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Production control systems (PCSs) control the flow of jobs in a production system. The selection of a suitable PCS in the context of make-to-order (MTO) is challenging, due to the characteristics of MTO businesses and the number of parameters or factors that comprise a PCS. The literature that compares PCSs in the MTO context reported contradictory results. In fact, there is a gap in the literature concerning which factors or parameters explain a PCS performance. This paper presents an analysis of comparative studies on PCS in the MTO context, using a systematic literature review, to reveal which control factors and manufacturing conditions influence a PCS performance. The analysis concentrates on studies that use simulation to assess the performance of PCSs. Our results indicate that the main difference in PCSs performance is the design of the control loops. Other important factors that must be considered in the choice of a PCS are the order release mechanism, the workload aggregation approach, and the workload estimation method used on control loops. A framework for choosing a suitable PCS for MTO companies is presented, considering these factors. [ABSTRACT FROM AUTHOR]

In complexity theory, there is a widely studied grouping of optimization problems that belongs to the non-deterministic polynomial-time hard set. One of them is the set covering problem, known as one of Karp's 21 NP -complete problems, and it consists of finding a subset of decision variables for satisfying a set of constraints at the minimum feasible cost. However, due to the nature of the problem, this cannot be solved using traditional complete algorithms for hard instances. In this work, we present an improved binary version of the monkey search algorithm for solving the set covering problem. Originally, this approximate method was naturally inspired by the cognitive behavior of monkeys for climbing mountains. We propose a new climbing process with a better exploratory capability and a new cooperation procedure to reduce the number of unfeasible solutions. For testing this approach, we present a detailed computational results section, where we illustrate how this variation of the monkey search algorithm is capable of reaching various global optimums for a well-known instance set from the Beasley's OR-Library and how it outperforms many other heuristics and meta-heuristics addressed in the literature. Moreover, we add a complete statistical analysis to show the effectiveness of the proposed approach with respect to the original version. [ABSTRACT FROM AUTHOR]

The multidimensional knapsack problem (MKP) is one of the widely known integer programming problems. The MKP has received significant attention from the operational research community for its large number of applications. Solving this NP-hard problem remains a very interesting challenge, especially when the number of constraints increases. In this paper we present a k-means transition ranking (KMTR) framework to solve the MKP. This framework has the property to binarize continuous population-based metaheuristics using a data mining k-means technique. In particular we binarize a Cuckoo Search and Black Hole metaheuristics. These techniques were chosen by the difference between their iteration mechanisms. We provide necessary experiments to investigate the role of key ingredients of the framework. Finally to demonstrate the efficiency of our proposal, MKP benchmark instances of the literature show that KMTR competes with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Constraint programming is an efficient and powerful paradigm for solving constraint satisfaction and optimization problems. Under this paradigm, problems are modeled as a sequence of variables and a set of constraints. The variables have a non-empty domain of candidate values and constraints restrict the values that variables can adopt. The solving process operates by assigning values to variables in order to produce potential solutions which are then evaluated. A main component in this process is the enumeration strategy, which decides the order in which variables and values are chosen to produce such potential solutions. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Unfortunately, selecting the proper strategy is known to be a hard task, as its behavior during search is generally unpredictable and certainly depends on the problem at hand. A recent trend to handle this concern, is to interleave a set of different strategies instead of using a single one during the whole process. The strategies are evaluated according to process indicators in order to use the most promising one on each part of the search process. This process is known as online control of enumeration strategies and its correct configuration can be seen itself as an optimization problem. In this paper, we present two new systems for online control of enumeration strategies based on recent nature-inspired metaheuristics: bat algorithm and black hole optimization. The bat algorithm mimics the location capabilities of bats that employ echoes to identify the objects in their surrounding areas, while black hole optimization inspires its behavior on the gravitational pull of black holes in space. We perform different experimental results by using different enumeration strategies and well-known benchmarks, where the proposed approaches are able to noticeably outperform previous work on online control. [ABSTRACT FROM AUTHOR]

The set covering problem is a classical optimization benchmark that finds application in several real-world domains, particularly in line balancing production, crew scheduling, and service installation. The problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. In this paper, we present two new approaches for efficiently solving this problem, the first one based on cuckoo search and the second one on black hole optimization. Both are relatively modern bio-inspired metaheuristics that have attracted much attention due to their rapid convergence, easy implementation, and encouraging obtained results. We integrate to the core of both metaheuristics an effective pre-processing phase as well as multiple transfer functions and discretization methods. Pre-processing is employed for filtering the values from domains leading to infeasible solutions, while transfers function and discretization methods are used for efficiently handling the binary nature of the problem. We illustrate interesting experimental results where the two proposed approaches are able to obtain various global optimums for a set of well-known set covering problem instances, outperforming also several recently reported techniques. [ABSTRACT FROM AUTHOR]

A biosorption process has been developed for the bioremediation of heavy metal-contaminated acid drainages from Merladet and Faith open-cast mines, located in western Spain. The process is based on the physico-chemical properties for the adsorption, ion exchange, and complexation of metal ions by biopolymers (chitin and α (1,3) β-D-glucan) from industrial biowaste exhausted brewer's yeast ( Saccharomyces cerevisiae L.). Firstly, the chemical composition (U, Mn, Al, Fe, Cu, Zn, and Ni) and the physico-chemical and ecological states of these acid mine drainages were characterised. Furthermore, the selectivity for Zn, Cu, Mn, Ni, and Al the first order kinetics and the performance of the metals biosorption process by exhausted brewer's yeast were evaluated with polluted acid synthetic waters and mine drainages. The biosorption equilibria were reached in 10 ∼ 15 min following Langmuir type isotherms with higher affinity constants for metal-biosorbent binding for synthetic waters than for acid mine drainages. The efficiency of the process with real water samples was markedly lower for the case of Mn, and zero for Zn and Al. An antagonistic interference on the biosorption of a metal due to the presence of other metals is proposed. Finally, the ecotoxicity of the acid mine drainage was removed when it was incubated with brewer's yeast trapped in polyurethane foam. [ABSTRACT FROM AUTHOR]