Asynchronous teams for joint lot-sizing and scheduling problem in flow shops

The integrated production scheduling and lot-sizing problem in a flow shop environment consists of establishing production lot sizes and allocating machines to process them within a planning horizon in a production line with machines arranged in series. The problem considers that demands must be met without backlogging, the capacity of the machines must be respected, and machine setups are sequence-dependent and preserved between periods of the planning horizon. The objective is to determine a production schedule to minimise the setup, production and inventory costs. A mathematical model from the literature is presented, as well as procedures for obtaining feasible solutions. However, some of the procedures have difficulty in obtaining feasible solutions for large-sized problem instances. In addition, we address the problem using different versions of the Asynchronous Team (A-Team) approach. The procedures were compared with literature heuristics based on Mixed Integer Programming. The proposed A-Team procedures outperformed the literature heuristics, especially for large instances. The developed methodologies and the results obtained are presented.     

A mixed-integer nonlinear program for the optimal design and dispatch of distributed generation systems

Maturing distributed generation (DG) technologies have promoted interest in alternative sources of energy for commercial building applications due to their potential to supply on-site heat and power at a lower cost and emissions rate compared to centralized generation. Accordingly, we present an optimization model that determines the mix, capacity, and operational schedule of DG technologies that minimize economic and environmental costs subject to the heat and power demands of a building and to the performance characteristics of the technologies. The technologies available to design the system include lead-acid batteries, photovoltaic cells, solid oxide fuel cells, heat exchangers, and a hot water storage tank. Modeling the acquisition and operation of discrete technologies requires integer restrictions, and modeling the variable electric efficiency of the fuel cells and the variable temperature of the tank water introduces nonlinear equality constraints. Thus, our optimization model is a nonconvex, mixed-integer nonlinear programming (MINLP) problem. Given the difficulties associated with solving large, nonconvex MINLPs to global optimality, we present convex underestimation and linearization techniques to bound and solve the problem. The solutions provided by our techniques are close to those provided by existing MINLP solvers for small problem instances. However, our methodology offers the possibility to solve large problem instances that exceed the capacity of existing solvers and that are critical to the real-world application of the model.     

CO-EVOLUTIONARY HYBRID DIFFERENTIAL EVOLUTION FOR MIXED-INTEGER OPTIMIZATION PROBLEMS

Evolutionary algorithms are promising candidates for obtaining the global optimum. Hybrid differential evolution is one or the evolutionary algorithms, which has been successfully applied to many real-world nonlinear programming problems. This paper proposes a co-evolutionary hybrid differential evolution to solve mixed-integer nonlinear programming (MINLP) problems. The key ingredients of the algorithm consist of an integer-valued variable evolution and a real-valued variable co-evolution, so that the algorithm can be used to solve MINLP problems or pure integer programming problems. Furthermore, the algorithm combines a local search heuristic (called acceleration) and a widespread search heuristic (called migration) to promote the search for a global optimum. Some numerical examples are tested to illustrate the performance of the proposed algorithm. Numerical examples show that the proposed algorithm converges to better solutions than the conventional MINLP optimization methods     

A MILP model for the Accessibility Windows Assembly Line Balancing Problem (AWALBP)

This work studies a novel assembly line balancing problem that has recently appeared in the literature, which we name Accessibility Windows Assembly Line Balancing Problem (AWALBP). AWALBP is a real-world industrial problem that arises in those assembly lines where, as opposed to the most common ones, the length of the workpiece is larger than the widths of the workstation. This means that, at any time, a workstation cannot access one whole workpiece, but only a restricted portion of one or two consecutive workpiece. In our problem the cycle decomposes into stationary stages separated by forward steps, according to a cyclic movement scheme. The aim of this paper is (i) to formalise the AWALBP and its variants, and (ii) to propose a Mixed Integer Linear Programming (MILP) model using two alternative formulations to solve the variant AWALBP-L2. This variant involves solving the task assignment and the movement scheme sub-problems (with the objective of minimising the cycle time). An extensive computational experiment is carried out to study the behaviour of the proposed model for different instance sizes. To the best of our knowledge, this is the first work in the literature which provides optimal solutions for AWALBP-L2. In addition, a set of benchmark instances is provided, which can be further used by the research community.     

Solving Large MINLPs on Computational Grids

We consider the solution of Mixed Integer Nonlinear Programming (MINLP) problems by a parallel implementation of nonlinear branch-and-bound on a computational grid or meta-computer. Computational experience on a set of large MINLPs is reported which indicates that this approach is efficient for the solution of these problems.     

Transmission system expansion planning by a branch-and-bound algorithm

A branch and bound (B&B) algorithm using the DC model, to solve the power system transmission expansion planning by incorporating the electrical losses in network modelling problem is presented. This is a mixed integer nonlinear programming (MINLP) problem, and in this approach, the so-called fathoming tests in the B&B algorithm were redefined and a nonlinear programming (NLP) problem is solved in each node of the B&B tree, using an interior-point method. Pseudocosts were used to manage the development of the B&B tree and to decrease its size and the processing time. There is no guarantee of convergence towards global optimisation for the MINLP problem. However, preliminary tests show that the algorithm easily converges towards the best-known solutions or to the optimal solutions for all the tested systems neglecting the electrical losses. When the electrical losses are taken into account, the solution obtained using the Garver system is better than the best one known in the literature.     

A sequence-pair representation and MIP-model-based heuristic for the facility layout problem with rectangular departments

In Facility Layout Problem (FLP) research, the continuous-representation-based FLP can consider all feasible all-rectangular-department solutions. Given this flexibility, this representation has become the representation of choice in FLP research. Much of this research is based on a methodology of Mixed-Integer Programming (MIP) models. However, these MIP-FLP models can only solve problems with a limited number of departments to optimality due to the large number of combinations of the binary variables used in the models to maintain feasibility with respect to departments overlapping. Our research centers around the sequence-pair representation, a concept that originated in the Very Large Scale Integration (VLSI) design literature. We show that an exhaustive search of the sequence-pair solution space will result in finding the optimal layout of the MIP-FLP and that every sequence-pair solution is position consistent (although possibly not layout feasible) in the MIP-FLP. We propose a genetic-algorithm-based heuristic that combines the sequence-pair representation with the MIP-FLP model. Numerical experiments based on different sized test problems from both the literature and industrial applications are provided and the solutions are compared with both the optimal solutions and the solutions from other heuristics to show the effectiveness and efficiency of our heuristic. For 11 data sets from the literature we provide solutions better than those previously found. For two large industrial application data sets we perform a sensitivity analysis with respect to the department aspect ratio constraint.     

The MINLP optimization approach to structural synthesis. Part I: A general view on simultaneous topology and parameter optimization

This paper presents the Mixed-Integer Non-linear Programming (MINLP) optimization approach to structural synthesis. Non-linear continuous/discrete non-convex problems of structural synthesis are proposed to be solved by means of simultaneous topology, parameter and standard dimension optimization. Part I of this three-part series of papers contains a general view of the MINLP approach to simultaneous topology and continuous parameter optimization. The MINLP optimization approach is performed through three steps. The first one includes the generation of a mechanical superstructure of different topology alternatives, the second one involves the development of an MINLP model formulation and the last one consists of a solution for the formulated MINLP problem. Some MINLP methods are also presented. A Modified OA/ER algorithm is applied to solve the MINLP problem and a simple example of a multiple cantilever beam is given to demonstrate the steps of the proposed MINLP optimization approach. As simultaneous optimization, extended to include also standard dimensions, requires additional effort, the development of suitable strategies to carry out the optimization is further discussed in Part II. The modelling of MINLP superstructures and the topology and parameter optimization of roller and sliding hydraulic steel gate structures are shown in Part III of the paper. An example of the synthesis of an already erected roller gate, i.e. the Intake Gate of Aswan II in Egypt, is presented as a comparative design research work. © 1998 John Wiley & Sons, Ltd.     

On the efficient modeling and solution of the multi-mode resource-constrained project scheduling problem with generalized precedence relations

For variants of the single-mode resource-constrained project scheduling problem, state-of-the-art exact algorithms combine a Branch and Bound algorithm with principles from Constraint Programming and Boolean Satisfiability Solving. In our paper, we propose new exact approaches extending the above principles to the multi-mode RCPSP (MRCPSP) with generalized precedence relations (GPRs). More precisely, we implemented two constraint handlers cumulativemm and gprecedencemm for the optimization framework SCIP. With the latter, one can model renewable resource constraints and GPRs in the context of multi-mode activities, respectively. Moreover, they integrate domain propagation and explanation generation techniques for the above problem characteristics. We formulate three SCIP-models for the MRCPSP with GPRs, two without and one with our constraint handler gprecedencemm. Our computational results on instances from the literature with 30, 50 and 100 activities show that the addition of this constraint handler significantly strengthens the SCIP-model. Moreover, we outperform the state-of-the-art exact approach on instances with 50 activities when imposing time limits of 27 s. In addition, we close (find the optimal solution and prove its optimality for) 289 open instances and improve the best known makespan for 271 instances from the literature.     

A robust Island Parallel Genetic Algorithm for the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) is a difficult and important problem studied in the domain of combinatorial optimisation. It is possible to solve QAP instances with 10--20 facilities using exhaustive parallel algorithms within a few days on a cluster machine. However, large QAP instances with more than 100 facilities are not solvable using exhaustive techniques. We have explored a variety of Genetic Algorithm crossover operators for this problem and verified its performance experimentally using well-known instances from the QAPLIB library. By increasing the number of processors, generations and population sizes we have been able to find solutions that are the same as (or very close to) the best reported solutions for large QAP instances in QAPLIB. In order to parallelise the Genetic Algorithm we generate and evolve separate solution pools on each cluster processor, using an island model. This model exchanges 10% of each processor’s solutions at the initial stages of optimisation. We show experimentally that both execution times and solution qualities are improved for large QAP instances by using our Island Parallel Genetic Algorithm.     

Heuristic and exact algorithms for the multi-pile vehicle routing problem

The multi-pile vehicle routing problem is a particular combination of loading and routing problems, in which items have to be loaded into different piles within vehicles, and then delivered with minimum cost. The problem is motivated by a real-world timber distribution problem, and is of both theoretical and practical interest. In this paper, we first develop heuristic and exact methods to solve the loading problem. We then include these methods into a tailored combination of Variable Neighborhood Search and Branch-and-Cut, to solve the overall problem. Extensive computational results show how the resulting algorithms are capable of solving to optimality a large number of small-size instances, and of consistently outperforming previous algorithms from the literature on large-size and real-world instances.     

Job-shop like manufacturing system with variable power threshold and operations with power requirements

This paper addresses an important issue in manufacturing by considering the scheduling of a Job-shop like manufacturing system involving a power threshold that must not be exceeded over time. A power profile is attached to operations that must be scheduled. This power profile presents a consumption peak at the start of process in order to model most of real-world machining operations. These operations must be scheduled according to the instantly available power threshold. A mathematical formulation of the problem is proposed; its main goal is to minimise the total completion time of all operations. A set of instances is built based on classical format of instances for the Job-shop problem. As it is time-consuming to obtain exact solutions on these instances with the CPLEX solver, a Greedy Randomised Adaptive Search Procedure hybridised with an Evolutionary Local Search (GRASP × ELS) metaheuristic is designed. The GRASP × ELS is compared with two other metaheuristics: a Variable Neighbourhood Search and a Memetic Algorithm. The GRASP × ELS is also compared with several algorithms developed in the literature for the classical job-shop problem. Results show the relevancy of the metaheuristic approaches both in terms of computational time and quality of solutions.     

Integrated process planning and scheduling for large-scale flexible job shops using metaheuristics

In this paper, we discuss an integrated process planning and scheduling problem in large-scale flexible job shops (FJSs). We assume that products can be manufactured in different ways, i.e. using different bills of materials (BOM) and routes for the same product. The total weighted tardiness is the performance measure of interest. A Mixed Integer Programming formulation is provided for the researched problem. Because of the NP-hardness of the investigated problem, an iterative scheme is designed that is based on variable neighbourhood search (VNS) on the process planning level. Appropriate neighbourhood structures for VNS are proposed. Because the evaluation of each move within VNS requires the solution of a large-scale FJS scheduling problem instance, efficient heuristics based on local search from previous research are considered on the scheduling level. Extensive computational experiments based on new randomly generated problem instances are conducted. In addition, a parallel version of the VNS is investigated within the computational experiments. The proposed iterative scheme is benchmarked against a genetic algorithm (GA) from the literature that simultaneously considers process planning and scheduling for the special case where a single BOM is available for each product. It turns out that the new iterative scheme outperforms the GA and a memetic algorithm based on the GA. It is able to solve even large-size problem instances in reasonable amount of time.     

Formulations and heuristics for the multi-item uncapacitated lot-sizing problem with inventory bounds

We consider the multi-item uncapacitated lot-sizing problem with inventory bounds, in which a production plan for multiple items has to be determined considering that they share a storage capacity. We present (a) a shortest path formulation and (b) a formulation based on the a priori addition of valid inequalities, which are compared with a facility location formulation available in the literature. Two easy-to-implement mixed integer programming heuristic frameworks are also presented, (a) a rounding scheme and (b) a relax-and-fix approach performed in a time partitioning fashion. Computational experiments are performed to evaluate the different approaches. The numerical results show that the proposed relax-and-fix heuristic outperforms all other approaches. Its solutions are within 4.0% of optimality in less than 10 minutes of running time for all tested instances, with mean gaps in the order of 2.1 and 1.8% for instances with more relaxed and tighter capacities, respectively. The obtained solutions were always better than those obtained by a commercial MIP solver running for one hour using any of the available formulations.     

MIXED INTEGER NON-LINEAR PROGRAMMING AND ITS APPLICATION TO THE MANAGEMENT OF UTILITY NETWORKS

This paper will focus on the application of MINLP (Mixed Integer Non Linear Programming). For the sake of application, we have chosen the utility network management problem which is important lo mechanical and chemical engineers.     

Supply chain optimisation with both production and transportation integration: multiple vehicles for a single perishable product

This paper deals with an extension of the integrated production and transportation scheduling problem (PTSP) by considering multiple vehicles (PTSPm) for optimisation of supply chains. The problem reflects a real concern for industry since production and transportation subproblems are commonly addressed independently or sequentially, which leads to sub-optimal solutions. The problem includes specific capacity constraints, the short lifespan of products and the special case of the single vehicle that has already been studied in the literature. A greedy randomised adaptive search procedure (GRASP) with an evolutionary local search (ELS) is proposed to solve the instances with a single vehicle as a special case. The method has been proven to be more effective than those published and provides shorter computational times with new best solutions for the single vehicle case. A new set of instances with multiple vehicles is introduced to favour equitable future research. Our study extends previous research using an indirect resolution approach and provides an algorithm to solve a wide range of one-machine scheduling problems with the proper coordination of single or multiple vehicles.     

A multi-objective particle swarm optimisation algorithm for unequal sized dynamic facility layout problem with pickup/drop-off locations

This paper deals with a multi-objective unequal sized dynamic facility layout problem (DFLP) with pickup/drop-off locations. First, a mathematical model to obtain optimal solutions for small size instances of the problem is developed. Then, a multi-objective particle swarm optimisation (MOPSO) algorithm is implemented to find near optimal solutions. Two new heuristics to prevent overlapping of the departments and to reduce ‘unused gaps’ between the departments are introduced. The performance of the MOPSO is examined using some sets of available test problems in the literature and various random test problems in small, medium, and large sizes. The percentage of improvements on the initial solutions is calculated for small, medium and large size instances. Also, the generation metric and the space metric for non-dominated solutions are examined. These experiments show the good performance of the developed MOPSO and sensitivity analysis show the robustness of the obtained solutions.     

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.     

One- and two-sided assembly line balancing problems with real-world constraints

In this study, we consider balancing problems of one- and two-sided assembly lines with real-world constraints like task or machine incompatibilities. First, we study the one-sided assembly line balancing problem (ALBP) with a limited number of machine types per workstation. Using a genetic algorithm (GA), we find optimal results for real-world instances. A set of larger test cases is used to compare two well-established solution approaches, namely GA and tabu search (TS). Additionally, we apply a specific differential evolution algorithm (DE), which has recently been proposed for the considered ALBP. Our computational results show that DE is clearly dominated by GA. Furthermore, we show that GA outperforms TS in terms of computational time, if capacity constraints are tight. Given the algorithm’s computational performance as well as the fact that it can easily be adapted to additional constraints, we then use it to solve two-sided ALBP. Three types of constraints and two different objectives are considered. We outperform all previously published methods in terms of solution quality and computational time. Finally, we are the first to provide feasible test instances as well as benchmark results for fully constrained two-sided ALB.     

The MINLP optimization approach to structural synthesis. Part III: synthesis of roller and sliding hydraulic steel gate structures

Part III of this three-part series of papers describes the synthesis of roller and sliding hydraulic steel gate structures performed by the Mixed-Integer Non-linear Programming (MINLP) approach. The MINLP approach enables the determination of the optimal number of gate structural elements (girders, plates), optimal gate geometry, optimal intermediate distances between structural elements and all continuous and standard crossectional sizes. For this purpose, special logical constraints for topology alterations and interconnection relations between the alternative and fixed structural elements are formulated. They have been embedded into a mathematical optimization model for roller and sliding steel gate structures GATOP. GATOP has been developed according to a special MINLP model formulation for mechanical superstructures (MINLP-MS), introduced in Parts I and II. The model contains an economic objective function of self-manufacturing and transportation costs of the gate. As the GATOP model is non-convex and highly non-linear, it is solved by means of the Modified OA/ER algorithm accompanied by the Linked Two-Phase MINLP Strategy, both implemented in the TOP computer code. An example of the synthesis is presented as a comparative design research work of the already erected roller gate, the so-called Intake Gate in Aswan II in Egypt. The optimal result yields 29·4 per cent of net savings when compared to the actual costs of the erected gate. © 1998 John Wiley & Sons, Ltd.