A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem

Flow-shop scheduling problem (FSP) deals with the scheduling of a set of n jobs that visit a set of m machines in the same order. As the FSP is NP-hard, there is no efficient algorithm to reach the optimal solution of the problem. To minimize the holding, delay and setup costs of large permutation flow-shop scheduling problems with sequence-dependent setup times on each machine, this paper develops a novel hybrid genetic algorithm (HGA) with three genetic operators. Proposed HGA applies a modified approach to generate a pool of initial solutions, and also uses an improved heuristic called the iterated swap procedure to improve the initial solutions. We consider the make-to-order production approach that some sequences between jobs are assumed as tabu based on maximum allowable setup cost. In addition, the results are compared to some recently developed heuristics and computational experimental results show that the proposed HGA performs very competitively with respect to accuracy and efficiency of solution

A green vehicle routing problem with customer satisfaction criteria

This paper develops an MILP model, named Satisfactory-Green Vehicle Routing Problem. It consists of routing a heterogeneous fleet of vehicles in order to serve a set of customers within predefined time windows. In this model in addition to the traditional objective of the VRP, both the pollution and customers' satisfaction have been taken into account. Meanwhile, the introduced model prepares an effective dashboard for decision-makers that determines appropriate routes, the best mixed fleet, speed and idle time of vehicles. Additionally, some new factors evaluate the greening of each decision based on three criteria. This model applies piecewise linear functions (PLFs) to linearize a nonlinear fuzzy interval for incorporating customers' satisfaction into other linear objectives. We have presented a mixed integer linear programming formulation for the S-GVRP. This model enriches managerial insights by providing trade-offs between customers' satisfaction, total costs and emission levels. Finally, we have provided a numerical study for showing the applicability of the model

Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs

Designing collaborative roles and responsibilities in supply chain

Different tasks in the supply chains should be accomplished by well-defined collaboration of the members and functional sections in the organisations. Therefore, optimising the definition of roles and responsibilities for several supply chain processes is critical. Defining roles and responsibilities in the supply chain could be considered as a part of a broader concept called enterprise architecture. Supply Chain Operations Reference (SCOR) model can be used for constructing supply chain-focused enterprise architecture. Relations between different levels of organisation are important in enterprise architecture. Roles and responsibilities are the essential concepts for relating organisations, processes, people and systems that must perform certain processes. Although the SCOR model gives very helpful standardisation and its best practices, it does not give any suggestion about the proper design of organisational roles involved in each process. This paper proposes a framework for accurate definition of roles and responsibilities based on SCOR model. This concept can be extended for other types of collaborative organisations

An improvement in calculation method for apparel assembly line balancing

259-264A calculation method has been developed to determine the number of workstations and cycle time, considering one machine type at each workstation, normally preferred in apparel industries. The findings are compared with the earlier findings with respect to criterions such as the number of workers, the level of workers' skill, the number of used machine and the number of worker's movement between machines. The results show improvement in line balancing when taking into consideration the machine type feature. Hence, a model is proposed for apparel assembly line balancing to take into consideration the machine type assigned to each workstation so that it has a specific advantage for apparel assembly lines

Designing collaborative roles and responsibilities in supply chain

Different tasks in the supply chains should be accomplished by well-defined collaboration of the members and functional sections in the organisations. Therefore, optimising the definition of roles and responsibilities for several supply chain processes is critical. Defining roles and responsibilities in the supply chain could be considered as a part of a broader concept called enterprise architecture. Supply Chain Operations Reference (SCOR) model can be used for constructing supply chain-focused enterprise architecture. Relations between different levels of organisation are important in enterprise architecture. Roles and responsibilities are the essential concepts for relating organisations, processes, people and systems that must perform certain processes. Although the SCOR model gives very helpful standardisation and its best practices, it does not give any suggestion about the proper design of organisational roles involved in each process. This paper proposes a framework for accurate definition of roles and responsibilities based on SCOR model. This concept can be extended for other types of collaborative organisations

A modified imperialist competitive algorithm for a two-agent single-machine scheduling under periodic maintenance consideration

Scheduling with periodic maintenance has been widely studied. However, multi-agent scheduling with simultaneous considerations of periodic maintenance has hardly been considered until now. In view of this, this research focuses on the problem of scheduling jobs that come from two agents on a single machine under periodic maintenance constraint with the objective of minimising the total completion time of the jobs of the first agent while keeping the maximum tardiness of other agent below or at a fixed level UB. We present some new dominance properties for this strongly NP-hard problem. And next, using these properties, we develop a novel imperialist competitive algorithm for the problem. Various parameters of the proposed algorithm are reviewed by means of Taguchi experimental design. For the evaluation of the proposed ICA, problem data was generated to compare it against a genetic algorithm. The results of computational experiments show the good performance of the proposed algorithm

A fuzzy clustering-based method for scenario analysis in strategic planning: The case of an Asian pharmaceutical company

In today's rapid changing market situations, many nations and companies try to keep or make better their situation and gain more market share by creating competitive advantages. Because of growing number of uncertain parameters in the environment and lack of information about the future, the strategic choice has become very complex and critical. One of the popular tools for solving the problem is scenario analysis. In this paper based on fuzzy clustering we propose a method for building, analyzing and ranking the possible scenarios. To cope with the Issue of uncertain parameters of the environment in strategic planning, we use the concept of fuzzy set theory to enhance the proposed method. Finally the performance of the proposed method is illustrated in a strategic planning case in a pharmaceutical company