Harmony search optimization algorithm for a novel transportation problem in a consolidation network

This article presents a new harmony search optimization algorithm to solve a novel integer programming model developed for a consolidation network. In this network, a set of vehicles is used to transport goods from suppliers to their corresponding customers via two transportation systems: direct shipment and milk run logistics. The objective of this problem is to minimize the total shipping cost in the network, so it tries to reduce the number of required vehicles using an efficient vehicle routing strategy in the solution approach. Solving several numerical examples confirms that the proposed solution approach based on the harmony search algorithm performs much better than CPLEX in reducing both the shipping cost in the network and computational time requirement, especially for realistic size problem instances.     

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.     

A new DEA common-weight multi-criteria decision-making approach for technology selection

This paper addresses an advanced manufacturing technology selection problem by proposing a new common-weight multi-criteria decision-making (MCDM) approach in the evaluation framework of data envelopment analysis (DEA). We improve existing technology selection models by giving a new mathematical formulation to simplify the calculation process and to ensure its use in more general situations with multiple inputs and multiple outputs. Further, an algorithm is provided to solve the proposed model based on mixed-integer linear programming and dichotomy. Compared with previous approaches for technology selection, our approach brings new contributions. First, it guarantees that only one decision-making unit (DMU) (referring to a technology) can be evaluated as efficient and selected as the best performer while maximising the minimum efficiency among all the DMUs. Second, the number of mixed-integer linear programs to solve is independent of the number of candidates. In addition, it guarantees the uniqueness of the final optimal set of common weights. Two benchmark instances are used to compare the proposed approach with existing ones. A computational experiment with randomly generated instances is further proceeded to show that the proposed approach is more suitable for situations with large datasets.     

A blockchain-based approach for a multi-echelon sustainable supply chain

Blockchain technology is destined to revolutionise supply chain processes. At the same time, governmental and regulatory policies are forcing firms to adjust their supply chains in response to environmental concerns. The objective of this study is therefore to develop a distributed ledger-based blockchain approach for monitoring supply chain performance and optimising both emission levels and operational costs in a synchronised fashion, producing a better outcome for the supply chain. We propose the blockchain approach for different production allocation problems within a multi-echelon supply chain (MESC) under a carbon taxation policy. As such, we couple recent advances in digitalisation of operations with increasingly stringent regulatory environmental policies. Specifically, with lead time considerations under emission rate constraints (imposed by a carbon taxation policy), we simultaneously consider the production, distribution and inventory control decisions in a production allocation-based MESC problem. The problem is then formulated as a Mixed Integer Non-Linear Programming (MINLP) model. We show that the distributed ledger-based blockchain approach minimises both total cost and carbon emissions. We then validate the feasibility of the proposed approach by comparing the results with a non-dominated sorting genetic algorithm (NSGA-II). The findings provide support for policymakers and supply chain executives alike.     

A desired load distribution model for scheduling of unrelated parallel machines

Scheduling problems concern the allocation of limited resources over time among both parallel and sequential activities. Load balancing has been adopted as an optimization criterion for several scheduling problems. However, in many practical situations, a load-balanced solution may not be feasible or attainable. To deal with this limitation, this paper presents a generic mathematical model of load distribution for resource allocation, called desired load distribution (DLD). The objective is to develop a DLD model for scheduling of unrelated parallel machines that can be used both in centralized resource management settings and in agent-based distributed scheduling systems. The paper describes the proposed DLD model in details, presents a dynamic programming based optimization algorithm for the proposed model, and then discusses its application to agent-based distributed scheduling.     

Lot streaming in a multiple product permutation flow shop with intermingling

In this paper we study the multi-product lot streaming problem in a permutation flow shop. The problem involves splitting given order quantities of different products into sublots and determining their optimal sequence. Each sublot has to be processed successively on all machines. The sublots of the particular products are allowed to intermingle, that is sublots of different jobs may be interleaved. A mixed integer programming formulation is presented which enables us to find optimal sublot sizes as well as the optimal sequence simultaneously. With this formulation, small- and medium-sized instances can be solved in a reasonable time. The model is further extended to deal with different settings and objectives. As no lot streaming instances are available in the literature, LSGen, a problem generator, is presented, facilitating valid and reproducible instances. First results concerning the average benefit of lot streaming with multiple products are presented, and are based on a computational study with 160 small- and medium-sized instances.     

The impact of dual-market on supply chain configuration for new products

Given a firm’s supply chain network, the key objective of supply chain configuration (SCC) is to determine a subset of supply chain partners to be involved in development, sourcing, production, distribution and support of a new product at the highest level of efficiency and expected responsiveness. Current literature on SCC realises the importance of considering the demand dynamics associated with the new product diffusion (NPD). However, these studies assume one-segment market for new products, a single homogenous consumer segment. Recent research in marketing indicates that such simplification might be fatal because a diverse and significant number of product categories may experience a dual-market structure, namely early and main markets, and generate a different demand dynamics. The objectives of this study are to: (i) develop a hybrid optimisation model, capturing both SCC decisions and the demand dynamics of dual-market NPD process; (ii) based on real-world data for a host of electronic product categories, various SCC networks and NPD demand dynamics, examine the new integrated optimisation model under one- and two-segment market; and (iii) present relevant managerial implications and guidelines for supply chain and marketing managers. Our extensive comparative computational experiment with 26 categories of consumer electronic products show that on average the relative net profit may improve significantly, when the market is considered as two-segment.     

THE COMPROMISE DECISION SUPPORT PROBLEM: MODELING THE DEVIATION FUNCTION AS IN PHYSICAL PROGRAMMING

The Archimedean and preemptive formulations that are currently used in the goal formulation of the Compromise Decision Support Problem (DSP) suffer two major drawbacks related to the arbitrary definition of priority levels and targets for multiple objectives. The Archimedean formulation is very difficult to implement because there is no consistent way to determine a prioria correct set of weights. Thus, choosing weights is either done arbitrarily or by cumbersome iterations. In the preemptive approach one objective is assumed to be infinitely more important than the others. Also, in the Compromise DSP it is necessary to define targets for each goal, and usually those targets are selected based on “educated guesses” or through an inefficient process of iteration. These shortcomings undermine the effectiveness of the Compromise DSP as a design tool. These problems can be amended by modifying the Compromise DSP according to the Linear Physical Programming (LPP) formulation. In this paper the modification of the Compromise DSP and its advantages are described, and the method is illustrated with a case example of the preliminary design of an absorption chiller.     

Flight path optimization passing through waypoints for autonomous flight control systems

Trajectory optimization is performed to generate a flight path passing specified waypoints. To deal with the unspecified time of passing through a waypoint, an auxiliary variable is introduced. Normalization of the time variable by the auxiliary variable transforms the waypoint optimization problem into the conventional optimization problem. The condition for passing through the waypoints can be relaxed, so that the vehicle passes specified waypoints within a certain acceptable range. Sequential quadratic programming is used to solve the optimization problem. As a numerical example, six degree-of-freedom vehicle dynamics is considered. Two-dimensional and three-dimensional trajectory optimization problems with several waypoints are solved to verify the effectiveness of the proposed algorithm.     

Explicit consideration of multiple objectives in cellular manufacturing

Although many methodologies have been proposed for solving the cell-formation problem, few of them explicitly consider the existence of multiple objectives in the design process. In this article, the development of multi-objective genetic programming single-linkage cluster analysis (GP-SLCA), an evolutionary methodology for the solution of the multi-objective cell-formation problem, is described. The proposed methodology combines an existing algorithm for the solution of single-objective cell-formation problems with NSGA-II, an elitist evolutionary multi-objective optimization technique. Multi-objective GP-SLCA is able to generate automatically a set of non-dominated solutions for a given multi-objective cell-formation problem. The benefits of the proposed approach are illustrated using an example test problem taken from the literature and an industrial case study.