Temperature cycling and its effect on mechanical behaviours of highporosity chalks

Temperature history can have a significant effect on the strength of water-saturated chalk.In this study,hydrostatic stress cycles are applied to understand the mechanical response of chalk samples exposed to temperature cycling between each stress cycle,compared to the samples tested at a constant temperature.The total accumulated strain during a stress cycle and the irreversible strain are reported.Chalk samples from Kansas(USA)and Mons(Belgium),with different degrees of induration(i.e.amount of contact cementation),were used.The samples were saturated with equilibrated water(polar)and nonpolar Isopar H oil to quantify water weakening.All samples tested during 10 stress cycles with varying temperature(i.e.temperature cycled in between each stress cycle)accumulated more strain than those tested at constant temperatures.All the stress cycles were performed at 30℃.The two chalk types behaved similarly when saturated with Isopar H oil,but differently when saturated with water.When saturated with water,the stronger Kansas chalk accumulated more total strain and more irreversible strain within each stress cycle than the weaker Mons chalk.     

钢筋混凝土非正常裂缝的产生原因及应对措施

根据钢筋混凝土结构中非正常裂缝的种类，分析了各种裂缝的产生原因，并有针对性地提出了应对措施，以保证建筑物和构件安全和稳定地工作。     

Single-machine parallel-batching scheduling with family jobs to minimize weighted number of tardy jobs

We consider the problem of scheduling n jobs in batches on a single parallel-batching machine, where the jobs are partitioned into jobs families and the jobs in each family have the same due date. The objective is to minimize the weighted number of tardy jobs. We first devise an efficient pseudo-polynomial time and a fully polynomial time approximation scheme for the weighted problem. Then we present $O(n^2)$-time and $O(n\log n)$-time algorithms for the case where the jobs have the same weight and for the case where the jobs have the same processing time, respectively.     

A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines

This paper dealt with an unrelated parallel machines scheduling problem with past-sequence-dependent setup times, release dates, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of its starting time, release time and position on the corresponding machine. In addition, the setup time of a job on each machine is proportional to the actual processing times of the already processed jobs on the corresponding machine, i.e., the setup times are past-sequence-dependent (p-s-d). The objective is to determine jointly the jobs assigned to each machine and the order of jobs such that the total machine load is minimized. Since the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, an efficient method based on the hybrid particle swarm optimization (PSO) and genetic algorithm (GA), denoted by HPSOGA, is proposed to solve the given problem. In view of the fact that efficiency of the meta-heuristic algorithms is significantly depends on the appropriate design of parameters, the Taguchi method is employed to calibrate and select the optimal levels of parameters. The performance of the proposed method is appraised by comparing its results with GA and PSO with and without local search through computational experiments. The computational results for small sized problems show that the mentioned algorithms are fully effective and viable to generate optimal/near optimal solutions, but when the size of the problem is increased, the HPSOGA obtains better results in comparison with other algorithms.     

A branch-and-bound algorithm to minimize total weighted completion time on identical parallel machines with job release dates

In this paper, we consider an identical parallel machine scheduling problem with release dates. The objective is to minimize the total weighted completion time. This problem is known to be strongly NP-hard. We propose some dominance properties and two lower bounds. We also present an efficient heuristic. A branch-and-bound algorithm, in which the heuristic, the lower bounds and the dominance properties are incorporated, is proposed and tested on a large set of randomly generated instances.     

Card-based delivery date promising in pure flow shops with order release control

Card-based systems are simple, effective means of controlling production. Yet most systems concentrate on controlling the shop floor. They neglect other planning tasks, like estimating short, feasible due dates during customer enquiry management. A card-based version of the workload control concept for job shops – COBACABANA (COntrol of BAlance by CArd-BAsed Navigation) – was proposed in the literature to overcome this shortcoming. COBACABANA uses cards for due date setting and order release, making it a potentially important solution for small shops with limited resources. But many such firms operate as flow shops rather than job shops. Research demonstrated that COBACABANA’s release mechanism must be adapted if applied to a pure flow shop, but its approach to due date setting has not been evaluated in such an environment. We show COBACABANA has the potential to improve pure flow shop performance, but its due date setting procedure should be adapted compared to job shops. In a flow shop, due date estimation can also be further simplified by considering the load awaiting release to the first (gateway) station only while maintaining most performance benefits. The results are important for all card-based systems that aim to stabilise work-in-process, including kanban and ConWIP (Constant Work-in-Process).     

Dynamic admission control for two customer classes with stochastic demands and strict due dates

We study a dynamic capacity allocation problem with admission control decisions of a company that caters for two demand classes with random arrivals, capacity requirements and strict due dates. We formulate the problem as a Markov decision process (MDP) in order to find the optimal admission control policy that maximises the expected profit of the company. Such a formulation suffers a state-space explosion. Moreover, it involves an additional dimension arising from the multiple possible order sizes that customers can request which further increases the complexity of the problem. To reduce the cardinality of possible policies, and, thus, the computational requirements, we propose a threshold-based policy. We formulate an MDP to generate such a policy. To deal with the curse of dimensionality, we develop threshold-based approximate algorithms based on the state-reduction heuristics with aggregation proposed previously. Our results reveal that for the majority of instances considered the optimal policy has a threshold structure. We then demonstrate the superiority of the proposed threshold-based approximate algorithms over two benchmark policies in terms of the generated profits and the robustness of the solutions to changes in operational conditions. Finally, we show that our proposed policies are also robust to changes in actual demand from its estimation.     

A note: Common due date assignment for a single machine scheduling with the rate-modifying activity

In this paper we study the single machine common due date assignment and scheduling problem with the possibility to perform a rate-modifying activity (RMA) for changing the processing times of the jobs following this activity. The objective is to minimize the total weighted sum of earliness, tardiness and due date costs. Placing the RMA to some position in the schedule can decrease the objective function value. Several properties of the problem are considered which in some cases can reduce the complexity of the solution algorithm.