Bi-objective Programming Approach Research Articles

Saving a path minimizing egress time of a dynamic contraflow: a bi-objective programming approach

Saving a path minimizing egress time of a dynamic contraflow: a bi-objective programming approach

A robust bi-objective programming approach to environmental closed-loop supply chain network design under uncertainty

Imposition of strict environmental protection acts and the imperative need of the best possible allocation of resources have given birth to the concept of low carbon logistics. Environmental laws force the manufacturers to extend their existing supply chains to form a closed-loop supply chain (CLSC) through the setup of an efficient recovery system. In this paper, a multi-objective robust optimisation model is proposed for the design of CLSC network under uncertainty. First, a deterministic bi-objective mixed integer linear programming (BOMILP) model is developed for designing a CLSC network. Then, the robust counterpart of the proposed BOMILP is presented to cope with the real-world uncertainty. The first objective aims to maximise the total profit generated in the CLSC network and the second objective minimises the environmental pollution of the CLSC network. The proposed bi-objective model is solved using a multi-choice goal programming (MCGP) approach.

A robust bi-objective programming approach to environmental closed-loop supply chain network design under uncertainty

Imposition of strict environmental protection acts and the imperative need of the best possible allocation of resources have given birth to the concept of low carbon logistics. Environmental laws force the manufacturers to extend their existing supply chains to form a closed-loop supply chain (CLSC) through the setup of an efficient recovery system. In this paper, a multi-objective robust optimisation model is proposed for the design of CLSC network under uncertainty. First, a deterministic bi-objective mixed integer linear programming (BOMILP) model is developed for designing a CLSC network. Then, the robust counterpart of the proposed BOMILP is presented to cope with the real-world uncertainty. The first objective aims to maximise the total profit generated in the CLSC network and the second objective minimises the environmental pollution of the CLSC network. The proposed bi-objective model is solved using a multi-choice goal programming (MCGP) approach.

Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty

For optimization of timetables in metro systems with regular cyclic operation, this paper develops a bi-objective programming approach addressed to minimization of net energy consumption and total travel time with provision for dwell time uncertainty. Firstly, we formulate the bi-objective timetable optimization problem as an expected value model with speed profile control. Secondly, we use the ɛ-constraint method within a genetic algorithm framework to determine the Pareto optimal solutions. Finally, numerical examples based on the real-life operation data from the Beijing Metro Yizhuang Line are presented in order to illustrate the practicability and effectiveness of the approach developed in the paper.

New criteria for configuration of cellular manufacturing considering product mix variation

This paper deals with configuring manufacturing cells when product mix variation occurs. Most of researches have addressed the cell formation problem when part-machine incidence matrix is constant even for dynamic/stochastic case. But to the nature of CMS in manufacturing products in mid-variety and mid-volume, the product mix variation is not too far-fetched. Product mix variation causes the part-machine incidence matrix to change. To formulate the proposed problem two different criteria are considered which one relates to worker experts and another to worker relations. The first object considers the maximizing the expert levels in manufacturing cells. While the second object tries to maximize the interest levels in manufacturing cells. To make these concepts practical, a mathematical formulation which minimizes the voids of both worker-machine and worker-worker incidence matrices is developed. Due to the non-homogenous nature of the objective functions and possible conflicts, a bi-objective programming approach is applied. To find the Pareto-optimal front, the augmented ε-constraint method (AUGMECON) is applied. Since AUGMECON may not provide non-dominated set in a reasonable time, especially for large-size instances, NSGAII algorithm is customized and applied to produce optimal/near optimal Pareto solutions. To assess the performance of the proposed NSGAII algorithm, several randomly generated test problems were solved for a set of well-known multi-objective performance metrics.

Ant colony optimisation for the bi-objective due-date-setting problem in the multi-class make-to-order firm

In this study, we develop a bi-objective programming approach for due-date setting in make-to-order manufacturing with different classes of customers. We formulate the problem of quoting due-dates under the assumption that demand is dependent on lead-time and price is determined by the length of the delivery time. In addition, other parameters such as production policy, inventory holding, delivery system and capacity utilisation should be considered in due-date decisions. To this purpose, we consider additional objective function in traditional due-date management problem. So, the proposed bi-objective model attempts to maximise total profit and minimise rates of changes in capacity utilisation simultaneously. To obtain a set of Pareto solutions efficiently, we propose an algorithm based on multi-objective ant colony optimisation. The proposed algorithm is compared with a noticeable multi-objective genetic algorithm, i.e. SPEA, based on some comparison metrics with random instances.

A bi-objective modeling approach applied to an urban semi-desirable facility location problem

This paper introduces a mixed-integer, bi-objective programming approach to identify the locations and capacities of semi-desirable (or semi-obnoxious) facilities. The first objective minimizes the total investment cost; the second one minimizes the dissatisfaction by incorporating together in the same function “pull” and “push” characteristics of the decision problem (individuals do not want to live too close, but they do not want to be too far, from facilities). The model determines the number of facilities to be opened, the respective capacities, their locations, their respective shares of the total demand, and the population that is assigned to each candidate site opened. The proposed approach was tested with a case study for a particular urban planning problem: the location of sorted waste containers. The complete set of (supported or unsupported) non-inferior solutions, consisting of combinations of multi-compartment containers for the disposal of four types of sorted waste in nineteen candidate sites, and population assignments, was generated. The results obtained for part of the historical center of an old European city (Coimbra, Portugal) show that this approach can be applied to a real-world planning scenario.

Properties of Pareto-efficient contracts and regulations for road franchising

Private provision of public roads through build–operate-transfer (BOT) contracts is increasing around the world. This paper investigates the properties of Pareto-efficient BOT contracts using a bi-objective programming approach under perfect information. Under certain conventional assumptions, we find that for any Pareto-efficient BOT contract: (1) the concession period should be set to be the whole road life; (2) the volume–capacity ratio (or the road service quality) and the average social cost per trip are constantly equal to those at the social optimum whenever there are constant returns to scale in road construction. Extensions are made to the cases with increasing (decreasing) returns to scale in road construction. A variety of regulatory regimes are investigated to analyze the behavior of the profit-maximizing private firm, and efficient regulations, including demand and markup charge regulations, are elucidated for both the public and private sectors to achieve a predetermined Pareto-optimal outcome.