Joint optimization of maintenance,buffers and machines in manufacturing lines

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.     

Integral methodology for distribution systems reconfiguration based on optimal power flow using Benders decomposition technique

A new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach is presented. The objective minimises power losses, balancing load among feeders and subject to constraints such as capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages and radial optimal operation of networks. A variant of the generalised Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages; the first one is the master problem and is formulated as a mixed integer non-linear programming problem. This stage determines the radial topology of the distribution network. The second stage is the slave problem and is formulated as a non-linear programming problem. This stage is used to determine the feasibility of the master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in GAMS mathematical modeling language. The effectiveness of the proposal is demonstrated through two examples extracted from the specialised literature.     

Dynamic-demand capacitated facility location problems with and without relocation

We consider two multi-period dynamic-demand capacitated location problems. In the first problem, the facilities are allowed to be relocated in each period, whereas in the second they are kept at a fixed location determined at the beginning of the planning horizon. We provide Lagrangian Relaxation and Benders Decomposition algorithms, including an ?-optimal BD algorithm, for the solution for the first model and a Benders Decomposition algorithm for the second. For detailed analysis, we generate a wide variety of instances to test the performance of the algorithms by taking into account varying number of customer locations and time periods in the planning horizon as well as fixed cost structures and facility capacities. We observe that the efficiency of the solution algorithms depends on the input data structure, specifically the cost structures, the facility capacities (which, in turn, dictate the expected number of open facilities), and the variation in the total customer demand from period to period.     

The constrained two-dimensional guillotine cutting problem with defects: an ILP formulation,a Benders decomposition and a CP-based algorithm

This paper addresses a variant of two-dimensional cutting problems in which rectangular small pieces are obtained by cutting a rectangular object through guillotine cuts. The characteristics of this variant are (i) the object contains some defects, and the items cut must be defective-free; (ii) there is an upper bound on the number of times an item type may appear in the cutting pattern; (iii) the number of guillotine stages is not restricted. This problem commonly arises in industrial settings that deal with defective materials, e.g. either by intrinsic characteristics of the object as in the cutting of wooden boards with knotholes in the wood industry, or by the manufacturing process as in the production of flat glass in the glass industry. We propose a compact integer linear programming (ILP) model for this problem based on the discretisation of the defective object. As solution methods for the problem, we develop a Benders decomposition algorithm and a constraint-programming (CP) based algorithm. We evaluate these approaches through computational experiments, using benchmark instances from the literature. The results show that the methods are effective on different types of instances and can find optimal solutions even for instances with dimensions close to real-size.     

Productivity improvement through a sequencing generalised assignment in an assembly line system

This paper considers the assignment of heterogeneous workers to workstations of an assembly line in order to minimise the total production time. As the structure of the system implies that each of the workstations needs at least one worker, thus the problem can be considered as a generalised assignment problem (GAP). The objective is to perform an efficient human resource planning for a specified horizon consisting of several periods. Hence, we present an extension of the generalised assignment problem, consisting of a set of GAPs (one for each planning period) in which each GAP depends on the previous ones. A mixed integer mathematical model is presented for this sequencing assignment problem. The model is solved by an exact algorithm using Gurobi solver. It is proved that the problem is NP-hard and solving the medium and large size instances is not possible by the exact algorithms. Hence, two matheuristic approaches based on the disaggregated formulation of GAP are proposed. The first approach solves the problem through two sub-problems as the transportation formulation and assignment formulation. The second approach solves the problem by decomposition of the problem into several classical GAPs. The approaches are examined by a total of 27 instances. The results illustrate the efficiency of the proposed algorithms in the computational time and accuracy of the solutions.     

Simultaneous balancing and sequencing of mixed-model parallel two-sided assembly lines

Growing interests from customers in customised products and increasing competitions among peers necessitate companies to configure their manufacturing systems more effectively than ever before. We propose a new assembly line system configuration for companies that need intelligent solutions to satisfy customised demands on time with existing resources. A mixed-model parallel two-sided assembly line system is introduced based on the parallel two-sided assembly line system previously proposed in the literature. The mixed-model parallel two-sided assembly line balancing problem is illustrated with examples from the perspective of simultaneous balancing and sequencing. An agent-based ant colony optimisation algorithm is proposed to solve the problem. This algorithm is the first attempt in the literature to solve an assembly line balancing problem with an agent-based ant colony optimisation approach. The algorithm is illustrated with an example and its operational procedures and principles are explained and discussed.     

Optimal cost design of water distribution networks using a decomposition approach

Water distribution network decomposition, which is an engineering approach, is adopted to increase the efficiency of obtaining the optimal cost design of a water distribution network using an optimization algorithm. This study applied the source tracing tool in EPANET, which is a hydraulic and water quality analysis model, to the decomposition of a network to improve the efficiency of the optimal design process. The proposed approach was tested by carrying out the optimal cost design of two water distribution networks, and the results were compared with other optimal cost designs derived from previously proposed optimization algorithms. The proposed decomposition approach using the source tracing technique enables the efficient decomposition of an actual large-scale network, and the results can be combined with the optimal cost design process using an optimization algorithm. This proves that the final design in this study is better than those obtained with other previously proposed optimization algorithms.     

A multi-objective genetic algorithm approach to the design of cellular manufacturing systems

In this paper, a multi-objective integer programming model is constructed for the design of cellular manufacturing systems with independent cells. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. There are some features that make the proposed algorithm different from other algorithms used in the design of cellular manufacturing systems. These include: (1) a systematic uniform design-based technique, used to determine the search directions, and (2) searching the solution space in multiple directions instead of single direction. Four problems are selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.     

A Benders decomposition approach to the weighted number of tardy jobs scheduling problem on unrelated parallel machines with production costs

This paper addresses the problem of scheduling on-time jobs on unrelated parallel machines with machine production costs. The objective is to maximise the net profit which is the sum of the weights of on-time jobs and the cost of using the machines. This scheduling problem is very important and frequent in industrial settings. It is herein solved using an exact approach that applies Benders decomposition to obtain tight upper and lower bounds and uses the bounds within a branch and bound. The computational investigation shows the efficacy of the approach in solving large instances. Most importantly, the proposed approach provides a new venue for solving large-scale scheduling problems.     

Evaluation of the influence of dominance rules for the assembly line design problem under consideration of product design alternatives

The current competitive situation increases the importance of realistically estimating product costs during the early phases of product and assembly line planning projects. In this article, several multi-objective algorithms using difference dominance rules are proposed to solve the problem associated with the selection of the most effective combination of product and assembly lines. The list of developed algorithms includes variants of ant colony algorithms, evolutionary algorithms and imperialist competitive algorithms. The performance of each algorithm and dominance rule is analysed by five multi-objective quality indicators and fifty problem instances. The algorithms and dominance rules are ranked using a non-parametric statistical test.     

A three-phase heuristic algorithm for fixed-charge capacitated material flow network design with input/output points location

In this paper, we consider the material flow network design problem in which locations of input and output points of departments and flow paths are determined concurrently on a given block layout. The objective of the problem is to minimize the sum of transportation cost, flow paths construction cost and penalty cost for non-smooth material flows, i.e., flows with turns. A mixed integer programming model is given for the problem and a three-phase heuristic algorithm is developed to solve the problem. In the suggested algorithm, we generate an initial flow network by determining locations of input/output points and flow paths sequentially in the first and second phases, respectively, and then improve it by changing locations of input/output points and flow paths iteratively in the third phase. To evaluate the performance of the suggested algorithms, a series of computational experiments are performed on well-known problem instances as well as randomly generated test problems. Results of computational experiments show that the suggested algorithm gives good solutions in a short computation time.     

Models and optimisation approaches for scheduling steelmaking–refining–continuous casting production under variable electricity price

This paper studies the steelmaking–refining–continuous casting (SRCC) scheduling problem with considering variable electricity price (SRCCSPVEP). SRCC is one of the critical production processes for steel manufacturing and energy intensive. Combining the technical rules used in iron-steel production practice, time-dependent electricity price is considered to reduce the electricity cost and the associate production cost. A decomposition approach is proposed for the SRCCSPVEP. Without considering the electrical factor, the first phase applies the mathematical programming method to determine the relative schedule plan for each cast. In the second phase, we formulate a scheduling problem of all casts subject to resource constraint and time-dependent electricity price. A heuristic algorithm combined with the constraint propagation is developed to solve this scheduling problem. To investigate and measure the performance of the proposed approach, numerous instances are randomly generated according to the collective data from a well-known iron-steel plant in China. Computational results demonstrate that our algorithm is very efficient and effective in providing high-quality scheduling plans, and the electricity cost can be reduced for the iron-steel plant.     

An improved harmony search algorithm for emergency inspection scheduling

The ability of nature-inspired search algorithms to efficiently handle combinatorial problems, and their successful implementation in many fields of engineering and applied sciences, have led to the development of new, improved algorithms. In this work, an improved harmony search (IHS) algorithm is presented, while a holistic approach for solving the problem of post-disaster infrastructure management is also proposed. The efficiency of IHS is compared with that of the algorithms of particle swarm optimization, differential evolution, basic harmony search and the pure random search procedure, when solving the districting problem that is the first part of post-disaster infrastructure management. The ant colony optimization algorithm is employed for solving the associated routing problem that constitutes the second part. The comparison is based on the quality of the results obtained, the computational demands and the sensitivity on the algorithmic parameters.     

Particle-swarm harmony search for water network design

The optimal design of water distribution networks is a non-linear, multi-modal, and constrained problem classified as an NP-hard combinatorial problem. Because of the drawbacks of calculus-based algorithms, the problem has been tackled by assorted stochastic algorithms, such as the genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping algorithm, ant colony optimization algorithm, harmony search, cross entropy, and scatter search. This study proposes a modified harmony search algorithm incorporating particle swarm concept. This algorithm was applied to the design of four bench-mark networks (two-loop, Hanoi, Balerma, and New York City networks), with good results.     

A coloured Petri net-based hybrid heuristic search approach to simultaneous scheduling of machines and automated guided vehicles

To achieve a significant improvement in the overall performance of a flexible manufacturing system, the scheduling process must consider the interdependencies that exist between the machining and transport systems. However, most works have addressed the scheduling problem as two independent decision making problems, assuming sufficient capacity in the transport system. In this paper, we study the simultaneous scheduling (SS) problem of machines and automated guided vehicles using a timed coloured Petri net (TCPN) approach under two performance objectives; makespan and exit time of the last job. The modelling approach allows the evaluation of all the feasible vehicle assignments as opposed to the traditional dispatching rules and demonstrates the benefits of vehicle-controlled assignments over machine-controlled for certain production scenarios. In contrast with the hierarchical decomposition technique of existing approaches, TCPN is capable of describing the dynamics and evaluating the performance of the SS problem in a single model. Based on TCPN modelling, SS is performed using a hybrid heuristic search algorithm to find optimal or near-optimal schedules by searching through the reachability graph of the TCPN with heuristic functions. Large-sized instances are solved in relatively short computation times, which were a priori unsolvable with conventional search algorithms. The algorithm’s performance is evaluated on a benchmark of 82 test problems. Experimental results indicate that the proposed algorithm performs better than the conventional ones and compares favourably with other approaches.     

Line-seru conversion towards reducing worker(s) without increasing makespan: models,exact and meta-heuristic solutions

Compared with the traditional assembly line, seru production can reduce worker(s) and decrease makespan. However, when the two objectives are considered simultaneously, Pareto-optimal solutions may save manpower but increase makespan. Therefore, we formulate line-seru conversion towards reducing worker(s) without increasing makespan and develop exact and meta-heuristic algorithms for the different scale instances. Firstly, we analyse the distinct features of the model. Furthermore, according to the feature of the solution space, we propose two exact algorithms to solve the small to medium-scale instances. The first exact algorithm searches the solution space from more workers to fewer workers. The second exact algorithm searches the solution space from fewer workers to more workers. The two exact algorithms search a part of solution space to obtain the optimal solution of reducing worker(s) without increasing makespan. According to the variable length of the feasible solutions, we propose a variable-length encoding heuristic algorithm for the large-scale instances. Finally, we use the extensive experiments to evaluate the performance of the proposed algorithms and to investigate some managerial insights on when and how to reduce worker(s) without increasing makespan by line-seru conversion.     

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.     

Balancing Multi-Manned Assembly Lines With Walking Workers: Problem Definition,Mathematical Formulation,and an Electromagnetic Field Optimisation Algorithm

Assembly lines are widely used in industrial environments that produce standardised products in high volumes. Multi-manned assembly line is a special version of them that allows simultaneous operation of more than one worker at the same workstation. These lines are widely used in large-sized product manufacturing since they have many advantages over the simple one. This article has dealt with multi-manned assembly line balancing problem with walking workers for minimising the number of workers and workstations as the first and second objectives, respectively. A linear mixed-integer programming formulation of the problem has been firstly addressed after the problem definition is given. Besides that, a metaheuristic based on electromagnetic field optimisation algorithm has been improved. In addition to the classical electromagnetic field optimisation algorithm, a regeneration strategy has been applied to enhance diversification. A particle swarm optimisation algorithm from assembly line balancing literature has been modified to compare with the proposed algorithm. A group of test instances from many precedence diagrams were generated for evaluating the performances of all solution methods. Deviations from lower bound values of the number of workers/workstations and the number of optimal solutions obtained by these methods are concerned as performance criteria. The results obtained by the proposed programming formulations have been also compared with the solutions obtained by the traditional mathematical model of the multi-manned assembly line. Through the experimental results, the performance of the metaheuristic has been found very satisfactory according to the number of obtained optimal solutions and deviations from lower bound values.     

An improved multi-objective evolutionary algorithm based on decomposition for energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time

With the increasing attention on environment issues, green scheduling in manufacturing industry has been a hot research topic. As a typical scheduling problem, permutation flow shop scheduling has gained deep research, but the practical case that considers both setup and transportation times still has rare research. This paper addresses the energy-efficient permutation flow shop scheduling problem with sequence-dependent setup time to minimise both makespan as economic objective and energy consumption as green objective. The mathematical model of the problem is formulated. To solve such a bi-objective problem effectively, an improved multi-objective evolutionary algorithm based on decomposition is proposed. With decomposition strategy, the problem is decomposed into several sub-problems. In each generation, a dynamic strategy is designed to mate the solutions corresponding to the sub-problems. After analysing the properties of the problem, two heuristics to generate new solutions with smaller total setup times are proposed for designing local intensification to improve exploitation ability. Computational tests are carried out by using the instances both from a real-world manufacturing enterprise and generated randomly with larger sizes. The comparisons show that dynamic mating strategy and local intensification are effective in improving performances and the proposed algorithm is more effective than the existing algorithms.     

An ant colony optimisation algorithm for scheduling in agile manufacturing

Producing customised products in a short time at low cost is one of the goals of agile manufacturing. To achieve this goal an assembly-driven differentiation strategy has been proposed in the agile manufacturing literature. In this paper, we address a manufacturing system that applies the assembly-driven differentiation strategy. The system consists of machining and assembly stages, where there is a single machine at the machining stage and multiple identical assembly stations at the assembly stage. An ant colony optimisation (ACO) algorithm is developed for solving the scheduling problem of determining the sequence of parts to be produced in the system so as to minimise the maximum completion time (or makespan). The ACO algorithm uses a new dispatching rule as the heuristic desirability and variable neighbourhood search as the local search to make it more efficient and effective. To evaluate the performance of heuristic algorithms, a branch-and-bound procedure is proposed for deriving the optimal solution to the problem. Computational results show that the proposed ACO algorithm is superior to the existing algorithm, not only improving the performance but also decreasing the computation time.