop type production system consisting of m machines. A material handling robot transports the parts between the machines and loads and unloads the machines. We consider the sequencing of the robot moves and determining the speeds of these moves simultaneously. These decisions affect both the robot’s energy consumption and the production speed of the system. In this study, these two objectives are considered simultaneously. We propose a second order cone programming formulation to find Pareto efficient solutions. We also develop a heuristic algorithm that finds a set of approximate Pareto efficient solutions. The conic formulation can find robot schedules for small cells with less number of machines in reasonable computation times. Our heuristic algorithm can generate a large set of approximate Pareto efficient solutions in a very short computational time. Proposed solution approaches help the decision-maker to achieve the best trade-off between the throughput of a cell and the energy efficiency of a material handling robot.     

Development of a fuzzy decision support framework for complex multi‐attribute decision problems: A case study for the selection of skilful basketball players

The selection of skilful players is a complicated process due to the problem criteria consisting of both qualitative and quantitative attributes as well as vague linguistic terms. This study seeks to develop a decision support framework for the selection of candidates eligible to become basketball players through the use of a fuzzy multi‐attribute decision making (MADM) algorithm. The proposed model is based on fuzzy analytic hierarchy process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The model was employed in the Youth and Sports Center of Mugla, Turkey, with the participation of seven junior basketball players aged between 7 and 14. In the present study, physical fitness measurement values and observation values of technical skills were utilized. FAHP was used to determine the weights of the criteria and the observation values of technical skills by decision makers. Physical fitness measurement values were converted to fuzzy values by using a fuzzy set approach. Subsequently, the overall ranking of the candidate players was determined by the TOPSIS method. Results were compared with human experts’ opinions. It is observed that the developed model is more reliable to be used in decision making. The model architecture and experimental results along with illustrative examples are further demonstrated in the study.     

Loosely coordinated coscheduling in the context of other approaches for dynamic job scheduling: a survey

Loosely coordinated (implicit/dynamic) coscheduling is a time‐sharing approach that originates from network of workstations environments of mixed parallel/serial workloads and limitedsoftware support. It is meant to be an easy‐to‐implement and scalable approach. Considering that the percentage of clusters in parallel computing is increasing and easily portable software is needed, loosely coordinated coscheduling becomes an attractive approach for dedicated machines. Loose coordination offers attractive features as a dynamic approach. Static approaches for local job scheduling assign resources exclusively and non‐preemptively. Such approaches still remain beyond the desirable resource utilization and average response times. Conversely, approaches for dynamic scheduling of jobs can preempt resources and/or adapt their allocation. They typically provide better resource utilization and response times. Existing dynamic approaches are full preemption with checkpointing, dynamic adaptation of node/CPU allocation, and time sharing via gang or loosely coordinated coscheduling. This survey presents and compares the different approaches, while particularly focusing on the less well‐explored loosely coordinated time sharing. The discussion particularly focuses on the implementation problems, in terms of modification of standard operating systems, the runtime system and the communication libraries. Copyright © 2005 John Wiley & Sons, Ltd.     

Testing the scenario hypothesis: An experimental comparison of scenarios and forecasts for decision support in a complex decision environment

Decision support tools are known to influence and facilitate decisionmaking through the thoughtful construction of the decision environment. However, little research has empirically evaluated the effects of using scenarios and forecasts. In this research, we asked participants to recommend a fisheries management strategy that achieved multiple objectives in the face of significant uncertainty. A decision support tool with one of two conditions—Scenario or Forecast—encouraged participants to explore a large set of diversified decision options. We found that participants in the two conditions explored the options similarly, but chose differently. Participants in the Scenario Condition chose the strategies that performed well over the full range of uncertainties (robust strategies) significantly more frequently than did those in the Forecast Condition. This difference seems largely to be because participants in the Scenario Condition paid increased attention to worst-case futures. The results offer lessons for designing decision support tools.