A branch and bound algorithm for scheduling problem of a robot‐centered manufacturing cell

Abstract

An alternative formulation of the scheduling problem in a robot‐centered manufacturing cell has been described here, which was originally formulated by Lin et al. [7] as a mixed integer programming problem. An efficient procedure based on the branch and bound technique has been proposed. In order to reduce the complexity of the branching procedure, several sequencing rules [4] have been imbedded into the proposed procedure and an integrated algorithm has then been presented. The computational results have indicated the proposed algorithm to be efficient. Finally, conclusions and some suggestions are given.     

Scheduling with multiple tasks per job – the case of quality control laboratories in the pharmaceutical industry

This paper addresses a complex scheduling problem encountered in a major pharmaceutical industry setting. Specifically, the problem deals with assigning tasks to technicians as part of the quality control phase in order to minimise the total flowtime, and the number of jobs not meeting a required time window. The problem considers test batching, overlapping tests, and resource assignments constrained by test specific capability requirements. Furthermore, batching tasks of similar types is possible, but batch sizes are particular to each product-test type combination. This is a significant difference from previous literature in batching parallel machines. The particular problem described in the paper is highly relevant to the pharmaceutical industry and has not been previously addressed in the literature. Various approaches to solve this particular problem are described and compared via statistical analyses. Finally, the authors present a software prototype with implemented solution algorithms.     

A new spanning tree-based genetic algorithm for the design of multi-stage supply chain networks with nonlinear transportation costs

The design of configuration and the transportation planning are crucial issues to the effectiveness of multi-stage supply chain networks. The decision makers are interested in the determination the optimal locations of the hubs and the optimal transportation routes to minimize the total costs incurred in the whole system. One may formulate this problem as a 0-1 mixed integer non-linear program though commercial packages are not able to efficiently solve this problem due to its complexity. This study proposes a new spanning tree-based Genetic Algorithm (GA) using determinant encoding for solving this problem. Also, we employ an efficient heuristic that fixes illegal spanning trees existing in the chromosomes obtained from the evolutionary process of the proposed GA. Our numerical experiments demonstrate that the proposed GA outperforms the other previously published GA in the solution quality and convergence rate.     

A new scheduling technique for the resource–constrained project scheduling problem with discounted cash flows

In this paper, we discuss the resource–constrained project scheduling problem with discounted cash flows. We introduce a new schedule construction technique which moves sets of activities to improve the project net present value and consists of two steps. In particular, the inclusion of individual activities into sets, which are then moved together, is crucial in both steps. The first step groups the activities based on the predecessors and successors in the project network, and adds these activities to a set based on their finish time and cash flow. The second step on the contrary does so based on the neighbouring activities in the schedule, which may but need not include precedence related activities. The proposed scheduling method is implemented in a genetic algorithm metaheuristic and we employ a penalty function to improve the algorithm’s feasibility with respect to a tight deadline. All steps of the proposed solution methodology are tested in detail and an extensive computational experiment shows that our results are competitive with existing work.     

A hybrid neural network–genetic algorithm approach for permutation flow shop scheduling

The objective of this paper is to find a sequence of jobs for the permutation flow shop to minimise the makespan. The shop consists of 10 machines. A feed-forward back-propagation artificial neural network (ANN) is used to solve the problem. The network is trained with the optimal sequences for five-, six- and seven-job problems. This trained network is then used to solve a problem with a greater number of jobs. The sequence obtained using the neural network is used to generate the initial population for the genetic algorithm (GA) using the random insertion perturbation scheme (RIPS). The makespan of the sequence obtained by this approach (ANN-GA-RIPS) is compared with that obtained using GA starting with a random population (ANN-GA). It was found that the ANN-GA-RIPS approach performs better than ANN-GA starting with a random population. The results obtained are compared with those obtained using the Nawaz, Enscore and Ham (NEH) heuristic and upper bounds of Taillard's benchmark problems. ANN-GA-RIPS performs better than the NEH heuristic and the results are found to be within 5% of the upper bounds.     

A data-driven ensemble ranking system of production quality across manufacturers—A case study for risk assessment in the solar industry

In many production industries, quality assurance and risk analysis are important aspects of the procurement of products. Such analysis often takes the form of during and after production inspections performed by quality assurance companies. These inspections give insight into the quality level of each individual production batch; however, it is also often important to understand a manufacturer's overall production quality and how its quality compares to other manufacturers. Therefore, in this paper, we propose a data-driven method to rank the production quality across manufacturers. The method is based on data from quality assurance inspections and consists of three steps. In the first step, a failure mode and effects analysis is applied for each individual manufacturer. This results in a risk assessment for identified failure modes. In the second step, the risk assessments are combined to create an overall failure index for each manufacturer. Step 2 can be designed in various ways. To ensure our final ranking is robust to the selected method, in step 3 we combine all methods into a unified rank of each manufacturer. We validate our proposed ensemble ranking method using a case study from the solar industry, where we compare our final ranking with the experts’ knowledge of manufacturer quality. It is shown that our data-driven method identifies the high-risk manufacturers in accordance with experts’ knowledge.     

Minimality and the Rauzy – Veech algorithm for interval exchange transformations with flips

Oriented interval exchange transformations (i.e.t.s) admitting infinite orbit of the Rauzy–Veech operator are minimal. In this paper, we analyse what happens for non-oriented i.e.t.s. In particular, we prove an analogous result for i.e.t.s with flips (Theorem A). We also show that the behaviour of i.e.t.s with flips is different to the one of oriented i.e.t.s, see Proposition 11.     

Integrated process planning and scheduling – multi-agent system with two-stage ant colony optimisation algorithm

In this paper, a two-stage ant colony optimisation (ACO) algorithm is implemented in a multi-agent system (MAS) to accomplish integrated process planning and scheduling (IPPS) in the job shop type flexible manufacturing environments. Traditionally, process planning and scheduling functions are performed sequentially and the actual status of the production facilities is not considered in either process planning or scheduling. IPPS is to combine both the process planning and scheduling problems in the consideration, that is, the actual process plan and the schedule are determined dynamically in accordance with the order details and the status of the manufacturing system. The ACO algorithm can be applied to solve IPPS problems. An innovative two-stage ACO algorithm is introduced in this paper. In the first stage of the algorithm, instead of depositing pheromones on graph edges as in common ant algorithms, ants are directed to deposit pheromones at the nodes to select a set of more favourable processes. In the second stage, the set of nodes not selected in the first stage will be ignored, and pheromones will be deposited along the graph edges while the ants traverse the paths connecting the selected set of nodes.     

A genetic algorithm with domain knowledge for weapon‐target assignment problems

Abstract

In this paper, a novel genetic algorithm, including domain specific knowledge into the crossover operator and the local search mechanism for solving weapon‐target assignment (WTA) problems is proposed. The WTA problem is a full assignment of weapons to hostile targets with the objective of minimizing the expected damage value to own‐force assets. It is an NP‐complete problem. In our study, a greedy reformation and a new crossover operator are proposed to improve the search efficiency. The proposed algorithm outperforms its competitors on all test problems.     

A flower pollination algorithm for the double-floor corridor allocation problem†

This research explores the double-floor corridor allocation problem (DFCAP), which deals with the optimal arrangement of departments over two floors and then place them along both sides against a corridor. This problem is a natural extension of the corridor allocation problem (CAP) to additional floors; the layout of each floor can be regarded as an approximately independent CAP. The DFCAP is commonly observed in manufacturing and service buildings. In this study, a mixed-integer programming formulation for the DFCAP is developed, and it is able to reduce to the classical CAP model. Then a novel flower pollination algorithm is provided, which is discretised using swap pair set approach to solve the considered DFCAP. In addition, to ameliorate the algorithm, three constructive heuristic rules are developed to produce a reasonably good initial population; meanwhile, a variable neighbourhood search structure is presented to prevent prematurity in arrival at a poor local solution. Finally, several instances for the DFCAP with a size of 9?≤?n?≤?80 are employed in the algorithms, as well as in mixed-integer non-linear programming (MINLP) formulations, which are solved with GUROBI 7.0.1. Moreover, the above-mentioned instances are utilized to show that the proposed algorithm performs better in comparison to the state-of-the-art optimization algorithms.     

Establishing optimal project-level strategies for pavement maintenance and rehabilitation – A framework and case study

This article presents a framework and an illustrative example for identifying the optimal pavement maintenance and rehabilitation (M&R) strategy using a mixed-integer nonlinear programming model. The objective function is to maximize the cost-effectiveness expressed as the ratio of the effectiveness to the cost. The constraints for the optimization problem are related to performance, budget, and choice. Two different formulations of effectiveness are derived using treatment-specific performance models for each constituent treatment of the strategy; and cost is expressed in terms of the agency and user costs over the life cycle. The proposed methodology is demonstrated using a case study. Probability distributions are established for the optimization input variables and Monte Carlo simulations are carried out to yield optimal solutions. Using the results of these simulations, M&R strategy contours are developed as a novel tool that can help pavement managers quickly identify the optimal M&R strategy for a given pavement section.     

Tabu-search simulation optimization approach for flow-shop scheduling with multiple processors — a case study

The flow shop with multiple processors (FSMP) environment is relatively common and has a variety of applications. The majority of academic authors solve the scheduling problem of FSMP using deterministic data that ignore the stochastic nature of a real-world problem. Discrete-event simulation can model a non-linear and stochastic problem and allows examination of the likely behaviour of a proposed manufacturing system under selected conditions. However, it does not provide a method for optimization. The present paper proposes to solve the FSMP scheduling problem by using a tabu-search simulation optimization approach. It features both the stochastically modelling capability of the discrete-event simulation and the efficient local-search algorithm of tabu search. A case study from a multilayer ceramic capacitor manufacturing illustrates the proposed solution methodology. Empirical results show promise for the practical application of the proposed methodologies. Future research opportunities are then addressed.     

A hybrid discrete biogeography-based optimization for the permutation flow shop scheduling problem

The permutation flow shop scheduling problem (PFSP) which is known to be NP-hard has been widely investigated in recent years. In this paper, an effective hybrid discrete biogeography-based optimization (HDBBO) algorithm is proposed for solving the PFSP with the objective to minimise the makespan. Opposition-based learning method and the NEH heuristic are utilised in the HDBBO to generate an initial population with certain quality and diversity. Moreover, a novel variable local search strategy is presented and incorporated within the biogeography-based optimization framework to improve the exploitation ability. Computational results on two typical benchmark suits and comparisons with some state-of-the-art algorithms are presented to show the effectiveness of the HDBBO scheme.     

Integrated aggregate supply chain planning using memetic algorithm – A performance analysis case study

This paper aims to examine how a complex supply chain yields cost reduction benefits through the global integration of production and distribution decisions. The research is motivated by a complex real world supply chain planning problem facing a large automotive company. A mixed-integer nonlinear production-distribution planning model is solved using a customised memetic algorithm. The performance and effectiveness of the developed model and solution approach in achieving the global optimisation is investigated through experiments comparing the numerical results from the proposed integrated approach with those of a typical non-integrated (hierarchical) production–distribution optimisation.     

An integrated neural network–simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities

This paper presents an integrated computer simulation and Artificial Neural Network (ANN) algorithm for a stochastic Two-Stage Assembly Flow-Shop Scheduling Problem (TSAFSP) with setup times under a weighted sum of makespan and mean completion time (MCT) criteria, known as bi-criteria. Significantly, it should be noted that there is no mathematical model to analyse the stochastic model, therefore simulation is used to solve the problem. The simulation model enables decision makers to consider the influence of job scheduling on machines in order to examine both criteria simultaneously. Since it is not possible to evaluate all sequence combinations using the simulation model in a reasonable time, multilayered neural network meta-models have been trained and used to estimate objective function values composed of both makespan and mean completion time criteria for the stochastic TSAFSP. To the best of the authors’ knowledge, this is the first study that considers stochastic machine breakdown, processing times, setup times, makespan and mean completion time as objectives concurrently. The TSAFSP is modelled by Visual SLAM simulation software. The simulation output results are then given to the ANN as inputs to build the meta-model. This meta-model is then used to obtain the results with the optimum values. The advantage of these meta-model applications is a reduction in the number of simulation runs and consequently a reduced run time. Also, this is the first study that introduces an intelligent and flexible algorithm for handling stochastic TSAFSP.     

Self-reported and observed seat belt use – A case study: Bosnia and Herzegovina

The issue of seat belt use in middle- and low-income countries is strongly evident and has as a result higher rates of fatalities and seriously injured on the roads. The first systematic field research of the seat belt use while driving was carried out in Bosnia and Herzegovina, in 2011–2012. Research methodology consisted of two mutually conditioned parts (observation and self-reported behavior). Specific features of the methodology used are in the relationship between the observed and interviewed drivers which enabled the analysis of their observed and self-reported behavior while driving. The logistic regression method was used in this work to make the analysis of the influence of personal human characteristics (gender, age, education, exposure) and vehicles’ characteristics (age) on the observed and self-reported driving behavior, from the point of view of seat belt use while driving. The influence of the listed factors on driving behavior, depending on road type (urban or rural), was given special attention in the analysis. The paper shows that certain factors do not have the same impact on driving behavior, in various conditions. Based on results from this study, it will be possible to define certain critical groups of road users and the way in which they must be addressed in order to increase the seat belt wearing rate.     

A hybrid cuckoo search via Lévy flights for the permutation flow shop scheduling problem

The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a cuckoo search (CS)-based memetic algorithm, called HCS, is proposed for the PFSSP. To make CS suitable for the PFSSP, a largest-ranked-value (LRV)-rule-based random key is used to convert the continuous position in CS into a discrete job permutation. The Nawaz-Enscore-Ham (NEH) heuristic is then combined with the random initialisation to initialise the population with a certain quality and diversity. After that, CS is employed to evolve nest vectors for exploration, and a fast local search is embedded to enhance the local exploitation ability. In addition, simulations and comparisons based on PFSSP benchmarks are carried out, which shows that our algorithm is both effective and efficient.     

Operator assignment with cell loading and product sequencing in labour-intensive assembly cells – a case study of a bicycle assembly company

This research deals with an operator assignment problem in which cell loading and product sequencing are taken into account in labour-intensive assembly cells. In each cell the number of assembly tasks is more than the number of operators, so multi-assembly tasks are assigned to each operator. Because the assembly procedure and time required for each assembly task are quite different for different products, some tasks will be transferred when the product changes. Reducing the number of task transfers can smooth the process of product change. A four-phase methodology is proposed to minimise the total manpower required and task transfers at the same time. The four phases are manpower configuration design, calculating the number of task transfers, manpower requirement minimization and cell loading and product sequencing optimization. A case study from a bicycle assembly company is introduced. For comparison, two methodologies are applied. The results show that the proposed four phase methodology can provided the solution with fewer task transfers based on the same total manpower requirement. However, when the number of products increases, the computation time of the proposed four-phase methodology increases rapidly.     

Triple Helix innovation in China’s dye-sensitized solar cell industry: hybrid methods with semantic TRIZ and technology roadmapping

In recent years, the Triple Helix model has identified feasible approaches to measuring relations among universities, industries, and governments. Results have been extended to different databases, regions, and perspectives. This paper explores how bibliometrics and text mining can inform Triple Helix analyses. It engages Competitive Technical Intelligence concepts and methods for studies of Newly Emerging Science & Technology (NEST) in support of technology management and policy. A semantic TRIZ approach is used to assess NEST innovation patterns by associating topics (using noun phrases to address subjects and objects) and actions (via verbs). We then classify these innovation patterns by the dominant categories of origination: Academy, Industry, or Government. We then use TRIZ tags and benchmarks to locate NEST progress using Technology Roadmapping. Triple Helix inferences can then be related to the visualized patterns. We demonstrate these analyses via a case study for dye-sensitized solar cells.     

Pricing structures for solutions: an exploratory study within the oil and gas industry†

A growing number of companies have begun to realise the potential for differentiating their product offerings by integrating services to provide customised solutions. Although there is now an extensive and growing literature on this trend, researchers have only recently begun to consider the pricing structures for such solutions. To address this shortcoming, the present study adopts an exploratory case-based approach to investigate a buyer (drilling contractor) and two suppliers of offshore capital equipment, each of whom provides condition-based maintenance solutions for offshore drilling units in the upstream oil and gas industry. The findings of the study identify a number of underlying mechanisms for solution offerings (i.e. innovativeness, benchmarking alternatives, measurability, replicability and operational risk) that are important considerations in the process of determining appropriate pricing structures based on the buyer’s business model, procurement practices and maintenance strategy vis-à-vis the supplier’s capabilities and the buyer–supplier relationship. The present study contributes to the literature by providing empirical evidence on and insight into the complexity of determining the pricing structure for solution offerings from the perspective of the supplier as well as the buyer.