A possibility theory‐based approach to the handling of uncertain relations between temporal points

Uncertain relations between temporal points are represented by means of possibility distributions over the three basic relations precedes, equals, and follows. Operations for computing inverse relation, for composing relations, for combining relations coming from different sources and pertaining to the same temporal points, or for representing negative information are defined. An illustrative example of representation and reasoning with uncertain temporal relations is provided. This article shows how possibilistic temporal uncertainty can be handled in the setting of point algebra. Moreover, the article emphasizes the advantages of the possibilistic approach over a probabilistic approach previously proposed. This work does for the temporal point algebra what the authors previously did for the temporal interval algebra. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 157–179, 2007.     

A refined cost-based approach to assembly scheduling

Increased emphasis on control of work-in-process costs in assembly scheduling of large, complex items leads to increased needs for aids to foremen in dealing with schedule changes. The task is complicated by constraints on resources that often require that activities are begun earlier than a just-in-time schedule would otherwise dictate. A criterion used in prototype tandem knowledge-based decision-aiding systems in the past was based on the assumption that investment costs do not compound. This can provide misleading choices in some cases. The present work refines the criterion previously used by including the compounding costs of holding subassemblies in inventory. A simplified version of the new formula is developed which provides simple rules for deciding which activities to start early if necessary. Numerical comparisons are made between the criteria.     

A hierarchical approach to parallel multiquery scheduling

There has been a good deal of progress made recently toward the efficient parallelization of individual phases of single queries in multiprocessor database systems. In this paper we devise and experimentally evaluate a number of scheduling algorithms designed to handle multiple parallel queries. (Scheduling in this context implies the determination of both processor allotments and temporal processor assignments to individual queries and query phases.) One of these algorithms performs the best in our experiments. This algorithm is hierarchical in nature: In the first phase, a good quality precedence based schedule is created for each individual query and each possible number of processors. This component employs dynamic programming. In the second phase, the results of the first phase are used to create an overall schedule of the full set of queries. This component is based on previously published work on nonprecedence-based malleable scheduling. Even though the problem we are considering is NP-hard in the strong sense, the multiple query schedules generated by our hierarchical algorithm are seen experimentally to achieve high quality results     

A DE-based approach to no-wait flow-shop scheduling

This paper proposes an effective hybrid differential evolution (HDE) for the no-wait flow-shop scheduling problem (FSSP) with the makespan criterion, which is a typical NP-hard combinational optimization problem. Firstly, a largest-order-value (LOV) rule is presented to transform individuals in DE from real vectors to job permutations so that the DE can be applied for solving FSSPs. Secondly, the DE-based parallel evolution mechanism and framework is applied to perform effective exploration, and a simple but efficient local search developed according to the landscape of FSSP is applied to emphasize problem-dependent local exploitation. Thirdly, a speed-up evaluation method and a fast Insert-based neighborhood examining method are developed based on the properties of the no-wait FSSPs. Due to the hybridization of DE-based evolutionary search and problem-dependent local search as well as the utilization of the speed-up evaluation and fast neighborhood examining, the no-wait FSSPs can be solved efficiently and effectively. Simulations and comparisons based on well-known benchmarks demonstrate the efficiency, effectiveness, and robustness of the proposed HDE.     

A decomposition approach to non-preemptive real-time scheduling

Consider the problem of scheduling a set ofn tasks on a uniprocessor such that a feasible schedule that satisfies each task's time constraints is generated. Traditionally, researchers have looked at all the tasks as a group and applied heuristic or enumeration search to it. We propose a new approach called thedecomposition scheduling where tasks are decomposed into a sequence of subsets. The subsets are scheduled independently, in the order of the sequence. It is proved that a feasible schedule can be generated as long as one exists for the tasks. In addition, the overall scheduling cost is reduced to the sum of the scheduling costs of the tasks in each subset.Simulation experiments were conducted to analyze the performance of decomposition scheduling approach. The results show that in many cases decomposition scheduling performs better than the traditional branch-and-bound algorithms in terms of scheduling cost, and heuristic algorithms in terms of percentage of finding feasible schedules over randomly-generated task sets.     

A neural network approach to job-shop scheduling

A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity.     

A holonic approach to dynamic manufacturing scheduling

Manufacturing scheduling is a complex combinatorial problem, particularly in distributed and dynamic environments. This paper presents a holonic approach to manufacturing scheduling, where the scheduling functions are distributed by several entities, combining their calculation power and local optimization capability. In this scheduling and control approach, the objective is to achieve fast and dynamic re-scheduling using a scheduling mechanism that evolves dynamically to combine centralized and distributed strategies, improving its responsiveness to emergence, instead of the complex and optimized scheduling algorithms found in traditional approaches.     

A hybrid approach to large-scale job shop scheduling

A decomposition based hybrid optimization algorithm is presented for large-scale job shop scheduling problems in which the total weighted tardiness must be minimized. In each iteration, a new subproblem is first defined by a simulated annealing approach and then solved using a genetic algorithm. We construct a fuzzy inference system to calculate the jobs’ bottleneck characteristic values which depict the characteristic information in different optimization stages. This information is then utilized to guide the process of subproblem-solving in an immune mechanism in order to promote the optimization efficiency. Numerical computational results show that the proposed algorithm is effective for solving large-scale scheduling problems.     

A simulated annealing approach to integrated production scheduling

This paper describes an approach to manufacturing planning that seeks to integrate both process planning and scheduling. We show that separating these two related tasks, as is the common practice, can impose constraints that substantially reduce the quality of the final schedule. These constraints arise from premature decisions regarding operation sequence and allocation of manufacturing resources. Having formulated an integrated process planning and scheduling problem, we describe a solution technique based on simulated annealing. We compare this approach with others reported in the literature, considering both their generality and performance. In particular, we perform a detailed empirical comparison between simulated annealing and the popular technique of dispatching rules. Our results, achieved with two distinct sets of example problems, show that simulated annealing can produce solutions of significantly higher quality than those achieved through a published dispatching rule approach.     

A temporal logic approach to object certification

A brief overview is made of the use of temporal logic formalisms for specifying and verifying concurrent systems in general and information systems in particular. The requirements imposed by object-orientation on such formalisms are examined. A logic is proposed fulfilling those requirements (except concerning non-monotonic features), allowing the uniform treatment of both local and global properties of systems with concurrent, interacting components organized in classes, and supporting specialization. A semantics and a calculus (following an axiomatic, Hilbert style) are presented in detail. The calculus includes rules for the sound inheritance and reflection of theorems between classes. Practical aspects of the usage of such a logic for both specification and verification are considered. To this end a set of metatheorems is provided for expediting the proof of invariants. Finally, the need and availability of automatic theorem proving for systems querying is briefly discussed.     

An optimization approach to relaxation labelling algorithms

It is shown that the relaxation labelling process of Rosenfeld, Hummel and Zucker is a suboptimal minimization of a cost function measuring inconsistency and ambiguity. Two new algorithms which minimize this cost function more efficiently are introduced. Finally, some general comments on relaxation are presented.     

A minimum-cost network flow approach to preemptive parallel-machine scheduling

We model and solve the problems of preemptive scheduling of n independent jobs with release dates on m parallel machines with machine availability and eligibility constraints to minimize the makespan and maximum lateness as the minimum-cost network flow problem. We show that the approach can be extended to solve the corresponding scheduling problems on two uniform parallel machines.     

A fluid approach to large volume job shop scheduling

We consider large volume job shop scheduling problems, in which there is a fixed number of machines, a bounded number of activities per job, and a large number of jobs. In large volume job shops it makes sense to solve a fluid problem and to schedule the jobs in such a way as to track the fluid solution. There have been several papers which used this idea to propose approximate solutions which are asymptotically optimal as the volume increases. We survey some of these results here. In most of these papers it is assumed that the problem consists of many identical copies of a fixed set of jobs. Our contribution in this paper is to extend the results to the far more general situation in which the many jobs are all different. We propose a very simple heuristic which can schedule such problems. We discuss asymptotic optimality of this heuristic, under a wide range of previously unexplored situations. We provide a software package to explore the performance of our policy, and present extensive computational evidence for its effectiveness.     

A mixed integer programming approach to multi-skilled workforce scheduling

The potential benefits of using human resources efficiently in the service sector constitute an incentive for decision makers in this industry to intelligently manage the work shifts of their employees, especially those dealing directly with customers. In the long term, they should attempt to find the right balance between employing as few labor resources as possible and keeping a high level of service. In the short run (e.g., 1 week), however, contracted staff levels cannot be adjusted, and management efforts thus focus on the efficient assignment of shifts and activities to each employee. This article proposes a mixed integer program model that solves the short-term multi-skilled workforce tour scheduling problem, enabling decision makers to simultaneously design workers’ shifts and days off, assign activities to shifts and assign those to employees so as to maximize and balance coverage of a firm’s demand for on-duty staff across multiple activities. Our model is simple enough to be solved with a commercial MIP solver calibrated by default without recurring to complex methodologies, such as extended reformulations and exact and/or heuristic column generation subroutines. A wide computational testing over 1000 randomly generated instances suggests that the model’s solution times are compatible with daily use and that multi-skilling is a significant source of labor flexibility to improve coverage of labor requirements, in particular when such requirements are negatively correlated and part-time workers are a scarce resource.